of total peptide

2 2 4.08

% sequence coverage

Domain Protein # of independent

Rho-associated protein

PKC and casein kinase substrate in neuron

Ubiquitin-conjugating

Rho-associated protein

Table 1. Identified Dvl2 interacting proteins.

DIX domain interacting proteins.

protein 1

SH3 binding

F9 cells were stimulated or unstimulated with Wnt3a for 30 min. Cell lysates were incubated with either GST domain beads or GST beads for 15 min. Bound proteins were eluted by SDS-running buffer and purified using ready prep 2-D cleanup kit. Samples were applied to pH 3-10 IPG strips, and then subjected to second dimensional gel electrophoresis.

Fig. 1. Second dimensional SDS-gel of GST pulls down.

Wnt-dependent spots were selected by comparing gel patterns with or without Wnt stimulation. Spots with red numbers were further analyzed.

Fig. 2. Analysis of Wnt-dependent spots on a second dimensional gel.

based sequencing was performed. Data were analyzed by the Mascot search engine to identify amino acid sequences. LC-ESI-MS-MS analyses were performed at the Stony Brook Proteomics Center.

#### **2.1.2 Identification of Dvl2 interacting proteins**

The identified Dvl2 interacting proteins by GST fusion pull down are summarized in Table 1. Dvls are known to oligomerize via their DIX domain. Purified DIX domain polymerizes in vitro to form fibris and to puncta in vivo (Schwarz-Romond et al., 2005). DIX domain mediates dynamic polymerization platform with a high concentration of binding sites for Wnt signaling partners such as Axin and GSK3β. Axin also has a DIX domain at the Cterminus which mediates self–interaction in a head to tail fashion, similar to Dvls (Schwarz-Romond et al., 2007b; Schwarz-Romond et al., 2005). Known interacting proteins pulled down by the DIX domain-GST included all three Dvl isoforms and Axin2. Presence of PDZ and DEP peptides of Dvl2 in GST-DIX pulled down fraction demonstrated that Dvl2 forms homo oligomer. Dvl2 also forms complexes with the other two isoforms, Dvl1 and Dvl3;

F9 cells were stimulated or unstimulated with Wnt3a for 30 min. Cell lysates were incubated with either GST domain beads or GST beads for 15 min. Bound proteins were eluted by SDS-running buffer and purified using ready prep 2-D cleanup kit. Samples were applied to pH 3-10 IPG strips, and then

Wnt-dependent spots were selected by comparing gel patterns with or without Wnt stimulation. Spots

based sequencing was performed. Data were analyzed by the Mascot search engine to identify amino acid sequences. LC-ESI-MS-MS analyses were performed at the Stony Brook

The identified Dvl2 interacting proteins by GST fusion pull down are summarized in Table 1. Dvls are known to oligomerize via their DIX domain. Purified DIX domain polymerizes in vitro to form fibris and to puncta in vivo (Schwarz-Romond et al., 2005). DIX domain mediates dynamic polymerization platform with a high concentration of binding sites for Wnt signaling partners such as Axin and GSK3β. Axin also has a DIX domain at the Cterminus which mediates self–interaction in a head to tail fashion, similar to Dvls (Schwarz-Romond et al., 2007b; Schwarz-Romond et al., 2005). Known interacting proteins pulled down by the DIX domain-GST included all three Dvl isoforms and Axin2. Presence of PDZ and DEP peptides of Dvl2 in GST-DIX pulled down fraction demonstrated that Dvl2 forms homo oligomer. Dvl2 also forms complexes with the other two isoforms, Dvl1 and Dvl3;

Fig. 2. Analysis of Wnt-dependent spots on a second dimensional gel.

subjected to second dimensional gel electrophoresis.

with red numbers were further analyzed.

**2.1.2 Identification of Dvl2 interacting proteins** 

Proteomics Center.

Fig. 1. Second dimensional SDS-gel of GST pulls down.


Table 1. Identified Dvl2 interacting proteins.

demonstrating Dvls assemble Dvls-based complexes and thus provide a Dvls platform. Polo-like kinase 4 (PLK4), Regulator of G protein signaling (RGS) 18, Rho-associated protein kinase and Receptor-type protein tyrosine phosphatase R (PTPRR) were identified as novel DIX domain interacting proteins.

PDZ domain beads pulled down interacting proteins Axin1 and Axin2. Novel interacting proteins were Tropomyosine α, cyclin, PKC and casein kinase substrate in neuron protein 1 and B-Raf proto-oncogen serine/threonine kinase. Adenylate kinase 1, cyclin and ubiquitinconjugating enzyme E2M were identified as novel DEP domain interacting proteins. SH3

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 195

(Cervenka et al., 2011). F9 cell extracts from stimulated or unstimulated cells were incubated with GST beads or one of the distinct GST-domain beads. PTPRR displayed prominent docking to DIX and DEP domains, modestly to the PDZ domain and weakly to the putative SH3 binding domain. Wnt stimulation slightly enhanced docking of PTPRR to Dvl2 DIX domain (fig. 3A). Association of Dvl2 and PTPRR was confirmed by Dvl2 immunoprecipitation followed by proved with either anti-Dvl2 antibody or anti-PTPRR antibody. Association of Dvl2 and PTPRR was enhanced by Wnt3a in a time-dependent manner (fig. 3B). Docking of PTPRR to Dvl2 increased over 1hour post Wnt3a stimulation. The functional role of PTPRR in Wnt/β-catenin pathway was investigated by measuring Lef/Tcf-sensitive transcriptional activity, a hallmark of activation of the Wnt/β−catenin pathway, by knocking down of PTPRR. siRNA mediated knockdown of PTPRR resulted in enhancement of Lef/Tcf-sensitive transcription activity, while overexpression of PTPRR attenuated Lef/Tcf-sensitive transcription activity (fig. 3C and 3D) confirming that PTPRR is a novel Dvl2 interacting protein and functions as a negative regulator of the Wnt/β-catenin

signaling pathway. Association of tyrosine phosphatase with Dvl2 is a novel finding.

interacting protein of Dvl2.

negatively (PTPRR and PLK4) or positively (B-Raf).

Polo-like kinase 4 (PLK4) is another novel DIX domain interacting protein which was identified in this study. PLK4 is the most structurally divergent Polo family kinase and is essential for mouse embryonic development (Swallow et al., 2005). PLK4 is required for the reproduction of centrosomes during cell cycle (Habedanck et al., 2005). Interaction of PLK4 and Dvl2 was confirmed by co-immunoprecipitation with an anti-Dvl2 antibody. Lef/Tcfsensitive transcription was enhanced by knockdown of PLK4, while overexpression of PLK4 attenuated Lef/Tcf-sensitive transcription, suggesting negative roles of PLK4 in Wnt/βcatenin signaling pathway (fig. 3E, 3F and 3G). Thus PLK4 is evaluated as functional novel

B-Raf proto-oncogen serine/threonine kinase, which transduces signals from Ras to MEK and ERK/MAPK, was identified as a novel PDZ interacting protein. These pathways regulate many cellular functions, including cell proliferation, differentiation, apoptosis, motility and metabolism (Wellbrock et al., 2004); and implicated in many human diseases such as cancer. (Rapp et al., 2006). Docking of B-Raf to PDZ domain was regulated by Wnt3a (fig. 4A). DIX, DEP and putative SH3 binding domains were also involved in the interaction of the two molecules. Docking of B-Raf to Dvl2 was Wnt-dependent and dramatically increased 15 min after Wnt3a stimulation. The association persisted for 2hours (fig. 4B). Attenuation of Lef/Tcf-sensitive transcription by knocking down of B-Raf demonstrated that B-Raf regulates positively Wnt/β-catenin signaling pathway (fig. 4C). Dose-dependent enhancement of Lef/Tcf-sensitive transcription is occurred by expression of B-Raf (fig. 4D). Thus, the newly identified Dvl2 interacting proteins, PTPRR, PLK4 and B-Raf are indeed docked to Dvl2 in a Wnt- and time-dependent manner and, regulate Wnt pathway either

**2.2 Tyrosine phosphorylation of Dvl2 regulates Wnt/**β**-catenin signaling pathway**  Dvl proteins are highly phosphorylated in response to Wnt stimulation and Wnt-dependent phosphorylation sites on Dvls have been poorly analyzed. Dvl phosphorylations regulate the Wnt signaling pathway, especially with regard to serine/threonine protein kinases (Kishida et al., 2001; Yanagawa et al., 1995). Involvement of tyrosine kinases in Wnt signaling pathway has not been established, except tyrosine phosphorylation of β-catenin. The phosphorylation sites on Dvls are presumably many and several mechanisms of Dvls

binding domain pulled down Tropomyosine α, HSP 70K, PARD-3, desmoplakin-3, plakoglobin, Rho-associated protein kinase, and Cullin 3. Actin, known Dvl interacting protein, was pulled down by PDZ, DEP and SH3 binding domains of Dvl2.

#### **2.1.3 Assay for Lef/Tcf-sensitive transcription**

Lef/Tcf-sensitive transcription is the read out of Wnt/beta-catenin signaling pathway. F9 cells were grown on 12-well plates and co-transfected with rat Fz1 (Rfz1) and Super8xTOPFlash (M50) (Seto and Bellen, 2006). After 1-2 days of transfection, cells were treated with Wnt3a (20 ng/ml) for up to 8 hours. Cells were lysed in a reporter gene lysis buffer [12.5 mM Tris-H3PO4 pH 7.8, 1 mM trans-1, 2-cyclohexanediaminetetraacetic acid (CDTA), 2 mM DTT, 10% glycerol and 1 % Triton X-100] (Promega) and further analyzed. Lef/Tcf-sensitive transcription activity was determined using cell lysates according to the manufacture's instructions (Stratagene) and displayed to relative to unstimulated cells (set to "1").

#### **2.1.4 Evaluation of novel Dvl2 interacting proteins**

Dvl proteins are phosphorylated in response to Wnt and display a shift of electrophoretic mobility on SDS-PAGE. Dvl activity itself is reported to be controlled by multiple phosphorylation events (McKay et al., 2001; Sun et al., 2001; Willert et al., 1997). Thus, Dvl phosphorylation is implicated in the Wnt signaling pathway. Many serine/threonine protein kinases and phosphatases are known to interact with Dvls. CK1ε, CK2 and Par1 are proposed to phosphorylate Dvls (Cong et al., 2004; Sun et al., 2001; Willert et al., 1997). Not only serine/threonine kinases, but also Src family tyrosine kinases phosphorylate Dvl2 (Yokoyama and Malbon, 2009). Protein phosphatase 2C (PP2C) and protein phosphatase 2A (PP2A) dock to Dvls (Strovel et al., 2000; Yokoyama and Malbon, 2007).

Treatment with okadaic acid (chemical inhibitor of serine/threonine phosphatases, PP1 and PP2A) mimics Wnt 3a action, increasing the cellular abundance of Axin and GSK3β and βcatenin as well as the trafficking of signaling elements in Wnt/β-catenin signaling pathway. Although mimicking effects of Wnt3a on the cellular abundance and trafficking of key signaling elements in Wnt /β-catenin signaling, suppression of PP2A alone does not provoke activation of Lef/Tcf-sensitive transcription, but potentiates its activation by Wnt3a. PP2A activity declines dramatically after Wnt stimulation and direct binding of Dvl2 to PP2A suppresses a phosphatase activity. PP2A dephosphorylates Dvl2 (Yokoyama and Malbon, 2007). Thus, phosphorylation-dephosphorylation is a central regulatory mechanism of docking proteins–Dvls interaction.

Consistent with the critical roles of phosphorylation-dephosphorylation in Wnt signaling pathway, several kinases and phosphatase were identified as novel Dvl2 interacting proteins. One tyrosine phosphatase PTPRR and two serine/threonine kinases, PLK4 and B-Raf, were further analyzed to confirm their functions in the Wnt/β-catenin signaling pathway.

Receptor-type protein tyrosine phosphatase R (PTPRR), also known as PTP-SL, contains a kinase interacting motif (KIM), located just N-terminal of the phosphatase domain, and associates with the mitogen-activated protein (MAP) kinase. PTPRR associates with MAPKs and inactivates kinases by dephosphorylating tyrosine residue (Hendriks et al., 2009). MAPKs promote Wnt/β-catenin signaling pathway via LRP6 phosphorylation suggesting convergence between Wnt/β-catenin signaling pathway and the mitogenic pathway

binding domain pulled down Tropomyosine α, HSP 70K, PARD-3, desmoplakin-3, plakoglobin, Rho-associated protein kinase, and Cullin 3. Actin, known Dvl interacting

Lef/Tcf-sensitive transcription is the read out of Wnt/beta-catenin signaling pathway. F9 cells were grown on 12-well plates and co-transfected with rat Fz1 (Rfz1) and Super8xTOPFlash (M50) (Seto and Bellen, 2006). After 1-2 days of transfection, cells were treated with Wnt3a (20 ng/ml) for up to 8 hours. Cells were lysed in a reporter gene lysis buffer [12.5 mM Tris-H3PO4 pH 7.8, 1 mM trans-1, 2-cyclohexanediaminetetraacetic acid (CDTA), 2 mM DTT, 10% glycerol and 1 % Triton X-100] (Promega) and further analyzed. Lef/Tcf-sensitive transcription activity was determined using cell lysates according to the manufacture's instructions (Stratagene) and displayed to relative to unstimulated cells (set

Dvl proteins are phosphorylated in response to Wnt and display a shift of electrophoretic mobility on SDS-PAGE. Dvl activity itself is reported to be controlled by multiple phosphorylation events (McKay et al., 2001; Sun et al., 2001; Willert et al., 1997). Thus, Dvl phosphorylation is implicated in the Wnt signaling pathway. Many serine/threonine protein kinases and phosphatases are known to interact with Dvls. CK1ε, CK2 and Par1 are proposed to phosphorylate Dvls (Cong et al., 2004; Sun et al., 2001; Willert et al., 1997). Not only serine/threonine kinases, but also Src family tyrosine kinases phosphorylate Dvl2 (Yokoyama and Malbon, 2009). Protein phosphatase 2C (PP2C) and protein phosphatase 2A

Treatment with okadaic acid (chemical inhibitor of serine/threonine phosphatases, PP1 and PP2A) mimics Wnt 3a action, increasing the cellular abundance of Axin and GSK3β and βcatenin as well as the trafficking of signaling elements in Wnt/β-catenin signaling pathway. Although mimicking effects of Wnt3a on the cellular abundance and trafficking of key signaling elements in Wnt /β-catenin signaling, suppression of PP2A alone does not provoke activation of Lef/Tcf-sensitive transcription, but potentiates its activation by Wnt3a. PP2A activity declines dramatically after Wnt stimulation and direct binding of Dvl2 to PP2A suppresses a phosphatase activity. PP2A dephosphorylates Dvl2 (Yokoyama and Malbon, 2007). Thus, phosphorylation-dephosphorylation is a central regulatory mechanism

Consistent with the critical roles of phosphorylation-dephosphorylation in Wnt signaling pathway, several kinases and phosphatase were identified as novel Dvl2 interacting proteins. One tyrosine phosphatase PTPRR and two serine/threonine kinases, PLK4 and B-Raf, were further analyzed to confirm their functions in the Wnt/β-catenin signaling

Receptor-type protein tyrosine phosphatase R (PTPRR), also known as PTP-SL, contains a kinase interacting motif (KIM), located just N-terminal of the phosphatase domain, and associates with the mitogen-activated protein (MAP) kinase. PTPRR associates with MAPKs and inactivates kinases by dephosphorylating tyrosine residue (Hendriks et al., 2009). MAPKs promote Wnt/β-catenin signaling pathway via LRP6 phosphorylation suggesting convergence between Wnt/β-catenin signaling pathway and the mitogenic pathway

protein, was pulled down by PDZ, DEP and SH3 binding domains of Dvl2.

**2.1.3 Assay for Lef/Tcf-sensitive transcription** 

**2.1.4 Evaluation of novel Dvl2 interacting proteins** 

of docking proteins–Dvls interaction.

(PP2A) dock to Dvls (Strovel et al., 2000; Yokoyama and Malbon, 2007).

to "1").

pathway.

(Cervenka et al., 2011). F9 cell extracts from stimulated or unstimulated cells were incubated with GST beads or one of the distinct GST-domain beads. PTPRR displayed prominent docking to DIX and DEP domains, modestly to the PDZ domain and weakly to the putative SH3 binding domain. Wnt stimulation slightly enhanced docking of PTPRR to Dvl2 DIX domain (fig. 3A). Association of Dvl2 and PTPRR was confirmed by Dvl2 immunoprecipitation followed by proved with either anti-Dvl2 antibody or anti-PTPRR antibody. Association of Dvl2 and PTPRR was enhanced by Wnt3a in a time-dependent manner (fig. 3B). Docking of PTPRR to Dvl2 increased over 1hour post Wnt3a stimulation. The functional role of PTPRR in Wnt/β-catenin pathway was investigated by measuring Lef/Tcf-sensitive transcriptional activity, a hallmark of activation of the Wnt/β−catenin pathway, by knocking down of PTPRR. siRNA mediated knockdown of PTPRR resulted in enhancement of Lef/Tcf-sensitive transcription activity, while overexpression of PTPRR attenuated Lef/Tcf-sensitive transcription activity (fig. 3C and 3D) confirming that PTPRR is a novel Dvl2 interacting protein and functions as a negative regulator of the Wnt/β-catenin signaling pathway. Association of tyrosine phosphatase with Dvl2 is a novel finding.

Polo-like kinase 4 (PLK4) is another novel DIX domain interacting protein which was identified in this study. PLK4 is the most structurally divergent Polo family kinase and is essential for mouse embryonic development (Swallow et al., 2005). PLK4 is required for the reproduction of centrosomes during cell cycle (Habedanck et al., 2005). Interaction of PLK4 and Dvl2 was confirmed by co-immunoprecipitation with an anti-Dvl2 antibody. Lef/Tcfsensitive transcription was enhanced by knockdown of PLK4, while overexpression of PLK4 attenuated Lef/Tcf-sensitive transcription, suggesting negative roles of PLK4 in Wnt/βcatenin signaling pathway (fig. 3E, 3F and 3G). Thus PLK4 is evaluated as functional novel interacting protein of Dvl2.

B-Raf proto-oncogen serine/threonine kinase, which transduces signals from Ras to MEK and ERK/MAPK, was identified as a novel PDZ interacting protein. These pathways regulate many cellular functions, including cell proliferation, differentiation, apoptosis, motility and metabolism (Wellbrock et al., 2004); and implicated in many human diseases such as cancer. (Rapp et al., 2006). Docking of B-Raf to PDZ domain was regulated by Wnt3a (fig. 4A). DIX, DEP and putative SH3 binding domains were also involved in the interaction of the two molecules. Docking of B-Raf to Dvl2 was Wnt-dependent and dramatically increased 15 min after Wnt3a stimulation. The association persisted for 2hours (fig. 4B). Attenuation of Lef/Tcf-sensitive transcription by knocking down of B-Raf demonstrated that B-Raf regulates positively Wnt/β-catenin signaling pathway (fig. 4C). Dose-dependent enhancement of Lef/Tcf-sensitive transcription is occurred by expression of B-Raf (fig. 4D). Thus, the newly identified Dvl2 interacting proteins, PTPRR, PLK4 and B-Raf are indeed docked to Dvl2 in a Wnt- and time-dependent manner and, regulate Wnt pathway either negatively (PTPRR and PLK4) or positively (B-Raf).

#### **2.2 Tyrosine phosphorylation of Dvl2 regulates Wnt/**β**-catenin signaling pathway**

Dvl proteins are highly phosphorylated in response to Wnt stimulation and Wnt-dependent phosphorylation sites on Dvls have been poorly analyzed. Dvl phosphorylations regulate the Wnt signaling pathway, especially with regard to serine/threonine protein kinases (Kishida et al., 2001; Yanagawa et al., 1995). Involvement of tyrosine kinases in Wnt signaling pathway has not been established, except tyrosine phosphorylation of β-catenin. The phosphorylation sites on Dvls are presumably many and several mechanisms of Dvls

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 197

(A) B-Raf docks to Dvl2. Lysates from cells treated or untreated with Wnt3a were incubated with either GST itself or one of the immobilized Dvl2 domains. Bound proteins were proved with either anti-B-Raf

immunoprecipitation. Bound proteins were analyzed by SDS-PAGE and immunoblotting with either

(D) Overexpression of B-Raf enhances Wnt3a-sensitive Lef/Tcf-sensitive transcription*.* F9 cells were cotransfected with Rfz1, M50, and B-Raf expression vector one day before cells were stimulated with or without purified Wnt3a for 7 hours. Cell lysates were assayed for Lef/Tcf-sensitive luciferase

phosphorylation may be involved; distributive phosphorylation, sequential priming phosphorylation, processive phosphorylation, and combination of distributive phosphorylation/sequential priming phosphorylation or processive phosphorylation. Similar to LRP5/6, several kinases may phosphorylate same sites on Dvls. Dvl kinases may play multiple opposing roles in Wnt signaling pathway like CK1 and GSK3β do. Thus, regulations by phosphorylation in Wnt signaling pathway are a complicated process, since many important molecules in the pathway are positively or negatively regulated by phosphorylation. To avoid such a complexity, in vitro systems are employed, although functional and physiological analysis *in vivo* is required. The amino acid region 370-376 of Dvl2 displays a consensus sequence for a class I core SH3 protein-binding motif RTEPVRP (Penton et al., 2002). This region is conserved in all three mammalian isoforms of Dvl suggesting that tyrosine phosphorylation may be functional in Dvl biology and Wnt signaling. In developmentally relevant signaling, Src is reported to elevate the expression (Karni et al., 2005) and phosphorylation at Tyr Y654 of β-catenin. The phosphorylation of Tyr654 blocked the E-cadherin-β-catenin interaction (Roura et al., 1999). In addition to Src, other tyrosine kinases such as Fyn, Fer, transmembrane tyrosine kinase EGFR and c-Met, downregulate E-cadherin-mediated adhesion via enhanced tyrosine phosphorylation of βcatenin (Lilien and Balsamo, 2005; Piedra et al., 2001). In chronic myeloid leukemia cells, oncogenic tyrosine kinase Bcr-Abl triggers tyrosine phosphorylation of β-catenin, stabilizes β-catenin levels and enhances nuclear signaling activation (Coluccia et al., 2007). A possible

(C) Effect of knockdown of B-Raf on Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting B-Raf one day before co-transfection of the cells with Rfz1 and M50. On the following day, the cells were stimulated and Lef/Tcf-sensitive transcription was assayed. Statistical significance is indicated (\*, *p*< 0.005). Cell lysates were analyzed by immunoblotting with either anti-B-Raf antibody or

(B) Time course of the association of Dvl2 and B-Raf. Cell lysates were subjected to Dvl2

transcription activity. Statistical significance is indicated (\*, *p*< 0.005). Fig. 4. Analysis of B-Raf in Wnt/β-catenin signaling pathway.

antibody or anti-GST antibody.

anti-Dvl2 antibody or anti-B-Raf antibody.

anti-GAPDH antibody (as a control).

A-D, analysis of PTPRR in Wnt/β-catenin signaling pathway. E-G, analysis of PLK4 in Wnt/β-catenin signaling pathway.

(A) PTPRR docks to Dvl2. Lysates from cell treated or untreated with Wnt3a were incubated with one of the immobilized Dvl2 domains or GST itself. Bound proteins were analyzed by SDS-PAGE and immunoblotting with either anti-PTPRR or anti-GST antibody.

(B) Time course of the association of Dvl2 and PTPRR. Cell lysates were immunoprecipitated with anti-Dvl2 antibody. Bound proteins were analyzed by SDS-PAGE and immunoblotting with either anti-Dvl2 or anti-PTPRR antibody.

(C) Effect of knockdown of PTPRR on Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting PTPRR one day before co-transfection of the cells with Rfz1 and Super8xTOPFlash reporter (M50). On the following day, the cells were stimulated with Wnt3a for 7 hours and cell lysates were assayed for Lef/Tcf-sensitive transcription. Statistical significance is indicated (\*, *p*< 0.005). Cell lysates were analyzed by immunoblotting with either anti-PTPRR or anti-GAPDH antibody (as a control).

(D) Overexpression of PTPRR attenuates Wnt3a-sensitive Lef/Tcf-sensitive transcription*.* F9 cells were co-transfected with Rfz1, M50, and a PTPRR expression vector one day before cells were stimulated without or with purified Wnt3a for 7 hours. Cell lysates were assayed for Lef/Tcf-sensitive transcription activity. Statistical significance is indicated (\*, *p*< 0.05).

(E) Dvl2 docks to PLK4 in a Wnt- and time-dependent manner. Cell lysates were subjected to immunoprecipitation with anti-Dvl2 antibody and analyzed by SDS-PAGE and then immunoblotted with either anti-Dvl2 antibody or anti-PLK4 antibody.

(F) Knockdown of PLK4 enhances Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting PLK4 one day before co-transfection of the cells with Rfz1 and M50. On the following day, the cells were stimulated with Wnt3a and assay for Lef/Tcf-sensitive transcription was performed as described previously. Statistical significance is indicated (\*, *p*< 0.005)

(G) Overexpression of PLK4 attenuates Lef/Tcf-sensitive transcription. Cells were co-transfected with PLK4, Rfz1 and M50 for one day before Wnt stimulation. Assay for Lef/Tcf-sensitive transcription was performed. Statistical significance is indicated (\*, *p*< 0.005).

Fig. 3. Analysis of novel Dvl2 interacting proteins.

A-D, analysis of PTPRR in Wnt/β-catenin signaling pathway. E-G, analysis of PLK4 in Wnt/β-catenin

the immobilized Dvl2 domains or GST itself. Bound proteins were analyzed by SDS-PAGE and

immunoblotting with either anti-PTPRR or anti-GST antibody.

transcription activity. Statistical significance is indicated (\*, *p*< 0.05).

described previously. Statistical significance is indicated (\*, *p*< 0.005)

with either anti-Dvl2 antibody or anti-PLK4 antibody.

performed. Statistical significance is indicated (\*, *p*< 0.005). Fig. 3. Analysis of novel Dvl2 interacting proteins.

(A) PTPRR docks to Dvl2. Lysates from cell treated or untreated with Wnt3a were incubated with one of

(B) Time course of the association of Dvl2 and PTPRR. Cell lysates were immunoprecipitated with anti-Dvl2 antibody. Bound proteins were analyzed by SDS-PAGE and immunoblotting with either anti-Dvl2

(C) Effect of knockdown of PTPRR on Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting PTPRR one day before co-transfection of the cells with Rfz1 and Super8xTOPFlash reporter (M50). On the following day, the cells were stimulated with Wnt3a for 7 hours and cell lysates were assayed for Lef/Tcf-sensitive transcription. Statistical significance is indicated (\*, *p*< 0.005). Cell lysates were analyzed by immunoblotting with either anti-PTPRR or anti-GAPDH antibody (as a

(D) Overexpression of PTPRR attenuates Wnt3a-sensitive Lef/Tcf-sensitive transcription*.* F9 cells were co-transfected with Rfz1, M50, and a PTPRR expression vector one day before cells were stimulated without or with purified Wnt3a for 7 hours. Cell lysates were assayed for Lef/Tcf-sensitive

(F) Knockdown of PLK4 enhances Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting PLK4 one day before co-transfection of the cells with Rfz1 and M50. On the following day, the cells were stimulated with Wnt3a and assay for Lef/Tcf-sensitive transcription was performed as

(G) Overexpression of PLK4 attenuates Lef/Tcf-sensitive transcription. Cells were co-transfected with PLK4, Rfz1 and M50 for one day before Wnt stimulation. Assay for Lef/Tcf-sensitive transcription was

(E) Dvl2 docks to PLK4 in a Wnt- and time-dependent manner. Cell lysates were subjected to immunoprecipitation with anti-Dvl2 antibody and analyzed by SDS-PAGE and then immunoblotted

signaling pathway.

or anti-PTPRR antibody.

control).

(A) B-Raf docks to Dvl2. Lysates from cells treated or untreated with Wnt3a were incubated with either GST itself or one of the immobilized Dvl2 domains. Bound proteins were proved with either anti-B-Raf antibody or anti-GST antibody.

(B) Time course of the association of Dvl2 and B-Raf. Cell lysates were subjected to Dvl2 immunoprecipitation. Bound proteins were analyzed by SDS-PAGE and immunoblotting with either anti-Dvl2 antibody or anti-B-Raf antibody.

(C) Effect of knockdown of B-Raf on Lef/Tcf-sensitive transcription. F9 cells were transfected with siRNA targeting B-Raf one day before co-transfection of the cells with Rfz1 and M50. On the following day, the cells were stimulated and Lef/Tcf-sensitive transcription was assayed. Statistical significance is indicated (\*, *p*< 0.005). Cell lysates were analyzed by immunoblotting with either anti-B-Raf antibody or anti-GAPDH antibody (as a control).

(D) Overexpression of B-Raf enhances Wnt3a-sensitive Lef/Tcf-sensitive transcription*.* F9 cells were cotransfected with Rfz1, M50, and B-Raf expression vector one day before cells were stimulated with or without purified Wnt3a for 7 hours. Cell lysates were assayed for Lef/Tcf-sensitive luciferase transcription activity. Statistical significance is indicated (\*, *p*< 0.005).

Fig. 4. Analysis of B-Raf in Wnt/β-catenin signaling pathway.

phosphorylation may be involved; distributive phosphorylation, sequential priming phosphorylation, processive phosphorylation, and combination of distributive phosphorylation/sequential priming phosphorylation or processive phosphorylation. Similar to LRP5/6, several kinases may phosphorylate same sites on Dvls. Dvl kinases may play multiple opposing roles in Wnt signaling pathway like CK1 and GSK3β do. Thus, regulations by phosphorylation in Wnt signaling pathway are a complicated process, since many important molecules in the pathway are positively or negatively regulated by phosphorylation. To avoid such a complexity, in vitro systems are employed, although functional and physiological analysis *in vivo* is required. The amino acid region 370-376 of Dvl2 displays a consensus sequence for a class I core SH3 protein-binding motif RTEPVRP (Penton et al., 2002). This region is conserved in all three mammalian isoforms of Dvl suggesting that tyrosine phosphorylation may be functional in Dvl biology and Wnt signaling. In developmentally relevant signaling, Src is reported to elevate the expression (Karni et al., 2005) and phosphorylation at Tyr Y654 of β-catenin. The phosphorylation of Tyr654 blocked the E-cadherin-β-catenin interaction (Roura et al., 1999). In addition to Src, other tyrosine kinases such as Fyn, Fer, transmembrane tyrosine kinase EGFR and c-Met, downregulate E-cadherin-mediated adhesion via enhanced tyrosine phosphorylation of βcatenin (Lilien and Balsamo, 2005; Piedra et al., 2001). In chronic myeloid leukemia cells, oncogenic tyrosine kinase Bcr-Abl triggers tyrosine phosphorylation of β-catenin, stabilizes β-catenin levels and enhances nuclear signaling activation (Coluccia et al., 2007). A possible

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 199

attenuated the formation of primitive endoderm (another readout of Wnt signaling pathway) as well as Lef/Tcf-sensitive transcription (Yokoyama and Malbon, 2009). Analysis of phosphorylation sites on Dvl2 by Src was performed *in vitro*. Purified rDvl2 from Sf9 cell was phosphorylated by a Src family kinase. The five tyrosine residues (Y18, Y27, Y275, Y295 and Y463) were identified by mass spectrometry. One example was shown in fig. 5B. Src family tyrosine kinases can phosphorylate *in vitro* two tyrosine residues (Y18 and Y27) of the DIX domain, two residues (Y275 and Y295) of the PDZ domain, and a single residue (Y463) in the DEP domain. Dvl2 levels were knocked-down by siRNA targeting Dvl2; the ability of YF mutants (Dvl2 Y18F, Y27F, Y275F, Y295F and Y463F) versus wild-type Dvl2 to rescue Wnt3a-stimulated Lef/Tcf-sensitive transcription was probed. Expression of the wild-type Dvl2 in Dvl2 knockdown cells restored the Wnt3a-stimulated Lef/Tcf-sensitive transcription, whereas the Y18F Dvl2 mutant in particular attenuated the response by more

(A) Dvl2 docks to Src and Hck tyrosine kinases. Cell lysates were incubated with one of the

analyzed by LC/MS/MS. Tyrosine 27 was identified as phosphorylation site.

Fig. 5. Src family tyrosine kinases are involved in Wnt/β-catenin signaling.

Malbon, C. C. (2009). J Cell Sci 122, 4439-4451).

immobilized Dvl2 domain, DIX, DEP, PDZ, putative SH3 binding region (SH3B), C-terminus (aa 511- 736) and GST itself. Proteins docking to the Dvl2 domains were resolved by SDS-PAGE and analyzed by immunoblotting, staining with: anti-Src, anti-Hck, and anti-GST antibodies (loading control). (B) Mass spectrum of the Y27 phosphopeptide. Phosphorylated Dvl2 was digested with trypsin and

(C) Tyrosine-to-phenylalanine substitution mutants of Dvl2 attenuate Lef/Tcf-sensitive transcription. F9 cells stably expressing Rfz1 and M50 were transfected with siRNA targeting Dvl2 one day before transfection of the cells with either wild-type or one of the YF-mutants of Dvl2 (Y18F, Y27F, Y275F, Y295F and Y463F). On the following day, cells were stimulated with Wnt3a. Assay for Lef/Tcf-sensitive transcription was performed. Data are adapted with permission from the publication (Yokoyama, N. &

At least two prominent components of the multiprotein complexes, Dvl2 and β-catenin, were phosphorylated on tyrosine residues in response to Wnt stimulation. Tyrosine phosphorylation of Y18, Y27 and Y275 of Dvl2 appears to contribute, in some complex manner, to the ability of Src to enhance Wnt3a/β-catenin signaling. Wnt stimulated Src docking to Dvl2 through the SH3-binding domain and the C-terminus proline-rich domain. Src activity is regulated by intramolecular interactions, an interaction between the SH2 domain and the C-terminal tail as well as an interaction between the SH3 domain and a polyproline-type helix in the SH2-kinase linker region (Sicheri et al., 1997; Williams et al.,

than 50 % (fig. 5C).

involvement of tyrosine kinases, particularly Src family tyrosine kinases, in Wnt/β-catenin signaling pathway was investigated.

#### **2.2.1 Baculovirus expression and purification of Dvl2**

Baculovirus/Sf9 cell system was employed to express Dvl2. Full length Dvl2 was subcloned into plasmid pBACgus-9 (N-terminal T7 tag and C-terminal CBD-tag and poly Histidinetag, Novagen), and expressed in Sf9 cells using the Bac Vector-3000 DNA transfection kit (Novagen). Sf9 cells were grown in Ex-cell-401 medium supplemented with L-glutamine and 2.5% fetal bovine serum. Sf9 cells were infected with recombinant Dvl2 baculovirus at a MOI (multiplicity of infection) of 5. Cells were harvested after 4 days of infection and lysed in a French pressure cell twice in 20 mM Tris-HCl buffer (pH 8.0) containing 1% deoxycholate, 2 mM Na3VO4, 20 mM NaF, 5 mM 2-mercaptoethanol, 10 μg/ml leupeptin, 10μg/ml aprotinin, and 1 mM phenylsulfonyl fluoride (PMSF). After centrifugation, rDvl2 was purified by Ni2+-affinity chromatography.

#### **2.2.2 Phosphorylation of Dvl2 by Src family kinase and identification of tyrosine phosphorylation sites on Dvl2**

Phosphorylation of rDvl2 was performed in a kinase buffer (10 mM Tris-HCl, pH7.4, 10mM MgCl2, 10 mM MnCl2, 1 mM Na3VO4, 50 mM NaF, 0.5 mM ATP) with purified rSrc (from Sf9 cell) for 1 hour at 30 oC. Phosphorylated rDvl2 was digested with trypsin and subjected to analysis using API QSTAR Pulsar LC/MS/MS (Applied Biosystems/MDS SCIEX) equipped with a Protana nanospray source (Protana Engineering A/S) and an UltiMate capillary high pressure liquid chromatography (LC Packings) with a PepMap C18 nanocolumn (75 μm x 15 cm, LC Packings). Phosphopeptides were detected by a data base search using Pro ID software (Applied Biosystems/MDS SCIEX).

#### **2.2.3 Functional analysis of tyrosine phosphorylation on Dvl2**

The functional relevance of the detected tyrosine phosphorylation sites was assessed following site-directed mutagenesis using Qickchange Mutagenesis system (Stratagene). The functional ability of the mutational constructs (YF mutants of Dvl2; Y18F, Y27F, Y275F, Y295F and Y463F) analyzed by Lef/Tcf-sensitive transcription in Dvl2-deficient cells. F9 cell stably expressing Rfz1 and M50 were transfected with siRNA targeting Dvl2 one day before transfection of cells with either wild-type or an YF mutant of Dvl2. On the following day, cells were stimulated with Wnt3a for 7 hours and Lef/Tcf-sensitive transcription was assayed.

#### **2.2.4 Dvl2 docks to and activates Src in a Wnt-dependent manner**

F9 cell extracts were incubated with immobilized Dvl2 domains (GST-DIX, GST-DEP, GST-PDZ, GST-putative SH3 binding site, and GST-C-terminus of Dvl2). Src family kinases (Src and Hck) displayed prominent docking on the putative SH3 binding region (aa 356-378) and the proline-rich region of the C-terminus of Dvl2 (aa 511-736) (fig. 5A). The ability of the SH3 domains of Src family tyrosine kinases, but not Nck SH3 domain to enable docking to Dvl2 reflects the specificity of the scaffold-kinase interaction (Yokoyama and Malbon, 2009). Positive roles of Src family tyrosine kinases in the Wnt/β-catenin signaling pathway were confirmed by either treatment with an inhibitor of the Src family tyrosine kinases (PP2) or with a siRNA-induced knockdown of Src. Treatment with PP2 or siRNA targeting Src

involvement of tyrosine kinases, particularly Src family tyrosine kinases, in Wnt/β-catenin

Baculovirus/Sf9 cell system was employed to express Dvl2. Full length Dvl2 was subcloned into plasmid pBACgus-9 (N-terminal T7 tag and C-terminal CBD-tag and poly Histidinetag, Novagen), and expressed in Sf9 cells using the Bac Vector-3000 DNA transfection kit (Novagen). Sf9 cells were grown in Ex-cell-401 medium supplemented with L-glutamine and 2.5% fetal bovine serum. Sf9 cells were infected with recombinant Dvl2 baculovirus at a MOI (multiplicity of infection) of 5. Cells were harvested after 4 days of infection and lysed in a French pressure cell twice in 20 mM Tris-HCl buffer (pH 8.0) containing 1% deoxycholate, 2 mM Na3VO4, 20 mM NaF, 5 mM 2-mercaptoethanol, 10 μg/ml leupeptin, 10μg/ml aprotinin, and 1 mM phenylsulfonyl fluoride (PMSF). After centrifugation, rDvl2

**2.2.2 Phosphorylation of Dvl2 by Src family kinase and identification of tyrosine** 

Phosphorylation of rDvl2 was performed in a kinase buffer (10 mM Tris-HCl, pH7.4, 10mM MgCl2, 10 mM MnCl2, 1 mM Na3VO4, 50 mM NaF, 0.5 mM ATP) with purified rSrc (from Sf9 cell) for 1 hour at 30 oC. Phosphorylated rDvl2 was digested with trypsin and subjected to analysis using API QSTAR Pulsar LC/MS/MS (Applied Biosystems/MDS SCIEX) equipped with a Protana nanospray source (Protana Engineering A/S) and an UltiMate capillary high pressure liquid chromatography (LC Packings) with a PepMap C18 nanocolumn (75 μm x 15 cm, LC Packings). Phosphopeptides were detected by a data base search

The functional relevance of the detected tyrosine phosphorylation sites was assessed following site-directed mutagenesis using Qickchange Mutagenesis system (Stratagene). The functional ability of the mutational constructs (YF mutants of Dvl2; Y18F, Y27F, Y275F, Y295F and Y463F) analyzed by Lef/Tcf-sensitive transcription in Dvl2-deficient cells. F9 cell stably expressing Rfz1 and M50 were transfected with siRNA targeting Dvl2 one day before transfection of cells with either wild-type or an YF mutant of Dvl2. On the following day, cells were stimulated with Wnt3a for 7 hours and Lef/Tcf-sensitive transcription was

F9 cell extracts were incubated with immobilized Dvl2 domains (GST-DIX, GST-DEP, GST-PDZ, GST-putative SH3 binding site, and GST-C-terminus of Dvl2). Src family kinases (Src and Hck) displayed prominent docking on the putative SH3 binding region (aa 356-378) and the proline-rich region of the C-terminus of Dvl2 (aa 511-736) (fig. 5A). The ability of the SH3 domains of Src family tyrosine kinases, but not Nck SH3 domain to enable docking to Dvl2 reflects the specificity of the scaffold-kinase interaction (Yokoyama and Malbon, 2009). Positive roles of Src family tyrosine kinases in the Wnt/β-catenin signaling pathway were confirmed by either treatment with an inhibitor of the Src family tyrosine kinases (PP2) or with a siRNA-induced knockdown of Src. Treatment with PP2 or siRNA targeting Src

signaling pathway was investigated.

**2.2.1 Baculovirus expression and purification of Dvl2** 

was purified by Ni2+-affinity chromatography.

using Pro ID software (Applied Biosystems/MDS SCIEX).

**2.2.3 Functional analysis of tyrosine phosphorylation on Dvl2** 

**2.2.4 Dvl2 docks to and activates Src in a Wnt-dependent manner** 

**phosphorylation sites on Dvl2** 

assayed.

attenuated the formation of primitive endoderm (another readout of Wnt signaling pathway) as well as Lef/Tcf-sensitive transcription (Yokoyama and Malbon, 2009). Analysis of phosphorylation sites on Dvl2 by Src was performed *in vitro*. Purified rDvl2 from Sf9 cell was phosphorylated by a Src family kinase. The five tyrosine residues (Y18, Y27, Y275, Y295 and Y463) were identified by mass spectrometry. One example was shown in fig. 5B. Src family tyrosine kinases can phosphorylate *in vitro* two tyrosine residues (Y18 and Y27) of the DIX domain, two residues (Y275 and Y295) of the PDZ domain, and a single residue (Y463) in the DEP domain. Dvl2 levels were knocked-down by siRNA targeting Dvl2; the ability of YF mutants (Dvl2 Y18F, Y27F, Y275F, Y295F and Y463F) versus wild-type Dvl2 to rescue Wnt3a-stimulated Lef/Tcf-sensitive transcription was probed. Expression of the wild-type Dvl2 in Dvl2 knockdown cells restored the Wnt3a-stimulated Lef/Tcf-sensitive transcription, whereas the Y18F Dvl2 mutant in particular attenuated the response by more than 50 % (fig. 5C).

(A) Dvl2 docks to Src and Hck tyrosine kinases. Cell lysates were incubated with one of the immobilized Dvl2 domain, DIX, DEP, PDZ, putative SH3 binding region (SH3B), C-terminus (aa 511- 736) and GST itself. Proteins docking to the Dvl2 domains were resolved by SDS-PAGE and analyzed by immunoblotting, staining with: anti-Src, anti-Hck, and anti-GST antibodies (loading control). (B) Mass spectrum of the Y27 phosphopeptide. Phosphorylated Dvl2 was digested with trypsin and analyzed by LC/MS/MS. Tyrosine 27 was identified as phosphorylation site. (C) Tyrosine-to-phenylalanine substitution mutants of Dvl2 attenuate Lef/Tcf-sensitive transcription. F9 cells stably expressing Rfz1 and M50 were transfected with siRNA targeting Dvl2 one day before transfection of the cells with either wild-type or one of the YF-mutants of Dvl2 (Y18F, Y27F, Y275F, Y295F and Y463F). On the following day, cells were stimulated with Wnt3a. Assay for Lef/Tcf-sensitive transcription was performed. Data are adapted with permission from the publication (Yokoyama, N. & Malbon, C. C. (2009). J Cell Sci 122, 4439-4451).

Fig. 5. Src family tyrosine kinases are involved in Wnt/β-catenin signaling.

At least two prominent components of the multiprotein complexes, Dvl2 and β-catenin, were phosphorylated on tyrosine residues in response to Wnt stimulation. Tyrosine phosphorylation of Y18, Y27 and Y275 of Dvl2 appears to contribute, in some complex manner, to the ability of Src to enhance Wnt3a/β-catenin signaling. Wnt stimulated Src docking to Dvl2 through the SH3-binding domain and the C-terminus proline-rich domain. Src activity is regulated by intramolecular interactions, an interaction between the SH2 domain and the C-terminal tail as well as an interaction between the SH3 domain and a polyproline-type helix in the SH2-kinase linker region (Sicheri et al., 1997; Williams et al.,

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 201

Size-exclusion chromatography (SEC) is one of the approaches for separating protein mixtures into numerous clusters of reduced complexity. Separation of Dvl3-based signalsomes was established by SEC for the first time. Cell lysates prepared from cells stimulated with or without Wnt3a were subjected to size-exclusion column chromatography. This high-pressure column (very long and well packed, HiLoad Superdex TM 200 prep grade 26/60) permitted high resolution over a *Mr* range of 43 kDa to ~2.0 MegaDa (i.e., 2,000,000 daltons). The operation was carried out with AKTA system (GE Healthcare). Molecular weight standards showed excellent and reproducible peak separation. The identified peaks are, discrete and highly reproducible *Mr* (fig. 6A). Thus size-exclusion column chromatography allows separation of Dvls-based supermolecular complexes for a reproducibly and accurate. Individual fractions were subjected to SDS-PAGE and analyzed by immunoblotting with isoform-specific antibodies. Dvl2, the major isoform of Dvls in F9 cell (>95%), displayed two major peaks (one with a peak *Mr* of ~1.6 MegaDa, and other centered around 0.5 MegaDa-*Mr*), and a minor peak (~80 kDa of *Mr*), likely a monomeric Dvl2. Dvl1 revealed the two similar high-*Mr* supermolecular forms of 1.6 and 0.5 MegaDa-*Mr*. In contrast, Dvl3 supermolecular complexes appeared a broad peak and the *Mr* of the resolved complexes spanning from the homodimeric Dvl3 (150-210 kDa) to the well-defined peaks with *Mr* from 0.8 to 2.0 MegaDa (fig. 6B). All three Dvls isoforms migrated to ~ 2 MegaDa regions without Wnt stimulation. GSK3β and Axin, components of the Dvls-based supermolecular complexes, also migrated to similar positions (Yokoyama et

**2.3.2 High–performance size-exclusion analysis of Dvls-based supermolecular** 

**2.3.3 Wnt stimulation provokes assembly of dynamic Dvl3-based supermolecular** 

MegaDa-*Mr*) in response to Wnt3a stimulation for 30 min (Yokoyama et al., 2010).

To ascertain whether mimicking Wnt3a action could result in the assembly of Dvl3-based supermolecular complexes, several distinct approaches were employed. The first approach is overexpression of Dvls. Assembly of Dvl3-based supermolecular complexes varied with the isoform of Dvls. It is known that overexpression of Dvl1 and Dvl3 in mouse F9 cells stimulated Lef/Tcf-sensitive transcription (Lee et al., 2008). Overexpression of Dvls provoked the formation of very large, Dvl3-based supermolecular complexes (fig. 7C), even larger than those observed in response to Wnt3a (fig. 7B). Overexpression of Dvl1 stimulated both activation of the canonical Lef/Tcf-sensitive transcription and an increase in the formation of the very large (>2.0 MegaDa-*Mr*) Dvl3-based supermolecular complexes

To address the functional significance of Dvls-based supermolecular complexes, we investigated whether their formation was regulated by Wnt3a stimulation. As seen in fig. 7A and 7B, Wnt3a provoked a dramatic shift in the apparent *Mr* of the Dvl3-based complexes to populations with sharply larger masses (>2.0 MegaDa-*Mr*). The abundance of Dvl3 based complexes (>2.0 MegaDa-*Mr*) was increased in a time-dependent manner (30min-60min after Wnt 3a stimulation) (fig. 7A and 7B). The upfield shift of Dvl3-based supermolecular complexes, derived at the expense of lower–*Mr* peaks, was detected as early as 5 min post-Wnt3a stimulation (unpublished data). In contrast, the shifts of other two isoforms, Dvl1 and Dvl2, to > 2.0 MegaDa*-Mr* were relatively small in response to Wnt. Dvl1/2-based complexes did not approach the limit size of those formed by Dvl3-based complexes. GSK3β and Axin also migrated with supermolecular complexes of increasing apparent mass (>2

**complexes** 

al., 2010).

**complexes** 

1997; Xu et al., 1997). Docking of Src to Dvl2 SH3 binding domain and C-terminus proline--rich domain disrupts Src autoinhibition, therefore enabling phosphorylation of Src substrates (Brown and Cooper, 1996; Miller, 2003). Activated Src enhances Wnt activation of the canonical pathway via phosphorylation of Dvl2 and β-catenin (Yokoyama and Malbon, 2009). Many Src family kinase substrates themselves possess SH2 and/or SH3 ligands, which couple enzyme activation to substrate phosphorylation (Brown and Cooper, 1996; Miller, 2003; Porter et al., 2000). In deed, total Src activity was increased after Wnt stimulation. Dvl2 bound form of Src showed higher activity than that of free form of Src. Furthermore, knockdown of Dvl2 blocked Wnt3a-induced activation of Src. Previously Y27D mutant of Dvl2 was shown as a polymerization defective mutant (Schwarz-Romond et al., 2007a). Roles of tyrosine residues Y18 and Y27 are the keenest interest. The tyrosine residue in the DIX domain plays an important role in polymerization of Dvl (i.e., form punctae). Application of GST pulled down and proteomics led to discover novel positive roles of Src family tyrosine kinase in Wnt/βcatenin signaling (Yokoyama and Malbon, 2009).

#### **2.3 Assembly of Dvls-based supermolecular complexes in response to Wnt stimulation**

Establishment of the physical nature and dynamic character of the Dvls-based complexes is a key to understanding Wnt signaling. The second approach is designed to probe Wntdependent assembly of Dvls-based supermolecular complexes. Dishevelled-based "punctae" have been observed earlier by fluorescence microscopy. Wnt treatment resulted in change of size of the "punctae" as well as their cellular localization. The physical evidence for the existence of these putative "aggregates" or "punctae" of Dvl3-based complexes was established using size-exclusion chromatography (SEC), affinity pull-downs, proteomics, and fluorescent correlation microscopy (*fcs*). Dvl3-based complexes were interrogated physically *in vitro* by SEC analysis of cell extracts and *in vivo* by *fcs* analysis in live cells (Yokoyama et al., 2010). *Fcs* enabled to analyze single molecules in live cells and is exploited for the study of the signalsomes mass and dynamic mobility. For the first time, assembly of supermolecular Dvl3-based complexes was shown in response to Wnt3a. Proteomics dissected the compositions of Dvls-based supermolecular complexes in response to Wnt stimulation.

#### **2.3.1 Preparation of Dvl3-based supermolecular complexes and quantification of proteins**

F9 cells co-expressing rat fz1(Rfz1) were suspended in ice-cold buffer (20 mM Tris-HCl pH 8.0, 0.2 M NaCl, 1 % NP-40, 1 mM PMSF, 10 µg/ml leupeptin, and 10 µg/ml aprotinin) and disrupted by repeated passage through a 23-gauge needle, and then centrifuged to remove unbroken cells, nucleus and mitochondria. Supernatants were filtered (0.45 μm) and diluted with buffer without detergent. 20 mg proteins were applied to a Superdex 200 gel filtration column (HiLoad Superdex TM 200 prep grade 26/60, fast-performance liquid chromatography system AKTA, GE Healthcare) which was preequilibrated with 20 mM Tris-HCl (pH 8.0), 0.2 M NaCl, and 10 % glycerol. Each fraction was analyzed by SDS-PAGE and Western immunoblotting. Protein concentration was determined by use of the Bradford assay. The immunoreactive bands were scanned by calibrated Umax 1000 scanner equipped with SilverFast software (LaserSoft Imaging Inc.). The bands were quantified by using Aida software (Raytest, Germany).

1997; Xu et al., 1997). Docking of Src to Dvl2 SH3 binding domain and C-terminus proline--rich domain disrupts Src autoinhibition, therefore enabling phosphorylation of Src substrates (Brown and Cooper, 1996; Miller, 2003). Activated Src enhances Wnt activation of the canonical pathway via phosphorylation of Dvl2 and β-catenin (Yokoyama and Malbon, 2009). Many Src family kinase substrates themselves possess SH2 and/or SH3 ligands, which couple enzyme activation to substrate phosphorylation (Brown and Cooper, 1996; Miller, 2003; Porter et al., 2000). In deed, total Src activity was increased after Wnt stimulation. Dvl2 bound form of Src showed higher activity than that of free form of Src. Furthermore, knockdown of Dvl2 blocked Wnt3a-induced activation of Src. Previously Y27D mutant of Dvl2 was shown as a polymerization defective mutant (Schwarz-Romond et al., 2007a). Roles of tyrosine residues Y18 and Y27 are the keenest interest. The tyrosine residue in the DIX domain plays an important role in polymerization of Dvl (i.e., form punctae). Application of GST pulled down and proteomics led to discover novel positive roles of Src family tyrosine kinase in Wnt/β-

**2.3 Assembly of Dvls-based supermolecular complexes in response to Wnt** 

Establishment of the physical nature and dynamic character of the Dvls-based complexes is a key to understanding Wnt signaling. The second approach is designed to probe Wntdependent assembly of Dvls-based supermolecular complexes. Dishevelled-based "punctae" have been observed earlier by fluorescence microscopy. Wnt treatment resulted in change of size of the "punctae" as well as their cellular localization. The physical evidence for the existence of these putative "aggregates" or "punctae" of Dvl3-based complexes was established using size-exclusion chromatography (SEC), affinity pull-downs, proteomics, and fluorescent correlation microscopy (*fcs*). Dvl3-based complexes were interrogated physically *in vitro* by SEC analysis of cell extracts and *in vivo* by *fcs* analysis in live cells (Yokoyama et al., 2010). *Fcs* enabled to analyze single molecules in live cells and is exploited for the study of the signalsomes mass and dynamic mobility. For the first time, assembly of supermolecular Dvl3-based complexes was shown in response to Wnt3a. Proteomics dissected the compositions of Dvls-based supermolecular complexes in response to Wnt

**2.3.1 Preparation of Dvl3-based supermolecular complexes and quantification of** 

F9 cells co-expressing rat fz1(Rfz1) were suspended in ice-cold buffer (20 mM Tris-HCl pH 8.0, 0.2 M NaCl, 1 % NP-40, 1 mM PMSF, 10 µg/ml leupeptin, and 10 µg/ml aprotinin) and disrupted by repeated passage through a 23-gauge needle, and then centrifuged to remove unbroken cells, nucleus and mitochondria. Supernatants were filtered (0.45 μm) and diluted with buffer without detergent. 20 mg proteins were applied to a Superdex 200 gel filtration column (HiLoad Superdex TM 200 prep grade 26/60, fast-performance liquid chromatography system AKTA, GE Healthcare) which was preequilibrated with 20 mM Tris-HCl (pH 8.0), 0.2 M NaCl, and 10 % glycerol. Each fraction was analyzed by SDS-PAGE and Western immunoblotting. Protein concentration was determined by use of the Bradford assay. The immunoreactive bands were scanned by calibrated Umax 1000 scanner equipped with SilverFast software (LaserSoft Imaging Inc.). The bands were quantified by using Aida

catenin signaling (Yokoyama and Malbon, 2009).

**stimulation** 

stimulation.

**proteins** 

software (Raytest, Germany).

#### **2.3.2 High–performance size-exclusion analysis of Dvls-based supermolecular complexes**

Size-exclusion chromatography (SEC) is one of the approaches for separating protein mixtures into numerous clusters of reduced complexity. Separation of Dvl3-based signalsomes was established by SEC for the first time. Cell lysates prepared from cells stimulated with or without Wnt3a were subjected to size-exclusion column chromatography. This high-pressure column (very long and well packed, HiLoad Superdex TM 200 prep grade 26/60) permitted high resolution over a *Mr* range of 43 kDa to ~2.0 MegaDa (i.e., 2,000,000 daltons). The operation was carried out with AKTA system (GE Healthcare). Molecular weight standards showed excellent and reproducible peak separation. The identified peaks are, discrete and highly reproducible *Mr* (fig. 6A). Thus size-exclusion column chromatography allows separation of Dvls-based supermolecular complexes for a reproducibly and accurate. Individual fractions were subjected to SDS-PAGE and analyzed by immunoblotting with isoform-specific antibodies. Dvl2, the major isoform of Dvls in F9 cell (>95%), displayed two major peaks (one with a peak *Mr* of ~1.6 MegaDa, and other centered around 0.5 MegaDa-*Mr*), and a minor peak (~80 kDa of *Mr*), likely a monomeric Dvl2. Dvl1 revealed the two similar high-*Mr* supermolecular forms of 1.6 and 0.5 MegaDa-*Mr*. In contrast, Dvl3 supermolecular complexes appeared a broad peak and the *Mr* of the resolved complexes spanning from the homodimeric Dvl3 (150-210 kDa) to the well-defined peaks with *Mr* from 0.8 to 2.0 MegaDa (fig. 6B). All three Dvls isoforms migrated to ~ 2 MegaDa regions without Wnt stimulation. GSK3β and Axin, components of the Dvls-based supermolecular complexes, also migrated to similar positions (Yokoyama et al., 2010).

#### **2.3.3 Wnt stimulation provokes assembly of dynamic Dvl3-based supermolecular complexes**

To address the functional significance of Dvls-based supermolecular complexes, we investigated whether their formation was regulated by Wnt3a stimulation. As seen in fig. 7A and 7B, Wnt3a provoked a dramatic shift in the apparent *Mr* of the Dvl3-based complexes to populations with sharply larger masses (>2.0 MegaDa-*Mr*). The abundance of Dvl3 based complexes (>2.0 MegaDa-*Mr*) was increased in a time-dependent manner (30min-60min after Wnt 3a stimulation) (fig. 7A and 7B). The upfield shift of Dvl3-based supermolecular complexes, derived at the expense of lower–*Mr* peaks, was detected as early as 5 min post-Wnt3a stimulation (unpublished data). In contrast, the shifts of other two isoforms, Dvl1 and Dvl2, to > 2.0 MegaDa*-Mr* were relatively small in response to Wnt. Dvl1/2-based complexes did not approach the limit size of those formed by Dvl3-based complexes. GSK3β and Axin also migrated with supermolecular complexes of increasing apparent mass (>2 MegaDa-*Mr*) in response to Wnt3a stimulation for 30 min (Yokoyama et al., 2010).

To ascertain whether mimicking Wnt3a action could result in the assembly of Dvl3-based supermolecular complexes, several distinct approaches were employed. The first approach is overexpression of Dvls. Assembly of Dvl3-based supermolecular complexes varied with the isoform of Dvls. It is known that overexpression of Dvl1 and Dvl3 in mouse F9 cells stimulated Lef/Tcf-sensitive transcription (Lee et al., 2008). Overexpression of Dvls provoked the formation of very large, Dvl3-based supermolecular complexes (fig. 7C), even larger than those observed in response to Wnt3a (fig. 7B). Overexpression of Dvl1 stimulated both activation of the canonical Lef/Tcf-sensitive transcription and an increase in the formation of the very large (>2.0 MegaDa-*Mr*) Dvl3-based supermolecular complexes

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 203

(A, B) Dvl3 assembles supermolecular multi-protein complexes in response to Wnt3a, in a time-dependent manner. F9 cells were stimulated with Wnt3a for the indicated times. Cells were lysed and subject to stericexclusion chromatography on Superdex 200. Fractions were analyzed by SDS-PAGE and resolved proteins immunoblotted with isoform-specific Dvl antibodies. Dvl3 blot (A) and quantitative analysis of Dvl3 (B) in

(C) Overexpression of Dvls provoked formation of supermolecular Dvl3-based complexes without Wnt3a stimulation*.* F9 cells were co-transfected with Rfz1 and either GFP- and HA-tagged mouse Dvl1, or Dvl2 or Dvl3. F9 cells were either unstimulated or stimulated with Wnt3a for 30 minutes. Cells lysates were applied to Superdex 200 gel filtration column. Fractions were analyzed by SDS-PAGE. Resolved proteins were immunoblotted with anti-Dvl3 antibody and quantified. Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J Cell Sci 123, 3693-3702).

Fig. 7. Assembly of Dvl3-based supermolecular complexes by Wnt stimulation and

the region above 750 kDa-*Mr.*

overexpression of Dvls.

(A) Resolution of Superdex 200 column**.** Mixtures of molecular weight markers (Blue Dextran 2000, thyroglobulin, ferritin, aldolase, conalbumin and ovaalbumin) were applied to Superdex 200 column. The elution profile was monitored by absorbance at 280 nm.

(B) F9 cells expressing Rfz1 were disrupted and cell lysates were applied to the Superdex 200 column. Proteins were analyzed by SDS-PAGE and immunoblotted with Dvl isoform-specific antibodies. Blots were quantified by the calibrated scanner. The calculated, relative molecular weight (*Mr*) positions from the calibration curve are labeled at the top, fraction numbers on the bottom. Arrows indicate the precise position at which calibration proteins elute from the Superdex 200 column. Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J Cell Sci 123, 3693-3702).

Fig. 6. Separation of Dvls-based supermolecular complexes using size-exclusion column chromatography.

(fig. 7C and Table 2). Overexpression of Dvl3 provoked a prominent activation of Lef/Tcfsensitive transcription and a sharp increase in the formation of very large (>2.0 MegaDa-*Mr*) Dvl3-based complexes (fig. 7C and Table 2). In contrast, overexpression of the most abundant Dvl isoform (i.e., Dvl2 which constitutes >95% of Dvls in F9 cell) provoked only a modest Lef/Tcf-sensitive transcriptional response and little formation of the very large, (>2.0 MegaDa-*Mr*) Dvl3-based complexes (fig. 7C and Table 2). Thus, the formation of the very large Dvl3-based supermolecular complexes can be specifically mimicked by the overexpression of either Dvl1 or Dvl3, which can uniquely activate the Lef/Tcf-sensitive pathway in the absence of Wnt3a. Lef/Tcf-sensitive transcriptional activation (the hallmark of activation of Wnt/beta-catenin pathway) and the assembly of Dvl3-based supermolecular complexes were followed and summarized in Table 2. The second approach to mimicking Wnt stimulation is the expression of constitutively active mutant (CA-delta-N) LRP6. Expression of the constitutively active mutant of LRP6 (CA-delta-N-LRP6) resulted in Wnt stimulation (Tamai et al., 2004). Mimicking Wnt3a action (in the absence of Wnt3a) by CAdelta-N-LRP6 was performed. Overexpression of CA-delta-N-LRP6 likewise provoked formation of the very large, supermolecular Dvl3-based complexes, just like Wnt3a itself (Table 2). Conversely, expression of the dominant-interfering delta-C-LRP6 blocked the canonical pathway and also abolished the formation of Dvl3-based supermolecular complexes. Inhibition of GSK3β activity is a third approach. The chemical inhibition of

(A) Resolution of Superdex 200 column**.** Mixtures of molecular weight markers (Blue Dextran 2000, thyroglobulin, ferritin, aldolase, conalbumin and ovaalbumin) were applied to Superdex 200 column.

(B) F9 cells expressing Rfz1 were disrupted and cell lysates were applied to the Superdex 200 column. Proteins were analyzed by SDS-PAGE and immunoblotted with Dvl isoform-specific antibodies. Blots were quantified by the calibrated scanner. The calculated, relative molecular weight (*Mr*) positions from the calibration curve are labeled at the top, fraction numbers on the bottom. Arrows indicate the precise position at which calibration proteins elute from the Superdex 200 column. Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J

Fig. 6. Separation of Dvls-based supermolecular complexes using size-exclusion column

(fig. 7C and Table 2). Overexpression of Dvl3 provoked a prominent activation of Lef/Tcfsensitive transcription and a sharp increase in the formation of very large (>2.0 MegaDa-*Mr*) Dvl3-based complexes (fig. 7C and Table 2). In contrast, overexpression of the most abundant Dvl isoform (i.e., Dvl2 which constitutes >95% of Dvls in F9 cell) provoked only a modest Lef/Tcf-sensitive transcriptional response and little formation of the very large, (>2.0 MegaDa-*Mr*) Dvl3-based complexes (fig. 7C and Table 2). Thus, the formation of the very large Dvl3-based supermolecular complexes can be specifically mimicked by the overexpression of either Dvl1 or Dvl3, which can uniquely activate the Lef/Tcf-sensitive pathway in the absence of Wnt3a. Lef/Tcf-sensitive transcriptional activation (the hallmark of activation of Wnt/beta-catenin pathway) and the assembly of Dvl3-based supermolecular complexes were followed and summarized in Table 2. The second approach to mimicking Wnt stimulation is the expression of constitutively active mutant (CA-delta-N) LRP6. Expression of the constitutively active mutant of LRP6 (CA-delta-N-LRP6) resulted in Wnt stimulation (Tamai et al., 2004). Mimicking Wnt3a action (in the absence of Wnt3a) by CAdelta-N-LRP6 was performed. Overexpression of CA-delta-N-LRP6 likewise provoked formation of the very large, supermolecular Dvl3-based complexes, just like Wnt3a itself (Table 2). Conversely, expression of the dominant-interfering delta-C-LRP6 blocked the canonical pathway and also abolished the formation of Dvl3-based supermolecular complexes. Inhibition of GSK3β activity is a third approach. The chemical inhibition of

The elution profile was monitored by absorbance at 280 nm.

Cell Sci 123, 3693-3702).

chromatography.

(A, B) Dvl3 assembles supermolecular multi-protein complexes in response to Wnt3a, in a time-dependent manner. F9 cells were stimulated with Wnt3a for the indicated times. Cells were lysed and subject to stericexclusion chromatography on Superdex 200. Fractions were analyzed by SDS-PAGE and resolved proteins immunoblotted with isoform-specific Dvl antibodies. Dvl3 blot (A) and quantitative analysis of Dvl3 (B) in the region above 750 kDa-*Mr.*

(C) Overexpression of Dvls provoked formation of supermolecular Dvl3-based complexes without Wnt3a stimulation*.* F9 cells were co-transfected with Rfz1 and either GFP- and HA-tagged mouse Dvl1, or Dvl2 or Dvl3. F9 cells were either unstimulated or stimulated with Wnt3a for 30 minutes. Cells lysates were applied to Superdex 200 gel filtration column. Fractions were analyzed by SDS-PAGE. Resolved proteins were immunoblotted with anti-Dvl3 antibody and quantified. Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J Cell Sci 123, 3693-3702).

Fig. 7. Assembly of Dvl3-based supermolecular complexes by Wnt stimulation and overexpression of Dvls.

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 205

not redundant with respect to function. Several key Dvl-interacting proteins (e.g., Dvl1, Dvl2, Dvl3, GSK3β and Axin) were present in these very large, Dvl3-based supermolecular complexes. Manipulations that provoked the formation of these Dvl3-based complexes also provoked activation of the Wnt/beta-catenin canonical pathway. Mutations of Dvl2 that block punctae formation inhibit the canonical Wnt signaling (Schwarz-Romond et al., 2007a). Dvl2/3 mutants, that do not form punctae, failed to assemble Dvl3-based supermolecular complexes (unpublished data). Phosphorylation-defective mutant of Dvl3 abolished assembly of Dvl3-based supermolecular complexes as well as activation of Wnt/beta-catenin signaling. On the other hand, Phosphorylation-mimetic mutant provoked both assembly of Dvl3-based supermolecular complexes and Lef/Tcf-sensitive transcription (unpublished data). The phosphorylation site was identified by CK1δ *in vitro* followed by proteomics. Data established that phosphorylation of Dvls is a crucial regulatory mechanism for the spatial/temporal assembly of dynamic supermolecular complexes to transduce Wnt signaling. Recent report provided compelling evidence that phosphorylation by CK1δ/ε sequentially regulates activation and de-activation of Dvls. Phosphorylated Dvls stimulate the oligomerization of Dvls, whereas the hyper-phosphorylated Dvls have less ability to

**2.3.4 Analysis of Dvl3-based supermolecular complexes by fluorescence correlation** 

**2.3.5 Proteomic analysis of Dvl3-based supermolecular complexes in response to** 

Thus the assembly of Dvl3-based supermolecular complexes in response to Wnt stimulation is established. Next important questions are "What are the compositions in Dvl3-based supermolecular complexes?" and "How is assembly/disassembly of Dvl3-based complexes regulated?" Proteomic analysis was employed to dissect the compositions of supermolecular

Fluorescence correlation spectroscopy (*fcs)* is used to probe the apparent size of the Dvl3 and Dvl2-based complexes in F9 cell. *Fcs* measurements were performed on a Zeiss LSM 510 Meta/Confocor 2 apparatus (Jena, Germany) fitted with a 40 × NA 1.2 C-Apochromat water immersion objective. *Fcs* of eGFP-tagged Dvl3 or Dvl2 in F9 cells was performed. In live cells the MW of Dvl3- and Dvl2-based supermolecular complexes was calculated. eGFP-tagged Dvl3 was tracked and the molecular weight calculated from the diffusion coefficients (Hess et al., 2002; Lakowicz, 2006; Schwille et al., 1999, Yokoyama et al., 2010). Two populations of eGFP-Dvl3 in unstimulated cells were obtained (Dvl3 dimers, i.e. ~132 kDa, and very large oligomers of ~35 MegaDa). Wnt stimulation slowed down the diffusion of the large complexes and increased the molecular mass of Dvl3-based complexes to ~40 MegaDa. The diffusion of the smaller complexes did not change in response to Wnt treatment. By sharp contrast, Dvl2-based complexes did not change molecular mass before and after stimulation with Wnt3a. This phenomenon is coinciding with those obtained by SEC. Wnt treatment resulted in increase of the mass of Dvl3-based supermolecular complexes, similar to those by SEC. The MW determined by *fcs* is much larger than that reported on the bases of SEC analysis with a Sephacryl S-400 column (HiPrep Sephacryl S400 high-resolution column 16/ 60, fast-performance AKTA liquid chromatography) (3-7 MegaDa-*Mr*). The size of Dvl3 based supermolecular complexes by SEC is reassessed and amended to 35 MegaDa, similar

oligomerize also form punctae (Bernatik et al., 2011).

to those identified by *fcs* (Patel & Winzor., 2010).

**spectroscopy (***fcs)*

**Wnt3a** 

GSK3β by LiCl stimulates Lef/Tcf-sensitive transcription (Stambolic et al., 1996). Inhibition of GSK3β provoked increased formation of the very large (>2.0 MegaDa-*Mr*) Dvl3-based supermolecular complexes (Table 2).

To define precisely roles of Dvl isoforms on the assembly of Dvl3-based supermolecular complexes, the effect of knockdown of each Dvl isoform was investigated (Table 2). Knockdown of each Dvl isoform resulted in attenuation of the assembly of Dvl3-based supermolecular complexes as well as Lef/Tcf-sensitive transcription. Knockdown of Dvl1 and Dvl3, lower abundance of Dvls in F9 cells, was more effective on both parameters. Knockdown of Dvl3 essentially precluded formation of the supermolecular complex in the absence or presence of Wnt3a. Knockdown of Dvl1 had little effect on the abundance of the basal Dvl3-based complex (i.e., without Wnt3a stimulation), but attenuated Wnt3a provoked formation of Dvl3-based supermolecular complexes (>2.0 MegaDa*-Mr*). Knockdown of the most abundant isoform Dvl2 severely reduced the abundance of the Dvl3-based complexes in the absence of Wnt3a. Wnt3a failed to stimulate the formation of supermolecular Dvl3 based complexes as well as activation of the canonical pathway. Thus, clearly Dvl1 and Dvl2 cooperate in catalyzing the formation of supermolecular, Dvl3-based complexes in either the absence or presence of Wnt3a. Finally, Dickkopf homologue 1 (DKK1), a well known Wnt antagonist (Nusse, 2001), blocked both activation of the Wnt canonical pathway and formation of very large, Dvl3-based supermolecular complexes (Table 2).


OE: overexpression; KD: knockdown; Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J Cell Sci 123, 3693-3702).

Table 2. Summary of Wnt activation and formation of Dvl3-based supermolecular complexes.

Thus, through targeted activation and disruption of Wnt3a signaling, a linkage between the ability to form very large (>2.0 MegaDa-*Mr*) Dvl3-based supermolecular complexes and the level of activation of Lef/Tcf-sensitive transcription (functional downstream signaling) was established. Unique rolls of Dvl3 in Wnt/β-catenin signaling demonstrate Dvl isoforms are

GSK3β by LiCl stimulates Lef/Tcf-sensitive transcription (Stambolic et al., 1996). Inhibition of GSK3β provoked increased formation of the very large (>2.0 MegaDa-*Mr*) Dvl3-based

To define precisely roles of Dvl isoforms on the assembly of Dvl3-based supermolecular complexes, the effect of knockdown of each Dvl isoform was investigated (Table 2). Knockdown of each Dvl isoform resulted in attenuation of the assembly of Dvl3-based supermolecular complexes as well as Lef/Tcf-sensitive transcription. Knockdown of Dvl1 and Dvl3, lower abundance of Dvls in F9 cells, was more effective on both parameters. Knockdown of Dvl3 essentially precluded formation of the supermolecular complex in the absence or presence of Wnt3a. Knockdown of Dvl1 had little effect on the abundance of the basal Dvl3-based complex (i.e., without Wnt3a stimulation), but attenuated Wnt3a provoked formation of Dvl3-based supermolecular complexes (>2.0 MegaDa*-Mr*). Knockdown of the most abundant isoform Dvl2 severely reduced the abundance of the Dvl3-based complexes in the absence of Wnt3a. Wnt3a failed to stimulate the formation of supermolecular Dvl3 based complexes as well as activation of the canonical pathway. Thus, clearly Dvl1 and Dvl2 cooperate in catalyzing the formation of supermolecular, Dvl3-based complexes in either the absence or presence of Wnt3a. Finally, Dickkopf homologue 1 (DKK1), a well known Wnt antagonist (Nusse, 2001), blocked both activation of the Wnt canonical pathway and

**(+) Wnt3a** 

 Oligomerization Lef/Tcf transcription

formation of very large, Dvl3-based supermolecular complexes (Table 2).

Control - - + + DKK1 - - - -

LiCl ++ ++ ++ ++ Dvl1 OE ++ ++ ++ ++ Dvl2 OE + + + + Dvl3 OE +++ +++ +++ +++ Dvl1KD + - + - Dvl2 KD + - + - Dvl3 KD - - - - OE: overexpression; KD: knockdown; Data are adapted with permission from the publication (Yokoyama, N., Golebiewska, U., Wang, H. Y. & Malbon, C. C. (2010) J Cell Sci 123, 3693-3702). Table 2. Summary of Wnt activation and formation of Dvl3-based supermolecular

Thus, through targeted activation and disruption of Wnt3a signaling, a linkage between the ability to form very large (>2.0 MegaDa-*Mr*) Dvl3-based supermolecular complexes and the level of activation of Lef/Tcf-sensitive transcription (functional downstream signaling) was established. Unique rolls of Dvl3 in Wnt/β-catenin signaling demonstrate Dvl isoforms are

+ + + +


**(-) Wnt3a** 

 Oligomerization Lef/Tcf transcription

delta-N-LRP6

delta-C-LRP6

complexes.

supermolecular complexes (Table 2).

not redundant with respect to function. Several key Dvl-interacting proteins (e.g., Dvl1, Dvl2, Dvl3, GSK3β and Axin) were present in these very large, Dvl3-based supermolecular complexes. Manipulations that provoked the formation of these Dvl3-based complexes also provoked activation of the Wnt/beta-catenin canonical pathway. Mutations of Dvl2 that block punctae formation inhibit the canonical Wnt signaling (Schwarz-Romond et al., 2007a). Dvl2/3 mutants, that do not form punctae, failed to assemble Dvl3-based supermolecular complexes (unpublished data). Phosphorylation-defective mutant of Dvl3 abolished assembly of Dvl3-based supermolecular complexes as well as activation of Wnt/beta-catenin signaling. On the other hand, Phosphorylation-mimetic mutant provoked both assembly of Dvl3-based supermolecular complexes and Lef/Tcf-sensitive transcription (unpublished data). The phosphorylation site was identified by CK1δ *in vitro* followed by proteomics. Data established that phosphorylation of Dvls is a crucial regulatory mechanism for the spatial/temporal assembly of dynamic supermolecular complexes to transduce Wnt signaling. Recent report provided compelling evidence that phosphorylation by CK1δ/ε sequentially regulates activation and de-activation of Dvls. Phosphorylated Dvls stimulate the oligomerization of Dvls, whereas the hyper-phosphorylated Dvls have less ability to oligomerize also form punctae (Bernatik et al., 2011).

#### **2.3.4 Analysis of Dvl3-based supermolecular complexes by fluorescence correlation spectroscopy (***fcs)*

Fluorescence correlation spectroscopy (*fcs)* is used to probe the apparent size of the Dvl3 and Dvl2-based complexes in F9 cell. *Fcs* measurements were performed on a Zeiss LSM 510 Meta/Confocor 2 apparatus (Jena, Germany) fitted with a 40 × NA 1.2 C-Apochromat water immersion objective. *Fcs* of eGFP-tagged Dvl3 or Dvl2 in F9 cells was performed. In live cells the MW of Dvl3- and Dvl2-based supermolecular complexes was calculated. eGFP-tagged Dvl3 was tracked and the molecular weight calculated from the diffusion coefficients (Hess et al., 2002; Lakowicz, 2006; Schwille et al., 1999, Yokoyama et al., 2010). Two populations of eGFP-Dvl3 in unstimulated cells were obtained (Dvl3 dimers, i.e. ~132 kDa, and very large oligomers of ~35 MegaDa). Wnt stimulation slowed down the diffusion of the large complexes and increased the molecular mass of Dvl3-based complexes to ~40 MegaDa. The diffusion of the smaller complexes did not change in response to Wnt treatment. By sharp contrast, Dvl2-based complexes did not change molecular mass before and after stimulation with Wnt3a. This phenomenon is coinciding with those obtained by SEC. Wnt treatment resulted in increase of the mass of Dvl3-based supermolecular complexes, similar to those by SEC. The MW determined by *fcs* is much larger than that reported on the bases of SEC analysis with a Sephacryl S-400 column (HiPrep Sephacryl S400 high-resolution column 16/ 60, fast-performance AKTA liquid chromatography) (3-7 MegaDa-*Mr*). The size of Dvl3 based supermolecular complexes by SEC is reassessed and amended to 35 MegaDa, similar to those identified by *fcs* (Patel & Winzor., 2010).

#### **2.3.5 Proteomic analysis of Dvl3-based supermolecular complexes in response to Wnt3a**

Thus the assembly of Dvl3-based supermolecular complexes in response to Wnt stimulation is established. Next important questions are "What are the compositions in Dvl3-based supermolecular complexes?" and "How is assembly/disassembly of Dvl3-based complexes regulated?" Proteomic analysis was employed to dissect the compositions of supermolecular

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 207

Tandem mass spectra were extracted by RawXtract version 1.9.7. All MS/MS samples were analyzed using Sequest (Thermo Fisher Scientific). Sequest was set up to search a mouse IPI database (ver. 3.75), including reversed sequences (in total 113990 entries) assuming no specific protease. Sequest was searched with a parent ion tolerance of 1.5Da. Iodoacetamide

Scaffold (version Scaffold\_3\_00\_08, Proteome Software Inc.) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller et al., 2002). Protein identifications were accepted if they could be established at greater than 99.0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii et al., 2003). Proteins that contained similar peptides and could not be differentiated based on

derivative of cysteine was specified in Sequest as a fixed modification.

MS/MS analysis alone were grouped to satisfy the principles of parsimony.

signaling related proteins and other signaling molecules.

PI4kinase Ephrin B1 CK1γ aPKC Src Par1 PTK7 Calpain AP-2 Daam 1 Actin CK1α LRP6 CK2α PP2A CK2β

Table 3. Identified known Dvls interaction proteins.

pathway and other signaling pathway.

proteins may assemble with Dvl3-based supermolecular complexes.

**response to Wnt3a** 

Ror2

**2.3.7 Evaluation of compositions of Dvl3-based supermolecular complexes in** 

Data were normalized between distinct time points and positive identification was ascribed to a protein which had at least 30 unique fragments. Proteins, which abundance was changed during Wnt stimulation, were further analyzed. This strategy may identify other supermolecular complexes occurred in advance of Wnt signaling as well. Analysis of proteomics identified both expected and also novel components. Identified compositions of Dvl3-based complexes included protein kinases and phosphatases (serine/threonine and tyrosine), guanine nucleotide binding proteins, inositol phosphate related proteins, Wnt

Detected known Dvls interacting proteins are listed in Table 3. Presence of Dvls interacting proteins (17 distinct proteins) in the above ~ 3 MegaDa*-Mr* region strongly indicated these

**Identified known Dvls interacting proteins** 

Identified key molecules involving in Wnt signaling pathway include Rho, Gα family, Gβ1 and mTOR, and abundance of these proteins was changed after Wnt stimulation (Table 4). Data represent the diversities and complexities of Wnt signaling pathways which Dvls are involved in. Proteomic analysis identified proteins related in the Wnt/β-catenin signaling pathway, but also proteins involved in the planar cell polarity (PCP) pathway, Wnt/Ca2+

Dvl3-based complexes. Over 3 MegaDa-*Mr* peak fractions of Dvl3-based supermolecular complexes separated by SEC from Wnt3a treated or untreated cells were subjected to LC-ESI-MS-MS. Dvl3-based supermolecular complexes (>3 MegaDa-*Mr*) isolated by SEC are distributed in a less populated region of the chromatogram (i.e., near void volume). Limited amount of proteins (~1% of total proteins) migrate to >3 MegaDa-*Mr* peak without Wnt stimulation. Wnt stimulation enhanced the abundance of these proteins 2-4 times in > 3 MegaDa-*Mr* region. The low amount of proteins in > 3 MegaDa-*Mr* region allowed executing proteomic analysis, although a main concerning of this application is potential contamination of proteins/complexes of *Mr* similar to that of the Dvl3-based supermolecular complexes. However, unrelated proteins/complexes (contamination) would not co-migrate with the Dvl3-based supermolecular complexes and also would not response to Wnt stimulation.

To minimize potential contamination, the analysis was carried out at distinct time points (0, 5, 10 and 30 min). By comparing the proteomic profiles from >3 MegaDa-*Mr* complexes with Wnt stimulation *versus* without Wnt stimulation, it will be possible to define the relative abundance of partners in >3 MegaDa-*Mr* peak in a Wnt- and time-dependent manner.

#### **2.3.6 Proteomics of Dvl3-based supermolecular complexes**

Pooled peaks (over 3 MegaDa-*Mr*) were subjected to TCA precipitation. Protein TCA pellets were resuspended in 10 μl of 8M urea, and diluted to 2M urea with 0.1M ammonium bicarbonate. The proteins were reduced with 5mM DTT and alkylated with 10mM iodoacetamide. 2 μg of trypsin was added to the proteins and incubated overnight at 37 oC. The digestion reaction was stopped with formic acid (5% final concentration).

Multidimensional chromatography was applied. Peptide mixtures were pressure-loaded onto a 250 µm inner diameter (i.d.) fused-silica capillary packed first with 3 cm of 5 μm strong cation exchange material (Partisphere SCX, Whatman), followed by 3 cm of 10 μm C18 reverse phase (RP) particles (Magic, Michrom). Loaded and washed microcapillaries were connected to a 100 µm i.d. column, which had been pulled to a 5 μm i.d. tip using a P-2000 CO2 laser puller (Sutter Instruments), then packed with 13 cm of 3 μm C18 reverse phase (RP) particles (Magic, Michrom) and equilibrated in 2% acetonitrile, 0.1 % formic acid (Buffer A). This split-column was then installed in-line with Thermo Surveyor MS HPLC pump. The flow rate was ~500 nl/min. Fully automated 12-step chromatography runs were carried out. Three different elution buffers were used: 2% acetonitrile, 0.1 % formic acid (Buffer A); 98% acetonitrile, 0.1% formic acid (Buffer B); and 0.5 M ammonium acetate, 2% acetonitrile, 0.1% formic acid (Buffer C). In such sequences of chromatographic events, peptides were sequentially eluted from the SCX resin to the RP resin by increasing salt steps (increase in Buffer C concentration), followed by organic gradients (increase in Buffer B concentration). The last chromatography step consisted in a high salt wash with 100% Buffer C followed by acetonitrile gradient. The application of a 1.8 kV distal voltage electrosprayed the eluting peptides directly into a LTQ mass spectrometer equipped with a nano-LC electrospray ionization source. Full MS spectra were recorded on the peptides over a 400 to 2000 m/z range, followed by five tandem mass (MS/MS) events sequentially generated in a data-dependent manner on the first, second, third, fourth, fifth most intense ions selected from the full MS spectrum (at 35% collision energy). Mass spectrometer scan functions and HPLC solvent gradients were controlled by the Xcalibur data system (ThermoFinnigan).

Dvl3-based complexes. Over 3 MegaDa-*Mr* peak fractions of Dvl3-based supermolecular complexes separated by SEC from Wnt3a treated or untreated cells were subjected to LC-ESI-MS-MS. Dvl3-based supermolecular complexes (>3 MegaDa-*Mr*) isolated by SEC are distributed in a less populated region of the chromatogram (i.e., near void volume). Limited amount of proteins (~1% of total proteins) migrate to >3 MegaDa-*Mr* peak without Wnt stimulation. Wnt stimulation enhanced the abundance of these proteins 2-4 times in > 3 MegaDa-*Mr* region. The low amount of proteins in > 3 MegaDa-*Mr* region allowed executing proteomic analysis, although a main concerning of this application is potential contamination of proteins/complexes of *Mr* similar to that of the Dvl3-based supermolecular complexes. However, unrelated proteins/complexes (contamination) would not co-migrate with the Dvl3-based supermolecular complexes and also would not response to Wnt

To minimize potential contamination, the analysis was carried out at distinct time points (0, 5, 10 and 30 min). By comparing the proteomic profiles from >3 MegaDa-*Mr* complexes with Wnt stimulation *versus* without Wnt stimulation, it will be possible to define the relative abundance of partners in >3 MegaDa-*Mr* peak in a Wnt- and time-dependent manner.

Pooled peaks (over 3 MegaDa-*Mr*) were subjected to TCA precipitation. Protein TCA pellets were resuspended in 10 μl of 8M urea, and diluted to 2M urea with 0.1M ammonium bicarbonate. The proteins were reduced with 5mM DTT and alkylated with 10mM iodoacetamide. 2 μg of trypsin was added to the proteins and incubated overnight at 37 oC.

Multidimensional chromatography was applied. Peptide mixtures were pressure-loaded onto a 250 µm inner diameter (i.d.) fused-silica capillary packed first with 3 cm of 5 μm strong cation exchange material (Partisphere SCX, Whatman), followed by 3 cm of 10 μm C18 reverse phase (RP) particles (Magic, Michrom). Loaded and washed microcapillaries were connected to a 100 µm i.d. column, which had been pulled to a 5 μm i.d. tip using a P-2000 CO2 laser puller (Sutter Instruments), then packed with 13 cm of 3 μm C18 reverse phase (RP) particles (Magic, Michrom) and equilibrated in 2% acetonitrile, 0.1 % formic acid (Buffer A). This split-column was then installed in-line with Thermo Surveyor MS HPLC pump. The flow rate was ~500 nl/min. Fully automated 12-step chromatography runs were carried out. Three different elution buffers were used: 2% acetonitrile, 0.1 % formic acid (Buffer A); 98% acetonitrile, 0.1% formic acid (Buffer B); and 0.5 M ammonium acetate, 2% acetonitrile, 0.1% formic acid (Buffer C). In such sequences of chromatographic events, peptides were sequentially eluted from the SCX resin to the RP resin by increasing salt steps (increase in Buffer C concentration), followed by organic gradients (increase in Buffer B concentration). The last chromatography step consisted in a high salt wash with 100% Buffer C followed by acetonitrile gradient. The application of a 1.8 kV distal voltage electrosprayed the eluting peptides directly into a LTQ mass spectrometer equipped with a nano-LC electrospray ionization source. Full MS spectra were recorded on the peptides over a 400 to 2000 m/z range, followed by five tandem mass (MS/MS) events sequentially generated in a data-dependent manner on the first, second, third, fourth, fifth most intense ions selected from the full MS spectrum (at 35% collision energy). Mass spectrometer scan functions and HPLC solvent gradients were

The digestion reaction was stopped with formic acid (5% final concentration).

**2.3.6 Proteomics of Dvl3-based supermolecular complexes** 

controlled by the Xcalibur data system (ThermoFinnigan).

stimulation.

Tandem mass spectra were extracted by RawXtract version 1.9.7. All MS/MS samples were analyzed using Sequest (Thermo Fisher Scientific). Sequest was set up to search a mouse IPI database (ver. 3.75), including reversed sequences (in total 113990 entries) assuming no specific protease. Sequest was searched with a parent ion tolerance of 1.5Da. Iodoacetamide derivative of cysteine was specified in Sequest as a fixed modification.

Scaffold (version Scaffold\_3\_00\_08, Proteome Software Inc.) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller et al., 2002). Protein identifications were accepted if they could be established at greater than 99.0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii et al., 2003). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

#### **2.3.7 Evaluation of compositions of Dvl3-based supermolecular complexes in response to Wnt3a**

Data were normalized between distinct time points and positive identification was ascribed to a protein which had at least 30 unique fragments. Proteins, which abundance was changed during Wnt stimulation, were further analyzed. This strategy may identify other supermolecular complexes occurred in advance of Wnt signaling as well. Analysis of proteomics identified both expected and also novel components. Identified compositions of Dvl3-based complexes included protein kinases and phosphatases (serine/threonine and tyrosine), guanine nucleotide binding proteins, inositol phosphate related proteins, Wnt signaling related proteins and other signaling molecules.

Detected known Dvls interacting proteins are listed in Table 3. Presence of Dvls interacting proteins (17 distinct proteins) in the above ~ 3 MegaDa*-Mr* region strongly indicated these proteins may assemble with Dvl3-based supermolecular complexes.


Table 3. Identified known Dvls interaction proteins.

Identified key molecules involving in Wnt signaling pathway include Rho, Gα family, Gβ1 and mTOR, and abundance of these proteins was changed after Wnt stimulation (Table 4). Data represent the diversities and complexities of Wnt signaling pathways which Dvls are involved in. Proteomic analysis identified proteins related in the Wnt/β-catenin signaling pathway, but also proteins involved in the planar cell polarity (PCP) pathway, Wnt/Ca2+ pathway and other signaling pathway.

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 209

(A) Time courses of LRP, CK1γ and PI4K in >3 MegaDa-*Mr* peak. Changes of LRP, CK1γ, PI4K and actin

antibody. Indeed, assembly of Dvl3-based supermolecular complexes was detected in Rfz2 expressing F9 cell in response to Wnt5a, demonstrating Wnt5a-dependent assembly of the large complexes is provoked in the PCP pathway as well (fig. 9). PCP signaling is pivotal for establishing cell polarity and activation of PCP pathway leads to the activation of Rhofamily GTPase and JNK through Dvls and Dishevelled-associated activator of morphogenesis 1 (Daam1) (Wallingford and Habas, 2005). Daam 1 bound to both Rho GTPase and Dvl, mediates Wnt-induced Dvl/Rho complex formation, which in turn activates Rho-associated protein kinase (ROCK) and remodels cytoskeleton (Habas et al., 2001). Daam1 is imprecated in cancer through its regulation of endocytosis of Ephrin B1 (Kida et al., 2007). Furthermore, proteomics also identified Ror2, which is involved in Wnt/Ca2+ pathway. Ror2 is a receptor tyrosine kinase. Wnt interaction with Fz and coreceptor Kyn or Ror2 provokes increase of intracellular Ca2+ level and subsequently activates calcium/calmodulin-dependent protein kinase II (CaMKII), protein kinase C (PKC) and nuclear factor of activated T cells (NFAT). Ca2+ is a central regulator of many cell functions and its downstream targets are numerous. Ror2 also shown as a receptor for Wnt5a, stimulating the non-canonical pathway (Oishi et al., 2003). In addition, Ror2 has a opposing role in the canonical signaling pathway and is able to inhibit Wnt/β-catenin signaling (Mikels and Nusse, 2006). Tyrosine kinase activity of Ror2 is indispensable for Wnt5ainduced inhibition of Wnt/β-catenin signaling (Mikels et al., 2009). Direct interaction of Ror2 with phosphorylated Dvl is required for the inhibition of Wnt/β-catenin signaling (Witte et al., 2010). Thus, Ror2 plays diverse roles in the discrete Wnt signaling pathways.

(B) Wnt3a responsive Dvls interacting proteins in >3 MegaDa-*Mr* peak are displayed.

Fig. 8. Time courses of key components presented in >3 MegaDa-*Mr*.*.*

in response to Wnt3a stimulation are displayed.


Table 4. Identified Wnt signaling related proteins.

Many proteins such as PI4 kinase, CK1γ, Src, LRP6, PP2A, Par1, CK1α, CK2α and CK2β are important key molecules in Wnt/β-catenin signaling pathway and are already known Dvl interacting proteins. Wnt treatment quickly produces plasma-membrane-associated LRP6 aggregates (Bilic et al., 2007). CK1γ phosphorylates LRP5/6 in response to Wnt3a (Davidson et al., 2005). Phosphorylation of LRP5/6 promotes Axin recruitment (Davidson et al., 2005; Tolwinski et al., 2003; Zeng et al., 2005). PI4 kinase together with PI5K kinase enhances PIP2 production and stimulates clustering of Dvls and LRP5/6 receptor (Pan et al., 2008). To see whether key parameters in the Dvl3-based supermolecular complexes will change in response to Wnt stimulation, tentative analysis of these parameters was carried out, although these data are not precisely quantitative (fig. 8A and 8B). First key step after Wnt stimulation is the phosphorylation of LRP6 by CK1γ. The abundance of LRP in the Dvl3 based supermolecular complexes was increased two times 5 min post stimulation by Wnt3a and sustained over 30 min. The abundance of CK1γ in the Dvl3-based supermolecular complexes was increased 5 min post Wnt stimulation and peaked at 10min thereafter decreased (fig. 8A). Thus, similar migration of two components was obtained in response to Wnt3a. These time courses fit quite well to a previous report, which demonstrated that LRP6 starts coalescing into punctate structures at or below the plasma membrane within 15min Wnt stimulation. PI4 kinase, which is involved in the production of PtdIns (4, 5)P2, dramatically increased 10 min post Wnt stimulation (fig. 8A). Data fits well with a previous report that significant PtdIns (4,5)P2 formation was detected 15-30 min after Wnt3a stimulation (Pan et al., 2008). In contrast, proteins like actin did not change significantly upon Wnt stimulation. Many Dvls interacting proteins, which are involved in the Wnt/βcatenin signaling, CK1α, CK2α, CK1γ, PI4kinaae and Src migrated at >3 MegaDa-*Mr* region after 5-10min Wnt stimulation (fig. 8B). Previous data showed that Src docking to Dvl2 is increased after 5-10min Wnt stimulation and sustained for 45min (Yokoyama and Malbon, 2007). Changing of Src abundance in the Dvl3-based supermolecular complexes was matched to the previous finding. Furthermore, not all proteins found in >3 MegaDa-*Mr* region were increased during Wnt stimulation. Migration of Par1 was decreased upon Wnt stimulation (fig. 8B). Also proteins such as tubulin, HSP60 and 40S ribosomal protein in >3 MegaDa-*Mr* region were decreased upon Wnt stimulation. Together, migrations of proteins found in the Dvl3-based supermolecular complexes were responsive to Wnt stimulation temporally and some of them were correlated well, suggesting that results obtained by proteomic analysis represent proper variations of compositions.

Interesting observations are several proteins involved in the PCP pathway, Daam 1, PTK7, AP-2, Par1, Ephrin B1 and Rho GTPase, were also found in >3 MegaDa-*Mr* peak. Therefore, whether or not activation of PCP pathway would provoke assembly of Dvl3-based supermolecular complexes was investigated. Wnt5a has been classified as a non-canonical Wnt family member. Rat Fz2 (Rfz2) expressing F9 cells were stimulated with Wnt5a for 30 min and cell lysates were separated by SEC and further analyzed by immunoblotting with anti-Dvl3

**Wnt signaling related proteins** 

Many proteins such as PI4 kinase, CK1γ, Src, LRP6, PP2A, Par1, CK1α, CK2α and CK2β are important key molecules in Wnt/β-catenin signaling pathway and are already known Dvl interacting proteins. Wnt treatment quickly produces plasma-membrane-associated LRP6 aggregates (Bilic et al., 2007). CK1γ phosphorylates LRP5/6 in response to Wnt3a (Davidson et al., 2005). Phosphorylation of LRP5/6 promotes Axin recruitment (Davidson et al., 2005; Tolwinski et al., 2003; Zeng et al., 2005). PI4 kinase together with PI5K kinase enhances PIP2 production and stimulates clustering of Dvls and LRP5/6 receptor (Pan et al., 2008). To see whether key parameters in the Dvl3-based supermolecular complexes will change in response to Wnt stimulation, tentative analysis of these parameters was carried out, although these data are not precisely quantitative (fig. 8A and 8B). First key step after Wnt stimulation is the phosphorylation of LRP6 by CK1γ. The abundance of LRP in the Dvl3 based supermolecular complexes was increased two times 5 min post stimulation by Wnt3a and sustained over 30 min. The abundance of CK1γ in the Dvl3-based supermolecular complexes was increased 5 min post Wnt stimulation and peaked at 10min thereafter decreased (fig. 8A). Thus, similar migration of two components was obtained in response to Wnt3a. These time courses fit quite well to a previous report, which demonstrated that LRP6 starts coalescing into punctate structures at or below the plasma membrane within 15min Wnt stimulation. PI4 kinase, which is involved in the production of PtdIns (4, 5)P2, dramatically increased 10 min post Wnt stimulation (fig. 8A). Data fits well with a previous report that significant PtdIns (4,5)P2 formation was detected 15-30 min after Wnt3a stimulation (Pan et al., 2008). In contrast, proteins like actin did not change significantly upon Wnt stimulation. Many Dvls interacting proteins, which are involved in the Wnt/βcatenin signaling, CK1α, CK2α, CK1γ, PI4kinaae and Src migrated at >3 MegaDa-*Mr* region after 5-10min Wnt stimulation (fig. 8B). Previous data showed that Src docking to Dvl2 is increased after 5-10min Wnt stimulation and sustained for 45min (Yokoyama and Malbon, 2007). Changing of Src abundance in the Dvl3-based supermolecular complexes was matched to the previous finding. Furthermore, not all proteins found in >3 MegaDa-*Mr* region were increased during Wnt stimulation. Migration of Par1 was decreased upon Wnt stimulation (fig. 8B). Also proteins such as tubulin, HSP60 and 40S ribosomal protein in >3 MegaDa-*Mr* region were decreased upon Wnt stimulation. Together, migrations of proteins found in the Dvl3-based supermolecular complexes were responsive to Wnt stimulation temporally and some of them were correlated well, suggesting that results obtained by

Rho GTPase 2 mTOR Gαi2 Gαi3 Gαs Gβ1

proteomic analysis represent proper variations of compositions.

Interesting observations are several proteins involved in the PCP pathway, Daam 1, PTK7, AP-2, Par1, Ephrin B1 and Rho GTPase, were also found in >3 MegaDa-*Mr* peak. Therefore, whether or not activation of PCP pathway would provoke assembly of Dvl3-based supermolecular complexes was investigated. Wnt5a has been classified as a non-canonical Wnt family member. Rat Fz2 (Rfz2) expressing F9 cells were stimulated with Wnt5a for 30 min and cell lysates were separated by SEC and further analyzed by immunoblotting with anti-Dvl3

Table 4. Identified Wnt signaling related proteins.

Gα13

(A) Time courses of LRP, CK1γ and PI4K in >3 MegaDa-*Mr* peak. Changes of LRP, CK1γ, PI4K and actin in response to Wnt3a stimulation are displayed.

(B) Wnt3a responsive Dvls interacting proteins in >3 MegaDa-*Mr* peak are displayed.

Fig. 8. Time courses of key components presented in >3 MegaDa-*Mr*.*.*

antibody. Indeed, assembly of Dvl3-based supermolecular complexes was detected in Rfz2 expressing F9 cell in response to Wnt5a, demonstrating Wnt5a-dependent assembly of the large complexes is provoked in the PCP pathway as well (fig. 9). PCP signaling is pivotal for establishing cell polarity and activation of PCP pathway leads to the activation of Rhofamily GTPase and JNK through Dvls and Dishevelled-associated activator of morphogenesis 1 (Daam1) (Wallingford and Habas, 2005). Daam 1 bound to both Rho GTPase and Dvl, mediates Wnt-induced Dvl/Rho complex formation, which in turn activates Rho-associated protein kinase (ROCK) and remodels cytoskeleton (Habas et al., 2001). Daam1 is imprecated in cancer through its regulation of endocytosis of Ephrin B1 (Kida et al., 2007). Furthermore, proteomics also identified Ror2, which is involved in Wnt/Ca2+ pathway. Ror2 is a receptor tyrosine kinase. Wnt interaction with Fz and coreceptor Kyn or Ror2 provokes increase of intracellular Ca2+ level and subsequently activates calcium/calmodulin-dependent protein kinase II (CaMKII), protein kinase C (PKC) and nuclear factor of activated T cells (NFAT). Ca2+ is a central regulator of many cell functions and its downstream targets are numerous. Ror2 also shown as a receptor for Wnt5a, stimulating the non-canonical pathway (Oishi et al., 2003). In addition, Ror2 has a opposing role in the canonical signaling pathway and is able to inhibit Wnt/β-catenin signaling (Mikels and Nusse, 2006). Tyrosine kinase activity of Ror2 is indispensable for Wnt5ainduced inhibition of Wnt/β-catenin signaling (Mikels et al., 2009). Direct interaction of Ror2 with phosphorylated Dvl is required for the inhibition of Wnt/β-catenin signaling (Witte et al., 2010). Thus, Ror2 plays diverse roles in the discrete Wnt signaling pathways.

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 211

phosphate 3-kinase, AKAP 1 and 14-3-3. Many proteolytic targeting proteins including E3 ubiquitin-protein ligase NEDD4, Cullin 4B *etc.* were also found in >3 MegaDa-*Mr* peak, suggesting an important implication for Wnt-initiated tumorigenesis. Functional analysis demonstrated that Dvls stability is regulated by Cullin 3 ubiquitin ligase complex containing KLH12 (Angers et al., 2006). Some of them were identified by two distinct approaches (GST fusion pulls down and application of proteomics to Dvl3-based supermolecular complexes). Overall, the combined approaches of SEC and proteomics are successful to dissect Dvl3 based supermolecular complexes. Key Dvls interacting proteins, known functions in Wnt/beta-catenin signaling, present in >3 MegaDa-*Mr* peak, suggesting a compelling evidence that these proteins form complexes with Dvl3. Dvl3-based supermolecular complexes assemble at early stage and their compositions are dynamically changed during Wnt stimulation. Data agrees well with the fact that the size and localization of Dishevelled-based "punctae" have been changed upon Wnt stimulation observed earlier by fluorescence microscopy. Further segregation of unrelated proteins from the enriched Dvl3-based complexes, if possible, will be a benefit to analyze dynamic assembly of

molecules central to the function of the Wnt/beta-catenin canonical signaling.

There are at least three major Wnt signaling branches, Wnt/β-catenin (canonical) pathway, PCP pathway (non-canonical) and Wnt/Ca2+ pathway. Proteomics in the >3 MegaDa-*Mr* region have identified proteins, which are involved in Wnt/β-catenin signaling pathway as well as the PCP and Wnt/Ca2+ pathways, suggest that these pathways may share a large signaling network. Wnt signaling pathways may not tightly isolate or share intracellular components between pathways. In this way, Wnt signaling pathways can cross talk with each other or other signaling pathways. Accumulated evidences show the cross talk between distinct Wnt pathways as well as other signaling pathways. Diversities of Wnt ligands and its receptors contribute to the complexities of Wnt signaling pathways. Distinct Wnt ligands can initiate discrete signaling pathways through its distinct receptors; however, signaling by different Wnt family members is not only intrinsically regulated by the Wnt proteins themselves, but also by receptor availability (cellular context of the receptor). Some Wnt ligand can bind to non-Frizzled receptor like Ror2 or RYK (an atypical Tyrosine kinase receptor) *etc*. RYK lacks a functional tyrosine kinase domain, but contains a module homologous to the Wnt-binding domain (Lu et al., 2004). Moreover, same Wnt ligand stimulates or inhibits distinct Wnt signaling pathways through its distinct receptors. Certain Wnt ligand activates the PCP and Wnt/Ca2+ pathways. Our SEC data demonstrate that Dvl3-based supermolecular complexes (~2 MegaDa-*Mr*) are assembling at the basal level (i.e., without Wnt stimulation) and Wnt stimulation provokes the upshift of molecular mass of Dvl3-based mutiprotein complexes. Data may suggest that without Wnt stimulation, Wnt signaling pathways may share fundamental platform of Dvls-based complexes, because Dvls are essential scaffold components in all three branches and transduce signals to distinct downstream pathways. In one way, we could say that distinct Wnt ligands provoke assembly of distinct multiprotein complexes. However, more detailed knowledge of the specificity of Wnt ligand and receptor, the receptor availability and protein components mediating signals to downstream are required to answer "How Dvls transduce signals to distinct Wnt signaling pathways?" and "How Wnt signaling integrates with other signaling pathways to form signaling network?" Dvls are a major player in Wnt signaling pathways and scaffold multiproteins to form the platform. Therefore, more precise analysis of protein compositions in the supermolecular complexes, not only Wnt/β-

F9 cells expressing either Rfz1 or Rfz2 were stimulated with Wnt3a or Wnt5a for 30min, respectively. Cell lysates were subjected to the gel filtration chromatography and then fractions were analyzed by SDS-PAGE and blotted with anti-Dvl3 antibody. The regions above 750 kDa-*Mr* were shown.

Fig. 9. Dvl3-based supermolecular complexes are assembled in the PCP pathway.

Similarly, Par1 is discovered as a Dsh-associated kinase in *Drosophila* (Sun et al., 2001) and an essential for canonical signaling pathway, but also functions in non-canonical pathway (Wharton, 2003). Thus, certain molecules are involved in several distinct pathways. Presence of mammalian target of rapamycin (mTOR) in the >3 MegaDa*-Mr* region also demonstrates diversity of Wnt signaling pathways (Table 4), and supporting a previous finding that Wntmediated signaling activates mTOR mediated translational regulation in tumorigenesis (Inoki et al., 2006). Wnt activates mTOR via inhibiting GSK3 (Inoki et al., 2006) and the phosphorylation of tuberous sclerosis complex 2 (TSC2), a tumor-suppressor which negatively regulates mTOR, is suppressed (Mak et al., 2005). These data suggesting that Wnt signaling pathway is integrated to control mTOR activity (Choo et al., 2006).

G proteins found in the Dvl3-based supermolecular complexes are reasonable and expected one. Requirement of Gαo and Gαq in Wnt/β-catenin signaling was established earlier in F9 cell (Liu et al., 1999). Gα13, essential for the formation of the primitive endoderm, induces the activation of Rho protein, mitogen-activated protein kinase kinase (MEKK) and Jun-Nterminal kinase-1 (JNK1) in P19 embryonal carcinoma cells (Jho and Malbon, 1997; Lee et al., 2004). Gα13and Gαi family functions in development and deficiency of these G proteins causes embryonic lethality (Offermanns et al., 1997; Wettschureck et al., 2004). Novel classes of signaling proteins found in this large *Mr* peak included cAMP-dependent protein kinase, cell division protein kinase, Yes, Fyn, serine/threonine and tyrosine protein phosphatases, inositol 1, 4, 5-triphosphate receptor, phosphoinositide 3-kinase, phosphatidylinositol-4-

F9 cells expressing either Rfz1 or Rfz2 were stimulated with Wnt3a or Wnt5a for 30min, respectively. Cell lysates were subjected to the gel filtration chromatography and then fractions were analyzed by SDS-PAGE and blotted with anti-Dvl3 antibody. The regions above 750 kDa-*Mr* were shown. Fig. 9. Dvl3-based supermolecular complexes are assembled in the PCP pathway.

Similarly, Par1 is discovered as a Dsh-associated kinase in *Drosophila* (Sun et al., 2001) and an essential for canonical signaling pathway, but also functions in non-canonical pathway (Wharton, 2003). Thus, certain molecules are involved in several distinct pathways. Presence of mammalian target of rapamycin (mTOR) in the >3 MegaDa*-Mr* region also demonstrates diversity of Wnt signaling pathways (Table 4), and supporting a previous finding that Wntmediated signaling activates mTOR mediated translational regulation in tumorigenesis (Inoki et al., 2006). Wnt activates mTOR via inhibiting GSK3 (Inoki et al., 2006) and the phosphorylation of tuberous sclerosis complex 2 (TSC2), a tumor-suppressor which negatively regulates mTOR, is suppressed (Mak et al., 2005). These data suggesting that Wnt

G proteins found in the Dvl3-based supermolecular complexes are reasonable and expected one. Requirement of Gαo and Gαq in Wnt/β-catenin signaling was established earlier in F9 cell (Liu et al., 1999). Gα13, essential for the formation of the primitive endoderm, induces the activation of Rho protein, mitogen-activated protein kinase kinase (MEKK) and Jun-Nterminal kinase-1 (JNK1) in P19 embryonal carcinoma cells (Jho and Malbon, 1997; Lee et al., 2004). Gα13and Gαi family functions in development and deficiency of these G proteins causes embryonic lethality (Offermanns et al., 1997; Wettschureck et al., 2004). Novel classes of signaling proteins found in this large *Mr* peak included cAMP-dependent protein kinase, cell division protein kinase, Yes, Fyn, serine/threonine and tyrosine protein phosphatases, inositol 1, 4, 5-triphosphate receptor, phosphoinositide 3-kinase, phosphatidylinositol-4-

signaling pathway is integrated to control mTOR activity (Choo et al., 2006).

phosphate 3-kinase, AKAP 1 and 14-3-3. Many proteolytic targeting proteins including E3 ubiquitin-protein ligase NEDD4, Cullin 4B *etc.* were also found in >3 MegaDa-*Mr* peak, suggesting an important implication for Wnt-initiated tumorigenesis. Functional analysis demonstrated that Dvls stability is regulated by Cullin 3 ubiquitin ligase complex containing KLH12 (Angers et al., 2006). Some of them were identified by two distinct approaches (GST fusion pulls down and application of proteomics to Dvl3-based supermolecular complexes). Overall, the combined approaches of SEC and proteomics are successful to dissect Dvl3 based supermolecular complexes. Key Dvls interacting proteins, known functions in Wnt/beta-catenin signaling, present in >3 MegaDa-*Mr* peak, suggesting a compelling evidence that these proteins form complexes with Dvl3. Dvl3-based supermolecular complexes assemble at early stage and their compositions are dynamically changed during Wnt stimulation. Data agrees well with the fact that the size and localization of Dishevelled-based "punctae" have been changed upon Wnt stimulation observed earlier by fluorescence microscopy. Further segregation of unrelated proteins from the enriched Dvl3-based complexes, if possible, will be a benefit to analyze dynamic assembly of molecules central to the function of the Wnt/beta-catenin canonical signaling.

There are at least three major Wnt signaling branches, Wnt/β-catenin (canonical) pathway, PCP pathway (non-canonical) and Wnt/Ca2+ pathway. Proteomics in the >3 MegaDa-*Mr* region have identified proteins, which are involved in Wnt/β-catenin signaling pathway as well as the PCP and Wnt/Ca2+ pathways, suggest that these pathways may share a large signaling network. Wnt signaling pathways may not tightly isolate or share intracellular components between pathways. In this way, Wnt signaling pathways can cross talk with each other or other signaling pathways. Accumulated evidences show the cross talk between distinct Wnt pathways as well as other signaling pathways. Diversities of Wnt ligands and its receptors contribute to the complexities of Wnt signaling pathways. Distinct Wnt ligands can initiate discrete signaling pathways through its distinct receptors; however, signaling by different Wnt family members is not only intrinsically regulated by the Wnt proteins themselves, but also by receptor availability (cellular context of the receptor). Some Wnt ligand can bind to non-Frizzled receptor like Ror2 or RYK (an atypical Tyrosine kinase receptor) *etc*. RYK lacks a functional tyrosine kinase domain, but contains a module homologous to the Wnt-binding domain (Lu et al., 2004). Moreover, same Wnt ligand stimulates or inhibits distinct Wnt signaling pathways through its distinct receptors. Certain Wnt ligand activates the PCP and Wnt/Ca2+ pathways. Our SEC data demonstrate that Dvl3-based supermolecular complexes (~2 MegaDa-*Mr*) are assembling at the basal level (i.e., without Wnt stimulation) and Wnt stimulation provokes the upshift of molecular mass of Dvl3-based mutiprotein complexes. Data may suggest that without Wnt stimulation, Wnt signaling pathways may share fundamental platform of Dvls-based complexes, because Dvls are essential scaffold components in all three branches and transduce signals to distinct downstream pathways. In one way, we could say that distinct Wnt ligands provoke assembly of distinct multiprotein complexes. However, more detailed knowledge of the specificity of Wnt ligand and receptor, the receptor availability and protein components mediating signals to downstream are required to answer "How Dvls transduce signals to distinct Wnt signaling pathways?" and "How Wnt signaling integrates with other signaling pathways to form signaling network?" Dvls are a major player in Wnt signaling pathways and scaffold multiproteins to form the platform. Therefore, more precise analysis of protein compositions in the supermolecular complexes, not only Wnt/β-

Proteomic Analysis of Wnt-Dependent Dishevelled-Based Supermolecular Complexes 213

Dvl3-based complexes interfered with further purification. Immunoprecipitation might be a useful approach, although there are difficulties to precipitate very large complexes, because epitope sites in the supermolecular complexes are not fully exposed. Under these circumstances, employed proteomics in the Dvl3-based supermolecular complexes provide invaluable information to address "What are the compositions of Dvl3-based supermolecular complexes?" and "How is the assembly/disassembly of Dvl3-based complexes temporally and spatially regulated?" More detailed analysis of the Dvls-based supermolecular complexes in all three Wnt signaling pathways is necessary. Thus, compelling, albeit indirect, studies established the natures of Dvl3-based supermolecular complexes by advanced proteomics. Novel proteins found in Dvl3-based supermolecular

The author thanks Dr. Craig. C. Malbon (Department of Pharmacology, School of Medicine, Health Sciences Center, State University of New York at Stony Brook) for helpful discussions and support. The author thanks the staff of the Proteomics Center (State University of New York at Stony Brook) for mass spectrometry analysis. The author acknowledges the contribution of Dr. Weiping Xie (present address: Pioneer Hi-Bred International DuPont Agricultural Biotechnology) in the first half of proteomics work. The author also thanks Drs. Antonius Koller and Emily Chen for technical support in the proteomic analysis. The author acknowledges the contribution of Drs. Urszula Golebiewska and Hsien-yu Wang (Department of Physiology and Biophysics, School of Medicine, Health Sciences Center, State University of New York at Stony Brook) for *fcs*

Angers S & Moon RT. (2009). *Proximal events in Wnt signal transduction*. Nat Rev Mol Cell

Angers S, Thorpe CJ, Biechele TL, Goldenberg SJ, Zheng N, MacCoss MJ & Moon RT. (2006).

Axelrod JD, Miller JR, Shulman JM, Moon RT & Perrimon N. (1998). *Differential recruitment of* 

Bernatik O, Ganji RS, Dijksterhuis JP, Konik P, Cervenka I, Polonio T, Krejci P, Schulte G &

Bilic J, Huang YL, Davidson G, Zimmermann T, Cruciat CM, Bienz M & Niehrs C. (2007).

Brown MT & Cooper JA. (1996). *Regulation, substrates and functions of src*. Biochim Biophys

*by targeting Dishevelled for degradation*. Nat Cell Biol 8:348-357.

*catenin pathway by casein kinases*. J Biol Chem 286:10396-10410.

*The KLHL12-Cullin-3 ubiquitin ligase negatively regulates the Wnt-beta-catenin pathway* 

*Dishevelled provides signaling specificity in the planar cell polarity and Wingless signaling* 

Bryja V. (2011). *Sequential activation and inactivation of Dishevelled in the Wnt/beta-*

*Wnt induces LRP6 signalosomes and promotes dishevelled-dependent LRP6* 

complexes need to be validated with functional analysis *in vivo* and *in vitro*.

**4. Acknowledgements** 

analysis.

**5. References** 

Biol 10:468-477.

Acta 1287:121-149.

*pathways.* Genes Dev 12:2610-2622.

*phosphorylation.* Science 316:1619-1622.

catenin signaling pathway as well as PCP pathway and Wnt/Ca2+ pathway, is required. Our data demonstrate that the assembly of Dvl3-based supermolecular complexes is provoked in the PCP pathway. Similar to the Wnt/β-catenin signaling, the molecular mass of Dvl3-based supermolecular complexes is upshifted in the response to Wnt5a. There is no information whether Dvls-based supermolecular complexes form in Wnt/Ca2+ signaling pathway. Comparison of proteomic profiles of Dvl3-based supermolecular complexes in the Wnt/β-catenin signaling *versus* the PCP pathway may provide a clearer image of signaling specific compositions of the supermolecular complexes. Precise regulations of Wnt signaling pathway by the Dvls-based supermolecular complexes are needed to analyze more widely in all branches. Advanced proteomic analysis of the Dvlsbased supermolecular complexes with high-throughput screening offers an ideal strategy to interrogate the composition of Dvl3-based supermolecular complexes in the Wnt/βcatenin signaling pathway as well as in the non-canonical signaling pathway.

#### **3. Conclusion**

Dvls are scaffold proteins and Wnt signaling is transduced through the dynamic assembly/disassembly of protein complexes with Dvls called "signalsomes". Dvls-based supermolecular complexes provide a platform for recruiting many docking proteins and organizing dynamic assembly of proteins. Dvls-based punctae have been visualized by fluorescence microscopy and displayed dynamic protein assemblies. Proteomics of immobilized GST-domain pull down identified several novel and functional Dvl2 interacting proteins. Novel positive roles for Src family tyrosine kinases in Wnt/β-catenin signaling also have been established by GST-domain pull down and proteomics. Identification of tyrosine phosphorylation sites on Dvl2 leads to the discovery of the positive role of Src family kinases in Wnt/β-catenin signaling. Wnt stimulates Src docking to Dvl2 and activates Src tyrosine kinase. Activated Src enhances Wnt activation of the canonical pathway via phosphorylation of Dvl2 and β-catenin.

The physical nature and the dynamic character of the Dvl3-based supermolecular complexes have been established first time. The formation of these very large, supermolecular Dvl3 based complexes in response to Wnt3a is found to be a time-dependent manner and dynamic in character. The assembly of Dvl3-based supermolecular complexes (>3 MegaDa-*Mr*) occurred at very early stage (detected as early as 5 min post-Wnt3a stimulation) and maximized 30-60 min post-Wnt stimulation, in advances of the Lef/Tcf sensitivetranscription. Formation of very large Dvl3-based supermolecular complexes is essential in Wnt canonical signaling. Data demonstrate "punctae", Lef/Tcf-sensitive transcription (hallmark of activation of Wnt/beta catenin pathway) and dynamically assembled Dvl3 based supermolecular complexes are functionally obligate to Wnt signaling. The mass of very huge Dvl3-based supermolecular complexes reveals MW ranging from 25-35 MegaDa by *fcs*. Reassessed size of Dvl3-based supermolecular complexes is 35 MegaDa by SEC, similar to those obtained by *fcs*. This study demonstrates successful probing of signalsomes by SEC, *fcs*, affinity pull down and advanced proteomics.

Application of the proteomics in Dvl3-based supermolecular complexes provides physical properties of the assemblies. This approach is still experimental and challenging efforts, because cardinal concern is that Dvl3-based supermolecular complexes separated by SEC may contain unrelated proteins. Unsuccessful further separation of pure Dvl3-based supermolecular complexes is the barrier of this approach, because the size of the very huge Dvl3-based complexes interfered with further purification. Immunoprecipitation might be a useful approach, although there are difficulties to precipitate very large complexes, because epitope sites in the supermolecular complexes are not fully exposed. Under these circumstances, employed proteomics in the Dvl3-based supermolecular complexes provide invaluable information to address "What are the compositions of Dvl3-based supermolecular complexes?" and "How is the assembly/disassembly of Dvl3-based complexes temporally and spatially regulated?" More detailed analysis of the Dvls-based supermolecular complexes in all three Wnt signaling pathways is necessary. Thus, compelling, albeit indirect, studies established the natures of Dvl3-based supermolecular complexes by advanced proteomics. Novel proteins found in Dvl3-based supermolecular complexes need to be validated with functional analysis *in vivo* and *in vitro*.

#### **4. Acknowledgements**

212 Proteomics – Human Diseases and Protein Functions

catenin signaling pathway as well as PCP pathway and Wnt/Ca2+ pathway, is required. Our data demonstrate that the assembly of Dvl3-based supermolecular complexes is provoked in the PCP pathway. Similar to the Wnt/β-catenin signaling, the molecular mass of Dvl3-based supermolecular complexes is upshifted in the response to Wnt5a. There is no information whether Dvls-based supermolecular complexes form in Wnt/Ca2+ signaling pathway. Comparison of proteomic profiles of Dvl3-based supermolecular complexes in the Wnt/β-catenin signaling *versus* the PCP pathway may provide a clearer image of signaling specific compositions of the supermolecular complexes. Precise regulations of Wnt signaling pathway by the Dvls-based supermolecular complexes are needed to analyze more widely in all branches. Advanced proteomic analysis of the Dvlsbased supermolecular complexes with high-throughput screening offers an ideal strategy to interrogate the composition of Dvl3-based supermolecular complexes in the Wnt/β-

catenin signaling pathway as well as in the non-canonical signaling pathway.

pathway via phosphorylation of Dvl2 and β-catenin.

by SEC, *fcs*, affinity pull down and advanced proteomics.

Dvls are scaffold proteins and Wnt signaling is transduced through the dynamic assembly/disassembly of protein complexes with Dvls called "signalsomes". Dvls-based supermolecular complexes provide a platform for recruiting many docking proteins and organizing dynamic assembly of proteins. Dvls-based punctae have been visualized by fluorescence microscopy and displayed dynamic protein assemblies. Proteomics of immobilized GST-domain pull down identified several novel and functional Dvl2 interacting proteins. Novel positive roles for Src family tyrosine kinases in Wnt/β-catenin signaling also have been established by GST-domain pull down and proteomics. Identification of tyrosine phosphorylation sites on Dvl2 leads to the discovery of the positive role of Src family kinases in Wnt/β-catenin signaling. Wnt stimulates Src docking to Dvl2 and activates Src tyrosine kinase. Activated Src enhances Wnt activation of the canonical

The physical nature and the dynamic character of the Dvl3-based supermolecular complexes have been established first time. The formation of these very large, supermolecular Dvl3 based complexes in response to Wnt3a is found to be a time-dependent manner and dynamic in character. The assembly of Dvl3-based supermolecular complexes (>3 MegaDa-*Mr*) occurred at very early stage (detected as early as 5 min post-Wnt3a stimulation) and maximized 30-60 min post-Wnt stimulation, in advances of the Lef/Tcf sensitivetranscription. Formation of very large Dvl3-based supermolecular complexes is essential in Wnt canonical signaling. Data demonstrate "punctae", Lef/Tcf-sensitive transcription (hallmark of activation of Wnt/beta catenin pathway) and dynamically assembled Dvl3 based supermolecular complexes are functionally obligate to Wnt signaling. The mass of very huge Dvl3-based supermolecular complexes reveals MW ranging from 25-35 MegaDa by *fcs*. Reassessed size of Dvl3-based supermolecular complexes is 35 MegaDa by SEC, similar to those obtained by *fcs*. This study demonstrates successful probing of signalsomes

Application of the proteomics in Dvl3-based supermolecular complexes provides physical properties of the assemblies. This approach is still experimental and challenging efforts, because cardinal concern is that Dvl3-based supermolecular complexes separated by SEC may contain unrelated proteins. Unsuccessful further separation of pure Dvl3-based supermolecular complexes is the barrier of this approach, because the size of the very huge

**3. Conclusion** 

The author thanks Dr. Craig. C. Malbon (Department of Pharmacology, School of Medicine, Health Sciences Center, State University of New York at Stony Brook) for helpful discussions and support. The author thanks the staff of the Proteomics Center (State University of New York at Stony Brook) for mass spectrometry analysis. The author acknowledges the contribution of Dr. Weiping Xie (present address: Pioneer Hi-Bred International DuPont Agricultural Biotechnology) in the first half of proteomics work. The author also thanks Drs. Antonius Koller and Emily Chen for technical support in the proteomic analysis. The author acknowledges the contribution of Drs. Urszula Golebiewska and Hsien-yu Wang (Department of Physiology and Biophysics, School of Medicine, Health Sciences Center, State University of New York at Stony Brook) for *fcs* analysis.

#### **5. References**


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**10** 

**Identification of the Novel** 

**Plasminogen Receptor, Plg-RKT**

Nagyung Baik1, Hongdong Bai4, Caitlin M. Parmer5,

Lindsey A. Miles1, Nicholas M. Andronicos2, Emily I. Chen3,

Initiation of the plasminogen activation system results in generation of the broad spectrum serine protease, plasmin, from the circulating zymogen, plasminogen. Plasminogen is activated to plasmin by plasminogen activators (PA's), either urokinase type-plasminogen activator (u-PA) or tissue-type plasminogen activator (t-PA), via specific proteolytic cleavage (Castellino & Ploplis, 2005). Plasmin is the major enzyme responsible for degradation of fibrin clots (fibrinolysis) to maintain normal blood homeostasis (Bugge et al., 1995; Ploplis et al., 1995). Dysregulation of the plasminogen activation system can result in hemorrhage (excess fibrinolysis) or thrombosis (insufficient fibrinolysis). The plasminogen activation system is regulated by direct inhibition of plasmin (by the circulating serpin, α2 antiplasmin) and by synthesis and secretion of plasminogen activators and the serpin, plasminogen activator inhibitor 1 (PAI-1)] (Collen, 1999). In a key regulatory step, plasminogen activation is promoted when plasminogen and its activator, t-PA, bind concomitantly to lysine residues on the surface of fibrin clots, resulting in a marked reduction in the Km for activation of plasminogen compared with the reaction in solution

In the past 25 years an additional mechanism for positive regulation of plasminogen activation has been recognized: co-localization of plasminogen and PA's on cell surfaces markedly decreases the Km for plasminogen activation in a mechanism analogous to assembly of components of the plasminogen activation system on fibrin (Miles et al., 2005). The plasmin produced is retained on the cell surface where it is protected from its inhibitor, α2-antiplasmin (Figure 1) (Hall et al., 1991; Plow et al., 1986). Thus, the cell surface becomes

**1. Introduction** 

(Hoylaerts et al., 1982).

*The Scripps Research Institute, USA* 

*University of California, San Diego, USA*

*Stony Brook University, USA* 

*Yale University, USA* 

 *1*

*2*

*3*

*4*

*6*

**1.1 The plasminogen activation system** 

**1.2 Functions of plasminogen receptors** 

*CSIRO Livestock Industries, Armidale, NSW, Australia* 

*Veterans Administration San Diego Healthcare System, USA 5*

Shahrzad Lighvani1, Samir Nangia4,6, William B. Kiosses1, Mark P. Kamps6, John R. Yates III1 and Robert J. Parmer4,6


### **Identification of the Novel Plasminogen Receptor, Plg-RKT**

Lindsey A. Miles1, Nicholas M. Andronicos2, Emily I. Chen3, Nagyung Baik1, Hongdong Bai4, Caitlin M. Parmer5, Shahrzad Lighvani1, Samir Nangia4,6, William B. Kiosses1, Mark P. Kamps6, John R. Yates III1 and Robert J. Parmer4,6

#### **1. Introduction**

218 Proteomics – Human Diseases and Protein Functions

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*counterregulates Wnt3a/beta-catenin signaling.* J Mol Signal 2:12.

*manner.* J Cell Sci 122:4439-4451.

438:873-877.

#### **1.1 The plasminogen activation system**

Initiation of the plasminogen activation system results in generation of the broad spectrum serine protease, plasmin, from the circulating zymogen, plasminogen. Plasminogen is activated to plasmin by plasminogen activators (PA's), either urokinase type-plasminogen activator (u-PA) or tissue-type plasminogen activator (t-PA), via specific proteolytic cleavage (Castellino & Ploplis, 2005). Plasmin is the major enzyme responsible for degradation of fibrin clots (fibrinolysis) to maintain normal blood homeostasis (Bugge et al., 1995; Ploplis et al., 1995). Dysregulation of the plasminogen activation system can result in hemorrhage (excess fibrinolysis) or thrombosis (insufficient fibrinolysis). The plasminogen activation system is regulated by direct inhibition of plasmin (by the circulating serpin, α2 antiplasmin) and by synthesis and secretion of plasminogen activators and the serpin, plasminogen activator inhibitor 1 (PAI-1)] (Collen, 1999). In a key regulatory step, plasminogen activation is promoted when plasminogen and its activator, t-PA, bind concomitantly to lysine residues on the surface of fibrin clots, resulting in a marked reduction in the Km for activation of plasminogen compared with the reaction in solution (Hoylaerts et al., 1982).

#### **1.2 Functions of plasminogen receptors**

In the past 25 years an additional mechanism for positive regulation of plasminogen activation has been recognized: co-localization of plasminogen and PA's on cell surfaces markedly decreases the Km for plasminogen activation in a mechanism analogous to assembly of components of the plasminogen activation system on fibrin (Miles et al., 2005). The plasmin produced is retained on the cell surface where it is protected from its inhibitor, α2-antiplasmin (Figure 1) (Hall et al., 1991; Plow et al., 1986). Thus, the cell surface becomes

 *1 The Scripps Research Institute, USA* 

*<sup>2</sup> CSIRO Livestock Industries, Armidale, NSW, Australia* 

*<sup>3</sup> Stony Brook University, USA* 

*<sup>4</sup> Veterans Administration San Diego Healthcare System, USA 5*

*Yale University, USA* 

*<sup>6</sup> University of California, San Diego, USA*

Identification of the Novel Plasminogen Receptor, Plg-RKT 221

additional known intracellular functions, including α-enolase (Miles et al., 1991; Redlitz et al., 1995), cytokeratin 8 (Hembrough et al., 1995, 1996), S100A10 (Choi et al., 2003; Kassam et al., 1998), TIP49a (Hawley et al., 2001) and histone H2B (Herren et al., 2006) and; 2) proteins requiring proteolytic processing in order to reveal a C-terminal basic residue (lysine), including actin (Dudani & Ganz, 1996; Miles et al., 2006) and annexin 2 (Hajjar et al., 1994). However, until recently, no integral membrane plasminogen binding proteins synthesized with a C-terminal basic residue had been identified. The existence of a plasminogen receptor with the latter characteristics would reveal a novel mechanism for stimulation of plasminogen activation because release and rebinding of intracellular proteins or proteolytic cleavage of membrane proteins to expose C-terminal basic residues

Plasminogen binding to carboxyl terminal lysines on the cell surface. Panel A) The binding of plasminogen to cell surface proteins occurs via receptors exposing carboxyl terminal lysines to the extracellular environment. Cell surface proteins with carboxyl terminal lysines that are masked or in other inaccessible orientations on the cell surface, or membrane-associated proteins with carboxyl terminal lysines that are located on the inner face of the membrane, cannot serve as plasminogen receptors. Panel B) CpB treatment of intact cells removes carboxyl terminal lysines from plasminogen receptors, and plasminogen binding to the cell surface is reduced. Reprinted with permission from

Fig. 2. Plasminogen binding to carboxyl terminal lysines on the cell surface.

**1.4 Need for a proteomics approach to identify integral membrane plasminogen** 

Previous methodologies and characteristics of plasminogen binding proteins may have precluded identification of an integral membrane plasminogen binding protein with a Cterminal basic residue. The identification of plasminogen receptors has relied previously on cell surface labeling followed by affinity chromatography on plasminogen-Sepharose columns and N-terminal sequencing of fractions eluted from SDS gels. Thus, many

would not be required.

(Hawley, Green, and Miles 2000, 84:882-890).

**receptor(s) with C-terminal basic residues** 

armed with the broad spectrum proteolytic activity of plasmin. Cell surface plasmin plays a key role in processes in which cells must degrade an extracellular matrix in order to migrate, including inflammation (Busuttil et al., 2004; Ploplis et al., 1998; Plow et al., 1999), wound healing (Creemers et al., 2000; Romer et al., 1996), metastasis (Palumbo et al., 2003; Ranson et al., 1998) and neurite outgrowth (Gutierrez-Fernandez et al., 2009; Jacovina et al., 2001). Cell surface plasmin also plays a key role in myogenesis (Lopez-Alemany et al., 2003) and prohormone processing (Jiang et al., 2001, 2002).

Activation of cell-associated plasminogen (Plg) to plasmin (Pm) by cell-associated plasminogen

activators (PA) is markedly enhanced compared to the reaction in solution. The Pm formed remains on the cell surface where it is relatively protected from its inhibitor, α2-antiplasmin (α2-AP).

Fig. 1. Enhancement of plasminogen activation on the cell surface.

#### **1.3 Characteristics of plasminogen receptors**

Cellular plasminogen binding sites are very broadly distributed on both prokaryotic and eukaryotic cells. Of the many cell types examined to date, only red cells do not exhibit detectable plasminogen binding ability (Miles et al., 2005). The interactions of plasminogen with cells are of very high capacity, reaching 3 X 107 molecules/cell on lung fibrolasts (Plow et al., 1986), for example. Thus, the plasminogen binding capacity of a cell is made up of contributions from a set of distinct cell surface proteins.

An important aspect of the mechanism of promotion of plasminogen activation on cell surfaces is that a subset of carboxypeptidase-B-sensitive plasminogen binding proteins is responsible for enhancement of plasminogen activation on eukaryotic cells. When cells are treated with carboxypeptidase B, the ability to stimulate plasminogen activation is lost (Félez et al., 1996). Since carboxypeptidase B removes C-terminal basic residues, these results imply that proteins exposing C-terminal basic residues on cell surfaces are responsible for stimulation of plasminogen activation (Figure 2,A). Known carboxypeptidase-B-sensitive cell surface plasminogen receptors could previously be divided into two classes: 1) proteins synthesized with C-terminal lysines and having

armed with the broad spectrum proteolytic activity of plasmin. Cell surface plasmin plays a key role in processes in which cells must degrade an extracellular matrix in order to migrate, including inflammation (Busuttil et al., 2004; Ploplis et al., 1998; Plow et al., 1999), wound healing (Creemers et al., 2000; Romer et al., 1996), metastasis (Palumbo et al., 2003; Ranson et al., 1998) and neurite outgrowth (Gutierrez-Fernandez et al., 2009; Jacovina et al., 2001). Cell surface plasmin also plays a key role in myogenesis (Lopez-Alemany et al., 2003) and

Activation of cell-associated plasminogen (Plg) to plasmin (Pm) by cell-associated plasminogen activators (PA) is markedly enhanced compared to the reaction in solution. The Pm formed remains on

Cellular plasminogen binding sites are very broadly distributed on both prokaryotic and eukaryotic cells. Of the many cell types examined to date, only red cells do not exhibit detectable plasminogen binding ability (Miles et al., 2005). The interactions of plasminogen with cells are of very high capacity, reaching 3 X 107 molecules/cell on lung fibrolasts (Plow et al., 1986), for example. Thus, the plasminogen binding capacity of a cell is made up of

An important aspect of the mechanism of promotion of plasminogen activation on cell surfaces is that a subset of carboxypeptidase-B-sensitive plasminogen binding proteins is responsible for enhancement of plasminogen activation on eukaryotic cells. When cells are treated with carboxypeptidase B, the ability to stimulate plasminogen activation is lost (Félez et al., 1996). Since carboxypeptidase B removes C-terminal basic residues, these results imply that proteins exposing C-terminal basic residues on cell surfaces are responsible for stimulation of plasminogen activation (Figure 2,A). Known carboxypeptidase-B-sensitive cell surface plasminogen receptors could previously be divided into two classes: 1) proteins synthesized with C-terminal lysines and having

the cell surface where it is relatively protected from its inhibitor, α2-antiplasmin (α2-AP).

Fig. 1. Enhancement of plasminogen activation on the cell surface.

**1.3 Characteristics of plasminogen receptors** 

contributions from a set of distinct cell surface proteins.

prohormone processing (Jiang et al., 2001, 2002).

additional known intracellular functions, including α-enolase (Miles et al., 1991; Redlitz et al., 1995), cytokeratin 8 (Hembrough et al., 1995, 1996), S100A10 (Choi et al., 2003; Kassam et al., 1998), TIP49a (Hawley et al., 2001) and histone H2B (Herren et al., 2006) and; 2) proteins requiring proteolytic processing in order to reveal a C-terminal basic residue (lysine), including actin (Dudani & Ganz, 1996; Miles et al., 2006) and annexin 2 (Hajjar et al., 1994). However, until recently, no integral membrane plasminogen binding proteins synthesized with a C-terminal basic residue had been identified. The existence of a plasminogen receptor with the latter characteristics would reveal a novel mechanism for stimulation of plasminogen activation because release and rebinding of intracellular proteins or proteolytic cleavage of membrane proteins to expose C-terminal basic residues would not be required.

Plasminogen binding to carboxyl terminal lysines on the cell surface. Panel A) The binding of plasminogen to cell surface proteins occurs via receptors exposing carboxyl terminal lysines to the extracellular environment. Cell surface proteins with carboxyl terminal lysines that are masked or in other inaccessible orientations on the cell surface, or membrane-associated proteins with carboxyl terminal lysines that are located on the inner face of the membrane, cannot serve as plasminogen receptors. Panel B) CpB treatment of intact cells removes carboxyl terminal lysines from plasminogen receptors, and plasminogen binding to the cell surface is reduced. Reprinted with permission from (Hawley, Green, and Miles 2000, 84:882-890).

Fig. 2. Plasminogen binding to carboxyl terminal lysines on the cell surface.

#### **1.4 Need for a proteomics approach to identify integral membrane plasminogen receptor(s) with C-terminal basic residues**

Previous methodologies and characteristics of plasminogen binding proteins may have precluded identification of an integral membrane plasminogen binding protein with a Cterminal basic residue. The identification of plasminogen receptors has relied previously on cell surface labeling followed by affinity chromatography on plasminogen-Sepharose columns and N-terminal sequencing of fractions eluted from SDS gels. Thus, many

Identification of the Novel Plasminogen Receptor, Plg-RKT 223

liquid chromatography upstream of reversed phase liquid chromatography (Larmann, Jr. et al., 1993; Link et al., 1999; Opiteck & Jorgenson, 1997; Wolters et al., 2001). Eluting peptides were electrosprayed onto an LTQ ion trap mass spectrometer equipped with a nano-LC electrospray ionization source. Full MS spectra were recorded over a 400–1600 m/z range, followed by three tandem mass (MS/MS) events sequentially generated in a data-dependent manner on the first, second, and third most intense ions selected from the full MS spectrum (at 35% collision energy). Mass spectrometer scan functions and HPLC solvent gradients

Database searching and interpretation of MS/MS Datasets were performed as described (Andronicos et al., 2010). Briefly, tandem mass spectra were extracted from raw files, and a binary classifier (Bern et al., 2004), previously trained on a manually validated data set, was used to remove low quality MS/MS spectra. Remaining spectra were searched against a mouse protein database containing 50,370 protein sequences downloaded as FASTAformatted sequences from EBI-IPI and 124 common contaminant proteins, for a total of 66,743 target database sequences (Peng et al., 2003). To calculate confidence levels and false positive rates, a decoy database containing the reverse sequences of the 66,743 proteins appended to the target database and the SEQUEST algorithm (Yates, III, 1998) were used to

SEQUEST searches were done on an Intel Xeon 80-processor cluster running under the Linux operating system. The peptide mass search tolerance was set to 3 Da. No differential modifications were considered. No enzymatic cleavage conditions were imposed on the database search, so the search space included all candidate peptides whose theoretical mass

The validity of peptide/spectrum matches was assessed in DTASelect2 (Tabb et al., 2002) using SEQUEST-defined parameters, the cross-correlation score (XCorr) and normalized difference in cross-correlation scores (DeltaCN). The search results were grouped by charge state (+1, +2, and +3) and tryptic status (fully tryptic, half-tryptic, and non-tryptic), resulting in 9 distinct sub-groups. In each one of the sub-groups, the distribution of XCorr and DeltaCN values for direct and decoy database hits was obtained, and the two subsets were separated by quadratic discriminant analysis. Outlier points in the two distributions (for example, matches with very low Xcorr but very high DeltaCN) were discarded. Full separation of the direct and decoy subsets is not generally possible; therefore, the discriminant score was set such that a false positive rate of 5% was determined based on the number of accepted decoy database peptides. This procedure was independently performed on each data subset, resulting in a false positive rate independent of tryptic status or charge

**3.1 Isolation of an integral membrane plasminogen receptor exposing a C-terminal** 

We used specific proteolysis followed by MudPIT to probe the membrane proteome of differentiated mouse monocyte progenitor cells (Hoxa9-ER4) for the presence of integral membrane plasminogen receptor(s) exposing a C-terminal basic residue on the cell surface, as outlined in Figure 3. [The Hoxa9-ER4 cell line was derived from primary murine bone

were controlled by the Xcalibur data system.

state.

**3. Results** 

**lysine on the cell surface** 

**2.3 Database search and interpretation of MS/MS datasets** 

find the best matching sequences from the combined database.

fell within the 3 Da mass tolerance window, despite their tryptic status.

intracellular proteins that are also present on the cell surface were readily identified because protein fractions that bound to plasminogen-Sepharose included the labeled, surfaceassociated protein, as well as nonlabeled intracellular protein. Using these methods, a lower abundance integral membrane plasminogen binding protein might not have been detectable.

Previously, we used a proteomics approach to examine monocytoid cell membranes for the presence of proteins exposing carboxyl terminal lysines on the extracellular face of the cell membrane (Hawley et al., 2000). We compared plasminogen ligand blots of 2-D gels of membrane fractions of intact cells treated with carboxypeptidase B with untreated membranes (e.g. Figure 2,B). We eluted a prominent carboxypeptidase B-sensitive protein from the 2-D gels and obtained two peptide sequences using tandem mass spectrometry. These peptide sequences corresponded to TATA-binding protein-interacting protein (TIP49a) (Hawley et al., 2001). However, TIP49a is a member of the class of cell surface plasminogen receptors synthesized with a C-terminal lysine and also having intracellular functions and is not an integral membrane protein.

The methodology used to identify TIP49a and other plasminogen receptors has required elution of candidate proteins from 2-D SDS polyacrylamide gels. However, many membrane proteins are not well resolved on SDS polyacrylamide gels. Therefore, we used an isolation method that used column chromatography instead of SDS polyacrylamide gel analysis: We took advantage of the exquisite sensitivity of multidimensional protein identification technology (MudPIT) to search for integral membrane plasminogen receptor(s) exposing a C-terminal basic residue on the cell surface and present on viable cells.

#### **2. Methods**

#### **2.1 Plasminogen receptor isolation**

Plasminogen receptor isolation was performed as described (Andronicos et al., 2010). Briefly, progenitor and M-CSF-differentiated Hoxa9-ER4 cells were separately biotinylated, using EZ-Link Amine-PEO3-Biotin. The cells were then subjected to dead cell removal on annexin V-coated magnetic microspheres that resulted in a 99% enrichment of viable cells. Membrane fractions were prepared from the viable cells by dounce homogenization in the presence of Complete Protease Inhibitor Cocktail in Invitrosol, followed by centrifugation steps as used in our laboratory (Hawley et al., 2000, 2001) and were applied to a plasminogen-Sepharose affinity column as described (Miles et al., 1991). The column was washed in phosphate buffered saline containing 1 X Invitrosol until no protein was detected at 280 nm followed by elution with the washing buffer containing 0.2 M ε-aminocaproic acid (EACA). The eluant from the plasminogen-Sepharose column was incubated with 50μl of immobilized avidin for 30 minutes at 4°C. The proteins bound to the immobilized avidin were resuspended in Invitrosol and heated at 60°C. Then, 80% acetonitrile was added and the samples were digested with trypsin. After 24 h, the solvent was evaporated in a speedvac, and peptides were dissolved in buffer A (95% H2O, 5% acetonitrile, and 0.1% formic acid).

#### **2.2 Multidimensional chromatography and tandem mass spectrometry**

Multidimensional chromatography and tandem mass spectrometry were performed as described (Andronicos et al., 2010). Briefly, the protein digest was subjected to MudPIT [reviewed in (Eng et al., 1994)]. Peptide mixtures were resolved by strong cation exchange

intracellular proteins that are also present on the cell surface were readily identified because protein fractions that bound to plasminogen-Sepharose included the labeled, surfaceassociated protein, as well as nonlabeled intracellular protein. Using these methods, a lower abundance integral membrane plasminogen binding protein might not have been

Previously, we used a proteomics approach to examine monocytoid cell membranes for the presence of proteins exposing carboxyl terminal lysines on the extracellular face of the cell membrane (Hawley et al., 2000). We compared plasminogen ligand blots of 2-D gels of membrane fractions of intact cells treated with carboxypeptidase B with untreated membranes (e.g. Figure 2,B). We eluted a prominent carboxypeptidase B-sensitive protein from the 2-D gels and obtained two peptide sequences using tandem mass spectrometry. These peptide sequences corresponded to TATA-binding protein-interacting protein (TIP49a) (Hawley et al., 2001). However, TIP49a is a member of the class of cell surface plasminogen receptors synthesized with a C-terminal lysine and also having intracellular

The methodology used to identify TIP49a and other plasminogen receptors has required elution of candidate proteins from 2-D SDS polyacrylamide gels. However, many membrane proteins are not well resolved on SDS polyacrylamide gels. Therefore, we used an isolation method that used column chromatography instead of SDS polyacrylamide gel analysis: We took advantage of the exquisite sensitivity of multidimensional protein identification technology (MudPIT) to search for integral membrane plasminogen receptor(s) exposing a

Plasminogen receptor isolation was performed as described (Andronicos et al., 2010). Briefly, progenitor and M-CSF-differentiated Hoxa9-ER4 cells were separately biotinylated, using EZ-Link Amine-PEO3-Biotin. The cells were then subjected to dead cell removal on annexin V-coated magnetic microspheres that resulted in a 99% enrichment of viable cells. Membrane fractions were prepared from the viable cells by dounce homogenization in the presence of Complete Protease Inhibitor Cocktail in Invitrosol, followed by centrifugation steps as used in our laboratory (Hawley et al., 2000, 2001) and were applied to a plasminogen-Sepharose affinity column as described (Miles et al., 1991). The column was washed in phosphate buffered saline containing 1 X Invitrosol until no protein was detected at 280 nm followed by elution with the washing buffer containing 0.2 M ε-aminocaproic acid (EACA). The eluant from the plasminogen-Sepharose column was incubated with 50μl of immobilized avidin for 30 minutes at 4°C. The proteins bound to the immobilized avidin were resuspended in Invitrosol and heated at 60°C. Then, 80% acetonitrile was added and the samples were digested with trypsin. After 24 h, the solvent was evaporated in a speedvac, and peptides were dissolved in buffer A (95% H2O, 5% acetonitrile, and 0.1%

detectable.

**2. Methods** 

formic acid).

functions and is not an integral membrane protein.

**2.1 Plasminogen receptor isolation** 

C-terminal basic residue on the cell surface and present on viable cells.

**2.2 Multidimensional chromatography and tandem mass spectrometry** 

Multidimensional chromatography and tandem mass spectrometry were performed as described (Andronicos et al., 2010). Briefly, the protein digest was subjected to MudPIT [reviewed in (Eng et al., 1994)]. Peptide mixtures were resolved by strong cation exchange liquid chromatography upstream of reversed phase liquid chromatography (Larmann, Jr. et al., 1993; Link et al., 1999; Opiteck & Jorgenson, 1997; Wolters et al., 2001). Eluting peptides were electrosprayed onto an LTQ ion trap mass spectrometer equipped with a nano-LC electrospray ionization source. Full MS spectra were recorded over a 400–1600 m/z range, followed by three tandem mass (MS/MS) events sequentially generated in a data-dependent manner on the first, second, and third most intense ions selected from the full MS spectrum (at 35% collision energy). Mass spectrometer scan functions and HPLC solvent gradients were controlled by the Xcalibur data system.

#### **2.3 Database search and interpretation of MS/MS datasets**

Database searching and interpretation of MS/MS Datasets were performed as described (Andronicos et al., 2010). Briefly, tandem mass spectra were extracted from raw files, and a binary classifier (Bern et al., 2004), previously trained on a manually validated data set, was used to remove low quality MS/MS spectra. Remaining spectra were searched against a mouse protein database containing 50,370 protein sequences downloaded as FASTAformatted sequences from EBI-IPI and 124 common contaminant proteins, for a total of 66,743 target database sequences (Peng et al., 2003). To calculate confidence levels and false positive rates, a decoy database containing the reverse sequences of the 66,743 proteins appended to the target database and the SEQUEST algorithm (Yates, III, 1998) were used to find the best matching sequences from the combined database.

SEQUEST searches were done on an Intel Xeon 80-processor cluster running under the Linux operating system. The peptide mass search tolerance was set to 3 Da. No differential modifications were considered. No enzymatic cleavage conditions were imposed on the database search, so the search space included all candidate peptides whose theoretical mass fell within the 3 Da mass tolerance window, despite their tryptic status.

The validity of peptide/spectrum matches was assessed in DTASelect2 (Tabb et al., 2002) using SEQUEST-defined parameters, the cross-correlation score (XCorr) and normalized difference in cross-correlation scores (DeltaCN). The search results were grouped by charge state (+1, +2, and +3) and tryptic status (fully tryptic, half-tryptic, and non-tryptic), resulting in 9 distinct sub-groups. In each one of the sub-groups, the distribution of XCorr and DeltaCN values for direct and decoy database hits was obtained, and the two subsets were separated by quadratic discriminant analysis. Outlier points in the two distributions (for example, matches with very low Xcorr but very high DeltaCN) were discarded. Full separation of the direct and decoy subsets is not generally possible; therefore, the discriminant score was set such that a false positive rate of 5% was determined based on the number of accepted decoy database peptides. This procedure was independently performed on each data subset, resulting in a false positive rate independent of tryptic status or charge state.

#### **3. Results**

#### **3.1 Isolation of an integral membrane plasminogen receptor exposing a C-terminal lysine on the cell surface**

We used specific proteolysis followed by MudPIT to probe the membrane proteome of differentiated mouse monocyte progenitor cells (Hoxa9-ER4) for the presence of integral membrane plasminogen receptor(s) exposing a C-terminal basic residue on the cell surface, as outlined in Figure 3. [The Hoxa9-ER4 cell line was derived from primary murine bone

Identification of the Novel Plasminogen Receptor, Plg-RKT 225

In MudPIT, the peptide mixtures were first resolved by strong cation exchange liquid chromatography followed by reversed phase liquid chromatography. Eluting peptides were electrosprayed onto an LTQ ion trap mass spectrometer and full MS spectra were recorded over a 400-1600 m/z range, followed by three tandem mass events. The spectra obtained were searched against a mouse protein database. Using this method, only one protein with a predicted transmembrane sequence and a C-terminal basic residue was identified: the hypothetical protein, C9orf46 homolog (IPI00136293), homologous to the protein predicted to be encoded by human chromosome 9, open reading frame 46. The peptides corresponding to C9orf46 homolog that were obtained in the MudPIT analysis are shown in Table 1. We have designated the protein, Plg-RKT, to indicate a plasminogen receptor with a C-terminal lysine and having a transmembrane domain (see below). [A limitation of shotgun proteomics, such as MudPIT, is that they typically under sample a proteome because they use data dependent data acquisition (a computer-driven data acquisition approach). This can lead to variations in the proteins identified, particularly amongst the lower abundant proteins. Thus, we cannot exclude the possibility that other membrane

proteins exposing C-terminal lysines were present in the membrane proteome.]

2.6749 0.1167 95.2% 1359.5521 1361.5181 K.NQQEFMVTHAR.L (2+) 2.6771 0.2534 99.8% 1160.4321 1160.3514 R.HLTMQNEMR.E (2+) 4.7468 0.3052 100% 1523.5322 1523.6954 R.MKSEAEDILETEK.T (2+)

3.775 0.3164 100% 1264.0922 1264.3287 K.SEAEDILETEK.T (2+) 2.995 0.0655 96.8% 1137.2722 1137.3184 K.GLITFESLEK.A (2+) 2.893 0.2591 99.7% 1364.4922 1364.5848 K.GLITFESLEKAR.R (2+)

Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface

plasminogen activation, Blood. 2010, 115: 1319-30.

annexin 2, and β2 integrin.

Table 1. Peptides obtained corresponding to C9orf46 homolog

3.8378 0.2884 99.8% 2195.8743 2196.4788 K.SMNENMKNQQEFMVTHAR.L (3+)

5.1774 0.3788 100% 2335.5544 2333.6997 R.MKSEAEDILETEKTKLELPK.G (3+)

SEQUEST-defined parameters (Xcorr, DeltCN, and Conf%) are shown for each peptide. (core: crosscorrelation score; DeltCN: normalized difference in cross-correlation scores; Conf%: confidence level of the peptide; ObsM+H+: observed peptide mass; CalcM+H+: theoretical peptide mass). Observed peptide mass, theoretical peptide mass, and charges of the peptide identified (3+ or 2+) are also shown to demonstrate accurate peptide identification. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III,

A key advantage of MudPIT is that proteins in a given proteome can be identified simultaneously. As proof of principle of our isolation method, peptides corresponding to other proteins previously identified as plasminogen binding proteins on monocytes were also detected in the membrane preparations: α-enolase, gamma actin, S100A10, histone H2B,

Xcorr DeltCN Conf% ObsM+H+ CalcM+H+ Peptide Sequences

marrow myeloid precursors immortalized with an estrogen regulated conditional oncoprotein, HoxA9-ER4 (Wang et al., 2006). The Hoxa9-ER4 line is factor-dependent and differentiates to monocytes when estrogen is removed from the medium, thereby inactivating the Hoxa9-ER protein. The mature monocytes respond to M-CSF (Odegaard et al., 2007)]. First, the Hoxa9-ER4 monocyte progenitor cells were differentiated with macrophage colony stimulating factor (M-CSF), which induces plasminogen receptors on these cells (Andronicos et al., 2010). Then intact cells were biotinylated. Because early apoptotic and non-viable/necrotic cells exhibit markedly enhanced plasminogen binding ability (Mitchell et al., 2006; O'Mullane & Baker, 1998, 1999) we passed the biotinylated cells over a dead cell removal column to enrich for live cells. Cells were then lysed and membrane fractions prepared and passed over a plasminogen-Sepharose affinity column and specifically eluted with ε-aminocaproic acid (EACA), a lysine analog that blocks the binding of plasminogen to cells (Miles & Plow, 1985). Biotinylated proteins bound to the avidin column and were digested with trypsin while still on the column. The peptide digest was then subjected to MudPIT.

Monocyte (Hoxa9-ER4) progenitor cells were differentiated with macrophage colony stimulating factor (M-CSF), which induces plasminogen receptors (▲) on these cells. Then intact cells were biotinylated (●) and passed over a dead cell removal column. Live cells were then lysed and membrane fractions prepared and passed over a plasminogen-Sepharose affinity column and specifically eluted. Biotinylated plasminogen receptors (▲●) were then bound to an avidin column and digested with trypsin.

Fig. 3. Isolation of plasminogen receptors.

marrow myeloid precursors immortalized with an estrogen regulated conditional oncoprotein, HoxA9-ER4 (Wang et al., 2006). The Hoxa9-ER4 line is factor-dependent and differentiates to monocytes when estrogen is removed from the medium, thereby inactivating the Hoxa9-ER protein. The mature monocytes respond to M-CSF (Odegaard et al., 2007)]. First, the Hoxa9-ER4 monocyte progenitor cells were differentiated with macrophage colony stimulating factor (M-CSF), which induces plasminogen receptors on these cells (Andronicos et al., 2010). Then intact cells were biotinylated. Because early apoptotic and non-viable/necrotic cells exhibit markedly enhanced plasminogen binding ability (Mitchell et al., 2006; O'Mullane & Baker, 1998, 1999) we passed the biotinylated cells over a dead cell removal column to enrich for live cells. Cells were then lysed and membrane fractions prepared and passed over a plasminogen-Sepharose affinity column and specifically eluted with ε-aminocaproic acid (EACA), a lysine analog that blocks the binding of plasminogen to cells (Miles & Plow, 1985). Biotinylated proteins bound to the avidin column and were digested with trypsin while still on the column. The peptide digest

Monocyte (Hoxa9-ER4) progenitor cells were differentiated with macrophage colony stimulating factor (M-CSF), which induces plasminogen receptors (▲) on these cells. Then intact cells were biotinylated (●) and passed over a dead cell removal column. Live cells were then lysed and membrane fractions prepared and passed over a plasminogen-Sepharose affinity column and specifically eluted. Biotinylated plasminogen receptors (▲●) were then bound to an avidin column and digested with

was then subjected to MudPIT.

trypsin.

Fig. 3. Isolation of plasminogen receptors.

In MudPIT, the peptide mixtures were first resolved by strong cation exchange liquid chromatography followed by reversed phase liquid chromatography. Eluting peptides were electrosprayed onto an LTQ ion trap mass spectrometer and full MS spectra were recorded over a 400-1600 m/z range, followed by three tandem mass events. The spectra obtained were searched against a mouse protein database. Using this method, only one protein with a predicted transmembrane sequence and a C-terminal basic residue was identified: the hypothetical protein, C9orf46 homolog (IPI00136293), homologous to the protein predicted to be encoded by human chromosome 9, open reading frame 46. The peptides corresponding to C9orf46 homolog that were obtained in the MudPIT analysis are shown in Table 1. We have designated the protein, Plg-RKT, to indicate a plasminogen receptor with a C-terminal lysine and having a transmembrane domain (see below). [A limitation of shotgun proteomics, such as MudPIT, is that they typically under sample a proteome because they use data dependent data acquisition (a computer-driven data acquisition approach). This can lead to variations in the proteins identified, particularly amongst the lower abundant proteins. Thus, we cannot exclude the possibility that other membrane proteins exposing C-terminal lysines were present in the membrane proteome.]


SEQUEST-defined parameters (Xcorr, DeltCN, and Conf%) are shown for each peptide. (core: crosscorrelation score; DeltCN: normalized difference in cross-correlation scores; Conf%: confidence level of the peptide; ObsM+H+: observed peptide mass; CalcM+H+: theoretical peptide mass). Observed peptide mass, theoretical peptide mass, and charges of the peptide identified (3+ or 2+) are also shown to demonstrate accurate peptide identification. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, Blood. 2010, 115: 1319-30.

Table 1. Peptides obtained corresponding to C9orf46 homolog

A key advantage of MudPIT is that proteins in a given proteome can be identified simultaneously. As proof of principle of our isolation method, peptides corresponding to other proteins previously identified as plasminogen binding proteins on monocytes were also detected in the membrane preparations: α-enolase, gamma actin, S100A10, histone H2B, annexin 2, and β2 integrin.

Identification of the Novel Plasminogen Receptor, Plg-RKT 227

The C9orf46 homolog/Plg-RKT murine DNA sequence encodes a protein of 147 amino acids with a molecular mass of 17,261 Da and a C-terminal lysine (Table 2, first line). We blasted the C9orf46 homolog/Plg-RKT sequence against all species using NCBI Blast and obtained unique human, rat, dog, cow, dog, giant panda, gibbon, horse, pig, rabbit, and rhesus monkey predicted orthologs, with high identity and homology (e.g. human versus rhesus monkey = 99% similarity), high identity (e.g. human vs rhesus monkey = 98% identity) and no gaps in the sequence (Table 2). Of key importance, a C-terminal lysine was predicted for all of the mammalian orthologs obtained in the blast search. In a query of the Ensembl Gene Report, DNA sequences of all 10 other sequenced mammalian orthologs encoded C-terminal

In addition, the DNA sequences of xenopus and the green lizard also encoded C-terminal lysines (Table 2). Furthermore, Plg-RKT orthologs with 149 amino acids with a C-terminal lysine were encoded in bony fish (salmon and zebrafish) and the high similarity with a mammalian ortholog is illustrated in the alignment of the mouse and zebrafish proteins in

The Plg-RKT sequence also encodes a putative conserved DUF2368 domain (encompassing amino acids 1-135), an uncharacterized protein with unknown function conserved from nematodes to humans. Notably, Plg-RKT orthologs of lower organisms were of different predicted lengths and did not consistently predict C-terminal lysines. It is interesting to note that the evolutionary origin of plasminogen is currently believed to originate with protochordates (Liu & Zhang, 2009), so that lower organisms without plasminogen would

It is also noteworthy that the primary sequence of C9orf46 homolog/Plg-RKT is apparently tightly conserved in humans, with no validated polymorphisms (cSNPs) within the 6 exons encoded by the gene (on chromosome 9p24.1) in the NCBI human genome sequence

The C9orf46 homolog/Plg-RKT sequence was analyzed in the TMpred site (www.ch.embnet.org/cgi-bin/TMPRED). The model predicted two transmembrane helices extending from F53-L73 (secondary helix, oriented from outside the cell to inside the cell) and P78-Y99 (primary helix, oriented from inside the cell to outside the cell) (Figure 4). Hence a 52 amino acid N-terminal region and a 48 amino acid C-terminal tail with a C-terminal lysine

not utilize the C-terminal lysine of Plg-RKT to bind plasminogen.

Table 3. Alignment of Mouse and Zebrafish Plg-RKT Sequences

variation database (dbSNP, http://www.ncbi.nlm.nih.gov/SNP).

were predicted to be exposed on the cell surface.

**3.2 Conservation of Plg-RKT across species** 

lysines (Table 2).

**3.3 Topology of Plg-RKT**

Table 3.


Table 2. Alignment of Orthologs of Plg-RKT

#### **3.2 Conservation of Plg-RKT across species**

226 Proteomics – Human Diseases and Protein Functions

Table 2. Alignment of Orthologs of Plg-RKT

The C9orf46 homolog/Plg-RKT murine DNA sequence encodes a protein of 147 amino acids with a molecular mass of 17,261 Da and a C-terminal lysine (Table 2, first line). We blasted the C9orf46 homolog/Plg-RKT sequence against all species using NCBI Blast and obtained unique human, rat, dog, cow, dog, giant panda, gibbon, horse, pig, rabbit, and rhesus monkey predicted orthologs, with high identity and homology (e.g. human versus rhesus monkey = 99% similarity), high identity (e.g. human vs rhesus monkey = 98% identity) and no gaps in the sequence (Table 2). Of key importance, a C-terminal lysine was predicted for all of the mammalian orthologs obtained in the blast search. In a query of the Ensembl Gene Report, DNA sequences of all 10 other sequenced mammalian orthologs encoded C-terminal lysines (Table 2).

In addition, the DNA sequences of xenopus and the green lizard also encoded C-terminal lysines (Table 2). Furthermore, Plg-RKT orthologs with 149 amino acids with a C-terminal lysine were encoded in bony fish (salmon and zebrafish) and the high similarity with a mammalian ortholog is illustrated in the alignment of the mouse and zebrafish proteins in Table 3.

The Plg-RKT sequence also encodes a putative conserved DUF2368 domain (encompassing amino acids 1-135), an uncharacterized protein with unknown function conserved from nematodes to humans. Notably, Plg-RKT orthologs of lower organisms were of different predicted lengths and did not consistently predict C-terminal lysines. It is interesting to note that the evolutionary origin of plasminogen is currently believed to originate with protochordates (Liu & Zhang, 2009), so that lower organisms without plasminogen would not utilize the C-terminal lysine of Plg-RKT to bind plasminogen.


Table 3. Alignment of Mouse and Zebrafish Plg-RKT Sequences

It is also noteworthy that the primary sequence of C9orf46 homolog/Plg-RKT is apparently tightly conserved in humans, with no validated polymorphisms (cSNPs) within the 6 exons encoded by the gene (on chromosome 9p24.1) in the NCBI human genome sequence variation database (dbSNP, http://www.ncbi.nlm.nih.gov/SNP).

#### **3.3 Topology of Plg-RKT**

The C9orf46 homolog/Plg-RKT sequence was analyzed in the TMpred site (www.ch.embnet.org/cgi-bin/TMPRED). The model predicted two transmembrane helices extending from F53-L73 (secondary helix, oriented from outside the cell to inside the cell) and P78-Y99 (primary helix, oriented from inside the cell to outside the cell) (Figure 4). Hence a 52 amino acid N-terminal region and a 48 amino acid C-terminal tail with a C-terminal lysine were predicted to be exposed on the cell surface.

Identification of the Novel Plasminogen Receptor, Plg-RKT 229

A. Membrane fractions or cytoplasmic fractions from either undifferentiated or M-CSF-treated Hoxa9- ER4 cells were electrophoresed on 12% sodium dodecyl sulfate polyacrylamide gels under reducing conditions and western blotted with either anti-Plg-RKT mAb, anti-α-enolase mAb as a loading control, or isotype control mAb. B. M-CSF-treated Hoxa9-ER4 cell membranes were solubilized in 3% Triton X-114. After heating at 37ºC and separation of the phases by centrifugation, an aliquot of both phases was electrophoresed and western blotted with anti-Plg-RKT mAb. This research was originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface

removed but intracellular C-terminal lysines are protected (see Figure 2,B). Under this condition, no peptides corresponding to Plg-RKT were obtained in the MudPIT analysis,

In order to experimentally evaluate whether the N-terminus of Plg-RKT was exposed on the cell surface, PC12 (rat pheochromocytoma) cells were stably transfected with V5-pCIneo-Plg-RKT that expressed a V5 tag at the N-terminus of Plg-RKT. (The V5 sequence was added in front of the mammalian expression vector, pCIneo using PCR and then the full-length 443 bp Plg-RKT cDNA was subcloned into the V5-pCIneo vector using the Xho1 and Sma1 cloning sites. Constructs were transfected into cells using Lipofectamine 2000 and stable

A specific band migrating with a Mrapp of 17,000 was detected in cell membranes of the stably transfected cells with both anti-V5 mAb and anti-Plg-RKT mAb (Figure 6, lane 1). The band was not detected by either mAb after trypsin digestion of the isolated membrane fraction (lane 2). When intact cells were incubated with trypsin and the trypsin neutralized with SBTI prior to preparation of the membrane fraction, the majority of the band detectable with either anti-V5 or anti-Plg-RKT was lost (lane 4). In controls, treatment with soybean trypsin inhibitor (SBTI) fully neutralized the ability of trypsin to degrade the V5-tagged Plg-RKT in purified membrane fractions (lane 3), demonstrating that the trypsin had been neutralized prior to membrane fractionation of the treated cells. These results suggest that the N- terminus of Plg-RKT is accessible to trypsin proteolysis of intact cells and is, therefore,

plasminogen activation, *Blood*. 2010, 115: 1319-30.

transfectants were selected with G418.)

Fig. 5. Plg-RKT behaves as a regulated integral membrane protein.

consistent with cell surface exposure of the C-terminal lysine of Plg-RKT.

Green indicates amino acids within the predicted primary transmembrane helix. Orange indicates amino acids within the predicted secondary transmembrane helix. Red indicates basic amino acids. This research was originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, *Blood*. 2010, 115: 1319-30.

Fig. 4. Structural model of Plg-RKT.

We experimentally tested predictions of the model. First, we raised a monoclonal antibody against the synthetic peptide, CEQSKLFSDK (corresponding to the nine C-terminal amino acids of murine Plg-RKT with an amino terminal cysteine added for coupling). To examine subcellular localization, membrane and cytoplasmic fractions from progenitor and differentiated Hoxa9-ER4 monocyte progenitor cells were electrophoresed and western blotted with anti-Plg-RKT mAb or isotype control mAb. A specific immunoreactive band migrating with an Mrapp of ~17,000, was detected in membrane fractions of differentiated monocyte progenitor cells, clearly demonstrating the existence of this new protein (Figure 5,A). The protein was not detected in undifferentiated cells or in the cytoplasmic fraction of the differentiated cells.

To test the prediction that Plg-RKT is an integral membrane protein, membranes from differentiated monocyte progenitor cells were subjected to phase separation in Triton X-114 as described (Bordier, 1981; Estreicher et al., 1989). In this method, integral membrane proteins form mixed micelles with the nonionic detergent and are recovered in the Triton X-114 detergent phase, whereas hydrophilic proteins remain in the aqueous phase. An immunoreactive band migrating with an Mrapp of ~17,000 was detected in the detergent phase in western blotting with anti-Plg-RKT mAb, but was not detected in the aqueous phase (Figure. 5,B). These data support the prediction that Plg-RKT is an integral membrane protein.

To experimentally test whether the C-terminal lysine of Plg-RKT was exposed on the cell surface, we treated intact biotinylated cells with carboxypeptidase B prior to performing our isolation procedure. Under this condition, C-terminal lysines exposed on the cell surface are

Green indicates amino acids within the predicted primary transmembrane helix. Orange indicates amino acids within the predicted secondary transmembrane helix. Red indicates basic amino acids. This research was originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major

We experimentally tested predictions of the model. First, we raised a monoclonal antibody against the synthetic peptide, CEQSKLFSDK (corresponding to the nine C-terminal amino acids of murine Plg-RKT with an amino terminal cysteine added for coupling). To examine subcellular localization, membrane and cytoplasmic fractions from progenitor and differentiated Hoxa9-ER4 monocyte progenitor cells were electrophoresed and western blotted with anti-Plg-RKT mAb or isotype control mAb. A specific immunoreactive band migrating with an Mrapp of ~17,000, was detected in membrane fractions of differentiated monocyte progenitor cells, clearly demonstrating the existence of this new protein (Figure 5,A). The protein was not detected in undifferentiated cells or in the cytoplasmic fraction of

To test the prediction that Plg-RKT is an integral membrane protein, membranes from differentiated monocyte progenitor cells were subjected to phase separation in Triton X-114 as described (Bordier, 1981; Estreicher et al., 1989). In this method, integral membrane proteins form mixed micelles with the nonionic detergent and are recovered in the Triton X-114 detergent phase, whereas hydrophilic proteins remain in the aqueous phase. An immunoreactive band migrating with an Mrapp of ~17,000 was detected in the detergent phase in western blotting with anti-Plg-RKT mAb, but was not detected in the aqueous phase (Figure. 5,B). These data support the prediction that Plg-RKT is an integral membrane

To experimentally test whether the C-terminal lysine of Plg-RKT was exposed on the cell surface, we treated intact biotinylated cells with carboxypeptidase B prior to performing our isolation procedure. Under this condition, C-terminal lysines exposed on the cell surface are

regulator of cell surface plasminogen activation, *Blood*. 2010, 115: 1319-30.

Fig. 4. Structural model of Plg-RKT.

the differentiated cells.

protein.

A. Membrane fractions or cytoplasmic fractions from either undifferentiated or M-CSF-treated Hoxa9- ER4 cells were electrophoresed on 12% sodium dodecyl sulfate polyacrylamide gels under reducing conditions and western blotted with either anti-Plg-RKT mAb, anti-α-enolase mAb as a loading control, or isotype control mAb. B. M-CSF-treated Hoxa9-ER4 cell membranes were solubilized in 3% Triton X-114. After heating at 37ºC and separation of the phases by centrifugation, an aliquot of both phases was electrophoresed and western blotted with anti-Plg-RKT mAb. This research was originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, *Blood*. 2010, 115: 1319-30.

Fig. 5. Plg-RKT behaves as a regulated integral membrane protein.

removed but intracellular C-terminal lysines are protected (see Figure 2,B). Under this condition, no peptides corresponding to Plg-RKT were obtained in the MudPIT analysis, consistent with cell surface exposure of the C-terminal lysine of Plg-RKT.

In order to experimentally evaluate whether the N-terminus of Plg-RKT was exposed on the cell surface, PC12 (rat pheochromocytoma) cells were stably transfected with V5-pCIneo-Plg-RKT that expressed a V5 tag at the N-terminus of Plg-RKT. (The V5 sequence was added in front of the mammalian expression vector, pCIneo using PCR and then the full-length 443 bp Plg-RKT cDNA was subcloned into the V5-pCIneo vector using the Xho1 and Sma1 cloning sites. Constructs were transfected into cells using Lipofectamine 2000 and stable transfectants were selected with G418.)

A specific band migrating with a Mrapp of 17,000 was detected in cell membranes of the stably transfected cells with both anti-V5 mAb and anti-Plg-RKT mAb (Figure 6, lane 1). The band was not detected by either mAb after trypsin digestion of the isolated membrane fraction (lane 2). When intact cells were incubated with trypsin and the trypsin neutralized with SBTI prior to preparation of the membrane fraction, the majority of the band detectable with either anti-V5 or anti-Plg-RKT was lost (lane 4). In controls, treatment with soybean trypsin inhibitor (SBTI) fully neutralized the ability of trypsin to degrade the V5-tagged Plg-RKT in purified membrane fractions (lane 3), demonstrating that the trypsin had been neutralized prior to membrane fractionation of the treated cells. These results suggest that the N- terminus of Plg-RKT is accessible to trypsin proteolysis of intact cells and is, therefore,

Identification of the Novel Plasminogen Receptor, Plg-RKT 231

A. M-CSF-differentiated (Hoxa9-ER4) cells were grown on coverslips and preincubated with either phosphate buffered saline (- plasminogen) or 2 μM plasminogen (+ plasminogen), then fixed in 1% formaldehyde, washed and stained with polyclonal anti-plasminogen IgG or anti-Plg-RKT mAb and stained with a combination of Alexa 488- F(ab')2 of goat anti-rabbit IgG and Alexa 568- F(ab')2 fragment

B. The number and size of each labeled aggregate was determined. The results reflect counts from over

To further address the plasminogen binding function of the C-terminus of Plg-RKT , we tested whether the synthetic peptide, corresponding to the C-terminus of Plg-RKT could bind plasminogen. The peptide, CEQSKLFSDK, was coupled to BSA and then coated onto wells of microtiter plates. Biotinylated Glu-plasminogen was incubated with the wells and specific binding was detected with HRP-streptavidin (Figure 8). We tested the ability of the soluble C-terminal peptide to inhibit Glu-plasminogen binding under solution phase equilibrium

originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of

p < 0.001. This research was

40 cells in 2 independent experiments. Data represent mean ± SEM. \*

cell surface plasminogen activation, *Blood*. 2010, 115: 1319-30. Fig. 7. Plg-RKT binds plasminogen on the cell surface.

of goat anti-mouse IgG.

Membrane fractions of PC12 cells stably transfected with V5-pCIneo-Plg-RKT were incubated with either buffer (lane 1), trypsin (1mg/ml ) (lane 2) or trypsin 1 mg/ml + soybean trypsin inhibitor (SBTI) (2 mg/ml) (lane 3) for 30 minutes at 37ºC or intact PC12 cells were incubated with 1 mg/ml trypsin for 2 hr at 37ºC, followed by 2 mg/ml SBTI for 15 min . Following neutralization of trypsin with SBTI, the membrane fraction was prepared from the treated, intact cells (lane 4). 30 µg/lane of membrane fractions were electrophoresed on 18% SDS PAGE under reducing conditions and western blotted with either anti-V5, anti-Plg-RKT mAb or isotype control.

Fig. 6. The N-termini and C-termini of Plg-RKT are exposed on the cell surface.

exposed on the extracellular face. Furthermore, because the anti-Plg-RKT mAb reacts with the C-terminus of Plg-RKT, these data also confirm the exposure of the C-terminus on the extracellular face of the cell membrane.

#### **3.4 Role of the C-terminal lysine of Plg-RKT in plasminogen binding**

We further addressed the exposure of the C-terminus of Plg-RKT on the cell surface using confocal microscopy with a mAb raised against the Plg-RKT C-terminal peptide. (The mAb reacted with the C-terminal peptide of murine Plg-RKT and blocked plasminogen binding to CEQSKLFSDK). When cells were incubated with anti-Plg-RKT mAb and a polyclonal antiplasminogen antibody, Plg-RKT and plasminogen were both immunodetected in small aggregates dispersed over the cell surface (Figure 7,A), in a similar distribution to that published for confocal analyses of monocyte-associated plasminogen (Das et al., 2007). Most importantly, after preincubation of monocytes with plasminogen, immunodetection of Plg-RKT was reduced by half (Figure 7,A,B). These results demonstrate that the C-terminus of Plg-RKT is exposed on the cell surface. Furthermore, these results show that plasminogen binds to the C-terminal domain of Plg-RKT on the cell surface.

Membrane fractions of PC12 cells stably transfected with V5-pCIneo-Plg-RKT were incubated with either buffer (lane 1), trypsin (1mg/ml ) (lane 2) or trypsin 1 mg/ml + soybean trypsin inhibitor (SBTI) (2 mg/ml) (lane 3) for 30 minutes at 37ºC or intact PC12 cells were incubated with 1 mg/ml trypsin for 2 hr at 37ºC, followed by 2 mg/ml SBTI for 15 min . Following neutralization of trypsin with SBTI, the membrane fraction was prepared from the treated, intact cells (lane 4). 30 µg/lane of membrane fractions were electrophoresed on 18% SDS PAGE under reducing conditions and western blotted with

exposed on the extracellular face. Furthermore, because the anti-Plg-RKT mAb reacts with the C-terminus of Plg-RKT, these data also confirm the exposure of the C-terminus on the

We further addressed the exposure of the C-terminus of Plg-RKT on the cell surface using confocal microscopy with a mAb raised against the Plg-RKT C-terminal peptide. (The mAb reacted with the C-terminal peptide of murine Plg-RKT and blocked plasminogen binding to CEQSKLFSDK). When cells were incubated with anti-Plg-RKT mAb and a polyclonal antiplasminogen antibody, Plg-RKT and plasminogen were both immunodetected in small aggregates dispersed over the cell surface (Figure 7,A), in a similar distribution to that published for confocal analyses of monocyte-associated plasminogen (Das et al., 2007). Most importantly, after preincubation of monocytes with plasminogen, immunodetection of Plg-RKT was reduced by half (Figure 7,A,B). These results demonstrate that the C-terminus of Plg-RKT is exposed on the cell surface. Furthermore, these results show that plasminogen

Fig. 6. The N-termini and C-termini of Plg-RKT are exposed on the cell surface.

**3.4 Role of the C-terminal lysine of Plg-RKT in plasminogen binding** 

binds to the C-terminal domain of Plg-RKT on the cell surface.

either anti-V5, anti-Plg-RKT mAb or isotype control.

extracellular face of the cell membrane.

A. M-CSF-differentiated (Hoxa9-ER4) cells were grown on coverslips and preincubated with either phosphate buffered saline (- plasminogen) or 2 μM plasminogen (+ plasminogen), then fixed in 1% formaldehyde, washed and stained with polyclonal anti-plasminogen IgG or anti-Plg-RKT mAb and stained with a combination of Alexa 488- F(ab')2 of goat anti-rabbit IgG and Alexa 568- F(ab')2 fragment of goat anti-mouse IgG.

B. The number and size of each labeled aggregate was determined. The results reflect counts from over 40 cells in 2 independent experiments. Data represent mean ± SEM. \* p < 0.001. This research was originally published in *Blood,* Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, *Blood*. 2010, 115: 1319-30.

Fig. 7. Plg-RKT binds plasminogen on the cell surface.

To further address the plasminogen binding function of the C-terminus of Plg-RKT , we tested whether the synthetic peptide, corresponding to the C-terminus of Plg-RKT could bind plasminogen. The peptide, CEQSKLFSDK, was coupled to BSA and then coated onto wells of microtiter plates. Biotinylated Glu-plasminogen was incubated with the wells and specific binding was detected with HRP-streptavidin (Figure 8). We tested the ability of the soluble C-terminal peptide to inhibit Glu-plasminogen binding under solution phase equilibrium

Identification of the Novel Plasminogen Receptor, Plg-RKT 233

Plasminogen activation was determined in either the presence or absence of differentiated Hoxa9-ER4 cells and in the presence of either anti-Plg-RKT mAb (filled bars) or isotype control rat IgG2a (open bars). \*\*\*p < 0.001, compared to the corresponding isotype control. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface

detected in spleen, lymph node, thymus, bone marrow, lung, intestine, adrenal, pituitary, and other endocrine tissues, vascular tissue, kidney, liver, stomach, bladder, and neuronal tissue (hippocampus, hypothalamus, cerebellum, cerebral cortex, olfactory bulb and dorsal

We also searched for C9orf46 homolog/Plg-RKT mRNA microarray expression data at http://www.ebi.ac.uk/microarray-as/aew/. C9orf46 homolog/Plg-RKT mRNA is present in monocytes, leukocytes, natural killer (NK) cells, T cells, myeloid, dendritic, and plasmacytoid cells, breast cancer, acute lymphoblastic leukemia and Molt-4 acute

plasminogen activation, Blood. 2010, 115: 1319-30.

lymphoblastic leukemia cells (Table 5).

root ganglion) (Table 4).

• Kidney

Fig. 9. Plg-RKT regulates cell surface plasminogen activation.

• Spleen • Liver • Thymus • Stomach • Lymph Node • Bladder • Lung • Hippocampus • Intestine • Hypothalamus • Bone Marrow • Cerebellum • Adrenal • Cerebral Cortex • Pituitary • Olfactory Bulb • Vascular Tissue • Dorsal Root Ganglion

Results of high-throughput gene expression profiling (54).

Table 4. Tissue Distribution of Plg-RKT mRNA

conditions. The soluble peptide competed for Glu-plasminogen binding in a dosedependent manner with an IC50 of 2 μM (Figure 8), similar to the Kd values we have previously determined for Glu-plasminogen binding to cells (Miles et al., 2005). In addition, a mutated peptide with the C-terminal lysine substituted with alanine did not compete for plasminogen binding at concentrations up to 1 mM (Figure 8), further supporting the role of the C-terminal lysine in the interaction of Plg-RKT with plasminogen.

The peptide, CEQSKLFSDK, was coupled to BSA and immobilized on microtiter wells. Biotinylated-Gluplasminogen (25 nM) was incubated with immobilized CEQSKLFSDK in the presence of increasing concentrations of CEQSKLFSDK (●) or a K147A mutant peptide, CEQSKLFSDA (○). Biotinylated Gluplasminogen binding was detected with HRP-streptavidin. Data are as mean ± SEM, n=3, for each determination. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, Blood. 2010, 115: 1319-30.

Fig. 8. Plasminogen binds to the C-terminal peptide of Plg-RKT.

#### **3.5 Plg-RKT regulates cell surface plasminogen activation**

We verified that plasminogen activation was promoted in the presence of differentiated Hoxa9-ER4 cells. Plasminogen activation was stimulated 12.7-fold in the presence of differentiated monocyte progenitor cells, compared to the reaction in the absence of cells (Figure 9). In order to test the role of Plg-RKT in plasminogen activation, we tested the effect of anti-Plg-RKT mAb raised against the synthetic peptide, CEQSKLFSDK . Anti-Plg-RKT mAb substantially suppressed cell-dependent plasminogen activation (Figure 9). In controls, plasminogen activation in the absence of cells was not affected by anti-Plg-RKT mAb.

#### **3.6 Tissue and cellular distribution and regulation of the Plg-RKT transcript**

We searched results of gene expression array analyses for expression of the C9orf46 homolog/Plg-RKT transcript. The transcript is broadly expressed in normal human and mouse tissues [as determined in high-throughput gene expression profiling in which RNA samples from human and murine tissues were hybridized to high-density gene expression arrays (Su et al., 2002; Su et al., 2004)]. The C9orf46 homolog/Plg-RKT transcript has been

conditions. The soluble peptide competed for Glu-plasminogen binding in a dosedependent manner with an IC50 of 2 μM (Figure 8), similar to the Kd values we have previously determined for Glu-plasminogen binding to cells (Miles et al., 2005). In addition, a mutated peptide with the C-terminal lysine substituted with alanine did not compete for plasminogen binding at concentrations up to 1 mM (Figure 8), further supporting the role of

The peptide, CEQSKLFSDK, was coupled to BSA and immobilized on microtiter wells. Biotinylated-Gluplasminogen (25 nM) was incubated with immobilized CEQSKLFSDK in the presence of increasing concentrations of CEQSKLFSDK (●) or a K147A mutant peptide, CEQSKLFSDA (○). Biotinylated Gluplasminogen binding was detected with HRP-streptavidin. Data are as mean ± SEM, n=3, for each determination. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a

We verified that plasminogen activation was promoted in the presence of differentiated Hoxa9-ER4 cells. Plasminogen activation was stimulated 12.7-fold in the presence of differentiated monocyte progenitor cells, compared to the reaction in the absence of cells (Figure 9). In order to test the role of Plg-RKT in plasminogen activation, we tested the effect of anti-Plg-RKT mAb raised against the synthetic peptide, CEQSKLFSDK . Anti-Plg-RKT mAb substantially suppressed cell-dependent plasminogen activation (Figure 9). In controls,

We searched results of gene expression array analyses for expression of the C9orf46 homolog/Plg-RKT transcript. The transcript is broadly expressed in normal human and mouse tissues [as determined in high-throughput gene expression profiling in which RNA samples from human and murine tissues were hybridized to high-density gene expression arrays (Su et al., 2002; Su et al., 2004)]. The C9orf46 homolog/Plg-RKT transcript has been

plasminogen activation in the absence of cells was not affected by anti-Plg-RKT mAb.

**3.6 Tissue and cellular distribution and regulation of the Plg-RKT transcript** 

the C-terminal lysine in the interaction of Plg-RKT with plasminogen.

major regulator of cell surface plasminogen activation, Blood. 2010, 115: 1319-30.

Fig. 8. Plasminogen binds to the C-terminal peptide of Plg-RKT.

**3.5 Plg-RKT regulates cell surface plasminogen activation** 

Plasminogen activation was determined in either the presence or absence of differentiated Hoxa9-ER4 cells and in the presence of either anti-Plg-RKT mAb (filled bars) or isotype control rat IgG2a (open bars). \*\*\*p < 0.001, compared to the corresponding isotype control. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, Blood. 2010, 115: 1319-30.

Fig. 9. Plg-RKT regulates cell surface plasminogen activation.

detected in spleen, lymph node, thymus, bone marrow, lung, intestine, adrenal, pituitary, and other endocrine tissues, vascular tissue, kidney, liver, stomach, bladder, and neuronal tissue (hippocampus, hypothalamus, cerebellum, cerebral cortex, olfactory bulb and dorsal root ganglion) (Table 4).

We also searched for C9orf46 homolog/Plg-RKT mRNA microarray expression data at http://www.ebi.ac.uk/microarray-as/aew/. C9orf46 homolog/Plg-RKT mRNA is present in monocytes, leukocytes, natural killer (NK) cells, T cells, myeloid, dendritic, and plasmacytoid cells, breast cancer, acute lymphoblastic leukemia and Molt-4 acute lymphoblastic leukemia cells (Table 5).


Results of high-throughput gene expression profiling (54).

Table 4. Tissue Distribution of Plg-RKT mRNA

Identification of the Novel Plasminogen Receptor, Plg-RKT 235

broad distribution of the Plg-RKT transcript and its regulation in tissues that have been demonstrated to express plasminogen binding sites, suggest that Plg-RKT provides plasminogen receptor function that may serve to modulate plasmin proteolytic functions (both physiologic and pathologic) specific to a large number of tissues. Furthermore, the potential function of Plg-RKT in the regulation of apoptosis and proliferation may play a key role in cancer and metastasis. Future studies with knockout mice should build on our initial

Supported by National Institutes of Health Grants (HL38272, Hl45934, and HL081046 to L.A.M., HL50398 to R.J.P., NIH P41 RR011823 to J.R.Y., NIAID sub-contract grant UCSD/MCB0237059 to E.I.C.) and Department of Veterans Affairs to R.J.P. S.L was supported by NIH training grant, T32 HL007195. We thank Dr. Ray Stevens at The Scripps Research Institute and Dr. Nuala Booth and Dr. Ian Booth, University of Aberdeen, for helpful discussions. We thank Ms. Linda Bonafede for manuscript preparation. This is

Andronicos, N. M., E. I. Chen, N. Baik, H. Bai, C. M. Parmer, W. B. Kiosses, M. P. Kamps, J. R.

Bugge, T. H., M. J. Flick, C. C. Daugherty, and J. L. Degen. 1995. Plasminogen deficiency

Busuttil, S. J., V. A. Ploplis, F. J. Castellino, L. Tang, J. W. Eaton, and E. F. Plow. 2004. A

Castellino, F. J. and V. A. Ploplis. 2005. Structure and function of the plasminogen/plasmin

Choi, K. S., D. K. Fogg, C. S. Yoon, and D. M. Waisman. 2003. p11 regulates extracellular

Collen, D. 1999. The plasminogen (fibrinolytic) system. *Thrombosis and Haemostasis* 82:259-270. Correc, P., M.-C. Fondanèche, M. Bracke, and P. Burtin. 1990. The presence of plasmin

Creemers, E., J. Cleutjens, J. Smits, S. Heymans, L. Moons, D. Collen, M. Daemen, and P.

Das, R., T. Burke, and E. F. Plow. 2007. Histone H2B as a functionally important plasminogen receptor on macrophages. *Blood* 110, no. 10:3763-3772.

Yates, III, R. J. Parmer, and L. A. Miles. 2010. Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation. *Blood* 115, no. 7:1319-1330. Bern, M., D. Goldberg, W. H. McDonald, and J. R. 3rd Yates. 2004. Automatic quality assessment of peptide tandem mass spectra. *Bioinformatics* 20, Suppl 1:I49-I54. Bordier, C. 1981. Phase separation of integral membrane proteins in Triton X-114 solution.

causes severe thrombosis but is compatible with development and reproducton.

central role for plasminogen in the inflammatory response to biomaterials.

plasmin production and invasiveness of HT1080 fibrosarcoma cells. *FASEB J* 17, no.

receptors on three mammary carcinoma MCF-7 sublines. *International Journal of* 

Carmeliet. 2000. Disruption of the plasminogen gene in mice abolishes wound healing after myocardial infarction. *American Journal of Pathology* 156, no. 6:1865-1873.

results using MudPIT to elucidate the role of Plg-RKT.

publication #21378 from The Scripps Research Institute.

*Journal of Biological Chemistry* 256, no. 4:1604-1607.

system. *Thrombosis and Haemostasis* 93, no. 4:647-654.

*Genes and Development* 9:794-807.

2:235-246.

*Cancer* 46:745-750.

*J.Thromb.Haemost.* 2, no. 10:1798-1805.

**5. Acknowledgments** 

**6. References** 


www.ebi.ae.uk/microarray-as/aew/

Table 5. Microarray Expression Data for Plg-RKT mRNA

These data are consistent with previous reports documenting expression of plasminogen binding sites on peripheral blood leukocytes (Miles & Plow, 1987), breast cancer cells (Correc et al., 1990; Ranson et al., 1998) and other tissues [reviewed in (Miles et al., 2005)]. In addition, results obtained by searching the ArrayExpress Warehouse (http://www.ebi.ac.uk/microarray) indicated that the C9orf46 homolog gene is also regulated in other tissues by lipopolysaccharide, aldosterone, canrenoate, H2O2, and dexamethasone (Table 6).

In a previously published genome-scale quantitative image analysis, overexpression of a cDNA that we now recognize to be the Plg-RKT cDNA, resulted in dramatic increases in cell proliferation whereas knockdown of the corresponding mRNA resulted in apoptosis (Harada et al., 2005). Consistent with an anti-apoptotic role of Plg-RKT, we have shown that cell-bound plasminogen inhibits TNFα-induced apoptosis (Mitchell et al., 2006). In microarray studies, C9orf46 homolog mRNA expression has a high power to predict cervical lymph node metastasis in oral squamous cell carcinoma (Nguyen et al., 2007).


Data were obtained from ArrayExpress Warehouse (http://www.ebi.ac.uk/microarray). \* ↓ = downregulation. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, Blood. 2010, 115: 1319-30.

Table 6. Regulation of C9orf46 homolog/Plg-RKT mRNA in Tissues

#### **4. Conclusions**

In conclusion, MudPIT has allowed us to identify a new protein, Plg-RKT, a novel plasminogen receptor with unique characteristics: integral to the cell membrane and exposing a C-terminal lysine on the cell surface in an orientation to bind plasminogen and promote plasminogen activation. Thus, Plg-RKT is likely to play a key role in plasminogendependent functions of cells including inflammation, wound healing, development, metastasis, neurite outgrowth, fibrinolysis, myogenesis and prohormone processing. The broad distribution of the Plg-RKT transcript and its regulation in tissues that have been demonstrated to express plasminogen binding sites, suggest that Plg-RKT provides plasminogen receptor function that may serve to modulate plasmin proteolytic functions (both physiologic and pathologic) specific to a large number of tissues. Furthermore, the potential function of Plg-RKT in the regulation of apoptosis and proliferation may play a key role in cancer and metastasis. Future studies with knockout mice should build on our initial results using MudPIT to elucidate the role of Plg-RKT.

#### **5. Acknowledgments**

234 Proteomics – Human Diseases and Protein Functions

• Myeloid cells • Breast cancer cells, SKBR3, MDA468, BT474, T47D

These data are consistent with previous reports documenting expression of plasminogen binding sites on peripheral blood leukocytes (Miles & Plow, 1987), breast cancer cells (Correc et al., 1990; Ranson et al., 1998) and other tissues [reviewed in (Miles et al., 2005)]. In addition, results obtained by searching the ArrayExpress Warehouse (http://www.ebi.ac.uk/microarray) indicated that the C9orf46 homolog gene is also regulated in other tissues by lipopolysaccharide, aldosterone, canrenoate, H2O2, and

In a previously published genome-scale quantitative image analysis, overexpression of a cDNA that we now recognize to be the Plg-RKT cDNA, resulted in dramatic increases in cell proliferation whereas knockdown of the corresponding mRNA resulted in apoptosis (Harada et al., 2005). Consistent with an anti-apoptotic role of Plg-RKT, we have shown that cell-bound plasminogen inhibits TNFα-induced apoptosis (Mitchell et al., 2006). In microarray studies, C9orf46 homolog mRNA expression has a high power to predict cervical

Experiment Tissue Agonist Effect\* E-MEXP-420 Hippocampal microglial cells lipopolysaccharide ↓ E-TABM-229 Kidney aldosterone ↓ E-TABM-229 Kidney canrenoate ↓ E-MEXP-710 Cholinergic cells H2O2 ↓ E-MEXP-774 Preadipocytes Dexamethasone ↓

\* ↓ = downregulation. This research was originally published in Blood, Andronicos, N.M., Chen, E.I., Baik, N., Bai, H., Parmer, C.M., Kiosses, W.B., Kamps, M.P., Yates, J.R., III, Parmer, R.J., Miles, L.A., Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation, Blood. 2010,

In conclusion, MudPIT has allowed us to identify a new protein, Plg-RKT, a novel plasminogen receptor with unique characteristics: integral to the cell membrane and exposing a C-terminal lysine on the cell surface in an orientation to bind plasminogen and promote plasminogen activation. Thus, Plg-RKT is likely to play a key role in plasminogendependent functions of cells including inflammation, wound healing, development, metastasis, neurite outgrowth, fibrinolysis, myogenesis and prohormone processing. The

lymph node metastasis in oral squamous cell carcinoma (Nguyen et al., 2007).

Data were obtained from ArrayExpress Warehouse (http://www.ebi.ac.uk/microarray).

Table 6. Regulation of C9orf46 homolog/Plg-RKT mRNA in Tissues

• Monocytes • B-cell precursor cells

Table 5. Microarray Expression Data for Plg-RKT mRNA

• Dendritic cells • Plasmacytoid cells

dexamethasone (Table 6).

115: 1319-30.

**4. Conclusions** 

www.ebi.ae.uk/microarray-as/aew/

• NK cells • Acute lymphoblastic leukemia cells • T cells • Molt-4 acute lymphoblastic leukemia cells

> Supported by National Institutes of Health Grants (HL38272, Hl45934, and HL081046 to L.A.M., HL50398 to R.J.P., NIH P41 RR011823 to J.R.Y., NIAID sub-contract grant UCSD/MCB0237059 to E.I.C.) and Department of Veterans Affairs to R.J.P. S.L was supported by NIH training grant, T32 HL007195. We thank Dr. Ray Stevens at The Scripps Research Institute and Dr. Nuala Booth and Dr. Ian Booth, University of Aberdeen, for helpful discussions. We thank Ms. Linda Bonafede for manuscript preparation. This is publication #21378 from The Scripps Research Institute.

#### **6. References**


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**11** 

*Canada* 

**Posttranslational Modifications of Myosin** 

The advances in proteomics over the last decade have made it possible for a more detailed study of protein posttranslational modifications. Posttranslational modification of proteins is an important signaling mechanism regulating vital pathways ranging from transcription to translation, in metabolism, cell survival, and cell death. Posttranslational modification of proteins has commonly been associated with the loss/gain of function and signal transduction with the concept of phosphorylation being the hallmark. However, many other posttranslational modifications of proteins have been detected and their implication to

The cardiovascular system, in particular the heart due to its high metabolic rates, sensitivity to oxidative stress and necessity to adapt quickly to new environments, is an ideal candidate to the study of posttranslational modifications in physiology and pathology. Cardiac contractile function relies significantly on the integrity of its contractile apparatus, with the myosin light chains being important contractile elements. We have recently described the role of nitration and nitrosylation of ventricular myosin light chains (MLCs) on its degradation by the proteolytic enzyme matrix metalloproteinase-2 (MMP-2) (Doroszko et al. 2010; Doroszko et al. 2009; Polewicz et al. 2010). Using distinct experimental models of oxidative stress, such as hypoxia-reoxygenation or ischemia/reperfusion, we have detected pathological nitration and nitrosylation of MLC induced by oxidative stress. According to our findings, nitration and nitrosylation of MLCs is associated with an increased affinity for MMP-2 and a consequent increase in degradation of these proteins that is associated with a

worsening in cardiac contractile function during either reoxygenation or reperfusion.

posttranslational modifications of proteins and its determination of protein fate.

Since contractile dysfunction is a predictor of patient outcome (Antman et al. 2004), it is crucial to understand the mechanisms behind the development of contractile dysfunction. Moreover, the identification of mechanisms that lead to contractile dysfunction can help and result in the development of new therapeutic approaches aiming at preventing and/or

This review will focus on the current knowledge of posttranslational modification of myosin light chain, a cardiac contractile protein, and how these modifications contribute to protection or pathogenesis in the setting of cardiac injury and contractile dysfunction triggered by oxidative stress. Moreover, this review will deal with the importance of

**1. Introduction** 

overall cellular homeostasis remains to be elucidated.

treating contractile dysfunction following oxidative stress.

**Light Chains Determine the Protein Fate** 

Virgilio J. J. Cadete and Grzegorz Sawicki *University of Saskatchewan, College of Medicine,* 

*Department of Pharmacology* 


### **Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate**

Virgilio J. J. Cadete and Grzegorz Sawicki *University of Saskatchewan, College of Medicine, Department of Pharmacology Canada* 

#### **1. Introduction**

238 Proteomics – Human Diseases and Protein Functions

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assembling and comparing protein identifications from shotgun proteomics.

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protein identification technology for shotgun proteomics. *Anal.Chem.* 73, no.

The advances in proteomics over the last decade have made it possible for a more detailed study of protein posttranslational modifications. Posttranslational modification of proteins is an important signaling mechanism regulating vital pathways ranging from transcription to translation, in metabolism, cell survival, and cell death. Posttranslational modification of proteins has commonly been associated with the loss/gain of function and signal transduction with the concept of phosphorylation being the hallmark. However, many other posttranslational modifications of proteins have been detected and their implication to overall cellular homeostasis remains to be elucidated.

The cardiovascular system, in particular the heart due to its high metabolic rates, sensitivity to oxidative stress and necessity to adapt quickly to new environments, is an ideal candidate to the study of posttranslational modifications in physiology and pathology. Cardiac contractile function relies significantly on the integrity of its contractile apparatus, with the myosin light chains being important contractile elements. We have recently described the role of nitration and nitrosylation of ventricular myosin light chains (MLCs) on its degradation by the proteolytic enzyme matrix metalloproteinase-2 (MMP-2) (Doroszko et al. 2010; Doroszko et al. 2009; Polewicz et al. 2010). Using distinct experimental models of oxidative stress, such as hypoxia-reoxygenation or ischemia/reperfusion, we have detected pathological nitration and nitrosylation of MLC induced by oxidative stress. According to our findings, nitration and nitrosylation of MLCs is associated with an increased affinity for MMP-2 and a consequent increase in degradation of these proteins that is associated with a worsening in cardiac contractile function during either reoxygenation or reperfusion.

Since contractile dysfunction is a predictor of patient outcome (Antman et al. 2004), it is crucial to understand the mechanisms behind the development of contractile dysfunction. Moreover, the identification of mechanisms that lead to contractile dysfunction can help and result in the development of new therapeutic approaches aiming at preventing and/or treating contractile dysfunction following oxidative stress.

This review will focus on the current knowledge of posttranslational modification of myosin light chain, a cardiac contractile protein, and how these modifications contribute to protection or pathogenesis in the setting of cardiac injury and contractile dysfunction triggered by oxidative stress. Moreover, this review will deal with the importance of posttranslational modifications of proteins and its determination of protein fate.

Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate 241

Fig. 1. Schematic representation of a proteomic method workflow. Samples are loaded and separated using 2-dimensional eletrophoresis (2-DE). Following 2-DE, protein spots of interest are identified and subjected to in-gel tryptic digestion followed by a mass spectrometry protocol (typically LC/MS/MS or MALDI TOF-TOF). Data generated from mass spectrometry can be used to identify the protein using the Mascot search database or, after protein identification, for detection of posttranslational modifications (PTMs) using the

technological advances gave rise to commercially available pre-cast gels (Criterion pre-cast gels, BioRad, Hercules, CA, USA) and the development of dodeca electrophoresis systems allowing for the simultaneous run of up to 12 gels (Criterion Dodeca Cell, BioRad, Hercules, CA, USA). These advances were very important in the achievement of reproducibility of

The majority of the results here described in terms of the study of posttranslational modifications of myosin light chain 1 and 2 were obtained using the following methodology

Protein samples for 2-DE were prepared by mixing frozen (-80°C), powdered heart tissue (40 to 60mg wet weight) with 200 µL rehydration buffer (8 mol/L urea, 4% CHAPS, 10 mmol/L DTT, 0.2% Bio-Lytes 3/10 [BioRad, Hercules, CA, USA]) at room temperature. Samples were sonicated for 2X5 seconds and centrifuged (10 minutes at 10,000g) to remove insoluble particles. Protein content of the heart extract in rehydration buffer was measured

Protein samples (400 g) were applied to each of 11 cm immobilized linear pH gradient (5-8) strips (IPG, BioRad, Hercules, CA, USA), with rehydration for 16–18 h at 20ºC.

ExPASy-FindMod tool (http://web.expasy.org/findmod/findmod\_masses.html).

sample generation by 2-DE.

with the BioRad Bradford protein assay.

as described by:

#### **2. Proteomics**

The term "PROTEOME" (PROTEin complement to the genOME), introduced in 1994, has attracted great attention, as approximately 30,000 human genes correspond to several million different gene products (proteins, peptides). The genome is intrinsically static and basically the same in every cell type, while the proteome is highly dynamic, differs between cell types, and does all the work. Proteins are the most common diagnostic and therapeutic targets in medicine, and the search for the proteome may lead to the discovery of new diagnostic and therapeutic targets. Classical proteomics, or what is now referred as "expression profiling", is a process in which total cellular or tissue proteins are separated on 2D gels and the visible protein spots are identified by peptide mass fingerprinting (Dunn 2000; Pandey and Mann 2000). This approach has been used to generate extensive proteomics online databases containing protein data obtained from the hearts of animals with cardiovascular disease states (Arrell et al. 2001a; Arrell et al. 2001b; Evans et al. 1997; Scheler et al. 1999).

The field of proteomics has its roots in the marriage between 2D electrophoresis and mass spectrometry. In most cases, 2-dimensional electrophoresis is used to separate individual proteins and their modified forms, which are then identified and further characterized/analyzed by mass spectrometry. To date, proteomics has identified changes in more than 40 proteins in heart diseases such as dilated cardiomyopathy, varying degrees of I/R injury, and heart failure (Arrell et al. 2001a; Corbett et al. 1998; Foster and Van Eyk 1999; Jager et al. 2002; Jiang et al. 2001; Schwertz et al. 2002).

Proteomics is an ideal approach to elucidate PTMs associated with kinase activity. Positive and negative modulation of heart contractility by short-term phosphorylation reactions at multiple sites in MLC2, TnI, TnT, α-tropomyosin, and myosin binding protein-C, have been known for almost a decade (Schaub et al. 1998). An example of this modification is the discovery of novel phosphorylation of MLC1 in preconditioned cardiomyocytes (Arrell et al. 2001a). However, the role of this PTM is not known. Phosphorylation of MLC1 was also detected in congestive heart failure (CHF) and this was associated with a decreased sensitivity to 8-Br-cGMP-mediated smooth muscle relaxation (Karim et al. 2004). Similarly, three different PTMs were found in functionally important N-terminal sites of MLC2, two occurred in normal hearts (phosphorylation and deamidation) and one (n-terminal truncation) was associated with I/R injury (White et al. 2003). We have found the same PTMs in MLC1 in our model IR with the exception that phosphorylation and deamidation were associated with truncated forms of MLC1. Thus, the use of the proteomics approach to investigate mechanisms underlying heart disease should result in the generation of new therapeutic strategies and the establishment of precise and sensitive diagnostic markers. A schematic representation of a proteomic workflow is given in figure 1.

#### **2.1 Methodology used in the study of myosin light chains posttranslational modifications**

Although new advances have been made recently in the development of new technology for protein separation, the proteomic method relies significantly on 2-dimensional electrophoresis (2-DE) for protein separation for further analysis by mass spectrometry. One of the early limitations of the use of 2-DE for sample generation for mass spectrometry analysis was reproducibility. The problem was generated by the fact that gradient gels are difficult to cast consistently and only 2 gels could be run simultaneously. Recent

The term "PROTEOME" (PROTEin complement to the genOME), introduced in 1994, has attracted great attention, as approximately 30,000 human genes correspond to several million different gene products (proteins, peptides). The genome is intrinsically static and basically the same in every cell type, while the proteome is highly dynamic, differs between cell types, and does all the work. Proteins are the most common diagnostic and therapeutic targets in medicine, and the search for the proteome may lead to the discovery of new diagnostic and therapeutic targets. Classical proteomics, or what is now referred as "expression profiling", is a process in which total cellular or tissue proteins are separated on 2D gels and the visible protein spots are identified by peptide mass fingerprinting (Dunn 2000; Pandey and Mann 2000). This approach has been used to generate extensive proteomics online databases containing protein data obtained from the hearts of animals with cardiovascular disease states (Arrell et al. 2001a; Arrell et al. 2001b; Evans et al. 1997;

The field of proteomics has its roots in the marriage between 2D electrophoresis and mass spectrometry. In most cases, 2-dimensional electrophoresis is used to separate individual proteins and their modified forms, which are then identified and further characterized/analyzed by mass spectrometry. To date, proteomics has identified changes in more than 40 proteins in heart diseases such as dilated cardiomyopathy, varying degrees of I/R injury, and heart failure (Arrell et al. 2001a; Corbett et al. 1998; Foster and Van Eyk

Proteomics is an ideal approach to elucidate PTMs associated with kinase activity. Positive and negative modulation of heart contractility by short-term phosphorylation reactions at multiple sites in MLC2, TnI, TnT, α-tropomyosin, and myosin binding protein-C, have been known for almost a decade (Schaub et al. 1998). An example of this modification is the discovery of novel phosphorylation of MLC1 in preconditioned cardiomyocytes (Arrell et al. 2001a). However, the role of this PTM is not known. Phosphorylation of MLC1 was also detected in congestive heart failure (CHF) and this was associated with a decreased sensitivity to 8-Br-cGMP-mediated smooth muscle relaxation (Karim et al. 2004). Similarly, three different PTMs were found in functionally important N-terminal sites of MLC2, two occurred in normal hearts (phosphorylation and deamidation) and one (n-terminal truncation) was associated with I/R injury (White et al. 2003). We have found the same PTMs in MLC1 in our model IR with the exception that phosphorylation and deamidation were associated with truncated forms of MLC1. Thus, the use of the proteomics approach to investigate mechanisms underlying heart disease should result in the generation of new therapeutic strategies and the establishment of precise and sensitive diagnostic markers. A

1999; Jager et al. 2002; Jiang et al. 2001; Schwertz et al. 2002).

schematic representation of a proteomic workflow is given in figure 1.

**2.1 Methodology used in the study of myosin light chains posttranslational** 

Although new advances have been made recently in the development of new technology for protein separation, the proteomic method relies significantly on 2-dimensional electrophoresis (2-DE) for protein separation for further analysis by mass spectrometry. One of the early limitations of the use of 2-DE for sample generation for mass spectrometry analysis was reproducibility. The problem was generated by the fact that gradient gels are difficult to cast consistently and only 2 gels could be run simultaneously. Recent

**2. Proteomics** 

Scheler et al. 1999).

**modifications** 

Fig. 1. Schematic representation of a proteomic method workflow. Samples are loaded and separated using 2-dimensional eletrophoresis (2-DE). Following 2-DE, protein spots of interest are identified and subjected to in-gel tryptic digestion followed by a mass spectrometry protocol (typically LC/MS/MS or MALDI TOF-TOF). Data generated from mass spectrometry can be used to identify the protein using the Mascot search database or, after protein identification, for detection of posttranslational modifications (PTMs) using the ExPASy-FindMod tool (http://web.expasy.org/findmod/findmod\_masses.html).

technological advances gave rise to commercially available pre-cast gels (Criterion pre-cast gels, BioRad, Hercules, CA, USA) and the development of dodeca electrophoresis systems allowing for the simultaneous run of up to 12 gels (Criterion Dodeca Cell, BioRad, Hercules, CA, USA). These advances were very important in the achievement of reproducibility of sample generation by 2-DE.

The majority of the results here described in terms of the study of posttranslational modifications of myosin light chain 1 and 2 were obtained using the following methodology as described by:

Protein samples for 2-DE were prepared by mixing frozen (-80°C), powdered heart tissue (40 to 60mg wet weight) with 200 µL rehydration buffer (8 mol/L urea, 4% CHAPS, 10 mmol/L DTT, 0.2% Bio-Lytes 3/10 [BioRad, Hercules, CA, USA]) at room temperature. Samples were sonicated for 2X5 seconds and centrifuged (10 minutes at 10,000g) to remove insoluble particles. Protein content of the heart extract in rehydration buffer was measured with the BioRad Bradford protein assay.

Protein samples (400 g) were applied to each of 11 cm immobilized linear pH gradient (5-8) strips (IPG, BioRad, Hercules, CA, USA), with rehydration for 16–18 h at 20ºC.

Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate 243

trigger sliding between filaments and the consequent contraction. The two light chains (myosin light chain 1 and 2, MLC1 and MLC2) confer stability to the myosin head and also have actin binding motifs. MLC1 is also referred to as the essential light chain (ELC) and is present in the hinge of the myosin head for stability purposes. MLC2 is also referred as regulatory light chain (RLC) and together with MLC1 forms the hinge region between the

The essential light chains (ELC) are expressed by three different genes (MYL1, 3 and 4) which give rise to four isoforms of ELC/MLC (Hernandez et al. 2007). The nomenclature adopted depends on the tissue expressed (ELCa and ELCv for atrium and ventricular ELC/MLC, respectively) or whether it is full or short MLC (MLC1 and MLC3 for long and short MLCs, respectively) (Hernandez et al. 2007). The nomenclature for myosin light chains is not always obvious and for this manuscript we will refer to MLC1 as the full length

It has been described that the amino terminus of MLC1 interacts with the carboxy terminus of actin during contraction (Andreev and Borejdo 1999; Efimova et al. 1998; Henry et al. 1985; Milligan et al. 1990; Miyanishi et al. 2002; Morano et al. 1995; Nieznanska et al. 1998; Nieznanska et al. 2002; Nieznanski et al. 2003; Timson et al. 1999; Trayer et al. 1987; VanBuren et al. 1994). This interaction of MLC1 with actin suggests an important role of MLC1 in the regulation of contraction. Indeed, selective removal of MLC1 from the myosin molecule resulted in a reduction of ~50% of the force generated (VanBuren et al. 1994). The regulatory light chain (RLC), referred as MLC2 in this review, is involved, as the name suggests, in the regulation of contraction. In the heart, two isoforms are found: a ventricular specific (MLC2v) and an atrium specific (MLC2a) isoform (Collins 2006). MLC2, together with MLC1, contributes to the mechanical stability of the hinge of the head region of the myosin molecule. MLC2 has been better studied and characterized due to the fact that it can be phosphorylated. MLC2 phosphorylation under basal conditions has been demonstrated to regulate Ca2+-dependent contraction (High and Stull 1980; Mizuno et al. 2008; Stull et al.

In order for proper sarcomeric contraction, the myosin structure has to be stable and fine tuned. It is the role of the light chains, present in the hinge of the head region, to assure stability of the head region and fine tune contraction by regulating the interaction between

Virtually all proteins are subjected to posttranslational modifications. In this text, posttranslational modification will refer to the addition of a chemical group to amino acid residue which has a biological functional. Mass spectrometry can be used to determine peptide masses belonging to the native protein. According to the mass of each peptide one can infer about the presence or absence of a posttranslational modification that has a unique mass signature. A useful tool in determining posttranslational modifications by using peptide masses is ExPASy-FindMod tool (available at http://web.expasy.org/findmod/findmod\_masses.html). Up to date the available information from ExPASy-FindMod tool, shows seventy one groups of posttranslational modifications that can be detected from analysis of peptide mass fingerprints. Of these, phosphorylation is by far the most studied and well know, mainly due to the identification of enzymes mediating phosphorylation of protein residues: protein kinases.

globular head and the α-helical tail of myosin.

1980; Sweeney and Stull 1986).

**4. Posttranslational modifications** 

MHC and actin.

myosin light chain present in the sarcomeres of the ventricle.

Isoelectrofocusing was performed using the BioRad Protean IEF cell with the following conditions at 20ºC with fast voltage ramping: step 1: 15 min with end voltage at 250 V; step 2: 150 min with end voltage at 8000 V; step 3: 35 000 V-hours (approximately 260 min). Following isoelectrofocusing the strips were equilibrated according to the manufacturer's instructions. The second dimension of 2-DE was performed with Criterion pre-cast gels (8 – 16%) (BioRad). After separation, proteins were detected with Coomassie Briliant Blue R250 (BioRad). To minimize variations in resolving proteins during the 2-DE run, 12 gels were run simultaneously using a Criterion Dodeca Cell (BioRad, Hercules, CA, USA). Because of this limitation for 2-DE analysis we used 4 hearts from each group. All the gels were stained in the same bath and next scanned with a calibrated densitometer GS-800 (BioRad, Hercules, CA, USA). Quantitative analysis of MLC1 and MLC2 spot intensities from 2-DE were measured with PDQuest 7.1 measurement software (BioRad, Hercules, CA, USA).

MLC1 and MLC2 protein spots were manually excised from the 2-DE gel. These spots were then processed using a MassPrep Station (Waters, Milford, MA, USA) using the methods supplied by the manufacturer. The excised gel fragment containing the protein spot was first destained in 200 µl of 50% acetonitrile with 50 mM ammonium bicarbonate at 37°C for 30 minutes. Next, the gel was washed twice with water. The protein extraction was performed overnight at room temperature with 50 µL of a mixture of formic acid, water, and isopropanol (1:3:2, vol:vol). The resulting solution was then analyzed by mass spectrometry (MS). For electrospray, quadruple time-of-flight (Q-TOF) analysis, 1 µl of the solution was used. Liquid chromatography/mass spectrometry (LC/MS) was performed on a CapLC high-performance liquid chromatography unit (Waters, Milford, MA, USA) coupled with Q-TOF-2 mass spectrometer (Waters, Milford, MA, USA). A mass deviation of 0.2 was tolerated and one missed cleavage site was allowed. Resulting values from mass spectrometry (MS/MS) analysis were used to search against the NCBInr and SwissProt databases with Mammalia specified. We used the Mascot (www.matrixscience.com) search engine to search the protein database. Posttranslational modifications were determined using the ExPASy-FindMod tool (http://web.expasy.org/findmod/findmod\_masses.html).

#### **3. Cardiac contractile proteins**

The heart is the central organ for the circulatory system and is responsible for providing an efficient flow of blood to the whole body in order to meet the metabolic demands of the organism by delivering oxygen and nutrients and, at the same time, removing metabolic waste. Often seen as a pump, the heart relies on the integrity of its contractile machinery in order to efficiently perform its function. The basic unit of contraction is the sarcomere. The sarcomere is constituted of thick and thin filaments that, during contraction, slide over each other leading to the shortening of the sarcomere and contraction. The thick filament is mainly constituted of myosin while the thin filament is mainly constituted of actin, tropomyosin, and troponins (Figure 2). The interaction between thin and thick filaments, the crucial component for the generation of a contractile force, occurs between actin and the myosin head.

#### **3.1 Myosins**

Myosin is a large complex molecule. It consists of two heavy chains, an �-helical tail, and four myosin light chains (Craig and Woodhead 2006; Dominguez et al. 1998; Rayment et al. 1993b). The heavy chains (myosin heavy chain, MHC) have the ATPase activity necessary to

Isoelectrofocusing was performed using the BioRad Protean IEF cell with the following conditions at 20ºC with fast voltage ramping: step 1: 15 min with end voltage at 250 V; step 2: 150 min with end voltage at 8000 V; step 3: 35 000 V-hours (approximately 260 min). Following isoelectrofocusing the strips were equilibrated according to the manufacturer's instructions. The second dimension of 2-DE was performed with Criterion pre-cast gels (8 – 16%) (BioRad). After separation, proteins were detected with Coomassie Briliant Blue R250 (BioRad). To minimize variations in resolving proteins during the 2-DE run, 12 gels were run simultaneously using a Criterion Dodeca Cell (BioRad, Hercules, CA, USA). Because of this limitation for 2-DE analysis we used 4 hearts from each group. All the gels were stained in the same bath and next scanned with a calibrated densitometer GS-800 (BioRad, Hercules, CA, USA). Quantitative analysis of MLC1 and MLC2 spot intensities from 2-DE were

measured with PDQuest 7.1 measurement software (BioRad, Hercules, CA, USA).

**3. Cardiac contractile proteins** 

myosin head.

**3.1 Myosins** 

MLC1 and MLC2 protein spots were manually excised from the 2-DE gel. These spots were then processed using a MassPrep Station (Waters, Milford, MA, USA) using the methods supplied by the manufacturer. The excised gel fragment containing the protein spot was first destained in 200 µl of 50% acetonitrile with 50 mM ammonium bicarbonate at 37°C for 30 minutes. Next, the gel was washed twice with water. The protein extraction was performed overnight at room temperature with 50 µL of a mixture of formic acid, water, and isopropanol (1:3:2, vol:vol). The resulting solution was then analyzed by mass spectrometry (MS). For electrospray, quadruple time-of-flight (Q-TOF) analysis, 1 µl of the solution was used. Liquid chromatography/mass spectrometry (LC/MS) was performed on a CapLC high-performance liquid chromatography unit (Waters, Milford, MA, USA) coupled with Q-TOF-2 mass spectrometer (Waters, Milford, MA, USA). A mass deviation of 0.2 was tolerated and one missed cleavage site was allowed. Resulting values from mass spectrometry (MS/MS) analysis were used to search against the NCBInr and SwissProt databases with Mammalia specified. We used the Mascot (www.matrixscience.com) search engine to search the protein database. Posttranslational modifications were determined using the ExPASy-FindMod tool (http://web.expasy.org/findmod/findmod\_masses.html).

The heart is the central organ for the circulatory system and is responsible for providing an efficient flow of blood to the whole body in order to meet the metabolic demands of the organism by delivering oxygen and nutrients and, at the same time, removing metabolic waste. Often seen as a pump, the heart relies on the integrity of its contractile machinery in order to efficiently perform its function. The basic unit of contraction is the sarcomere. The sarcomere is constituted of thick and thin filaments that, during contraction, slide over each other leading to the shortening of the sarcomere and contraction. The thick filament is mainly constituted of myosin while the thin filament is mainly constituted of actin, tropomyosin, and troponins (Figure 2). The interaction between thin and thick filaments, the crucial component for the generation of a contractile force, occurs between actin and the

Myosin is a large complex molecule. It consists of two heavy chains, an �-helical tail, and four myosin light chains (Craig and Woodhead 2006; Dominguez et al. 1998; Rayment et al. 1993b). The heavy chains (myosin heavy chain, MHC) have the ATPase activity necessary to trigger sliding between filaments and the consequent contraction. The two light chains (myosin light chain 1 and 2, MLC1 and MLC2) confer stability to the myosin head and also have actin binding motifs. MLC1 is also referred to as the essential light chain (ELC) and is present in the hinge of the myosin head for stability purposes. MLC2 is also referred as regulatory light chain (RLC) and together with MLC1 forms the hinge region between the globular head and the α-helical tail of myosin.

The essential light chains (ELC) are expressed by three different genes (MYL1, 3 and 4) which give rise to four isoforms of ELC/MLC (Hernandez et al. 2007). The nomenclature adopted depends on the tissue expressed (ELCa and ELCv for atrium and ventricular ELC/MLC, respectively) or whether it is full or short MLC (MLC1 and MLC3 for long and short MLCs, respectively) (Hernandez et al. 2007). The nomenclature for myosin light chains is not always obvious and for this manuscript we will refer to MLC1 as the full length myosin light chain present in the sarcomeres of the ventricle.

It has been described that the amino terminus of MLC1 interacts with the carboxy terminus of actin during contraction (Andreev and Borejdo 1999; Efimova et al. 1998; Henry et al. 1985; Milligan et al. 1990; Miyanishi et al. 2002; Morano et al. 1995; Nieznanska et al. 1998; Nieznanska et al. 2002; Nieznanski et al. 2003; Timson et al. 1999; Trayer et al. 1987; VanBuren et al. 1994). This interaction of MLC1 with actin suggests an important role of MLC1 in the regulation of contraction. Indeed, selective removal of MLC1 from the myosin molecule resulted in a reduction of ~50% of the force generated (VanBuren et al. 1994).

The regulatory light chain (RLC), referred as MLC2 in this review, is involved, as the name suggests, in the regulation of contraction. In the heart, two isoforms are found: a ventricular specific (MLC2v) and an atrium specific (MLC2a) isoform (Collins 2006). MLC2, together with MLC1, contributes to the mechanical stability of the hinge of the head region of the myosin molecule. MLC2 has been better studied and characterized due to the fact that it can be phosphorylated. MLC2 phosphorylation under basal conditions has been demonstrated to regulate Ca2+-dependent contraction (High and Stull 1980; Mizuno et al. 2008; Stull et al. 1980; Sweeney and Stull 1986).

In order for proper sarcomeric contraction, the myosin structure has to be stable and fine tuned. It is the role of the light chains, present in the hinge of the head region, to assure stability of the head region and fine tune contraction by regulating the interaction between MHC and actin.

#### **4. Posttranslational modifications**

Virtually all proteins are subjected to posttranslational modifications. In this text, posttranslational modification will refer to the addition of a chemical group to amino acid residue which has a biological functional. Mass spectrometry can be used to determine peptide masses belonging to the native protein. According to the mass of each peptide one can infer about the presence or absence of a posttranslational modification that has a unique mass signature. A useful tool in determining posttranslational modifications by using peptide masses is ExPASy-FindMod tool (available at http://web.expasy.org/findmod/findmod\_masses.html). Up to date the available information from ExPASy-FindMod tool, shows seventy one groups of posttranslational modifications that can be detected from analysis of peptide mass fingerprints. Of these, phosphorylation is by far the most studied and well know, mainly due to the identification of enzymes mediating phosphorylation of protein residues: protein kinases.

Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate 245

Muscles contract when filaments containing a molecular motor, myosin, pull against another set of filaments containing mainly actin. The source of energy for this directional movement is provided by the hydrolysis of ATP, which is catalyzed by myosin. Muscle myosin is a hexamer consisting of two heavy chains (MHC), two regulatory (or phoshorylatable) light chains (known as MLC2 or RLC) and two essential chains (known as MLC1, and alkali or ELC). The myosin heavy chain is an elongated molecule where more then 90% of the protein is a coiled coil tail formed by the two heavy chains. However, the Nterminus of MHC is globular and contains ATPase activity, the actin binding site, and MLC1 and MLC2 binding sites (Rayment et al. 1993a; Rayment et al. 1993b). The light chains from cardiac and skeletal muscles are not directly involved in the regulation of contraction.

The precise molecular basis for myocardial stunning remains unknown, but protein damage within the myofilament is a likely mechanism. It is almost certain that stunning is a multifactoral process. One potential target is ventricular MLC2, which via changes in its phosphorylation status, modulates contractile force generation arising from actin-myosin MHC interaction (the structure, function and malfunction of MLC2 have been reviewed by Szczesna, (Szczesna 2003)). Three years ago an Australian group, using an experimental protocol similar to ours, found changes in phosphorylation of MLC2 and showed how these changes are correlated with the function of stunned myocardium (White et al. 2003). In another model, involving pharmacologically preconditioned isolated cardiomyocytes, altered phosphorylation of MLC1 was also found, but the role of this modification is not yet

Not only does heart injury induce chemical modification of MLCs, but during acute congestive heart failure entire MLC molecules, or their degradation products, are released into the circulation (Goto et al. 2003; Hansen et al. 2002). Van Eyk and colleagues have shown that the release of degradation products of MLC1 to coronary effluent is positively correlated with the duration of ischemia (Van Eyk et al. 1998). And White and co-workers found that both to MLC1 and MLC2 are released into the effluent of ischemic hearts (White et al. 2003). There was no evidence as to what proteolytic enzyme could be responsible for MLC degradation or what molecular mechanism could account for the release of their products into the circulation. Our work on the degradation of MLC1 in I/R heart shows that MMP-2 is responsible (at least in part) for the degradation of this protein (Doroszko et al. 2009; Polewicz et al. 2010; Sawicki et al. 2005). Although, the mechanism of release is still unknown, it could result from a loss of cell membrane integrity. Despite the many unanswered questions about the molecular basis of I/R injury in the heart, cardiac MLC1 is

Phosphorylation is a posttranslational modification that consists of the addition of a phosphate group to serine (Ser), threonine (Thr) or tyrosine (Tyr). The addition of the phosphate group to these amino acids is catalyzed by kinases. The currently described mechanism of phosphorylation is that it essentially works as a switch, turning the function the phosphorylated protein on or off. Other consequences of protein phosphorylation may involve subcellular localization of proteins, protein-protein interaction, and proteolytic degradation. In fact, our ongoing studies on role of posttranslational modifications in the development of cardiac contractile dysfunction implies that the phosphorylation of MLC1

However, both MLC1 and MLC2 can exert a subtle modulatory effect.

becoming a very important candidate as a biomarker of heart injury.

**4.2 MLCs in heart injury** 

known (Arrell et al. 2001a).

**4.3 Phosphorylation** 

Phosphorylation is commonly associated with signal transduction, the hallmark of signaling cascades mediated by kinases. Other posttranslational modifications have recently gained more attention, mainly due to the fact that they are associated with oxidative stress. Protein nitration and nitrosylation are common events occurring in cells subjected to oxidative stress. Contrary to phosphorylation, no enzyme has been described to mediate nitration and nitrosylation and these modifications are often seen as a non-enzymatic posttranslational modification dependent on the presence, identity and concentration of reactive nitrogen species. The role of protein nitration on cellular signal transduction pathways has been reviewed by Yakovlev and Mikkelsen (Yakovlev and Mikkelsen 2010). The authors conclude that the gathered evidence supports the notion of protein nitration being a specific reaction. Though not entirely clear, it appears that nitration of protein residues by reactive nitrogen species is dependent on the tertiary structure of the protein and in particular the chemical environment of the tyrosin residues.

Due to the number of possible posttranslational modifications currently identified and the fact that the same posttranslational modification can occur in different amino acids, it is clear that the study of posttranslational modifications of protein under physiological and pathological conditions is a difficult task. Moreover, posttranslational modifications are not isolated reactions. A protein molecule present in physiological or pathological conditions may have more than one posttranslational modification. Also, the same protein can exhibit different types of posttranslational modifications at one time. Hence, the study of posttranslational modifications of proteins is difficult but also of high importance due to the nature of physiological and pathological consequences these modifications often cause. Also of importance is the fact that the study of posttranslational modification of cardiac contractile proteins can result in the identification of disease-specific markers of heart injury, hence contributing to the development of more sensitive and specific biomarkers of heart injury.

#### **4.1 Biomarkers of heart injury**

A biomarker is defined as a reproducibly detectable molecular feature, usually present in an accessible bodily fluid or tissue, that is correlated with a disease state. Cardiac enzymes have long been used as front-line diagnostic tools in the detection of myocardial injury caused by myocardial ischemia. However, the most commonly used enzymes (such as creatine kinase (CK) and its myocardial fraction CK myocardial band (MB), aspartic aminotransferase, and lactate dehydrogenase) are limited in their ability to detect myocardial injury by short diagnosis windows, have limited sensitivities, and lack specificity because of their presence in skeletal muscle. Similarly, myoglobin also lacks specificity because its release from skeletal muscle cannot be distinguished from its release from the heart muscle (Christenson and Azzazy 1998). Thus, there is a need to develop novel biomarkers in order to more effectively treat and diagnose myocardial infarction (MI). Using the proteomics approach a time-dependent increase of TnI in the serum from patients with MI was reported (Labugger et al. 2000). This new finding led to the suggestion that MLC1, as a contractile protein, could be considered as a new protein biomarker in I/R injury of the heart (Lee and Vasan 2005; Sato et al. 2004). The list of biomarkers in cardiovascular diseases will grow, particularly when the proteomics approach is used. This method has already identified 177 different proteins (including their different molecular forms) with the potential to be good candidates as biomarkers (Anderson 2005) in cardiovascular disease such as stroke.

#### **4.2 MLCs in heart injury**

244 Proteomics – Human Diseases and Protein Functions

Phosphorylation is commonly associated with signal transduction, the hallmark of signaling cascades mediated by kinases. Other posttranslational modifications have recently gained more attention, mainly due to the fact that they are associated with oxidative stress. Protein nitration and nitrosylation are common events occurring in cells subjected to oxidative stress. Contrary to phosphorylation, no enzyme has been described to mediate nitration and nitrosylation and these modifications are often seen as a non-enzymatic posttranslational modification dependent on the presence, identity and concentration of reactive nitrogen species. The role of protein nitration on cellular signal transduction pathways has been reviewed by Yakovlev and Mikkelsen (Yakovlev and Mikkelsen 2010). The authors conclude that the gathered evidence supports the notion of protein nitration being a specific reaction. Though not entirely clear, it appears that nitration of protein residues by reactive nitrogen species is dependent on the tertiary structure of the protein and in particular the chemical

Due to the number of possible posttranslational modifications currently identified and the fact that the same posttranslational modification can occur in different amino acids, it is clear that the study of posttranslational modifications of protein under physiological and pathological conditions is a difficult task. Moreover, posttranslational modifications are not isolated reactions. A protein molecule present in physiological or pathological conditions may have more than one posttranslational modification. Also, the same protein can exhibit different types of posttranslational modifications at one time. Hence, the study of posttranslational modifications of proteins is difficult but also of high importance due to the nature of physiological and pathological consequences these modifications often cause. Also of importance is the fact that the study of posttranslational modification of cardiac contractile proteins can result in the identification of disease-specific markers of heart injury, hence contributing to the development of more sensitive and specific biomarkers of heart

A biomarker is defined as a reproducibly detectable molecular feature, usually present in an accessible bodily fluid or tissue, that is correlated with a disease state. Cardiac enzymes have long been used as front-line diagnostic tools in the detection of myocardial injury caused by myocardial ischemia. However, the most commonly used enzymes (such as creatine kinase (CK) and its myocardial fraction CK myocardial band (MB), aspartic aminotransferase, and lactate dehydrogenase) are limited in their ability to detect myocardial injury by short diagnosis windows, have limited sensitivities, and lack specificity because of their presence in skeletal muscle. Similarly, myoglobin also lacks specificity because its release from skeletal muscle cannot be distinguished from its release from the heart muscle (Christenson and Azzazy 1998). Thus, there is a need to develop novel biomarkers in order to more effectively treat and diagnose myocardial infarction (MI). Using the proteomics approach a time-dependent increase of TnI in the serum from patients with MI was reported (Labugger et al. 2000). This new finding led to the suggestion that MLC1, as a contractile protein, could be considered as a new protein biomarker in I/R injury of the heart (Lee and Vasan 2005; Sato et al. 2004). The list of biomarkers in cardiovascular diseases will grow, particularly when the proteomics approach is used. This method has already identified 177 different proteins (including their different molecular forms) with the potential to be good candidates as biomarkers (Anderson 2005) in

environment of the tyrosin residues.

**4.1 Biomarkers of heart injury** 

cardiovascular disease such as stroke.

injury.

Muscles contract when filaments containing a molecular motor, myosin, pull against another set of filaments containing mainly actin. The source of energy for this directional movement is provided by the hydrolysis of ATP, which is catalyzed by myosin. Muscle myosin is a hexamer consisting of two heavy chains (MHC), two regulatory (or phoshorylatable) light chains (known as MLC2 or RLC) and two essential chains (known as MLC1, and alkali or ELC). The myosin heavy chain is an elongated molecule where more then 90% of the protein is a coiled coil tail formed by the two heavy chains. However, the Nterminus of MHC is globular and contains ATPase activity, the actin binding site, and MLC1 and MLC2 binding sites (Rayment et al. 1993a; Rayment et al. 1993b). The light chains from cardiac and skeletal muscles are not directly involved in the regulation of contraction. However, both MLC1 and MLC2 can exert a subtle modulatory effect.

The precise molecular basis for myocardial stunning remains unknown, but protein damage within the myofilament is a likely mechanism. It is almost certain that stunning is a multifactoral process. One potential target is ventricular MLC2, which via changes in its phosphorylation status, modulates contractile force generation arising from actin-myosin MHC interaction (the structure, function and malfunction of MLC2 have been reviewed by Szczesna, (Szczesna 2003)). Three years ago an Australian group, using an experimental protocol similar to ours, found changes in phosphorylation of MLC2 and showed how these changes are correlated with the function of stunned myocardium (White et al. 2003). In another model, involving pharmacologically preconditioned isolated cardiomyocytes, altered phosphorylation of MLC1 was also found, but the role of this modification is not yet known (Arrell et al. 2001a).

Not only does heart injury induce chemical modification of MLCs, but during acute congestive heart failure entire MLC molecules, or their degradation products, are released into the circulation (Goto et al. 2003; Hansen et al. 2002). Van Eyk and colleagues have shown that the release of degradation products of MLC1 to coronary effluent is positively correlated with the duration of ischemia (Van Eyk et al. 1998). And White and co-workers found that both to MLC1 and MLC2 are released into the effluent of ischemic hearts (White et al. 2003). There was no evidence as to what proteolytic enzyme could be responsible for MLC degradation or what molecular mechanism could account for the release of their products into the circulation. Our work on the degradation of MLC1 in I/R heart shows that MMP-2 is responsible (at least in part) for the degradation of this protein (Doroszko et al. 2009; Polewicz et al. 2010; Sawicki et al. 2005). Although, the mechanism of release is still unknown, it could result from a loss of cell membrane integrity. Despite the many unanswered questions about the molecular basis of I/R injury in the heart, cardiac MLC1 is becoming a very important candidate as a biomarker of heart injury.

#### **4.3 Phosphorylation**

Phosphorylation is a posttranslational modification that consists of the addition of a phosphate group to serine (Ser), threonine (Thr) or tyrosine (Tyr). The addition of the phosphate group to these amino acids is catalyzed by kinases. The currently described mechanism of phosphorylation is that it essentially works as a switch, turning the function the phosphorylated protein on or off. Other consequences of protein phosphorylation may involve subcellular localization of proteins, protein-protein interaction, and proteolytic degradation. In fact, our ongoing studies on role of posttranslational modifications in the development of cardiac contractile dysfunction implies that the phosphorylation of MLC1

Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate 247

To our knowledge these are the first observations concerning phosphorylation of a target protein contributing to the direct proteolytic degradation of that protein. Since it is well known that during several distinct disease processes the activation of phosphorylation cascades occur we speculate that besides up- and down-regulation of protein activity, phosphorylation is responsible for signaling protein degradation contributing directly to the

Protein tyrosine nitration has been implicated in many pathological conditions and diseases such as inflammation, chronic hypoxia, myocardial infarction and diabetes among others (Blantz and Munger 2002; Brindicci et al. 2010; Donnini et al. 2008; Giasson et al. 2000; Jones et al. 2009; Kang et al. 2010; Koeck et al. 2009; MacMillan-Crow et al. 1996; Naito et al. 2008; Pacher et al. 2007; Pavlides et al. 2010; Pieper et al. 2009; Reyes et al. 2008; Reynolds et al. 2005; 2007; Smith 2009; Upmacis 2008; Zhang et al. 2010). However, a physiological role for protein tyrosine nitration should not be excluded. Not all the tyrosine residues in a protein are targets for nitration either *in vitro* or *in vivo*. Moreover, the observed nitrations of tyrosine very seldom coincide between *in vitro* and *in vivo* studies. Of importance is the fact that nitration of tyrosine residues is a selective process that appears to be under tight control, even though the exact

Nitration of protein tyrosine residues (formation of nitrotyrosine) has been suggested to facilitate proteolysis of the nitrated protein (Yakovlev and Mikkelsen 2010). We have recently shown that the contractile proteins MLC1 and MLC2 (part of the thick filament of the sarcomere) are subjected to tyrosine nitration and cysteine s-nitrosylation in cardiac models of oxidative stress (Doroszko et al. 2010; Doroszko et al. 2009; Polewicz et al. 2010). Using an *in vivo* model of neonatal asphyxia in piglets we have shown that both MLC1 and MLC2 are significantly decreased following hypoxia-reoxygenation (Doroszko et al. 2010; Doroszko et al. 2009). Mass spectrometry analysis for nitration and nitrosylation revealed that MLC1 is S-notrosylated at Cys 138 and nitrated at Tyr 141. Interestingly, these residues are located at the positions P3 and P1' of the cleavage site for MMP-2 and hypoxiareoxygenation was associated with an increase in MMP-2 activity. Also, MLC2 from hearts subjected to hypoxia-reoxygenation was nitrated at Tyr 118 and Tyr 152, while no nitration was observed for the control group (Table 1). These data suggest a pathological role for MLC2 tyrosine nitration associated with hypoxia-reoxygenation. Using human recombinant mutant MLC2, in which the tyrosine residue is replaced with phenylalanine, (Y152F) the *in vitro* incubation with peroxynitrite as a nitrating agent resulted in the prevention of MLC2 degradation by MMP-2, with no nitration observed at position 152. These observations indicate that although MLC2 has two nitration sites, it is Tyr 152 that mediates the signaling of degradation by MMP-2. MLC1 was also studied in a model of isolated adult rat cardiomyocytes subjected to simulated ischemia. Mass spectrometry analysis revealed nitration of Tyr 190, consistent with what was observed in piglet hearts. However, the Cys in the P3 position of the MMP-2 cleavage site was not S-nitrosylated as observed in MLC1 from piglet hearts. Moreover, MLC1 from rat cardiomyocytes was also nitrated at Tyr 78 and S-nitrosylated at Cys 81. *In vitro* human recombinant MLC1 was nitrated by peroxynitrite (used as a nitrating agent) at Tyr 73 (corresponding to rat MLC1 Tyr 78) Tyr 185 (corresponding to rat MLC1 Tyr 190), Tyr 140 and S-nytrosilated at Cys 76 (corresponding to rat Cys 81) and Cys 67. *In vitro* nitrated and S-nitrosylated MLC1 was

mechanisms for the regulation of tyrosine nitration remain unknown.

progression of the disease process.

**4.4 Nitration and S-nitrosylation** 

more susceptible to degradation by MMP-2.

during ischemia/reperfusion results in its increase degradation, possibly by MMP-2, contributing to ischemia/reperfusion injury.

Phosphorylation of MLC1 has been demonstrated previously (Arrell et al. 2001a) but it has been associated with stability of the myosin head. The authors reported phosphorylation of rat/human Thr 69/64 and Ser 200/194 or 195. Our unpublished data demonstrates that phosphorylation of MLC1 has direct implications in its degradation by MMP-2. We observed *in vitro* phosphorylation (by myosin light chain kinase) of human recombinant MLC1 at Thr127, Thr129 or Tyr 130, as well as Ser179 and Tyr186. In MLC1 from isolated rat hearts subjected to ischemia/reperfusion we observed six phosphorylated residues: Thr69, Thr77 or Tyr78, Thr132, Thr134 or Tyr135, Thr164, Ser184 and Tyr190. Our data suggests a physiological role for MLC1 phosphorylation of Thr69 and Thr132, Thr134 or Tyr135, since these phosphorylations are present in aerobic control hearts, with the remaining four phosphorylations being induced by ischemia/reperfusion (Table 1). The observed phosphorylations of MLC1 induced by ischemia/reperfusion resulted in an increased degradation of MLC1. In an unpublished *in vitro* study we observed that when MLC1 was phosphorylated by the myosin light chain kinase (MLCK), the affinity of MMP-2 for MLC1 was increased and this increase in affinity resulted in an increase in the degradation of MLC1. Taken together, these observations suggest a role for protein phosphorylation in the induction of proteolytic degradation, namely by MMP-2.


Table 1. Identification of MLC1 and MLC2 protein residues subjected to posttranslational modification leading to protein degradation.

To our knowledge these are the first observations concerning phosphorylation of a target protein contributing to the direct proteolytic degradation of that protein. Since it is well known that during several distinct disease processes the activation of phosphorylation cascades occur we speculate that besides up- and down-regulation of protein activity, phosphorylation is responsible for signaling protein degradation contributing directly to the progression of the disease process.

#### **4.4 Nitration and S-nitrosylation**

246 Proteomics – Human Diseases and Protein Functions

during ischemia/reperfusion results in its increase degradation, possibly by MMP-2,

Phosphorylation of MLC1 has been demonstrated previously (Arrell et al. 2001a) but it has been associated with stability of the myosin head. The authors reported phosphorylation of rat/human Thr 69/64 and Ser 200/194 or 195. Our unpublished data demonstrates that phosphorylation of MLC1 has direct implications in its degradation by MMP-2. We observed *in vitro* phosphorylation (by myosin light chain kinase) of human recombinant MLC1 at Thr127, Thr129 or Tyr 130, as well as Ser179 and Tyr186. In MLC1 from isolated rat hearts subjected to ischemia/reperfusion we observed six phosphorylated residues: Thr69, Thr77 or Tyr78, Thr132, Thr134 or Tyr135, Thr164, Ser184 and Tyr190. Our data suggests a physiological role for MLC1 phosphorylation of Thr69 and Thr132, Thr134 or Tyr135, since these phosphorylations are present in aerobic control hearts, with the remaining four phosphorylations being induced by ischemia/reperfusion (Table 1). The observed phosphorylations of MLC1 induced by ischemia/reperfusion resulted in an increased degradation of MLC1. In an unpublished *in vitro* study we observed that when MLC1 was phosphorylated by the myosin light chain kinase (MLCK), the affinity of MMP-2 for MLC1 was increased and this increase in affinity resulted in an increase in the degradation of MLC1. Taken together, these observations suggest a role for protein phosphorylation in the

Modification MLC1 MLC2

Thr127/Thr129/Tyr130, Ser179, Tyr186

Thr69, Thr77/Tyr78, Thr132/Thr134/Tyr135, Thr164, Ser184, Tyr190

Tyr78, Tyr190

Cys67, Cys76

Cys138

Cys81 Table 1. Identification of MLC1 and MLC2 protein residues subjected to posttranslational

Identified posttranslational modified residues

*in vitro* (human recombinant)

*ex vivo* (rat heart)

*in vitro* (human recombinant) Tyr73, Tyr130, Tyr185 Tyr152 *in vivo* (piglet heart)

*ex vivo* (rat heart)

*in vitro* ( human recombinant)

*in vivo* (piglet heart)

*ex vivo* (rat heart)

Tyr141 Tyr118, Tyr152

contributing to ischemia/reperfusion injury.

induction of proteolytic degradation, namely by MMP-2.

Posttranslational

Phosphorylation

Tyr nitration

Cys S-nitrosylation

modification leading to protein degradation.

Protein tyrosine nitration has been implicated in many pathological conditions and diseases such as inflammation, chronic hypoxia, myocardial infarction and diabetes among others (Blantz and Munger 2002; Brindicci et al. 2010; Donnini et al. 2008; Giasson et al. 2000; Jones et al. 2009; Kang et al. 2010; Koeck et al. 2009; MacMillan-Crow et al. 1996; Naito et al. 2008; Pacher et al. 2007; Pavlides et al. 2010; Pieper et al. 2009; Reyes et al. 2008; Reynolds et al. 2005; 2007; Smith 2009; Upmacis 2008; Zhang et al. 2010). However, a physiological role for protein tyrosine nitration should not be excluded. Not all the tyrosine residues in a protein are targets for nitration either *in vitro* or *in vivo*. Moreover, the observed nitrations of tyrosine very seldom coincide between *in vitro* and *in vivo* studies. Of importance is the fact that nitration of tyrosine residues is a selective process that appears to be under tight control, even though the exact mechanisms for the regulation of tyrosine nitration remain unknown.

Nitration of protein tyrosine residues (formation of nitrotyrosine) has been suggested to facilitate proteolysis of the nitrated protein (Yakovlev and Mikkelsen 2010). We have recently shown that the contractile proteins MLC1 and MLC2 (part of the thick filament of the sarcomere) are subjected to tyrosine nitration and cysteine s-nitrosylation in cardiac models of oxidative stress (Doroszko et al. 2010; Doroszko et al. 2009; Polewicz et al. 2010). Using an *in vivo* model of neonatal asphyxia in piglets we have shown that both MLC1 and MLC2 are significantly decreased following hypoxia-reoxygenation (Doroszko et al. 2010; Doroszko et al. 2009). Mass spectrometry analysis for nitration and nitrosylation revealed that MLC1 is S-notrosylated at Cys 138 and nitrated at Tyr 141. Interestingly, these residues are located at the positions P3 and P1' of the cleavage site for MMP-2 and hypoxiareoxygenation was associated with an increase in MMP-2 activity. Also, MLC2 from hearts subjected to hypoxia-reoxygenation was nitrated at Tyr 118 and Tyr 152, while no nitration was observed for the control group (Table 1). These data suggest a pathological role for MLC2 tyrosine nitration associated with hypoxia-reoxygenation. Using human recombinant mutant MLC2, in which the tyrosine residue is replaced with phenylalanine, (Y152F) the *in vitro* incubation with peroxynitrite as a nitrating agent resulted in the prevention of MLC2 degradation by MMP-2, with no nitration observed at position 152. These observations indicate that although MLC2 has two nitration sites, it is Tyr 152 that mediates the signaling of degradation by MMP-2. MLC1 was also studied in a model of isolated adult rat cardiomyocytes subjected to simulated ischemia. Mass spectrometry analysis revealed nitration of Tyr 190, consistent with what was observed in piglet hearts. However, the Cys in the P3 position of the MMP-2 cleavage site was not S-nitrosylated as observed in MLC1 from piglet hearts. Moreover, MLC1 from rat cardiomyocytes was also nitrated at Tyr 78 and S-nitrosylated at Cys 81. *In vitro* human recombinant MLC1 was nitrated by peroxynitrite (used as a nitrating agent) at Tyr 73 (corresponding to rat MLC1 Tyr 78) Tyr 185 (corresponding to rat MLC1 Tyr 190), Tyr 140 and S-nytrosilated at Cys 76 (corresponding to rat Cys 81) and Cys 67. *In vitro* nitrated and S-nitrosylated MLC1 was more susceptible to degradation by MMP-2.

Posttranslational Modifications of Myosin Light Chains Determine the Protein Fate 249

With the development of proteomics technology over the last two decades, more and more information about protein posttranslational modifications has been gathered. The difficulty of studying posttranslational modification of proteins and their physiological and pathological consequences lies on the fact that often (if not always) a protein will exhibit more than one type of posttranslational modification at any given time or more than one

Classically, enzymatic production of a certain product, from a given substrate, was limited by the enzyme activity. Also, posttranslational modification of the enzyme, such as phosphorylation, is a valid process to increase enzyme activity. We propose a new paradigm in the regulation of enzymatic activity by modification of proteins previously resistant to degradation. Here we have described the role of nitrosylation, nitration and phosphorylation of cardiac contractile proteins, as substrates for enzymatic reaction, in models of oxidative stress which result in their increased degradation by a proteolytic

It has been described that posttranslational modification of MMP-2 triggered by oxidative stress can activate the enzyme (Viappiani et al. 2009). Although this may be the case in the *in vivo* and *ex vivo* models, the same observations were made in *in vitro* experiments in which MMP-2 is not posttranslational modified. This new paradigm, that posttranslational modification determine fate of proteins, is an important advance in the understanding of the molecular mechanisms by which oxidative stress can trigger cardiac contractile dysfunction in pathological processes such as ischemia/reperfusion and hypoxia-reoxygenation. activation of MLCK and phosphorylation of MLC1. These posttranslational modifications increase the affinity of MMP-2 for MLC1 and MLC2. MMP-2 degrades MLC1 and MLC2

We would like to thank Steve Arcand for the editorial contribution to this work. Also Jolanta Sawicka for the contribution in gathering the unpublished, ongoing data on

Virgilio J. J. Cadete is funded by the James Regan Graduate Scholarship in Cardiology from

Grzegorz Sawicki is a scholar of the Heart and Stroke Foundation of Canada and the

This project was funded by grants from Canadian Institutes of Health Research and the

Anderson, L. (2005). Candidate-based proteomics in the search for biomarkers of

Andreev, O. A. and J. Borejdo (1999). Binding of myosin cross-bridges to thin filaments of

cardiovascular disease. *J Physiol* 563, Pt 1, (Feb 15 2005), 23-60.0022-3751 (Print)

rabbit skeletal muscle. *Biochem Biophys Res Commun* 258, 3, (May 19 1999), 628-

**5. Conclusion** 

posttranslational modification of the same type.

enzyme (MMP-2) both *in vitro* and *in vivo* (Figure 2).

leading to cardiac contractile dysfunction.

Canadian Institutes of Health Research.

0022-3751 (Linking)

Saskatchewan Health Research Foundation.

the College of Medicine, University of Saskatchewan.

31.0006-291X (Print) 0006-291X (Linking)

**6. Acknowledgements** 

phosphorylation of MLC1.

**7. References** 

These data support the concept of highly regulated nitration and S-nitrosylation of proteins previously suggested, even though the exact mechanism remains unknown. Moreover, not only these processes are highly specific, they are also tightly associated with pathophysiological consequences. In this case, nitration and S-nitrosylation of protein residues is associated with an increase in its degradation by the proteolytic enzyme MMP-2 both *in vitro* and *in vivo.* 

Fig. 2. Cartoon representation of our proposed model for regulation of contractile protein fate by posttranslational modifications. Reactive oxygen species (ROS) generated during ischemia/reperfusion or hypoxya-reoxygenation can lead to the direct nitration/Snitrosylation of tyrosine and cysteine residues of MLC1 and MLC2. Also, ROS can lead to the phosphorylation of MLC1 and MLC2.

#### **5. Conclusion**

248 Proteomics – Human Diseases and Protein Functions

These data support the concept of highly regulated nitration and S-nitrosylation of proteins previously suggested, even though the exact mechanism remains unknown. Moreover, not only these processes are highly specific, they are also tightly associated with pathophysiological consequences. In this case, nitration and S-nitrosylation of protein residues is associated with an increase in its degradation by the proteolytic enzyme MMP-2

Fig. 2. Cartoon representation of our proposed model for regulation of contractile protein fate by posttranslational modifications. Reactive oxygen species (ROS) generated during ischemia/reperfusion or hypoxya-reoxygenation can lead to the direct nitration/Snitrosylation of tyrosine and cysteine residues of MLC1 and MLC2. Also, ROS can lead to

the phosphorylation of MLC1 and MLC2.

both *in vitro* and *in vivo.* 

With the development of proteomics technology over the last two decades, more and more information about protein posttranslational modifications has been gathered. The difficulty of studying posttranslational modification of proteins and their physiological and pathological consequences lies on the fact that often (if not always) a protein will exhibit more than one type of posttranslational modification at any given time or more than one posttranslational modification of the same type.

Classically, enzymatic production of a certain product, from a given substrate, was limited by the enzyme activity. Also, posttranslational modification of the enzyme, such as phosphorylation, is a valid process to increase enzyme activity. We propose a new paradigm in the regulation of enzymatic activity by modification of proteins previously resistant to degradation. Here we have described the role of nitrosylation, nitration and phosphorylation of cardiac contractile proteins, as substrates for enzymatic reaction, in models of oxidative stress which result in their increased degradation by a proteolytic enzyme (MMP-2) both *in vitro* and *in vivo* (Figure 2).

It has been described that posttranslational modification of MMP-2 triggered by oxidative stress can activate the enzyme (Viappiani et al. 2009). Although this may be the case in the *in vivo* and *ex vivo* models, the same observations were made in *in vitro* experiments in which MMP-2 is not posttranslational modified. This new paradigm, that posttranslational modification determine fate of proteins, is an important advance in the understanding of the molecular mechanisms by which oxidative stress can trigger cardiac contractile dysfunction in pathological processes such as ischemia/reperfusion and hypoxia-reoxygenation. activation of MLCK and phosphorylation of MLC1. These posttranslational modifications increase the affinity of MMP-2 for MLC1 and MLC2. MMP-2 degrades MLC1 and MLC2 leading to cardiac contractile dysfunction.

#### **6. Acknowledgements**

We would like to thank Steve Arcand for the editorial contribution to this work. Also Jolanta Sawicka for the contribution in gathering the unpublished, ongoing data on phosphorylation of MLC1.

Virgilio J. J. Cadete is funded by the James Regan Graduate Scholarship in Cardiology from the College of Medicine, University of Saskatchewan.

Grzegorz Sawicki is a scholar of the Heart and Stroke Foundation of Canada and the Canadian Institutes of Health Research.

This project was funded by grants from Canadian Institutes of Health Research and the Saskatchewan Health Research Foundation.

#### **7. References**


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Nitration and inactivation of manganese superoxide dismutase in chronic rejection of human renal allografts. *Proc Natl Acad Sci U S A* 93, 21, (Oct 15 1996), 11853-

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actin-binding regions on the myosin heads of cardiac muscle. *Biochemistry* 41, 17,

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actin on the accessibility of myosin essential light chain A1 to papain cleavage. *Biochim Biophys Acta* 1383, 1, (Mar 3 1998), 71-81.0006-3002 (Print) 0006 3002 (Linking)

myosin essential light chain isoforms with actin in skeletal muscles. *Acta Biochim* 

Ca2+ binding to myosin regulatory light chain affects the conformation of the Nterminus of essential light chain and its binding to actin. *Arch Biochem Biophys* 417,


**Part 3** 

**Proteomic Approaches to** 

**Dissecting Disease Processes** 

protein expression following myocardial ischemia and reperfusion in rabbits. *Proteomics* 2, 8, (Aug 2002), 988-95.1615-9853 (Print) 1615-9853 (Linking)


### **Part 3**

**Proteomic Approaches to Dissecting Disease Processes** 

254 Proteomics – Human Diseases and Protein Functions

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Timson, D. J., H. R. Trayer, K. J. Smith and I. P. Trayer (1999). Size and charge requirements for

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Peroxynitrite-mediated oxidative modifications of complex II: relevance in myocardial infarction. *Biochemistry* 49, 11, (Mar 23 2010), 2529-39.1520-4995

**12** 

**Proteomic Study of Esophageal** 

Comprehensive profiling of genome and transcriptome has identified myriads of alternations at the level of gene and gene expression, which drive malignant development and progression in context of oncology. As a result, qualitative or quantitative changes of protein expression pattern will inevitably ensue during multi-stage of carcinogenesis. In this sense, the proteome is a functional translation of the genome and is the actual manipulator of cellular behavior. Therefore, proteomic profiling of cellular protein constituents should generate the most relevant marker of the functional state of a cell. On the other hand, lack of correlation between mRNA and protein expression have been documented for a variety of genes. Unlike the genome which is static in certain sense, the proteome of a cell is dynamic and changes over time in terms of protein pattern, protein interactions and modifications triggered by external or internal signals[Kolch et al., 2004; Kolch et al., 2005]. Only dynamic information flow through protein circuitry reflects the course of a disease and allows us to track the pathogenetic mechanisms as well as treatment response[Kolch et al., 2005]. Furthermore, examining DNA sequences and measuring mRNA expression do not specify splicing, post-translational modifications, cleavages, protein subcellular localization and complex formations[Banks et al., 2000; Chambers et al., 2000]. There exists a huge information gulf between RNA transcription and protein expression. Proteome represents a much richer source for the functional description of diseases and the biomarker discovery implicated in cancer. Moreover, most of diagnostic assays currently applied in clinical practice are protein-based immunological methods, which are well adapted to standardization and clinical implementation. Proteomic profiling during disease formation and evolution not only provides an integrated understanding of pathogenesis in context of genome and proteome but also holds greater promise to identify the biomarkers of

Accounting for more than 400,000 deaths per year, esophageal cancer (EC) ranks as the sixth most common cause of cancer-related mortality worldwide[Parkin et al., 2005]. Moreover, about half of world's EC cases newly diagnosed each year occurred in China[Holmes & Vaughan, 2007]. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal

diagnosis and therapeutic targets for diseases such as cancer.

**1.1 Epidemiology and etiology of ESCC** 

**1. Introduction** 

**Squamous Cell Carcinoma** 

Yi-Jun Qi1 and Jen-Fu Chiu2 *1College of Medicine, Henan University 2College of Medicine, Shantou University* 

*and University of Hong Kong* 

*P.R. China* 

#### Yi-Jun Qi1 and Jen-Fu Chiu2

*1College of Medicine, Henan University 2College of Medicine, Shantou University and University of Hong Kong P.R. China* 

#### **1. Introduction**

Comprehensive profiling of genome and transcriptome has identified myriads of alternations at the level of gene and gene expression, which drive malignant development and progression in context of oncology. As a result, qualitative or quantitative changes of protein expression pattern will inevitably ensue during multi-stage of carcinogenesis. In this sense, the proteome is a functional translation of the genome and is the actual manipulator of cellular behavior. Therefore, proteomic profiling of cellular protein constituents should generate the most relevant marker of the functional state of a cell. On the other hand, lack of correlation between mRNA and protein expression have been documented for a variety of genes. Unlike the genome which is static in certain sense, the proteome of a cell is dynamic and changes over time in terms of protein pattern, protein interactions and modifications triggered by external or internal signals[Kolch et al., 2004; Kolch et al., 2005]. Only dynamic information flow through protein circuitry reflects the course of a disease and allows us to track the pathogenetic mechanisms as well as treatment response[Kolch et al., 2005]. Furthermore, examining DNA sequences and measuring mRNA expression do not specify splicing, post-translational modifications, cleavages, protein subcellular localization and complex formations[Banks et al., 2000; Chambers et al., 2000]. There exists a huge information gulf between RNA transcription and protein expression. Proteome represents a much richer source for the functional description of diseases and the biomarker discovery implicated in cancer. Moreover, most of diagnostic assays currently applied in clinical practice are protein-based immunological methods, which are well adapted to standardization and clinical implementation. Proteomic profiling during disease formation and evolution not only provides an integrated understanding of pathogenesis in context of genome and proteome but also holds greater promise to identify the biomarkers of diagnosis and therapeutic targets for diseases such as cancer.

#### **1.1 Epidemiology and etiology of ESCC**

Accounting for more than 400,000 deaths per year, esophageal cancer (EC) ranks as the sixth most common cause of cancer-related mortality worldwide[Parkin et al., 2005]. Moreover, about half of world's EC cases newly diagnosed each year occurred in China[Holmes & Vaughan, 2007]. Histologically, esophageal squamous cell carcinoma (ESCC) and esophageal

with diverse functions, such as vulnerable genes to chemicals, tumor-related genes, tumor suppressor genes, metastasis genes, apoptosis gene, proliferation genes, etc[Enzinger & Mayer, 2003; Greenawalt et al., 2007; Kwong, 2005; Lin et al., 2009]. Moreover, epigenetic alterations, chromosomal changes and transcriptional changes have also been found to play crucial roles in the pathogenesis of ESCC[Abnet et al., 2010; Greenawalt et al., 2007; Wang et al., 2010]. Although these findings improve our general understanding about the molecular biology of ESCC, the appropriate biomarkers for high-risk population screening, for clinical diagnosis and prognosis, for evaluation of treatment efficiency have not been identified yet.

The completion of human genome sequence did not ensure panacea solutions to all problems related to biological deregulation. In fact, human proteome is far more complex and dynamic than genome sequence. It is estimated that the human genome contains about 32 000 protein coding genes, which code for 100 000 to 10 million proteins due to alternative RNA splicing, overlapping of transcription units, post-translational processing and modifications[Lander et al., 2001; Venter et al., 2001]. Thus, a big disparity between genome and proteome exists, which indicates that the combinatorial diversification of regulatory networks lead to functional evolution of proteins. Through detecting the functioning units, proteomic studies generate a protein fingerprint, which reflects both the intrinsic genetic programme of the cell and the impact of its immediate environment. Therefore, proteomics is valuable for biomarker discovery since its application provides higher opportunity to identify genuine determinants or causal factors involved in biological functions or the

Therefore, it is imperative to search more effective biomarkers for such purposes.

**2.2 Two-dimensional electrophoresis-based proteomic findings of ESCC** 

Two-dimensional electrophoresis (2DE) has been used for over 30 years now due to its high resolution for the separation of complex protein mixtures. In combination with mass spectrometry, 2DE has been so far the most commonly used method for analyzing protein expression and identity. Our laboratory used 2DE to profile the proteome from ESCC tumors and matched adjacent non-cancer mucosa, and proteome from immortalized esophageal cell line and cancer cell lines. Comparative analysis and MS for protein identification showed that the over-expressions of four proteins were common in ESCC tissues and cancer cell lines, which include tropomyosin isoform 4 (TPM4), prohibitin, peroxiredoxin (PRX1) and manganese superoxide dismutase (MnSOD); the expressions of another three proteins, i.e. stratifin, prohibitin, squamous cell carcinoma antigen 1 (SCCA1), were correlated inversely with dedifferentiation of ESCC[Qi et al., 2005; Qi et al., 2008]. Immunohistochemistry (IHC) analysis showed that loss of expressions of annexin A2 and stratifin were 45% and 64% in ESCC, respectively[Qi et al., 2007a; Qi et al., 2007b; Ren et al., 2010]. Differential expressions of ten proteins including TPM1, SCCA1, stratifin, peroxiredoxin 2 isoform a, alpha B-crystalline, annexin A2, heterogeneous nuclear ribonucleoprotein L (hnRNP L), triosephosphate isomerase1 (TPI), laminA/C, and cyclophilin A (CypA) can be observed as well. Our findings may suggest that these differential proteins contribute to the multistage process of carcinogenesis, tumor progression, and invasiveness of ESCC. Published in the same issue, Zhou et al found 28

**2. ESCC analysis by proteomics** 

pathogenesis of disease.

**2.1 Advantages of proteomics compared with genomics** 

adenocarcinoma (EAC) contribute to more than 90% of EC[Daly et al., 2000]. In China, ESCC is the predominant histological subtype and account for nearly 90% of all EC[Li et al., 2011]. In developed countries, in contrast, EAC has been increasingly more frequent over the past two decades and has now surpassed the previously more predominant ESCC[Brown et al., 2008; Trivers et al., 2008]. The incidence of ESCC is characterized by its striking geographical distribution across the world. In the extremely high incidence areas, e.g. northern China, the incidence of EC exceeds 100/100 000/year, while the incidence is less than 5/100 000/year in Europe and the USA[Cheng & Day, 1996]. Heavy smoking and alcohol consumption are associated with increased risk of ESCC in developed countries[Brown et al., 2008; Messmann, 2001; Morita et al., 2010], but not major contributing factors in the pathogenesis of ESCC in China, where major risk factors include nutritional deficiency, consumption of pickled vegetables, dietary contamination with nitrosamine or mycotoxin, and low socioeconomic status[Kamangar et al., 2009; Yang et al., 1984]. In light of the poor nutrition status in Linxian, one of the highest incidence areas for ESCC in the world, two large nutrition intervention studies implemented in the late 1980s reported that the combination of selenium/vitamin E/βcarotene significantly reduced total mortality, total cancer mortality and stomach cancer incidence[Blot et al., 1993; Li et al., 1993]. High baseline serum selenium concentrations showed strong protective effects on ESCC and stomach cancer in prospective studies[Mark et al., 2000]. Recently, opposing trends in incidence of EAC and ESCC, i.e. decrease of ESCC incidence and reciprocal increase of EAC incidence has been observed not only worldwide but also in high risk areas in China, pointing to the roles of economic level and lifestyle factors in EC pattern change[Devesa et al., 1998; Fan et al., 2008; Hongo et al., 2009]. In addition, familial aggregation of ESCC has been reported in high-risk areas for ESCC[Chang-Claude et al., 1997]. Taken together, these facts indicate that both genetic susceptibility and environmental risk factors contribute to the etiology of ESCC.

#### **1.2 Current situation of clinical management of ESCC**

Early detection of ESCC is formidable and the majority of ESCC patients have advanced metastatic disease at initial diagnosis. Therefore, 40-60% ESCC patients are inappropriate for curative resection, which remains the primary treatment of ESCC as it provides sustained palliation of dysphagia and the best chance of cure[Hagymasi & Tulassay, 2007; Triboulet et al., 2001]. Nonetheless, more than 50% ESCC develop recurrence within 2-3 years after surgery[Dresner & Griffin, 2000; Hulscher et al., 2000; Nakagawa et al., 2004]. Moreover, the overall 5-year survival rate is < 10% despite significant improvements in surgical techniques and adjuvant chemoradiation[Lightdale, 1999]. In contrast, the 5-year survival rate for EC patients at early stages could be as high as 90%[Hu et al., 2001]. Long-term survival correlates with stages of EC, as evidenced by 40-62% of 5-year survival rate for stage I and IIA contrasting with 18-25% for stage IIB and III of EC[Iizuka et al., 1989]. This suggests that the reasons for this disappointingly low survival rate include ineffective screening tools for high-risk population, cancer detection at an advanced stage, high-risk for recurrence, lack of targets for treatment, unreliable noninvasive tools to monitor complete response to chemoradiotherapy and so on. Clearly, identification of effective biomarkers for early detection, monitoring tumor progression and potential therapeutic targets offer the best chances to lower the morbidity and mortality of ESCC.

#### **1.3 Molecular biology studies of ESCC and its contribution to clinical management**

Extensive molecular biology studies of ESCC have identified a wealth of dysregulated molecular events involved in esophageal carcinogenesis, which cover a broad range of genes with diverse functions, such as vulnerable genes to chemicals, tumor-related genes, tumor suppressor genes, metastasis genes, apoptosis gene, proliferation genes, etc[Enzinger & Mayer, 2003; Greenawalt et al., 2007; Kwong, 2005; Lin et al., 2009]. Moreover, epigenetic alterations, chromosomal changes and transcriptional changes have also been found to play crucial roles in the pathogenesis of ESCC[Abnet et al., 2010; Greenawalt et al., 2007; Wang et al., 2010]. Although these findings improve our general understanding about the molecular biology of ESCC, the appropriate biomarkers for high-risk population screening, for clinical diagnosis and prognosis, for evaluation of treatment efficiency have not been identified yet. Therefore, it is imperative to search more effective biomarkers for such purposes.

#### **2. ESCC analysis by proteomics**

258 Proteomics – Human Diseases and Protein Functions

adenocarcinoma (EAC) contribute to more than 90% of EC[Daly et al., 2000]. In China, ESCC is the predominant histological subtype and account for nearly 90% of all EC[Li et al., 2011]. In developed countries, in contrast, EAC has been increasingly more frequent over the past two decades and has now surpassed the previously more predominant ESCC[Brown et al., 2008; Trivers et al., 2008]. The incidence of ESCC is characterized by its striking geographical distribution across the world. In the extremely high incidence areas, e.g. northern China, the incidence of EC exceeds 100/100 000/year, while the incidence is less than 5/100 000/year in Europe and the USA[Cheng & Day, 1996]. Heavy smoking and alcohol consumption are associated with increased risk of ESCC in developed countries[Brown et al., 2008; Messmann, 2001; Morita et al., 2010], but not major contributing factors in the pathogenesis of ESCC in China, where major risk factors include nutritional deficiency, consumption of pickled vegetables, dietary contamination with nitrosamine or mycotoxin, and low socioeconomic status[Kamangar et al., 2009; Yang et al., 1984]. In light of the poor nutrition status in Linxian, one of the highest incidence areas for ESCC in the world, two large nutrition intervention studies implemented in the late 1980s reported that the combination of selenium/vitamin E/βcarotene significantly reduced total mortality, total cancer mortality and stomach cancer incidence[Blot et al., 1993; Li et al., 1993]. High baseline serum selenium concentrations showed strong protective effects on ESCC and stomach cancer in prospective studies[Mark et al., 2000]. Recently, opposing trends in incidence of EAC and ESCC, i.e. decrease of ESCC incidence and reciprocal increase of EAC incidence has been observed not only worldwide but also in high risk areas in China, pointing to the roles of economic level and lifestyle factors in EC pattern change[Devesa et al., 1998; Fan et al., 2008; Hongo et al., 2009]. In addition, familial aggregation of ESCC has been reported in high-risk areas for ESCC[Chang-Claude et al., 1997]. Taken together, these facts indicate that both genetic susceptibility and environmental risk

Early detection of ESCC is formidable and the majority of ESCC patients have advanced metastatic disease at initial diagnosis. Therefore, 40-60% ESCC patients are inappropriate for curative resection, which remains the primary treatment of ESCC as it provides sustained palliation of dysphagia and the best chance of cure[Hagymasi & Tulassay, 2007; Triboulet et al., 2001]. Nonetheless, more than 50% ESCC develop recurrence within 2-3 years after surgery[Dresner & Griffin, 2000; Hulscher et al., 2000; Nakagawa et al., 2004]. Moreover, the overall 5-year survival rate is < 10% despite significant improvements in surgical techniques and adjuvant chemoradiation[Lightdale, 1999]. In contrast, the 5-year survival rate for EC patients at early stages could be as high as 90%[Hu et al., 2001]. Long-term survival correlates with stages of EC, as evidenced by 40-62% of 5-year survival rate for stage I and IIA contrasting with 18-25% for stage IIB and III of EC[Iizuka et al., 1989]. This suggests that the reasons for this disappointingly low survival rate include ineffective screening tools for high-risk population, cancer detection at an advanced stage, high-risk for recurrence, lack of targets for treatment, unreliable noninvasive tools to monitor complete response to chemoradiotherapy and so on. Clearly, identification of effective biomarkers for early detection, monitoring tumor progression and potential therapeutic targets offer the best

**1.3 Molecular biology studies of ESCC and its contribution to clinical management**  Extensive molecular biology studies of ESCC have identified a wealth of dysregulated molecular events involved in esophageal carcinogenesis, which cover a broad range of genes

factors contribute to the etiology of ESCC.

**1.2 Current situation of clinical management of ESCC** 

chances to lower the morbidity and mortality of ESCC.

#### **2.1 Advantages of proteomics compared with genomics**

The completion of human genome sequence did not ensure panacea solutions to all problems related to biological deregulation. In fact, human proteome is far more complex and dynamic than genome sequence. It is estimated that the human genome contains about 32 000 protein coding genes, which code for 100 000 to 10 million proteins due to alternative RNA splicing, overlapping of transcription units, post-translational processing and modifications[Lander et al., 2001; Venter et al., 2001]. Thus, a big disparity between genome and proteome exists, which indicates that the combinatorial diversification of regulatory networks lead to functional evolution of proteins. Through detecting the functioning units, proteomic studies generate a protein fingerprint, which reflects both the intrinsic genetic programme of the cell and the impact of its immediate environment. Therefore, proteomics is valuable for biomarker discovery since its application provides higher opportunity to identify genuine determinants or causal factors involved in biological functions or the pathogenesis of disease.

#### **2.2 Two-dimensional electrophoresis-based proteomic findings of ESCC**

Two-dimensional electrophoresis (2DE) has been used for over 30 years now due to its high resolution for the separation of complex protein mixtures. In combination with mass spectrometry, 2DE has been so far the most commonly used method for analyzing protein expression and identity. Our laboratory used 2DE to profile the proteome from ESCC tumors and matched adjacent non-cancer mucosa, and proteome from immortalized esophageal cell line and cancer cell lines. Comparative analysis and MS for protein identification showed that the over-expressions of four proteins were common in ESCC tissues and cancer cell lines, which include tropomyosin isoform 4 (TPM4), prohibitin, peroxiredoxin (PRX1) and manganese superoxide dismutase (MnSOD); the expressions of another three proteins, i.e. stratifin, prohibitin, squamous cell carcinoma antigen 1 (SCCA1), were correlated inversely with dedifferentiation of ESCC[Qi et al., 2005; Qi et al., 2008]. Immunohistochemistry (IHC) analysis showed that loss of expressions of annexin A2 and stratifin were 45% and 64% in ESCC, respectively[Qi et al., 2007a; Qi et al., 2007b; Ren et al., 2010]. Differential expressions of ten proteins including TPM1, SCCA1, stratifin, peroxiredoxin 2 isoform a, alpha B-crystalline, annexin A2, heterogeneous nuclear ribonucleoprotein L (hnRNP L), triosephosphate isomerase1 (TPI), laminA/C, and cyclophilin A (CypA) can be observed as well. Our findings may suggest that these differential proteins contribute to the multistage process of carcinogenesis, tumor progression, and invasiveness of ESCC. Published in the same issue, Zhou et al found 28

oxioreduction, proliferation, glycolysis, cell motility, transcription, signal transduction, suggesting multiple dysregulated pathways involved in ESCC. For better understanding the pathogenesis of ESCC and development of biomarkers, integrated and comprehensive

An alternative approach to identify novel tumor biomarkers is the assessment of immune response elicited by tumor antigen since the humoral immune response to cancer in humans has been evidenced by the identification of autoantibodies to a variety of intracellular and surface antigens in cancer patients with different types of tumors[Chen et al., 2007; Disis et al., 1997; Hong et al., 2004; Soussi, 2000]. In ESCC, a number of reports have documented the presence of autoantibodies in serum against various proteins, including p53, cytokeratins, myomegalin, TRIM21, peroxiredoxin VI proteins, Hsp70, and CDC25B[Bergqvist et al., 2001; Fujita et al., 2006; Fujita et al., 2008; Liu et al., 2008; Shimada et al., 2007; Shimada et al., 2005; Veale et al., 1988]. The proteomic-based approach to identify panels of tumor antigens and related autoantibodies was introduced by Brichory et al. in 2001, which identified antiannexin I and II antibodies in sera from patients with lung cancer[Brichory et al., 2001]. There have been four articles published by two research groups, which reported the existence of autoantibodies in sera of ESCC patients. The first report was published by Fujita et al from Japan, who used 2DE to resolve protein extracts from ESCC cell line TE-2 as tumor antigens and then probed the blot with sera of ESCC patients, healthy controls and patients with other cancers[Fujita et al., 2006]. One positive spot was identified as PRX VI by MALDI TOF/TOF MS. The frequency of autoantibody against PRX VI was 50% (15/30) in ESCC, only 6.6% (2/30) in health controls and 3.3% (1/30) in colon cancer. Two years later, the same research group discovered augmented concentration of Hsp70 autoantibody in the serum of ESCC patients, which was significantly higher in ESCC patients than gastric and colon cancer, healthy controls[Fujita et al., 2008]. On the other hand, Liu et al. used ESCC tissue protein extracts and autologous sera to search for autoantibodies in ESCC patients and identified autoantibody CDC25B[Liu et al., 2008]. Furthermore, CDC25B expression was significantly higher in ESCC tissues with positive autoantibody CDC25B and significantly correlated with tumor stage. The sensitivity and specificity of autoantibody CDC25B for ESCC detection was 56.7% and 91%, respectively[Dong et al., 2010]. The autoantibodydriven research is indeed a promising approach for the identification of novel serum biomarkers present in ESCC and for the tumor antigen itself, which may aid the diagnosis of

Similar to other cancers, development of multiple drug resistance in ESCC is one of major causes of failure to chemotherapy treatment. Furthermore, recent studies have shown that there exists intrinsic sensitivity and resistance to chemotherapy and/or radiotherapy in malignant cells of ESCC, which may predict clinical outcome of ESCC patients receiving neoadjuvant chemotherapy. Prior stratification of ESCC patients according to reliable biomarkers could not only save patients unnecessary adverse effects of chemotherapeutic agents but also render patients more chance to access to alternative curative treatment options. Therefore, it is imperative to define new diagnostic indicators that can reliably predict response to chemotherapy and radiotherapy in advance. A recent study compared the 2DE gels of parental esophageal cancer cell line EC109 and its resistant sub-cell line EC109/CDDP to determine the different proteins spots and identified 44 proteins with potential contribution to chemotherapy resistance[Wen et al., 2010]. In another study, radioactive 2DE proteomic comparative analysis was performed using protein extracts of biopsies from 34 patients with locally advanced EAC receiving neoadjuvant chemotherapy.

studies on these protein candidates are needed.

ESCC and development of more effective immunotherapies.

proteins aberrantly expressed in ESCC cancer cells with at least three-fold difference between ESCC and normal epithelial cells[Zhou et al., 2005]. The overlap between these two studies was quite small. Only expression of SCCA1 was commonly down-expressed in ESCC, but transgelin showed increased expression in tumor in our study and decreased expression in Zhou's study. The disparity of proteins identified between these two studies may be due to different sample source, different methods used by these two groups, such as laser capture microdissection vs. bulk tissues, 2D-DIGE vs. silver staining. Later, five groups reported proteomic signatures associated with ESCC using ESCC samples collected from different regions of China, including high risk areas for ESCC such as Linzhou, Xinjiang and low risk areas like Beijing and Guangdong, but only four reports displayed details of identified proteins. Interestingly, more overlap of the identified proteins came from Fu's study and ours, both of which used ESCC samples from Linzhou, one of the highest areas for ESCC adjacent to Taihang Mountain[Fu et al., 2007]. The commonly identified proteins with the same change direction included alpha enolase, TPM, tubulin, prohibitin and PRX2. Although the prevalence of ESCC in Xinjiang is comparable to Linzhou, the protein signatures were unique to sample origin, indicative of more important roles of environmental, ethnic or hereditary factors in the carcinogenesis of ESCC[Liu et al., 2011]. It seems that hsp27 was a general molecular events involved in ESCC since four out of five studies observed down-expression in ESCC except ours. Only one among seven studies performed survival assay after identifying the candidate proteins by ESCC proteomic profiling. Du et al. reported that over-expression of calreticulin and GRP78 could predicate poor prognosis of ESCC[Du et al., 2007]. Although 2DE is indeed a very useful method for biomarker discovery, more examinations of the biological functions and the clinical relevance of biomarker candidates involved in ESCC are necessary to verify its clinical value.

Two reports described the proteomic signatures of ESCC with samples from Japan. Nishimori et al. used the agarose IEF gel in the first dimension, which not only allows for large-scale quantitative comparisons of protein expression but also is able to resolve high molecular mass proteins larger than 150 kDa[Nishimori et al., 2006]. As a result, a different protein pattern was revealed, including a few protein candidates with MW > 70 kDa. Western blot and IHC verified the different expression of a 195 kDa protein, periplakin, between cancer and adjacent non-cancer tissues. Not only was the expression of periplakin significantly down-regulated in ESCC but also translocation of periplakin was observed, which localized at cell-cell boundaries in normal epithelium and dysplastic precursor lesions, and disappeared from cell boundaries and shifted to cell cytoplasm in early cancers. The other research group from Japan used unsupervised classification to analyze the 2D-DIGE protein spots and procured the protein signatures most relevant to clinical parameters with progression of ESCC[Hatakeyama et al., 2006]. The authors developed the largest protein database relevant to ESCC, which identified 240 proteins with expression level associated with carcinogenesis, histological differentiation and the number of lymph node metastases. A significant overlapping was observed between the proteins identified in ESCC with other different types of tumor. In addition, Jazii et al did proteomic profiling using ESCC samples from Iran, another high incidence area for ESCC like northern China, and identified six over-expressed proteins and six under-expressed proteins associated with ESCC[Jazii et al., 2006]. However, the authors only used RT-PCR to verify the loss of βtropomyosin in ESCC. The functions of identified proteins associated with the development and progression of ESCC include cytoskeletal/structural organization, transport, chaperon,

proteins aberrantly expressed in ESCC cancer cells with at least three-fold difference between ESCC and normal epithelial cells[Zhou et al., 2005]. The overlap between these two studies was quite small. Only expression of SCCA1 was commonly down-expressed in ESCC, but transgelin showed increased expression in tumor in our study and decreased expression in Zhou's study. The disparity of proteins identified between these two studies may be due to different sample source, different methods used by these two groups, such as laser capture microdissection vs. bulk tissues, 2D-DIGE vs. silver staining. Later, five groups reported proteomic signatures associated with ESCC using ESCC samples collected from different regions of China, including high risk areas for ESCC such as Linzhou, Xinjiang and low risk areas like Beijing and Guangdong, but only four reports displayed details of identified proteins. Interestingly, more overlap of the identified proteins came from Fu's study and ours, both of which used ESCC samples from Linzhou, one of the highest areas for ESCC adjacent to Taihang Mountain[Fu et al., 2007]. The commonly identified proteins with the same change direction included alpha enolase, TPM, tubulin, prohibitin and PRX2. Although the prevalence of ESCC in Xinjiang is comparable to Linzhou, the protein signatures were unique to sample origin, indicative of more important roles of environmental, ethnic or hereditary factors in the carcinogenesis of ESCC[Liu et al., 2011]. It seems that hsp27 was a general molecular events involved in ESCC since four out of five studies observed down-expression in ESCC except ours. Only one among seven studies performed survival assay after identifying the candidate proteins by ESCC proteomic profiling. Du et al. reported that over-expression of calreticulin and GRP78 could predicate poor prognosis of ESCC[Du et al., 2007]. Although 2DE is indeed a very useful method for biomarker discovery, more examinations of the biological functions and the clinical relevance of biomarker candidates involved in ESCC are necessary to verify its clinical

Two reports described the proteomic signatures of ESCC with samples from Japan. Nishimori et al. used the agarose IEF gel in the first dimension, which not only allows for large-scale quantitative comparisons of protein expression but also is able to resolve high molecular mass proteins larger than 150 kDa[Nishimori et al., 2006]. As a result, a different protein pattern was revealed, including a few protein candidates with MW > 70 kDa. Western blot and IHC verified the different expression of a 195 kDa protein, periplakin, between cancer and adjacent non-cancer tissues. Not only was the expression of periplakin significantly down-regulated in ESCC but also translocation of periplakin was observed, which localized at cell-cell boundaries in normal epithelium and dysplastic precursor lesions, and disappeared from cell boundaries and shifted to cell cytoplasm in early cancers. The other research group from Japan used unsupervised classification to analyze the 2D-DIGE protein spots and procured the protein signatures most relevant to clinical parameters with progression of ESCC[Hatakeyama et al., 2006]. The authors developed the largest protein database relevant to ESCC, which identified 240 proteins with expression level associated with carcinogenesis, histological differentiation and the number of lymph node metastases. A significant overlapping was observed between the proteins identified in ESCC with other different types of tumor. In addition, Jazii et al did proteomic profiling using ESCC samples from Iran, another high incidence area for ESCC like northern China, and identified six over-expressed proteins and six under-expressed proteins associated with ESCC[Jazii et al., 2006]. However, the authors only used RT-PCR to verify the loss of βtropomyosin in ESCC. The functions of identified proteins associated with the development and progression of ESCC include cytoskeletal/structural organization, transport, chaperon,

value.

oxioreduction, proliferation, glycolysis, cell motility, transcription, signal transduction, suggesting multiple dysregulated pathways involved in ESCC. For better understanding the pathogenesis of ESCC and development of biomarkers, integrated and comprehensive studies on these protein candidates are needed.

An alternative approach to identify novel tumor biomarkers is the assessment of immune response elicited by tumor antigen since the humoral immune response to cancer in humans has been evidenced by the identification of autoantibodies to a variety of intracellular and surface antigens in cancer patients with different types of tumors[Chen et al., 2007; Disis et al., 1997; Hong et al., 2004; Soussi, 2000]. In ESCC, a number of reports have documented the presence of autoantibodies in serum against various proteins, including p53, cytokeratins, myomegalin, TRIM21, peroxiredoxin VI proteins, Hsp70, and CDC25B[Bergqvist et al., 2001; Fujita et al., 2006; Fujita et al., 2008; Liu et al., 2008; Shimada et al., 2007; Shimada et al., 2005; Veale et al., 1988]. The proteomic-based approach to identify panels of tumor antigens and related autoantibodies was introduced by Brichory et al. in 2001, which identified antiannexin I and II antibodies in sera from patients with lung cancer[Brichory et al., 2001]. There have been four articles published by two research groups, which reported the existence of autoantibodies in sera of ESCC patients. The first report was published by Fujita et al from Japan, who used 2DE to resolve protein extracts from ESCC cell line TE-2 as tumor antigens and then probed the blot with sera of ESCC patients, healthy controls and patients with other cancers[Fujita et al., 2006]. One positive spot was identified as PRX VI by MALDI TOF/TOF MS. The frequency of autoantibody against PRX VI was 50% (15/30) in ESCC, only 6.6% (2/30) in health controls and 3.3% (1/30) in colon cancer. Two years later, the same research group discovered augmented concentration of Hsp70 autoantibody in the serum of ESCC patients, which was significantly higher in ESCC patients than gastric and colon cancer, healthy controls[Fujita et al., 2008]. On the other hand, Liu et al. used ESCC tissue protein extracts and autologous sera to search for autoantibodies in ESCC patients and identified autoantibody CDC25B[Liu et al., 2008]. Furthermore, CDC25B expression was significantly higher in ESCC tissues with positive autoantibody CDC25B and significantly correlated with tumor stage. The sensitivity and specificity of autoantibody CDC25B for ESCC detection was 56.7% and 91%, respectively[Dong et al., 2010]. The autoantibodydriven research is indeed a promising approach for the identification of novel serum biomarkers present in ESCC and for the tumor antigen itself, which may aid the diagnosis of ESCC and development of more effective immunotherapies.

Similar to other cancers, development of multiple drug resistance in ESCC is one of major causes of failure to chemotherapy treatment. Furthermore, recent studies have shown that there exists intrinsic sensitivity and resistance to chemotherapy and/or radiotherapy in malignant cells of ESCC, which may predict clinical outcome of ESCC patients receiving neoadjuvant chemotherapy. Prior stratification of ESCC patients according to reliable biomarkers could not only save patients unnecessary adverse effects of chemotherapeutic agents but also render patients more chance to access to alternative curative treatment options. Therefore, it is imperative to define new diagnostic indicators that can reliably predict response to chemotherapy and radiotherapy in advance. A recent study compared the 2DE gels of parental esophageal cancer cell line EC109 and its resistant sub-cell line EC109/CDDP to determine the different proteins spots and identified 44 proteins with potential contribution to chemotherapy resistance[Wen et al., 2010]. In another study, radioactive 2DE proteomic comparative analysis was performed using protein extracts of biopsies from 34 patients with locally advanced EAC receiving neoadjuvant chemotherapy.

Protein name T/N ratio Functions References

Annexin A8 ↓ [Nishimori et al., 2006]

Annexin V ↑ [Du et al., 2007] Annexin VI ↓ [Nishimori et al., 2006] Reticulocalbin ↑ [Zhou et al., 2005]

Syntaxin binding protein ↑ [Liu et al., 2011]

protein <sup>↑</sup> [Zhu et al., 2010] Zinc finger protein 410 ↑ Zink/DNA binding [Du et al., 2007] Mutuant hemoglobin beta chain <sup>↓</sup> Heme binding [Du et al., 2007] Myoglobin ↓ [Zhu et al., 2010]

TPM1 ↓ [Qi et al., 2005]

TPM4 ↑ [Qi et al., 2005]

Gamma-actin ↑ [Qi et al., 2005] Beta-actin ↑ [Fu et al., 2007] ACTB protein ↑ [Liu et al., 2011] Profilin-1 ↑ [Zhu et al., 2010] Periplakin ↓ [Nishimori et al., 2006]

Calreticulin ↑ Stress response and

MnSOD <sup>↑</sup> Superoxide dismutase

Calcium-dependent phospholipid binding calcium ion binding

calcium ion binding

Cytoskeleton constituent

TPM-4-ALK fusion oncoprotein type 2 ↑ NA [Du et al., 2007; Jazii et al., 2006]

TPM2 <sup>↓</sup> [Jazii et al., 2006; Nishimori et al.,

TPM3 ↑or *↓* [Fu et al., 2007; *Zhu et al., 2010]* 

TPM isoform ↑ [Fu et al., 2007; Qi et al., 2005] Vinculin ↓ [Nishimori et al., 2006] Capping protein, gelsolin-like ↓ [Fu et al., 2007]

Tubulin beta-5 chain ↑ [Fu et al., 2007; Qi et al., 2005]

immunity

Keratin 13 <sup>↓</sup> [Nishimori et al., 2006; Zhou et al.,

Desmin <sup>↓</sup> [Nishimori et al., 2006; Zhou et al.,

(PCNA) ↑ DNA polymerase activity [Du et al., 2007; Zhou et al., 2005]

HSPAIL ↑ or *<sup>↓</sup>* [Du et al., 2007; *Nishimori et al., 2006*]

Intermediate filament

Stress response and chaperone binding

Calreticulin precursor ↑ [Nishimori et al., 2006] Keratin 6A ↑ Intermediate filament [Du et al., 2007]

Keratin 6 ↑ [Nishimori et al., 2006] Keratin 8 ↓ [Zhou et al., 2005]

Vimentin ↑ [Nishimori et al., 2006]

DnaJ(Hsp40) homolog ↑ [Nishimori et al., 2006]

isoform SM1 <sup>↓</sup> [Nishimori et al., 2006] Smooth muscle protein ↓ [Liu et al., 2011] Apha-actinin 4 ↑ [Fu et al., 2007] Tubulin alpha-6, ubiquitous ↑ [Fu et al., 2007]

[Du et al., 2007; *Liu et al., 2011*]

[Fu et al., 2007; Liu et al., 2011; Nishimori et al., 2006; Zhou et al.,

[Du et al., 2007; Jazii et al., 2006]

[Jazii et al., 2006; Zhou et al., 2005]

2006; Zhu et al., 2010]

[Zhou et al., 2005]

2005]

2005]

2005]

activity [Du et al., 2007; Qi et al., 2005]

[Du et al., 2007]

[Du et al., 2007; Jazii et al., 2006; Liu et al., 2011; Nishimori et al., 2006; Zhou et al., 2005; *Zhu et al., 2010]* 

Annexin A2 ↑or *↓*

Annexin I ↓or*↑*

S100 A9 ↓

TPM ↓

Smooth muscle myosin heavy chain 11

Keratin 1 ↑

PRO1708 ↑

Proliferation cell nuclear antigen

Dank-type molecular chaperone

Translationally controlled tumor

The identified proteins with different expression between responders and non-responders were classified into two major families, cytoskeleton proteins and molecular chaperon proteins. Further validation by IHC and RT-PCR showed that weak expression of HSP27 at protein level and mRNA level were associated with non-response to platin-based chemotherapy[Langer et al., 2008]. As serum represents a rich source for biomarker discovery, proteomic spectra were examined using 27 and 12 serum samples of responders and non-responders, respectively, to preoperative chemoradiotherapy in a training set by surface-enhanced laser desorption and ionization coupled with mass spectrometry analysis. A proteomic classifier comprising four mass peaks, at 7 420, 9 112, 17 123 and 12 867 m/z was identified with 93.3% predicative accuracy in the validation set[Hayashida et al., 2005]. Since chemotherapy resistance is a complex and multi-factorial event, proteomic-based studies enable comprehensive characterization of resistance phenotype of malignant cancers, which may lead to identification of potential distinguishing biomarkers between responders and non-responders and lay foundation for further molecular mechanism studies.

In addition of 2DE gel for proteomic studies, surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an alternative proteomic tool to profile the serum or other body fluids and define potential protein pattern with diagnostic potential. By profiling of the serum proteome with SELDI-TOF-MS combined with bioinformatics tools, a number of highly sensitive and specific potential diagnosis markers have been revealed in various types of cancers. Wang et al. used weak cation exchange (WCX2) protein chips and SELDI-TOF-MS to profile 130 symptom-free serum samples collected from high-incidence area of ESCC in northern China, Linzhou, which included 63 subjects with normal esophageal mucosa, 40 subjects with basal cell hyperplasia, 27 subjects with dysplasia and 30 ESCC patients. Biomarker pattern's software identified four protein features at m/z of 9 306.61, 13 765.9, 2 942.15 and 15 953.4, which could distinguish normal esophageal epithelium, basal cell hyperplasia, dysplasia and ESCC with satisfactory diagnostic accuracy[Wang et al., 2006]. Xinjiang is one of the high-incidence areas for ESCC and comprise different ethnic peoples including Han decent. Using CM10 protein chips to capture targets from serum, SELDI-TOF-MS and bioinformatics analysis resulted in identification of six protein peaks (m/z 5667, 5790, 5876, 5979, 6043 and 6102) with diagnostic power with sensitivity and specificity of 91.43% and 88.89%, respectively[Xu et al., 2009]. In the case of ESCC profiled by SELDI-TOF-MS, further purification and identification of discriminatory peaks is necessary for development of simple methods for wider clinical application, and to enhance our understanding of the molecular mechanisms of esophageal carcinogenesis as well.

#### **2.3 SILAC-based proteomic findings of ESCC**

Quantitative proteomics is one of the hot research fields in post-genomic era, which has been used extensively in oncology to identify biomarkers with diagnostic and therapeutic potential, thereby avoiding proteins without biological importance. In traditional 2DE, quantitative information of protein spots on 2DE gels is represented by staining intensity. Although 2DE is a versatile tool for visualization of thousands of proteins, detection of posttranslational modified isoforms and targeting of protein expression alternations, it has inherent limitations, such as limited resolution of membrane or extreme pI proteins, low sensitivity and throughput, poor reproducibility, etc., which result in only part of proteome uncovered[Ong & Mann, 2005]. In this context, two classes of MS-based quantitative

262 Proteomics – Human Diseases and Protein Functions

The identified proteins with different expression between responders and non-responders were classified into two major families, cytoskeleton proteins and molecular chaperon proteins. Further validation by IHC and RT-PCR showed that weak expression of HSP27 at protein level and mRNA level were associated with non-response to platin-based chemotherapy[Langer et al., 2008]. As serum represents a rich source for biomarker discovery, proteomic spectra were examined using 27 and 12 serum samples of responders and non-responders, respectively, to preoperative chemoradiotherapy in a training set by surface-enhanced laser desorption and ionization coupled with mass spectrometry analysis. A proteomic classifier comprising four mass peaks, at 7 420, 9 112, 17 123 and 12 867 m/z was identified with 93.3% predicative accuracy in the validation set[Hayashida et al., 2005]. Since chemotherapy resistance is a complex and multi-factorial event, proteomic-based studies enable comprehensive characterization of resistance phenotype of malignant cancers, which may lead to identification of potential distinguishing biomarkers between responders and non-responders and lay foundation for further molecular mechanism

In addition of 2DE gel for proteomic studies, surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an alternative proteomic tool to profile the serum or other body fluids and define potential protein pattern with diagnostic potential. By profiling of the serum proteome with SELDI-TOF-MS combined with bioinformatics tools, a number of highly sensitive and specific potential diagnosis markers have been revealed in various types of cancers. Wang et al. used weak cation exchange (WCX2) protein chips and SELDI-TOF-MS to profile 130 symptom-free serum samples collected from high-incidence area of ESCC in northern China, Linzhou, which included 63 subjects with normal esophageal mucosa, 40 subjects with basal cell hyperplasia, 27 subjects with dysplasia and 30 ESCC patients. Biomarker pattern's software identified four protein features at m/z of 9 306.61, 13 765.9, 2 942.15 and 15 953.4, which could distinguish normal esophageal epithelium, basal cell hyperplasia, dysplasia and ESCC with satisfactory diagnostic accuracy[Wang et al., 2006]. Xinjiang is one of the high-incidence areas for ESCC and comprise different ethnic peoples including Han decent. Using CM10 protein chips to capture targets from serum, SELDI-TOF-MS and bioinformatics analysis resulted in identification of six protein peaks (m/z 5667, 5790, 5876, 5979, 6043 and 6102) with diagnostic power with sensitivity and specificity of 91.43% and 88.89%, respectively[Xu et al., 2009]. In the case of ESCC profiled by SELDI-TOF-MS, further purification and identification of discriminatory peaks is necessary for development of simple methods for wider clinical application, and to enhance our understanding of the molecular mechanisms

Quantitative proteomics is one of the hot research fields in post-genomic era, which has been used extensively in oncology to identify biomarkers with diagnostic and therapeutic potential, thereby avoiding proteins without biological importance. In traditional 2DE, quantitative information of protein spots on 2DE gels is represented by staining intensity. Although 2DE is a versatile tool for visualization of thousands of proteins, detection of posttranslational modified isoforms and targeting of protein expression alternations, it has inherent limitations, such as limited resolution of membrane or extreme pI proteins, low sensitivity and throughput, poor reproducibility, etc., which result in only part of proteome uncovered[Ong & Mann, 2005]. In this context, two classes of MS-based quantitative

studies.

of esophageal carcinogenesis as well.

**2.3 SILAC-based proteomic findings of ESCC** 


Protein name T/N ratio Functions References Prohibitin ↑or *↓* Transcription regulation [*Fu et al., 2007*; Qi et al., 2005]

calmodulin binding

[Nishimori et al., 2006]

[Nishimori et al., 2006]

[Jazii et al., 2006]

[Fu et al., 2007]

[Zhu et al., 2010]

[Zhu et al., 2010]

muscle isoform [Jazii et al., 2006; Zhu et al., 2010]

calmodulin binding [Nishimori et al., 2006]

binding [Fu et al., 2007]

activity [Liu et al., 2011]

protein [Zhu et al., 2010]

isomerase activity [Zhu et al., 2010]

endopeptidase inhibitor [Zhu et al., 2010]

Signal transduction

Cysteine-type endopeptidase inhibitor

activity

activity

Neuronal protein ↑ Neuronal growth [Qi et al., 2005]

Myosin heavy chain nonmuscle form A ↓ Actin binding or

Myosin regulatory light chain 2 <sup>↓</sup> Ventricular/cardiac

ribonucleoprotein A2/B1:B1 <sup>↑</sup> RNA binding and

Calponin 1, basic <sup>↓</sup> actin binding ;

OPTN protein <sup>↓</sup> Protein C-terminus

Cathepsin D <sup>↑</sup> Aspartyl proteinase

Transthyretin [Precursor] <sup>↑</sup> Thyroid hormone-binding

Peptidyl-prolyl cis-trans isomerase A <sup>↑</sup> Peptidyl-prolyl cis-trans

Carbonic anhydrase 1 ↓ Carbonate dehydratase

Table 1. Reported differential proteins in esophageal cancer tissues

protein1 <sup>↓</sup> Serine-type

Myosin light chain 2 ↓ Regulatory light chain of

isoform 1 ↑ Hsp90 protein binding [Nishimori et al., 2006]

Caldesmon 1 isoform 1 ↓ [Nishimori et al., 2006]

ribonucleoprotein A2/B1:A2 <sup>↑</sup> [Nishimori et al., 2006] Myosin light chain 3 <sup>↓</sup> Regulatory light chain [Zhu et al., 2010] Myosin light polypeptide 6 ↑ [Jazii et al., 2006] Myosin light chain 6B ↓ Regulatory light chain [Zhu et al., 2010] Similar to alpha-fetoprotein ↓ NA [Nishimori et al., 2006] Trnasferrin ↓ ferric iron binding [Nishimori et al., 2006] Alpha-1-antitrypsin precursor <sup>↓</sup> Proteinase inhibitor [Nishimori et al., 2006] Alpha-1-antitrypsin ↑ [Fu et al., 2007] Procollagen-proline ↓ Oxidoreductase activity [Nishimori et al., 2006]

Myosin light chain 1 ↓ [Zhu et al., 2010]

(ropB) ↑ Transcription [Jazii et al., 2006] GH16431P ↑ NA [Jazii et al., 2006]

TNF receptor associated factor 7 ↑ [Liu et al., 2011]

Chromosome1 open reading frame 8 ↑ NA [Liu et al., 2011] Cdc42 ↑ GTPase activator activity [Liu et al., 2011] LLDBP ↑ NA [Liu et al., 2011] Adenylate kinase 1 ↓ Adenylate kinase activity [Liu et al., 2011] General transcription factor IIH ↓ Transcription [Liu et al., 2011] Serpin B5 precursor <sup>↑</sup> serine proteinase inhibitor [Zhu et al., 2010] Serpin B3 ↑ [Zhu et al., 2010]

Apolipoprotein A-I [Precursor] ↑ lipid metabolism [Zhu et al., 2010]

[Precursor] ↓ Protein binding [Zhu et al., 2010]

Carbonic anhydrase 3 ↓ [Zhu et al., 2010] Creatine kinase M-type ↓ Creatine kinase activity [Zhu et al., 2010]

Stratifin ↓ [Du et al., 2007; Qi et al., 2005]

myosin

processing

Nuclear autoantigenic sperm protein

Heterogeneous nuclear

Heterogeneous nuclear

DNA directed RNA polymerase B

67 kDa laminin receptor ↑

Cystatin-B ↑

Serum amyloid P-component

Phosphatidylethanolamine-binding


264 Proteomics – Human Diseases and Protein Functions

Protein name T/N ratio Functions References Heat shock protein 27 kDa ↓ or *<sup>↑</sup>* [Du et al., 2007; Fu et al., 2007; *Liu et* 

Alpha-B-Crystalline ↓ [Qi et al., 2005; Zhu et al., 2010]

Energy metabolism

Protein binding

Protein binding

Protein degradation

Redox homeostasis

Protein degradation

Peroxiredoxin 1 ↑or *↓* [*Fu et al., 2007*; Qi et al., 2005] Peroxiredoxin 2 ↓ [Jazii et al., 2006; Qi et al., 2005]

Transgelin ↓or *<sup>↑</sup>* [Liu et al., 2011; Qi et al., 2005; Zhou

Phosphoglycerate kinase 1 ↑ [Du et al., 2007; Nishimori et al., 2006] Alpha enolase <sup>↑</sup> [Du et al., 2007; Fu et al., 2007;

protein <sup>↑</sup> [Du et al., 2007] Heat shock 70kDa protein 8 ↓ [Nishimori et al., 2006] Heat shock protein 70 kDa ↑ [Jazii et al., 2006] gp96 ↑ [Zhou et al., 2005] GRP78 ↑ [Du et al., 2007]

Fibrin beta ↓ [Liu et al., 2011]

procathepsin B ↑ NA [Du et al., 2007]

Mutant beta-actin(Q6F5I1) ↑ [Du et al., 2007]

Beat-enolase ↑ [Fu et al., 2007] Triosephosphate isomerase ↑ [Zhu et al., 2010] GAPDH ↑ [Qi et al., 2005] Aldolase A ↓ [Nishimori et al., 2006] Fructose-bisphosphate aldolase A ↓ [Zhu et al., 2010]

Translation initiation factor Eif-1A ↑ Translation [Zhou et al., 2005]

COMT protein ↑ [Liu et al., 2011]

fibrinogen fragment <sup>↑</sup> [Nishimori et al., 2006]

complex core protein2 <sup>↑</sup> [Nishimori et al., 2006] Proteosome ↑ [Liu et al., 2011]

Ubiquitin C-terminal esterase ↑ [Zhou et al., 2005]

Fatty acid-binding protein ↓ Lipid metabolism [Zhou et al., 2005] TGase ↓ Protein modification [Zhou et al., 2005] Fascin ↑ actin cross-lining [Zhou et al., 2005]

ARK family 1 ↑ Carcinogen metabolism [Zhou et al., 2005]

Proteasome subunit βtype 9 ↓ [Zhou et al., 2005] Prosomal protein p30-33k ↑ [Zhou et al., 2005] Elongation factor Tu ↑ Translation [Qi et al., 2005] (NADP) cytoplasmic ↑ NAD binding [Qi et al., 2005]

RNA binding motif protein 8A <sup>↑</sup> mRNA/nucleotide/protei

Galectin-7 <sup>↓</sup> Interactionof cells and

SCCA1 <sup>↓</sup> Cysteine proteinase

Proteinase inhibitor, Clade B <sup>↓</sup> Neutrophil elastase

GST M2 <sup>↑</sup> glutathione transferase

Similar to heat shock congnate 71-kDa

Crystal structure of huma recombinant

M2-type pyruvate kinase ↑or *↑*

Transmembrane protein 4 ↑

Early endosome antigen 1 ↓

enzyme E2 variant 1 isoform <sup>↓</sup>

Thioredoxin perosidase ↑

Proteasome subunit βtype 4 ↑

Crystal structure of recombinant human

Similar to ubiquitin -conjugating

Ubiquinol-cytochrome C reductase

*al., 2011*; Zhou et al., 2005]

[Du et al., 2007; Fu et al., 2007; Liu et

Nishimori et al., 2006; Qi et al., 2005]

al., 2011]

[Zhou et al., 2005]

[Liu et al., 2011]

[Du et al., 2007]

cell-matrix [Zhou et al., 2005; Zhu et al., 2010]

inhibitor [Qi et al., 2005; Zhou et al., 2005]

inhibitor [Zhou et al., 2005]

activity [Zhou et al., 2005]

et al., 2005; Zhu et al., 2010]

[Zhou et al., 2005; Zhu et al., 2010]

[Zhou et al., 2005]

n binding [Zhou et al., 2005]


Table 1. Reported differential proteins in esophageal cancer tissues

TPM3 HUMAN Tropomyosin alpha-3 chain 32.80/4.53 330.06 0.47 2 Actin binding TPM4 HUMAN Tropomyosin alpha-4 chain 28.50/4.52 199.64 0.37 2

LEG1 HUMAN Galectin-1 14.71/5.18 424.98 0.49 3 Signal transduction CLIC1 HUMAN Chloride channel ABP 26.91/4.94 447.94 0.63 4

[NADPH]1 30.36/9.53 467.30 0.59 2

complex II 72.65/7.31 207.55 0.5 2

Translation RSSA HUMAN 40S ribosomal protein SA 32.83/4.64 298.67 0.58 2

NPM HUMAN Nucleophosmin 32.55/4.49 444.46 0.52 2 DNA binding

binding CH10 HUMAN Hsp 10 10.92/9.44 219.29 0.40 3

isomerase 63.11/9.10 510.30 0.48 5

dehydrogenase 54.99/6.89 604.20 0.53 2

aldolase A 39.40/9.18 386.91 0.59 2

164.83/6.3

regulation CAND1\_HUMAN TBP-interacting protein 120A 136.3/5.4 617.2 1.8 15

complex subunit2 27.34/5.33 367.19 0.48 2

factor MCM7 81.3/6.1 510.8 1.97 13

**(T/N)**

<sup>8</sup>233.25 0.59 2

<sup>0</sup>3115.1 0.24 6

<sup>1</sup>546.79 0.47 2 Transcription

**Matched** 

<sup>9</sup>0.50 14 Chaperone

**peptides Functions** 

Redox homeostasis

Metabolic process

Energy metabolism

Cell cycle

**Accession no. Protein name MW/PI Scores Ratio**

K2C8 HUMAN Keratin, type II cytoskeletal 8 53.67/5.38 907.48 0.51 4 FSCN1 HUMAN Fascin 54.50/7.02 296.56 0.45 2

1433E HUMAN 14-3-3 protein epsilon 29.16/4.48 400.71 0.66 3 PRDX1 HUMAN Peroxiredoxin-1 22.10/9.22 689.77 0.55 7

PRDX2 HUMAN Peroxiredoxin-2 21.88/5.59 238.11 0.65 5 PRDX4 HUMAN Peroxiredoxin-4 30.52/5.85 367.60 0.34 2 PRDX5 HUMAN Peroxiredoxin-5 22.01/9.93 522.84 0.60 2

KCRB HUMAN Creatine kinase B-type 42.62/5.25 711.33 1.67 4

GSTP1 HUMAN Glutathione S-transferase P 23.34/5.32 1140.8 0.45 6 GDIB HUMAN Rab GDI beat 50.63/6.08 614.67 0.47 2

ACBP HUMAN Acyl-CoA-binding protein 10.04/6.16 135.03 0.64 2 PHS HUMAN PHS 2 11.99/6.33 170.64 0.43 3

IF4G1\_HUMAN eIF-4-gamma 1 175.4/5.1 650.5 2.15 14

PPIA HUMAN Peptidyl-prolyl isomerase A 18.00/9.05 770.25 0.59 9

PGK1 HUMAN Phosphoglycerate kinase 1 44.59/9.22 1020.8 0.50 6 G3P HUMAN GAPDH 36.03/9.26 1127.9 0.52 8 IPYR HUMAN Inorganic pyrophosphatase 32.64/5.47 485.51 0.45 3 ENOA HUMAN Alpha-enolase 47.14/7.71 1998.1 0.55 15 CYTB HUMAN Cystatin-B 11.13/7.85 144.98 0.43 2

CBR1 HUMAN Carbonyl reductase

DHSA HUMAN Favoprotein subunit

G6PI HUMAN Glucose-6-phosphate

ALDOA HUMAN Fructose-bisphosphate

CPSM HUMAN Carbamoyl-phosphate synthase 1

PSME2 HUMAN Proteasome activator

MCM7\_HUMAN DNA replication licensing

PHB2 HUMAN Prohibitin-2 33.28/10.2

UGDH HUMAN UDP-glucose 6-

RL27A HUMAN 60S ribosomal protein L27a 16.55/11.7

GRP78 HUMAN GRP78 72.29/4.92 1869.0

proteomics methods have been developed, which include extracted ion current (XIC)-based label-free quantification and stable isotope labeling quantification. Stable isotope labeling by amino acids in cell culture (SILAC) is an in vivo metabolic labeling method in which stable isotope-labeled amino acids (Heavy vs. Light amino acids) replace the natural amino acids of preexisting proteome[Ong & Mann, 2006]. We used SILAC medium to label immortalized cells (NE3 and NE6) with heavy stable isotope [U-13C6]-H-Lysine and [U-13C6]-H-Arginine and cancer cells (EC1, EC109, EC9706) with light stable isotope [12C6]-L-Lysine and [12C6]-L-Arginine, respectively. After complete labeling of the cellular proteome, equal quantity of proteins from immortalized cells and cancer cells were mixed and then subjected to SDS-PAGE separation, in-gel trypsin digestion and high performance liquid chromatography online with electrospray ionization-MS/MS analysis (HPLC-ESI-MS/MS). Forty-seven candidate proteins with differential expression were identified with our arbitrary criteria, which contains ratio change > 1.5 folds, ≥ 2 peptides for quantification and coefficient of variation < 50%. Then, we characterized the cellular protein expression pattern and secretome derived from cisplatin-resistant sub-cell line EC9706 and its parental sensitive cell line EC9706. By SILAC labeling and MS-based quantification, we successfully identified 74 proteins of cellular origin and 57 proteins of secretome with altered expression levels. Similar to our approach, Kashyap et al. used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells and identified 120 up-regulated proteins with >2-fold difference in the ESCC secretome[Kashyap et al., 2010]. In addition of previously known increased ESCC biomarkers, i.e. matrix metalloproteinase 1, transferrin receptor, and transforming growth factor beta-induced 68 kDa, a number of novel proteins showed distinct expression pattern, among which protein disulfide isomerase family a member 3 (PDIA3), GDP dissociation inhibitor 2 (GDI2), and lectin galactoside binding soluble 3 binding protein (LGALS3BP) were further validated by immunoblot analysis and immunohistochemical labeling using tissue microarrays. These identified proteins participate in multiple biological functions, including molecular chaperones, cytoskeletal proteins, and members of protein inhibitors family, reducing protein, etc., suggesting multiple dysregulated pathways involving in ESCC.

#### **2.4 Clinical relevance of potential protein biomarkers in ESCC**

To answer clinical questions, the protein biomarkers identified by proteomic techniques with potential diagnosis and therapeutic targets for ESCC need to be translated into clinical scenario, which is realized by using clinical samples, such as biopsy samples, resected tissue samples, plasma or serum samples, urine samples, saliva samples, etc. The methods used for validation generally comprise Western blot, IHC and ELISA at protein level, and RT-PCR at transcription level. Using 2DE- and SILAC-based quantitative proteomic approaches, we have identified a total of 78 non-redundant proteins with aberrant expression associated with ESCC, suggesting that these proteins may play functional roles in carcinogenesis of ESCC and may have clinical values. Afterwards, Western blot analysis verified the decreased expressions of three proteins, i.e. SCCA1, TPM1 and αB-Cryst in cancer, in accordance with 2DE quantitative results. At transcription level, SCCA1 mRNA was downregulated in tumor as well. More importantly, the expression of SCCA1 decreased step by step as a function of precancer lesions progression, which suggests that SCCA1 may take part in the multi-stage transformation of ESCC, even in the earliest stages[Qi et al., 2005]. In the 2DE-based comparative proteomic study using immortalized and cancer cell model, we

proteomics methods have been developed, which include extracted ion current (XIC)-based label-free quantification and stable isotope labeling quantification. Stable isotope labeling by amino acids in cell culture (SILAC) is an in vivo metabolic labeling method in which stable isotope-labeled amino acids (Heavy vs. Light amino acids) replace the natural amino acids of preexisting proteome[Ong & Mann, 2006]. We used SILAC medium to label immortalized cells (NE3 and NE6) with heavy stable isotope [U-13C6]-H-Lysine and [U-13C6]-H-Arginine and cancer cells (EC1, EC109, EC9706) with light stable isotope [12C6]-L-Lysine and [12C6]-L-Arginine, respectively. After complete labeling of the cellular proteome, equal quantity of proteins from immortalized cells and cancer cells were mixed and then subjected to SDS-PAGE separation, in-gel trypsin digestion and high performance liquid chromatography online with electrospray ionization-MS/MS analysis (HPLC-ESI-MS/MS). Forty-seven candidate proteins with differential expression were identified with our arbitrary criteria, which contains ratio change > 1.5 folds, ≥ 2 peptides for quantification and coefficient of variation < 50%. Then, we characterized the cellular protein expression pattern and secretome derived from cisplatin-resistant sub-cell line EC9706 and its parental sensitive cell line EC9706. By SILAC labeling and MS-based quantification, we successfully identified 74 proteins of cellular origin and 57 proteins of secretome with altered expression levels. Similar to our approach, Kashyap et al. used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells and identified 120 up-regulated proteins with >2-fold difference in the ESCC secretome[Kashyap et al., 2010]. In addition of previously known increased ESCC biomarkers, i.e. matrix metalloproteinase 1, transferrin receptor, and transforming growth factor beta-induced 68 kDa, a number of novel proteins showed distinct expression pattern, among which protein disulfide isomerase family a member 3 (PDIA3), GDP dissociation inhibitor 2 (GDI2), and lectin galactoside binding soluble 3 binding protein (LGALS3BP) were further validated by immunoblot analysis and immunohistochemical labeling using tissue microarrays. These identified proteins participate in multiple biological functions, including molecular chaperones, cytoskeletal proteins, and members of protein inhibitors family, reducing protein, etc., suggesting multiple dysregulated pathways involving in

**2.4 Clinical relevance of potential protein biomarkers in ESCC** 

To answer clinical questions, the protein biomarkers identified by proteomic techniques with potential diagnosis and therapeutic targets for ESCC need to be translated into clinical scenario, which is realized by using clinical samples, such as biopsy samples, resected tissue samples, plasma or serum samples, urine samples, saliva samples, etc. The methods used for validation generally comprise Western blot, IHC and ELISA at protein level, and RT-PCR at transcription level. Using 2DE- and SILAC-based quantitative proteomic approaches, we have identified a total of 78 non-redundant proteins with aberrant expression associated with ESCC, suggesting that these proteins may play functional roles in carcinogenesis of ESCC and may have clinical values. Afterwards, Western blot analysis verified the decreased expressions of three proteins, i.e. SCCA1, TPM1 and αB-Cryst in cancer, in accordance with 2DE quantitative results. At transcription level, SCCA1 mRNA was downregulated in tumor as well. More importantly, the expression of SCCA1 decreased step by step as a function of precancer lesions progression, which suggests that SCCA1 may take part in the multi-stage transformation of ESCC, even in the earliest stages[Qi et al., 2005]. In the 2DE-based comparative proteomic study using immortalized and cancer cell model, we

ESCC.


of therapeutic efficiency, prognostic evaluation, and molecular targets of developing novel therapeutic regimen as well. In addition of our proteomic results in ESCC, several other reports have looked at the clinical value of potential biomarkers, including cytokeratin 14, Annexin I, SCCA1/2, calgulanulin B and HSP 60, alpha-actinin 4 and 67 kDa laminin receptor, cathepsin D and PKM2, periplakin, calreticulin and GRP78, galectin-7, anti-CD25B antibody[Dong et al., 2010; Du et al., 2007; Fu et al., 2007; Hatakeyama et al., 2006; Liu et al., 2011; Nishimori et al., 2006; Zhu et al., 2010]. Nevertheless, further extensive studies are still necessary to determine the clinical utility of the identified proteins in tumorigenesis and

Nowadays, the dilemma for cancer control and management is not due to lack of efficient treatment options but diagnosis at late stages. In the case of ESCC in China, five-year survival rate for early stage tumor reaches around 90%[Hu et al., 2001]. Obviously, to detect tumor as early as possible is the key for reducing the mortality and morbidity of ESCC. It is believed that development of ESCC from normal esophageal epithelium takes at least about 10 years, during which diseased epithelium manifests as basal cell hyperproliferation, dysplasia, carcinoma in situ in terms of morphology and finally evolves to malignant neoplasms. As such, carcinogenesis of ESCC is a multi-stage and dynamic process which

Proteomic studies from various research groups worldwide have identified distinct dysregulated protein expression pattern associated with ESCC. The discrepancy might reflect the different etiology, different stages of disease and diverse pathways involved, which makes identification of biomarkers for ESCC difficult. In light of a wealth of potential biomarkers associated with ESCC identified so far in the exploratory phase, future largescale validation studies involving symptom-free patients with precursor lesions in highincidence area and ESCC patients compared with controls are essential toward clinical application. Therefore, ultimate translation from laboratory into bedside for ESCC biomarkers will require close collaboration and cooperation between researchers and clinicians to look into the clinical utility in diagnosis at early stage, prognosis and

This work was supported in part by National Natural Science Founding of China (No.

Abnet, C. C., Freedman, N. D., Hu, N., et al. (2010). A shared susceptibility locus in PLCE1

Banks, R. E., Dunn, M. J., Hochstrasser, D. F., et al. (2000). Proteomics: new perspectives,

at 10q23 for gastric adenocarcinoma and esophageal squamous cell carcinoma. *Nat* 

new biomedical opportunities. *Lancet,* Vol.356, No.9243, (Nov 18), pp: 1749-1756,

30700366 and No. 81072039) and Cancer Research UK (to Yi-Jun Qi).

*Genet,* Vol.42, No.9, (Sep), pp: 764-767, ISSN 1546-1718

accumulates ongoing changes at the level of both gene and protein expression.

progression of ESCC.

monitoring treatment efficiency for ESCC.

**4. Acknowledgement** 

ISSN 0140-6736

**5. References** 

**3. Conclusions** 


Table 2. Differential proteins between immortalized and cancer cell lines derived from ESCC identified by SILAC-based proteomics

selected Annexin A2 for validation by Western blot and IHC. Stepwise decrease in annexin A2 protein expression was observed when epithelial cell was transformed malignantly. In poorly-differentiated squamous carcinoma, 46% (5/11) of cancer tissue sample lost annexin A2 protein and 36% (4/11) expressed at weak intensity[Qi et al., 2007b]. In a separate study, IHC was used to determine 14-3-3σ in 60 cases of ESCC, nearby matched normal esophageal epithelium and a variety of ESCC precursor lesions. High level of 14-3-3σ expression was found ubiquitously in normal esophageal epithelium with an immuonstaining score of 8.22 in expression. Protein 14-3-3σ was down-regulated stepwise during the multi-stage development of ESCC. Sixty-four percent of poorly-differentiated squamous cancer lost 14- 3-3σ expression with a score of 0.45[Qi et al., 2007a]. In agreement with our results, Ren et al. documented that the level of 14-3-3σ in terms of mRNA and protein was markedly downregulated in ESCC compared with nearby matched non-cancer tissues. Furthermore, decrease of 14-3-3σ expression was correlated with tumor infiltration depth, lymph node metastasis, distant metastasis and lymphovascular invasion and shorter 5-year survival rate[Ren et al., 2010]. Among the different proteins identified by SILAC-based quantitative analysis using immortal cell and cancer cell model, the clinical values of MIF in tumorigenesis of ESCC was determined as well. Not only the increased expression of MIF was detected in cellular protein but also in the conditioned medium of esophageal cancer cell lines EC1, EC109 and EC9706 compared with immortal cell lines NE3 and NE6. Low frequency and very weak expression of MIF was detected predominantly in basal cells in normal esophageal epithelium, with an immunostaining score of 1.13. Pronouncedly upregulated expression of MIF occurred in severe dysplasia compared with weak immunostaining in mild and moderate dysplasia. In ESCC, high frequency of intense expression of MIF was observed with a score of 5.46. Furthermore, high expression of MIF was significantly correlated with advanced clinical stages. ELISA tests revealed that there was an increase trend in serum level of MIF in clinically advanced stage IV compared to stage I-III. Functional studies on MIF indicated that MIF knockdown resulted in decrease in proliferation, clonogenicity, non-adherent growth and invasive potential. Our findings indicate that MIF may play crucial roles in malignant transformation of pathogenesis of EC and MIF could become a potential biomarker for high-risk population screening, assessment of therapeutic efficiency, prognostic evaluation, and molecular targets of developing novel therapeutic regimen as well. In addition of our proteomic results in ESCC, several other reports have looked at the clinical value of potential biomarkers, including cytokeratin 14, Annexin I, SCCA1/2, calgulanulin B and HSP 60, alpha-actinin 4 and 67 kDa laminin receptor, cathepsin D and PKM2, periplakin, calreticulin and GRP78, galectin-7, anti-CD25B antibody[Dong et al., 2010; Du et al., 2007; Fu et al., 2007; Hatakeyama et al., 2006; Liu et al., 2011; Nishimori et al., 2006; Zhu et al., 2010]. Nevertheless, further extensive studies are still necessary to determine the clinical utility of the identified proteins in tumorigenesis and progression of ESCC.

#### **3. Conclusions**

268 Proteomics – Human Diseases and Protein Functions

ACADV HUMAN VLCAD 70.35/9.63 841.39 0.35 2 Lipid metabolism ATPA HUMAN ATP5A1 59.71/9.61 963.07 0.47 5

ATPB HUMAN ATPB-3 56.52/5.14 1704.2 0.40 5 Ion transport VDAC1 HUMAN VDAC-1 30.75/9.22 548.36 2.32 2 Anion

VPS35\_HUMAN hVPS35 91.6/5.2 602.6 1.67 12 Protein

Table 2. Differential proteins between immortalized and cancer cell lines derived from ESCC

selected Annexin A2 for validation by Western blot and IHC. Stepwise decrease in annexin A2 protein expression was observed when epithelial cell was transformed malignantly. In poorly-differentiated squamous carcinoma, 46% (5/11) of cancer tissue sample lost annexin A2 protein and 36% (4/11) expressed at weak intensity[Qi et al., 2007b]. In a separate study, IHC was used to determine 14-3-3σ in 60 cases of ESCC, nearby matched normal esophageal epithelium and a variety of ESCC precursor lesions. High level of 14-3-3σ expression was found ubiquitously in normal esophageal epithelium with an immuonstaining score of 8.22 in expression. Protein 14-3-3σ was down-regulated stepwise during the multi-stage development of ESCC. Sixty-four percent of poorly-differentiated squamous cancer lost 14- 3-3σ expression with a score of 0.45[Qi et al., 2007a]. In agreement with our results, Ren et al. documented that the level of 14-3-3σ in terms of mRNA and protein was markedly downregulated in ESCC compared with nearby matched non-cancer tissues. Furthermore, decrease of 14-3-3σ expression was correlated with tumor infiltration depth, lymph node metastasis, distant metastasis and lymphovascular invasion and shorter 5-year survival rate[Ren et al., 2010]. Among the different proteins identified by SILAC-based quantitative analysis using immortal cell and cancer cell model, the clinical values of MIF in tumorigenesis of ESCC was determined as well. Not only the increased expression of MIF was detected in cellular protein but also in the conditioned medium of esophageal cancer cell lines EC1, EC109 and EC9706 compared with immortal cell lines NE3 and NE6. Low frequency and very weak expression of MIF was detected predominantly in basal cells in normal esophageal epithelium, with an immunostaining score of 1.13. Pronouncedly upregulated expression of MIF occurred in severe dysplasia compared with weak immunostaining in mild and moderate dysplasia. In ESCC, high frequency of intense expression of MIF was observed with a score of 5.46. Furthermore, high expression of MIF was significantly correlated with advanced clinical stages. ELISA tests revealed that there was an increase trend in serum level of MIF in clinically advanced stage IV compared to stage I-III. Functional studies on MIF indicated that MIF knockdown resulted in decrease in proliferation, clonogenicity, non-adherent growth and invasive potential. Our findings indicate that MIF may play crucial roles in malignant transformation of pathogenesis of EC and MIF could become a potential biomarker for high-risk population screening, assessment

111.27/5.0

13.91/11.0

inhibitory factor 12.47/9.12 267.01 0.61 3 Cytokine

**(T/N)**

**Matched** 

<sup>2</sup>1206.8 0.56 2 ATP binding

7 330.93 0.49 2 mRNA

**peptides Functions** 

activity

transport

transport

processing

**Accession no. Protein name MW/PI Scores Ratio**

MIF HUMAN Macrophage migration

HYOU1 HUMAN Hypoxia up-regulated protein 1

identified by SILAC-based proteomics

ribonucleoprotein 3

SMD3 HUMAN Small nuclear

THIL HUMAN Acetoacetyl-CoA thiolase 45.17/9.63 330.39 0.45 2

Nowadays, the dilemma for cancer control and management is not due to lack of efficient treatment options but diagnosis at late stages. In the case of ESCC in China, five-year survival rate for early stage tumor reaches around 90%[Hu et al., 2001]. Obviously, to detect tumor as early as possible is the key for reducing the mortality and morbidity of ESCC. It is believed that development of ESCC from normal esophageal epithelium takes at least about 10 years, during which diseased epithelium manifests as basal cell hyperproliferation, dysplasia, carcinoma in situ in terms of morphology and finally evolves to malignant neoplasms. As such, carcinogenesis of ESCC is a multi-stage and dynamic process which accumulates ongoing changes at the level of both gene and protein expression.

Proteomic studies from various research groups worldwide have identified distinct dysregulated protein expression pattern associated with ESCC. The discrepancy might reflect the different etiology, different stages of disease and diverse pathways involved, which makes identification of biomarkers for ESCC difficult. In light of a wealth of potential biomarkers associated with ESCC identified so far in the exploratory phase, future largescale validation studies involving symptom-free patients with precursor lesions in highincidence area and ESCC patients compared with controls are essential toward clinical application. Therefore, ultimate translation from laboratory into bedside for ESCC biomarkers will require close collaboration and cooperation between researchers and clinicians to look into the clinical utility in diagnosis at early stage, prognosis and monitoring treatment efficiency for ESCC.

#### **4. Acknowledgement**

This work was supported in part by National Natural Science Founding of China (No. 30700366 and No. 81072039) and Cancer Research UK (to Yi-Jun Qi).

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**13** 

*Canada* 

*Université de Montréal* 

**Multidimensional Proteomics for the** 

**Identification of Endothelial Post Mortem** 

Isabelle Sirois, Alexey V. Pshezhetsky and Marie-Josée Hébert

**Signals of Importance in Vascular Remodeling** 

Atherosclerotic diseases (AD) and immune-mediated vasculopathy of the transplanted organ (referred to as transplant vasculopathy (TV)) are both characterized by vessel wall thickening and fibrotic changes that lead to progressive vascular obliteration (Al-Lamki et al., 2008; Cailhier et al., 2006; Cornell et al., 2008; Mitchell, 2009; Rahmani et al., 2006; Valantine, 2003). The endothelium, positioned at the interface of blood flow and the vessel wall, serves as a physiological barrier and sensor of environmental stress. The "response to injury hypothesis" proposed by Russell Ross in the 70's suggested that endothelial injury prompts vascular smooth muscle cell (VSMC) migration and proliferation, therefore initiating neointima formation (Ross et al., 1977; Ross and Glomset, 1976). Initially, vascular remodeling is beneficial but repeated cycles of injury, proliferation and repair lead to maladaptive remodeling and lumen narrowing. To date, *in vitro* and *in vivo* studies in animals and humans confirmed that endothelial apoptosis is a key determinant in the development of AD and TV (Rossig et al., 2001). Various immune and non-immune factors, such as cytotoxic T-cells, donor-specific antibodies, high cholesterol and hyperglycemia account for increased endothelial apoptosis (Cailhier et al., 2006). In turn, migration and accumulation of VSMC, surviving and accumulating within a hostile environment through acquisition of an anti-apoptotic phenotype, form the initial neointima. Histological and biochemical features characterizing AD and TV include 1) extracellular matrix (ECM) degradation that likely facilitate VSMC migration; 2) acquisition of a synthetic and antiapoptotic phenotype by neointimal cells (VSMC), mesenchymal stem cells (MSC) and fibroblasts associated with Bcl-xl overexpression (Gennaro et al., 2004; Hirata et al., 2000; Pollman et al., 1998) and 3) differentiation of fibroblasts into myofibroblasts of importance in fibrogenic vascular changes (Tomasek et al., 2002) (Figure 1). The molecular interplay regulating intercellular communication between apoptotic endothelial cells (EC) and

**1.2 Proteomics for studying Post Mortem Signals (PMS) exported by apoptotic EC**  Apoptotic programmed cell death is classically considered a silent process. The first clues suggesting that apoptotic endothelial cells may not "go quietly" stems from pharmacological

**1. Introduction** 

**1.1 Endothelial apoptosis and vascular remodeling** 

neointimal cells are only beginning to be unraveled.


### **Multidimensional Proteomics for the Identification of Endothelial Post Mortem Signals of Importance in Vascular Remodeling**

Isabelle Sirois, Alexey V. Pshezhetsky and Marie-Josée Hébert *Université de Montréal Canada* 

#### **1. Introduction**

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#### **1.1 Endothelial apoptosis and vascular remodeling**

Atherosclerotic diseases (AD) and immune-mediated vasculopathy of the transplanted organ (referred to as transplant vasculopathy (TV)) are both characterized by vessel wall thickening and fibrotic changes that lead to progressive vascular obliteration (Al-Lamki et al., 2008; Cailhier et al., 2006; Cornell et al., 2008; Mitchell, 2009; Rahmani et al., 2006; Valantine, 2003). The endothelium, positioned at the interface of blood flow and the vessel wall, serves as a physiological barrier and sensor of environmental stress. The "response to injury hypothesis" proposed by Russell Ross in the 70's suggested that endothelial injury prompts vascular smooth muscle cell (VSMC) migration and proliferation, therefore initiating neointima formation (Ross et al., 1977; Ross and Glomset, 1976). Initially, vascular remodeling is beneficial but repeated cycles of injury, proliferation and repair lead to maladaptive remodeling and lumen narrowing. To date, *in vitro* and *in vivo* studies in animals and humans confirmed that endothelial apoptosis is a key determinant in the development of AD and TV (Rossig et al., 2001). Various immune and non-immune factors, such as cytotoxic T-cells, donor-specific antibodies, high cholesterol and hyperglycemia account for increased endothelial apoptosis (Cailhier et al., 2006). In turn, migration and accumulation of VSMC, surviving and accumulating within a hostile environment through acquisition of an anti-apoptotic phenotype, form the initial neointima. Histological and biochemical features characterizing AD and TV include 1) extracellular matrix (ECM) degradation that likely facilitate VSMC migration; 2) acquisition of a synthetic and antiapoptotic phenotype by neointimal cells (VSMC), mesenchymal stem cells (MSC) and fibroblasts associated with Bcl-xl overexpression (Gennaro et al., 2004; Hirata et al., 2000; Pollman et al., 1998) and 3) differentiation of fibroblasts into myofibroblasts of importance in fibrogenic vascular changes (Tomasek et al., 2002) (Figure 1). The molecular interplay regulating intercellular communication between apoptotic endothelial cells (EC) and neointimal cells are only beginning to be unraveled.

#### **1.2 Proteomics for studying Post Mortem Signals (PMS) exported by apoptotic EC**

Apoptotic programmed cell death is classically considered a silent process. The first clues suggesting that apoptotic endothelial cells may not "go quietly" stems from pharmacological

Multidimensional Proteomics for the Identification of

Lauber et al., 2003; Truman et al., 2008).

intercellular communication.

stage of vascular remodeling.

Endothelial Post Mortem Signals of Importance in Vascular Remodeling 277

remodeling, suggesting a paracrine role for the apoptotic endothelium in triggering pathways of importance in neointima formation (Cailhier et al., 2006; Choy et al., 2004a; Choy et al., 2004b; Shimizu et al., 2000a; Shimizu et al., 2000b, 2002a, b). Cell biology approaches supported this contention and showed that medium conditioned by apoptotic EC regulates the survival and differentiation of major cellular constituents of the vessel wall (Cailhier et al., 2006; Laplante et al., 2005; Raymond et al., 2004; Soulez et al., 2010). Execution of the apoptotic program relies mainly on post-translational modifications, such as protein-protein interactions, protein translocation and proteolysis that will set in motion the molecular pathways regulating the various phases of apoptosis (Thiede and Rudel, 2004;Wang and Chen, 2011; Mahrus et al., 2008). The caspase family of cysteine proteases is central to the regulation of the various phases of apoptosis. Their activation in association with mitochondrial destabilization or extracellular death receptor activation leads to modifications in the architecture of intracellular organelles and fragmentation of the cytoskeleton, the ER and the nucleus (Taylor et al., 2008). Apoptosis triggers changes in the cell membrane including blebbing and extracellular exposure of PS of importance as a phagocyte recognition signal (Leroyer et al., 2008; Martinez et al., 2005; Pober and Sessa, 2007; Verhoven et al., 1995). In addition, mounting evidence suggests that the apoptotic program also regulates the extracellular export of a finely regulated set of signals of importance in leukocyte trafficking, phagocytosis and coagulation (Bournazou et al., 2009;

The complete set of mediators released by a cell at a given time, defined as a secretome, can be decrypted through high-throughput methods based on mass-spectrometry. Use of technology focusing on post-transcriptional events bears special importance in dying cells where the various levels of molecular regulation depend on protein degradation, translocation and specific protein-protein interactions rather than gene transcription. Proteomics was instrumental in characterizing the complex mixture of several secretomes composed of both soluble and vesicular mediators including microparticles and exosomes (Mathivanan and Simpson, 2009). As illustrated by the following reports, large-scale mass-spectrometry also eased the identification of paracrine signals (lipids, proteins and microparticles) specifically enriched within the secretome of apoptotic cells. For example, apoptotic Burkitt lymphoma cells release lysophosphatidylcholine (LPC) through activated caspase-3 dependent mechanisms, which in turn favors recruitment of macrophages and clearance of apoptotic bodies (Lauber et al., 2003). Apoptotic MCF7 epithelial cells secrete lactoferrin as a means of promoting migration of mononuclear leukocytes while inhibiting migration of polymorphonuclear leukocytes (Bournazou et al., 2009). Apoptotic EC shed microparticles with potent immunogenic and pro-coagulant abilities (Smalley and Ley, 2008; Smalley et al., 2007). In sum, these proteomic-based reports suggested that a paracrine response embedded within the apoptotic program and herein referred to as post mortem signals (PMS), controls a finely orchestrated network of

In the following sections, we will highlight the advantage of different proteomic strategies for characterization of PMS released by apoptotic cells. The systematic analysis of the secretome of apoptotic EC is central to gain insights into novel mechanisms of intercellular communication of importance in TV and AD. Also, the characterization of endothelial apoptotic secretome represents a unique opportunity to identify biomarkers of the initial

Fig. 1. Schematic diagram of the initiation of vascular remodeling characteristic of AD and TV. Immune and non-immune factors induce endothelial apoptosis. Endothelial apoptosis precedes neo-intima formation. The latter is accompanied by ECM degradation and proliferation and resistance to apoptosis of neo-intimal cells (VSMC, MSC, EPC, fibroblasts and myofibroblasts). Homing of MSC and EPC as well as myofibroblast differentiation contribute to fibrogenic changes observed with vascular remodeling.

or genetic approaches aimed at inhibiting endothelial apoptosis in models of AD or TV. Inhibition of endothelial apoptosis was shown to block the development of vascular

Fig. 1. Schematic diagram of the initiation of vascular remodeling characteristic of AD and TV. Immune and non-immune factors induce endothelial apoptosis. Endothelial apoptosis precedes neo-intima formation. The latter is accompanied by ECM degradation and proliferation and resistance to apoptosis of neo-intimal cells (VSMC, MSC, EPC, fibroblasts and myofibroblasts). Homing of MSC and EPC as well as myofibroblast differentiation

or genetic approaches aimed at inhibiting endothelial apoptosis in models of AD or TV. Inhibition of endothelial apoptosis was shown to block the development of vascular

contribute to fibrogenic changes observed with vascular remodeling.

remodeling, suggesting a paracrine role for the apoptotic endothelium in triggering pathways of importance in neointima formation (Cailhier et al., 2006; Choy et al., 2004a; Choy et al., 2004b; Shimizu et al., 2000a; Shimizu et al., 2000b, 2002a, b). Cell biology approaches supported this contention and showed that medium conditioned by apoptotic EC regulates the survival and differentiation of major cellular constituents of the vessel wall (Cailhier et al., 2006; Laplante et al., 2005; Raymond et al., 2004; Soulez et al., 2010). Execution of the apoptotic program relies mainly on post-translational modifications, such as protein-protein interactions, protein translocation and proteolysis that will set in motion the molecular pathways regulating the various phases of apoptosis (Thiede and Rudel, 2004;Wang and Chen, 2011; Mahrus et al., 2008). The caspase family of cysteine proteases is central to the regulation of the various phases of apoptosis. Their activation in association with mitochondrial destabilization or extracellular death receptor activation leads to modifications in the architecture of intracellular organelles and fragmentation of the cytoskeleton, the ER and the nucleus (Taylor et al., 2008). Apoptosis triggers changes in the cell membrane including blebbing and extracellular exposure of PS of importance as a phagocyte recognition signal (Leroyer et al., 2008; Martinez et al., 2005; Pober and Sessa, 2007; Verhoven et al., 1995). In addition, mounting evidence suggests that the apoptotic program also regulates the extracellular export of a finely regulated set of signals of importance in leukocyte trafficking, phagocytosis and coagulation (Bournazou et al., 2009; Lauber et al., 2003; Truman et al., 2008).

The complete set of mediators released by a cell at a given time, defined as a secretome, can be decrypted through high-throughput methods based on mass-spectrometry. Use of technology focusing on post-transcriptional events bears special importance in dying cells where the various levels of molecular regulation depend on protein degradation, translocation and specific protein-protein interactions rather than gene transcription. Proteomics was instrumental in characterizing the complex mixture of several secretomes composed of both soluble and vesicular mediators including microparticles and exosomes (Mathivanan and Simpson, 2009). As illustrated by the following reports, large-scale mass-spectrometry also eased the identification of paracrine signals (lipids, proteins and microparticles) specifically enriched within the secretome of apoptotic cells. For example, apoptotic Burkitt lymphoma cells release lysophosphatidylcholine (LPC) through activated caspase-3 dependent mechanisms, which in turn favors recruitment of macrophages and clearance of apoptotic bodies (Lauber et al., 2003). Apoptotic MCF7 epithelial cells secrete lactoferrin as a means of promoting migration of mononuclear leukocytes while inhibiting migration of polymorphonuclear leukocytes (Bournazou et al., 2009). Apoptotic EC shed microparticles with potent immunogenic and pro-coagulant abilities (Smalley and Ley, 2008; Smalley et al., 2007). In sum, these proteomic-based reports suggested that a paracrine response embedded within the apoptotic program and herein referred to as post mortem signals (PMS), controls a finely orchestrated network of intercellular communication.

In the following sections, we will highlight the advantage of different proteomic strategies for characterization of PMS released by apoptotic cells. The systematic analysis of the secretome of apoptotic EC is central to gain insights into novel mechanisms of intercellular communication of importance in TV and AD. Also, the characterization of endothelial apoptotic secretome represents a unique opportunity to identify biomarkers of the initial stage of vascular remodeling.

Multidimensional Proteomics for the Identification of

Endothelial Post Mortem Signals of Importance in Vascular Remodeling 279

Fig. 2. Schematic representation of the experimental strategy for generating serum-free media (conditioned by equal EC numbers in equal volumes of serum-free media for 4 hours) by apoptotic (SSC-Apo) and non-apoptotic EC (SSC-No-Apo). Secretomes were collected

#### **2. Studying the secretome of apoptotic EC: Methodological aspects**

#### **2.1 In vitro experimental systems aimed at studying endothelial apoptosis**

Two major pathways, the intrinsic and extrinsic pathways, regulate the initiation of apoptosis. The intrinsic pathway is activated by metabolic disturbances, such as nutrient deprivation and oxidative stress, leading to mitochondrial permeabilization, release of cytochrome C and activation of caspase-9. The extrinsic pathway is activated by death receptors that, upon ligand-mediated activation, recruit an initiator caspase (ex. caspase-8). The effector phase of apoptosis responsible for cleavage of key substrates that bring about the morphological changes of apoptosis is controlled by a common phase regulated by effector caspases (-3, -6, -7) (Taylor et al., 2008). Serum starvation (SS) is a classical inducer of the intrinsic apoptotic pathway in EC and offers several advantages for the characterization of an apoptotic secretome. First, four hours of SS in cultured EC induces sequentially mitochondrial permeabilization, activation of caspases -9 and -3, PARP cleavage and chromatin condensation characteristic of apoptotic cell death. The functional importance of caspase activation in SS-induced apoptosis was validated with caspase inhibitors (the pan-caspase inhibitor (ZVAD-FMK) and caspase-3 inhibitor (DEVD-FMK)) as well as small interfering RNA (siRNA) targeting caspase-3 (Sirois et al., 2011). Second, apoptosis induced by brief SS does not induce necrotic features and cell membrane permeabilization, as assessed by fluorescence microscopy with propidium iodide and evaluation of lactacte dehydrogenase (LDH) activity in medium conditioned by serumstarved EC (Laplante et al., 2010; Sirois et al., 2011). The absence of necrosis in this system is an asset for studying secretory events in absence of uncontrolled leakage secondary to cell membrane damage. Finally, SS circumvents contamination of the secretome by residual components of culture medium (such as albumin) that could interfere with the identification of less abundant proteins specifically released by apoptotic EC downstream of caspase activation.

#### **2.2 Identification of endothelial PMS by multidimensional proteomics**

A comparative and multidimensional proteomic analysis was undertaken to characterize the secretome of apoptotic EC (Sirois et al., 2011) (Figure 2). Proteins specifically released by apoptotic EC were identified through comparison of the secretomes generated by equal numbers of serum-starved apoptotic EC (SSC-apo) and serum-starved EC in which apoptosis was blocked by the irreversible pan-caspase inhibitor ZVAD-fmk (SSC-no-apo). Cell media were cleared of cell debris and apoptotic blebs prior to proteomic analysis (Cailhier et al., 2008; Laplante et al., 2010; Sirois et al., 2011; Soulez et al., 2010). An equivalent amount of proteins were fractionated either by SDS-PAGE or by HPLC anion exchange chromatography followed by protein identification by MS/MS (Pshezhetsky et al., 2007). The two comparative strategies were complemented by a functional approach aimed at identifying proteins with an anti-apoptotic activity on VSMC, therefore recapitulating induction of the neointimal anti-apoptotic phenotype (Raymond et al., 2004). Proteins present in SSC-apo were fractionated by ultrafiltration followed by ion-exchange FPLC. Eluted fractions were individually tested *in vitro* for their ability to inhibit apoptosis of VSMC and the fraction displaying a significant anti-apoptotic activity was further fractionated by SDS-PAGE followed by protein identification by LC-MS/MS. Computational analysis of the peptides identified by mass-spectrometry generated three lists built by the functional and the two semi-quantitative comparative approaches.

Two major pathways, the intrinsic and extrinsic pathways, regulate the initiation of apoptosis. The intrinsic pathway is activated by metabolic disturbances, such as nutrient deprivation and oxidative stress, leading to mitochondrial permeabilization, release of cytochrome C and activation of caspase-9. The extrinsic pathway is activated by death receptors that, upon ligand-mediated activation, recruit an initiator caspase (ex. caspase-8). The effector phase of apoptosis responsible for cleavage of key substrates that bring about the morphological changes of apoptosis is controlled by a common phase regulated by effector caspases (-3, -6, -7) (Taylor et al., 2008). Serum starvation (SS) is a classical inducer of the intrinsic apoptotic pathway in EC and offers several advantages for the characterization of an apoptotic secretome. First, four hours of SS in cultured EC induces sequentially mitochondrial permeabilization, activation of caspases -9 and -3, PARP cleavage and chromatin condensation characteristic of apoptotic cell death. The functional importance of caspase activation in SS-induced apoptosis was validated with caspase inhibitors (the pan-caspase inhibitor (ZVAD-FMK) and caspase-3 inhibitor (DEVD-FMK)) as well as small interfering RNA (siRNA) targeting caspase-3 (Sirois et al., 2011). Second, apoptosis induced by brief SS does not induce necrotic features and cell membrane permeabilization, as assessed by fluorescence microscopy with propidium iodide and evaluation of lactacte dehydrogenase (LDH) activity in medium conditioned by serumstarved EC (Laplante et al., 2010; Sirois et al., 2011). The absence of necrosis in this system is an asset for studying secretory events in absence of uncontrolled leakage secondary to cell membrane damage. Finally, SS circumvents contamination of the secretome by residual components of culture medium (such as albumin) that could interfere with the identification of less abundant proteins specifically released by apoptotic EC downstream

**2. Studying the secretome of apoptotic EC: Methodological aspects 2.1 In vitro experimental systems aimed at studying endothelial apoptosis** 

**2.2 Identification of endothelial PMS by multidimensional proteomics** 

A comparative and multidimensional proteomic analysis was undertaken to characterize the secretome of apoptotic EC (Sirois et al., 2011) (Figure 2). Proteins specifically released by apoptotic EC were identified through comparison of the secretomes generated by equal numbers of serum-starved apoptotic EC (SSC-apo) and serum-starved EC in which apoptosis was blocked by the irreversible pan-caspase inhibitor ZVAD-fmk (SSC-no-apo). Cell media were cleared of cell debris and apoptotic blebs prior to proteomic analysis (Cailhier et al., 2008; Laplante et al., 2010; Sirois et al., 2011; Soulez et al., 2010). An equivalent amount of proteins were fractionated either by SDS-PAGE or by HPLC anion exchange chromatography followed by protein identification by MS/MS (Pshezhetsky et al., 2007). The two comparative strategies were complemented by a functional approach aimed at identifying proteins with an anti-apoptotic activity on VSMC, therefore recapitulating induction of the neointimal anti-apoptotic phenotype (Raymond et al., 2004). Proteins present in SSC-apo were fractionated by ultrafiltration followed by ion-exchange FPLC. Eluted fractions were individually tested *in vitro* for their ability to inhibit apoptosis of VSMC and the fraction displaying a significant anti-apoptotic activity was further fractionated by SDS-PAGE followed by protein identification by LC-MS/MS. Computational analysis of the peptides identified by mass-spectrometry generated three

lists built by the functional and the two semi-quantitative comparative approaches.

of caspase activation.

Fig. 2. Schematic representation of the experimental strategy for generating serum-free media (conditioned by equal EC numbers in equal volumes of serum-free media for 4 hours) by apoptotic (SSC-Apo) and non-apoptotic EC (SSC-No-Apo). Secretomes were collected

Multidimensional Proteomics for the Identification of

Endothelial Post Mortem Signals of Importance in Vascular Remodeling 281

Abbreviations: Mem: Membranne; V.E.: endocytic pathway including endosomes, MVB and lysosomes; C: cytoplasmic; N: nuclear, Ext.: Identified in the extracellular milieu; N.D.: information non available; Mito: mitochondria; WPBs: Weibel Palade Bodies; \*\*: shedding; Sec. Gran. : Secretory granules Table 1. Specific components of the apoptotic secretome (SSC-apo) regrouping 27 mediators selected according to stringent criteria (see Figure 2 and the text). Proteins were listed according to their mode of secretion, the presence of a signal peptide and their intracellular localization. Classical type of secretion was defined as a protein containing a signal peptide with secretion mechanism described in the literature. Non-classical type of secretion was defined by the absence of a signal peptide or by reports describing their non-classical secretion.

and depleted of cell debris and apoptotic blebs prior to fractionation. Multidimensional proteomics of the secretomes was performed using one functional and two comparative approaches. SSC-apo was fractionated by FPLC and each eluted fraction was tested for its anti-apoptotic activity in serum-starved VSMC. The fraction with the most significant activity was further separated by SDS-PAGE followed by silver staining and in-gel trypsin digestion. SSC-apo and SSC-no-apo proteins were also compared and fractionated by HPLC or SDS-PAGE prior to protein identification by mass-spectrometry analysis. Identification of specific components of the SSC-apo was achieved using stringent selection criteria. To be considered a specific component of the apoptotic secretome, the protein had to meet the following criteria: protein present in SSC-apo only; protein identified by 2 out of the 3 proteomic approaches; protein of human origin. 27 proteins were identified and classified according to their mode of secretion and the presence of signal peptide, generating novel hypotheses that were further validated by cell biology methods.

#### **3. The caspase-specific endothelial secretome**

A targeted screening strategy was developed to focus on the proteins with the highest likelihood of representing caspase-specific secretome components of importance in vascular remodeling. 1300 proteins were identified by LC- MS/MS analysis, 2385 were detected by SDS-PAGE-MS/MS and 28 proteins were identified by the functional approach. To be considered a specific component of the secretome of apoptotic EC, identified proteins had to meet concomitantly the following criteria: 1) they had to be identified by at least 2 out of the 3 different MS/MS approaches, 2) they had to be found exclusively in SSC-Apo, and 3) they had to be of human origin. According to these criteria, 27 proteins were classified as specific components of endothelial apoptotic secretome (Table 1) (Sirois et al., 2011). In the following section we will describe some of the observed changes and discuss the potential function of this apoptotic secretome.

#### **3.1 Enrichment of proteins associated with non-classical modes of secretion**

Most proteins that are directed to the cell surface or the extracellular space through a conventional secretory pathway contain a signal peptide (Nickel and Rabouille, 2009). Recent evidence suggests alternative modes of secretion for leaderless proteins, i.e. proteins without a signal peptide (Schotman et al., 2008) (Nickel and Rabouille, 2009). To define the contribution of classical and non-classical secretory pathways during apoptotic cell death, the 27 specific constituents of the endothelial apoptotic secretome were classified according to the presence of a signal peptide in their primary amino acid sequence, their mode of secretion, and their intracellular distribution (Table 1). This analysis showed that 25 out of the 27 proteins appeared to be associated with non-classical modes of secretion, based on recent literature and/or the absence of a secretion signal. 13 out of the 27 proteins were previously identified as a component of exosomal nanovesicles. Reevaluation of the comparative and functional proteomic results identified ten additional exosomal proteins in SSC-apo only, whereas only two exosomal proteins were identified in SSC-no-apo (Sirois et al., 2011). Finally, 4 proteins (Table 1 group 2) were annotated as potential components of exosome-like nanovesicles in other cell types. In total, 31 proteins associated with exosomelike nanovesicles were considered to be specific components of the secretome of apoptotic EC.

A targeted screening strategy was developed to focus on the proteins with the highest likelihood of representing caspase-specific secretome components of importance in vascular remodeling. 1300 proteins were identified by LC- MS/MS analysis, 2385 were detected by SDS-PAGE-MS/MS and 28 proteins were identified by the functional approach. To be considered a specific component of the secretome of apoptotic EC, identified proteins had to meet concomitantly the following criteria: 1) they had to be identified by at least 2 out of the 3 different MS/MS approaches, 2) they had to be found exclusively in SSC-Apo, and 3) they had to be of human origin. According to these criteria, 27 proteins were classified as specific components of endothelial apoptotic secretome (Table 1) (Sirois et al., 2011). In the following section we will describe some of the observed changes and discuss the potential

**3.1 Enrichment of proteins associated with non-classical modes of secretion** 

Most proteins that are directed to the cell surface or the extracellular space through a conventional secretory pathway contain a signal peptide (Nickel and Rabouille, 2009). Recent evidence suggests alternative modes of secretion for leaderless proteins, i.e. proteins without a signal peptide (Schotman et al., 2008) (Nickel and Rabouille, 2009). To define the contribution of classical and non-classical secretory pathways during apoptotic cell death, the 27 specific constituents of the endothelial apoptotic secretome were classified according to the presence of a signal peptide in their primary amino acid sequence, their mode of secretion, and their intracellular distribution (Table 1). This analysis showed that 25 out of the 27 proteins appeared to be associated with non-classical modes of secretion, based on recent literature and/or the absence of a secretion signal. 13 out of the 27 proteins were previously identified as a component of exosomal nanovesicles. Reevaluation of the comparative and functional proteomic results identified ten additional exosomal proteins in SSC-apo only, whereas only two exosomal proteins were identified in SSC-no-apo (Sirois et al., 2011). Finally, 4 proteins (Table 1 group 2) were annotated as potential components of exosome-like nanovesicles in other cell types. In total, 31 proteins associated with exosomelike nanovesicles were considered to be specific components of the secretome of apoptotic

and depleted of cell debris and apoptotic blebs prior to fractionation. Multidimensional proteomics of the secretomes was performed using one functional and two comparative approaches. SSC-apo was fractionated by FPLC and each eluted fraction was tested for its anti-apoptotic activity in serum-starved VSMC. The fraction with the most significant activity was further separated by SDS-PAGE followed by silver staining and in-gel trypsin digestion. SSC-apo and SSC-no-apo proteins were also compared and fractionated by HPLC or SDS-PAGE prior to protein identification by mass-spectrometry analysis. Identification of specific components of the SSC-apo was achieved using stringent selection criteria. To be considered a specific component of the apoptotic secretome, the protein had to meet the following criteria: protein present in SSC-apo only; protein identified by 2 out of the 3 proteomic approaches; protein of human origin. 27 proteins were identified and classified according to their mode of secretion and the presence of signal peptide, generating novel

hypotheses that were further validated by cell biology methods.

**3. The caspase-specific endothelial secretome** 

function of this apoptotic secretome.

EC.


Abbreviations: Mem: Membranne; V.E.: endocytic pathway including endosomes, MVB and lysosomes; C: cytoplasmic; N: nuclear, Ext.: Identified in the extracellular milieu; N.D.: information non available; Mito: mitochondria; WPBs: Weibel Palade Bodies; \*\*: shedding; Sec. Gran. : Secretory granules

Table 1. Specific components of the apoptotic secretome (SSC-apo) regrouping 27 mediators selected according to stringent criteria (see Figure 2 and the text). Proteins were listed according to their mode of secretion, the presence of a signal peptide and their intracellular localization. Classical type of secretion was defined as a protein containing a signal peptide with secretion mechanism described in the literature. Non-classical type of secretion was defined by the absence of a signal peptide or by reports describing their non-classical secretion.

Multidimensional Proteomics for the Identification of

adding specificity to the secreted signals.

fibrogenic activity of medium conditioned by apoptotic EC.

**3.2.3 PMS with potential biological activity on vascular repair** 

**3.2.2 Fibrogenic PMS** 

**3.2.1 Anti-apoptotic PMS** 

Endothelial Post Mortem Signals of Importance in Vascular Remodeling 283

activity of the endothelial apoptotic secretome (Cailhier et al., 2008; Laplante et al., 2006;

The importance of ECM proteolysis in association with endothelial apoptosis was highlighted by the identification of the C-terminal perlecan fragment referred to as LG3 by MS/MS and validated by western blot analysis (Raymond et al., 2004). This fragment induces a significant anti-apoptotic activity on MSC through alpha-integrin-dependent activation of the ERK1-2 pathway leading to Bcl-xl overexpression (Soulez et al., 2010). LG3 also interacts with beta-integrins on fibroblasts to induce an anti-apoptotic response but the intermediate signaling component differs (Laplante et al., 2006). LG3–integrin interactions in fibroblasts leads to sequential activation of Src family kinases with downstream phosphatidylinositol 3-kinase (PI3K)-dependent induction of Bcl-xl (Laplante et al., 2006). In support of a functionally important role for LG3 in TV, increased LG3 urinary levels were

Raymond et al., 2004; Raymond et al., 2002; Sirois et al., 2011; Soulez et al., 2010).

reported in renal allograft recipients with chronic rejection (Goligorsky et al., 2007).

Comparative and functional proteomics of media conditioned by apoptotic and non-apoptotic EC also revealed the presence of proteases, including ADAM17, ADMTS4, SPUVE, tPA and cathepsin L of potential importance in ECM proteolysis (Cailhier et al., 2008). The extracellular export of cathepsin L, which was validated by WB analysis and functional studies, was found to occur through caspase-3 dependent pathways and to play a central role in cleavage of perlecan and generation of the bioactive LG3 anti-apoptotic fragment (Cailhier et al., 2008). Apoptotic EC export a complex array of soluble and vesicular transport-assisted mediators sharing a common anti-apoptotic activity. Interestingly, these mediators target differentially the cellular components of the vascular wall through non-redundant signaling mechanisms,

Vascular remodeling is associated with fibrogenic changes characterized by the accumulation of myofibroblasts within the vessel wall. Myofibroblasts represent a differentiated and activated subset of fibroblasts characterized by *de novo* expression of contractile stress fibers and alpha-smooth-muscle actin (α-SMA) and enhanced production of collagen I and II. The accumulation of myofibroblasts plays an important role in myointimal thickening and vascular stiffness characteristic of AD and TV. The fibrogenic mediator Connective Tissue Growth Factor (CTGF) was identified with an abundance ratio of 2.5 in medium conditioned by apoptotic EC as compared with medium conditioned by non-apoptotic EC (Laplante et al., 2010). Western blotting confirmed that caspase activation significantly increased the release of CTGF by EC during apoptosis. The central importance of CTGF in the fibrogenic response triggered by the endothelial secretome was highlighted by injecting mice sub-cutaneously with medium conditioned by apoptotic or non-apoptotic EC. A significant fibrogenic response with increased skin thickness and enhanced production of collagen I developed in mice injected with medium conditioned by apoptotic EC. Also, CTGF immunodepletion abrogated the

Besides PMS characterized and described above, other components of the secretome released by apoptotic EC are potential regulators of vascular remodeling. PLA2G2D was

Initially characterized by Rose Johnstone in the 80's, exosomes are now recognized as important intercellular carrier devices detected in most biological liquids including plasma and urine as well as in the media of cultured mammalian cells (Mathivanan et al., 2010; Pan and Johnstone, 1983; Pan et al., 1985). These nanovesicles with a diameter ranging for 50-100 nm are generated from inward budding of multivesicular bodies (MVB). Exosomes contain proteins of the MVB machinery including TSG101 and Alix, both considered classical exosome markers (Keller et al., 2006; Thery et al., 2002). Exosomes express MHC class I and II associated proteins and play important role in the innate immune system and in antigen presentation (Thery et al., 2009). They also contain different cargos including proteins, lipids, microRNAs and mRNA (Valadi et al., 2007). Their extracellular release stems from the fusion of MVB with the cell membrane but the molecular regulation of MVB exocytosis remains ill defined. A wide diversity of cell types have been shown to secrete exosomes but their protein composition appears to be cell specific and/or dependent on the metabolic state of the cell.

Guided by the proteomic results, we hypothesized that apoptotic cells release nanovesicleassociated mediators and that this process was triggered by caspase activation. This hypothesis was further validated by several biochemical techniques, cell biology approaches and electron microscopy (Sirois et al., 2011). Apoptotic nanovesicles were shown to express classical constituents of exosomes. Electron microscopy with morphometry analysis demonstrated that secreted nanovesicles are structurally and functionally distinct from apoptotic bodies and represent a novel entity of potential significance in vascular remodeling.

#### **3.1.1 Nanovesicular PMS as novel anti-apoptotic factors exported by apoptotic EC**

Translationally Controlled Tumour Protein (TCTP) was identified by both functional and comparative proteomics in SSC-apo (Table 1, group 1). TCTP is an evolutionarily conserved protein of crucial importance during development (Chen et al., 2007) and for intracellular inhibition of apoptosis (Telerman and Amson, 2009). TCTP does not contain a secretion peptide signal and its extracellular export depends on the exosomal pathway (Amzallag et al., 2004; Lespagnol et al., 2008). Using electron microscopy in association with immunogold labeling we showed that TCTP was present on the outer surface of endothelial apoptotic nanovesicles (Sirois et al., 2011). Caspase-activated apoptotic VSMC and fibroblasts also released TCTP-positive nanovesicles in association with apoptosis, suggesting that this pathway is active in various cellular components of the vessel wall. TCTP was found to play a central role in the activation of an anti-apoptotic phenotype in neointimal cells. VSMC exposed to TCTP(+) apoptotic nanovesicles mounted a robust anti-apoptotic response whereas VSMC exposed to nanovesicles generated by TCTP-silenced EC failed to develop an anti-apoptotic phenotype. Collectively these results suggest that TCTP released by apoptotic nanovesicles is a novel and central inducer of resistance to apoptosis in VSMC and a biomarker of apoptotic endothelial nanovesicles.

#### **3.2 PMS characterized as biological mediators of vascular remodeling**

We further addressed the relevance of the secretome released by apoptotic EC in vascular remodeling. Since development of AD and TV depends initially on ECM degradation and phenotypical changes within neointimal cells (i.e. anti-apoptotic and fibrogenic), the list of proteins generated by the multidimensional proteomic strategy was screened for the presence of mediators sharing these biological functions. Functional studies on EC, VSMC, MSC and fibroblasts highlighted a multifunctional and biochemically complex paracrine activity of the endothelial apoptotic secretome (Cailhier et al., 2008; Laplante et al., 2006; Raymond et al., 2004; Raymond et al., 2002; Sirois et al., 2011; Soulez et al., 2010).

#### **3.2.1 Anti-apoptotic PMS**

282 Proteomics – Human Diseases and Protein Functions

Initially characterized by Rose Johnstone in the 80's, exosomes are now recognized as important intercellular carrier devices detected in most biological liquids including plasma and urine as well as in the media of cultured mammalian cells (Mathivanan et al., 2010; Pan and Johnstone, 1983; Pan et al., 1985). These nanovesicles with a diameter ranging for 50-100 nm are generated from inward budding of multivesicular bodies (MVB). Exosomes contain proteins of the MVB machinery including TSG101 and Alix, both considered classical exosome markers (Keller et al., 2006; Thery et al., 2002). Exosomes express MHC class I and II associated proteins and play important role in the innate immune system and in antigen presentation (Thery et al., 2009). They also contain different cargos including proteins, lipids, microRNAs and mRNA (Valadi et al., 2007). Their extracellular release stems from the fusion of MVB with the cell membrane but the molecular regulation of MVB exocytosis remains ill defined. A wide diversity of cell types have been shown to secrete exosomes but their protein composition

Guided by the proteomic results, we hypothesized that apoptotic cells release nanovesicleassociated mediators and that this process was triggered by caspase activation. This hypothesis was further validated by several biochemical techniques, cell biology approaches and electron microscopy (Sirois et al., 2011). Apoptotic nanovesicles were shown to express classical constituents of exosomes. Electron microscopy with morphometry analysis demonstrated that secreted nanovesicles are structurally and functionally distinct from apoptotic bodies and represent a novel entity of potential significance in vascular remodeling.

**3.1.1 Nanovesicular PMS as novel anti-apoptotic factors exported by apoptotic EC**  Translationally Controlled Tumour Protein (TCTP) was identified by both functional and comparative proteomics in SSC-apo (Table 1, group 1). TCTP is an evolutionarily conserved protein of crucial importance during development (Chen et al., 2007) and for intracellular inhibition of apoptosis (Telerman and Amson, 2009). TCTP does not contain a secretion peptide signal and its extracellular export depends on the exosomal pathway (Amzallag et al., 2004; Lespagnol et al., 2008). Using electron microscopy in association with immunogold labeling we showed that TCTP was present on the outer surface of endothelial apoptotic nanovesicles (Sirois et al., 2011). Caspase-activated apoptotic VSMC and fibroblasts also released TCTP-positive nanovesicles in association with apoptosis, suggesting that this pathway is active in various cellular components of the vessel wall. TCTP was found to play a central role in the activation of an anti-apoptotic phenotype in neointimal cells. VSMC exposed to TCTP(+) apoptotic nanovesicles mounted a robust anti-apoptotic response whereas VSMC exposed to nanovesicles generated by TCTP-silenced EC failed to develop an anti-apoptotic phenotype. Collectively these results suggest that TCTP released by apoptotic nanovesicles is a novel and central inducer of resistance to apoptosis in VSMC and

appears to be cell specific and/or dependent on the metabolic state of the cell.

a biomarker of apoptotic endothelial nanovesicles.

**3.2 PMS characterized as biological mediators of vascular remodeling** 

We further addressed the relevance of the secretome released by apoptotic EC in vascular remodeling. Since development of AD and TV depends initially on ECM degradation and phenotypical changes within neointimal cells (i.e. anti-apoptotic and fibrogenic), the list of proteins generated by the multidimensional proteomic strategy was screened for the presence of mediators sharing these biological functions. Functional studies on EC, VSMC, MSC and fibroblasts highlighted a multifunctional and biochemically complex paracrine The importance of ECM proteolysis in association with endothelial apoptosis was highlighted by the identification of the C-terminal perlecan fragment referred to as LG3 by MS/MS and validated by western blot analysis (Raymond et al., 2004). This fragment induces a significant anti-apoptotic activity on MSC through alpha-integrin-dependent activation of the ERK1-2 pathway leading to Bcl-xl overexpression (Soulez et al., 2010). LG3 also interacts with beta-integrins on fibroblasts to induce an anti-apoptotic response but the intermediate signaling component differs (Laplante et al., 2006). LG3–integrin interactions in fibroblasts leads to sequential activation of Src family kinases with downstream phosphatidylinositol 3-kinase (PI3K)-dependent induction of Bcl-xl (Laplante et al., 2006). In support of a functionally important role for LG3 in TV, increased LG3 urinary levels were reported in renal allograft recipients with chronic rejection (Goligorsky et al., 2007).

Comparative and functional proteomics of media conditioned by apoptotic and non-apoptotic EC also revealed the presence of proteases, including ADAM17, ADMTS4, SPUVE, tPA and cathepsin L of potential importance in ECM proteolysis (Cailhier et al., 2008). The extracellular export of cathepsin L, which was validated by WB analysis and functional studies, was found to occur through caspase-3 dependent pathways and to play a central role in cleavage of perlecan and generation of the bioactive LG3 anti-apoptotic fragment (Cailhier et al., 2008). Apoptotic EC export a complex array of soluble and vesicular transport-assisted mediators sharing a common anti-apoptotic activity. Interestingly, these mediators target differentially the cellular components of the vascular wall through non-redundant signaling mechanisms, adding specificity to the secreted signals.

#### **3.2.2 Fibrogenic PMS**

Vascular remodeling is associated with fibrogenic changes characterized by the accumulation of myofibroblasts within the vessel wall. Myofibroblasts represent a differentiated and activated subset of fibroblasts characterized by *de novo* expression of contractile stress fibers and alpha-smooth-muscle actin (α-SMA) and enhanced production of collagen I and II. The accumulation of myofibroblasts plays an important role in myointimal thickening and vascular stiffness characteristic of AD and TV. The fibrogenic mediator Connective Tissue Growth Factor (CTGF) was identified with an abundance ratio of 2.5 in medium conditioned by apoptotic EC as compared with medium conditioned by non-apoptotic EC (Laplante et al., 2010). Western blotting confirmed that caspase activation significantly increased the release of CTGF by EC during apoptosis. The central importance of CTGF in the fibrogenic response triggered by the endothelial secretome was highlighted by injecting mice sub-cutaneously with medium conditioned by apoptotic or non-apoptotic EC. A significant fibrogenic response with increased skin thickness and enhanced production of collagen I developed in mice injected with medium conditioned by apoptotic EC. Also, CTGF immunodepletion abrogated the fibrogenic activity of medium conditioned by apoptotic EC.

#### **3.2.3 PMS with potential biological activity on vascular repair**

Besides PMS characterized and described above, other components of the secretome released by apoptotic EC are potential regulators of vascular remodeling. PLA2G2D was

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enriched in the secretome of apoptotic EC (Table 1, Group 1) and recent evidence suggests that it could participate in vascular remodeling. PLA2G2D belongs to a family of secreted phospholipases (sPLA2), which catalyze hydrolysis of membrane glycerophospholipids to release fatty acids and lysophospholipids (Murakami et al., 2010). PLA2G2D secreted through the exosomal pathway favors intercellular transfer of inflammatory molecules, including prostaglandins (Subra et al., 2010). Tissue plasminogen activator (tPA) was also identified in the secretome of apoptotic EC (Table 1, Group 4) (Cailhier et al., 2008). Recent studies suggest that extracellular release of tPA fosters the development of fibrogenic changes (Edgtton et al., 2004; Hu et al., 2008b; Zhang et al., 2007). Convincing evidence also suggests a predominant role for tPA in atherosclerotic diseases (Gramling and Church, 2010). In fibroblasts and myofibroblasts, tPA favors myofibroblast differentiation and induces anti-apoptotic phenotypes through phosphorylation of Bad and the inhibition of the intrinsic apoptotic pathway (Hu et al., 2008a).

#### **4. Conclusion**

Characterizing secretomes released by apoptotic cells implies inherent experimental challenges. Cell death is regulated by post-transcriptional events based on protein translocation and cleavage. Failure to take into consideration the importance of proteolysis, protein translocation and activation of non-classical secretion pathways during apoptosis will undermine the experimental strategy. The type of initiating apoptotic signal and the phase of apoptosis to be studied should also guide the design of the proteomic strategy. Creative data mining based on a combination of technical and functional criteria is necessary to gain novel insights into the modes of intercellular communication associated with cell death. The use of a multidimensional proteomics was instrumental in characterizing the importance of caspase activation as a novel regulator of non-classical modes of secretion. It allowed us to demonstrate that apoptotic cells release apoptotic nanovesicles, a novel type of membrane vesicle distinct from apoptotic bodies and reminiscent of exosomes. Mediators of importance in vascular remodeling and of potential use as biomarkers of endothelial injury, such as TCTP, LG3, CTGF, cathepsin L, EGF, PLA2GD2 and tPA were also identified. Further analysis of the complex secretome of apoptotic cells, including biochemical and functional characterization of apoptotic blebs and nanovesicles, should provide further insights into the mechanisms of intercellular communication between dying cells and the local microenvironment.

#### **5. Acknowledgment**

This work was supported by research grants from the Canadian Institutes of Health Research (CIHR) (MOP-15447 and MOP-89869) and Fonds de la recherche en santé du Québec (FRSQ) to MJH. MJH is the holder of the Shire Chair in Nephrology, Transplantation and Renal Regeneration of Université de Montréal. We thank the J.-L. Lévesque Foundation for renewed support.

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enriched in the secretome of apoptotic EC (Table 1, Group 1) and recent evidence suggests that it could participate in vascular remodeling. PLA2G2D belongs to a family of secreted phospholipases (sPLA2), which catalyze hydrolysis of membrane glycerophospholipids to release fatty acids and lysophospholipids (Murakami et al., 2010). PLA2G2D secreted through the exosomal pathway favors intercellular transfer of inflammatory molecules, including prostaglandins (Subra et al., 2010). Tissue plasminogen activator (tPA) was also identified in the secretome of apoptotic EC (Table 1, Group 4) (Cailhier et al., 2008). Recent studies suggest that extracellular release of tPA fosters the development of fibrogenic changes (Edgtton et al., 2004; Hu et al., 2008b; Zhang et al., 2007). Convincing evidence also suggests a predominant role for tPA in atherosclerotic diseases (Gramling and Church, 2010). In fibroblasts and myofibroblasts, tPA favors myofibroblast differentiation and induces anti-apoptotic phenotypes through phosphorylation of Bad and the inhibition of the

Characterizing secretomes released by apoptotic cells implies inherent experimental challenges. Cell death is regulated by post-transcriptional events based on protein translocation and cleavage. Failure to take into consideration the importance of proteolysis, protein translocation and activation of non-classical secretion pathways during apoptosis will undermine the experimental strategy. The type of initiating apoptotic signal and the phase of apoptosis to be studied should also guide the design of the proteomic strategy. Creative data mining based on a combination of technical and functional criteria is necessary to gain novel insights into the modes of intercellular communication associated with cell death. The use of a multidimensional proteomics was instrumental in characterizing the importance of caspase activation as a novel regulator of non-classical modes of secretion. It allowed us to demonstrate that apoptotic cells release apoptotic nanovesicles, a novel type of membrane vesicle distinct from apoptotic bodies and reminiscent of exosomes. Mediators of importance in vascular remodeling and of potential use as biomarkers of endothelial injury, such as TCTP, LG3, CTGF, cathepsin L, EGF, PLA2GD2 and tPA were also identified. Further analysis of the complex secretome of apoptotic cells, including biochemical and functional characterization of apoptotic blebs and nanovesicles, should provide further insights into the mechanisms of intercellular

This work was supported by research grants from the Canadian Institutes of Health Research (CIHR) (MOP-15447 and MOP-89869) and Fonds de la recherche en santé du Québec (FRSQ) to MJH. MJH is the holder of the Shire Chair in Nephrology, Transplantation and Renal Regeneration of Université de Montréal. We thank the J.-L.

Al-Lamki, R.S., Bradley, J.R., and Pober, J.S. (2008). Endothelial cells in allograft rejection.

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**4. Conclusion** 

**5. Acknowledgment** 

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**14** 

*France* 

**The Microtubule-Dissociating** 

**Tau in Neurological Disorders** 

Around 24 million of people worldwide have some kind of dementia, and most of them are diagnosed to suffer from Alzheimer's disease (AD). In fact, every seven second a new case of dementia is identified, arriving to the rate of 4,6 new million cases per year. It is expected that by 2040 over 80 million of people will be affected. Neurological diseases are therefore a major public health problem due to the rise in the aging population, only in Europe these disorders cover approximately 35% of the burden of all diseases. In economical terms, brain diseases in Europe cost a total of 386 billion of euros per year, with an average of 829€ per inhabitant. AD and other dementias represent the second-leading cause of brain disorders after affective ones and equal with addiction diseases (Wittchen and Jacobi, 2005). Altogether dementias, and in particular AD represent a huge socio-economical impact, not only regarding the cost from the pharmacological point of view but also familiar cares which increase in an alarming rate in the last stages of the disease. Worthy to mention is the role of the family during the progression of this kind of diseases, relatives have to watch the patient every moment above all during the first lapses of memory and some of them need

The incidence and prevalence of this group of diseases explain the need to understand mechanisms underlying dementia to uncover early and discriminative diagnostic markers as well as new therapeutic targets in order to improve the quality of life of these patients and the efficacy of the treatements. For these reasons research in AD is currently considered as a priority. At this time, the pharmacological treatments available aim to enhance the cognitive impairments once the disease is diagnosed, only cholinesterase inhibitors and one NMDA receptor antagonist are commercialized. Despite these products can alleviate the symptomatology, they are far away to constitute an effective remedy to cure or prevent the deleterious effect of the disease. In line of these observations, methods for improving diagnosis are needed, the search of biomarkers and neuroimaging techniques might help to support clinical diagnosis and detect the disease in the earliest stages. The identification of potential genetic and environmental risk factors as well as protective ones may provide a new window of action even if interventions at this level are more complex and controversial

psychological help to assume the situation and the change in their lifestyle.

**1. Introduction** 

(Ballard C et al., 2011).

Francisco José Fernández-Gómez,

*Jean-Pierre Aubert Reserch Center, Université de Lille Droit & Santé, Lille* 

Susanna Schraen-Maschke and Luc Buée *Inserm UMR837 - Alzheimer & Tauopathies -* 

and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation. J Biol Chem *278*, 10963-10972.


### **The Microtubule-Dissociating Tau in Neurological Disorders**

Francisco José Fernández-Gómez, Susanna Schraen-Maschke and Luc Buée *Inserm UMR837 - Alzheimer & Tauopathies - Jean-Pierre Aubert Reserch Center, Université de Lille Droit & Santé, Lille France* 

#### **1. Introduction**

290 Proteomics – Human Diseases and Protein Functions

Yu, X., Harris, S.L., and Levine, A.J. (2006). The regulation of exosome secretion: a novel

Zhang, G., Kernan, K.A., Collins, S.J., Cai, X., Lopez-Guisa, J.M., Degen, J.L., Shvil, Y., and

function of the p53 protein. Cancer Res *66*, 4795-4801.

Nephrol *18*, 846-859.

and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation. J Biol Chem *278*, 10963-10972.

Eddy, A.A. (2007). Plasmin(ogen) promotes renal interstitial fibrosis by promoting epithelial-to-mesenchymal transition: role of plasmin-activated signals. J Am Soc

> Around 24 million of people worldwide have some kind of dementia, and most of them are diagnosed to suffer from Alzheimer's disease (AD). In fact, every seven second a new case of dementia is identified, arriving to the rate of 4,6 new million cases per year. It is expected that by 2040 over 80 million of people will be affected. Neurological diseases are therefore a major public health problem due to the rise in the aging population, only in Europe these disorders cover approximately 35% of the burden of all diseases. In economical terms, brain diseases in Europe cost a total of 386 billion of euros per year, with an average of 829€ per inhabitant. AD and other dementias represent the second-leading cause of brain disorders after affective ones and equal with addiction diseases (Wittchen and Jacobi, 2005). Altogether dementias, and in particular AD represent a huge socio-economical impact, not only regarding the cost from the pharmacological point of view but also familiar cares which increase in an alarming rate in the last stages of the disease. Worthy to mention is the role of the family during the progression of this kind of diseases, relatives have to watch the patient every moment above all during the first lapses of memory and some of them need psychological help to assume the situation and the change in their lifestyle.

> The incidence and prevalence of this group of diseases explain the need to understand mechanisms underlying dementia to uncover early and discriminative diagnostic markers as well as new therapeutic targets in order to improve the quality of life of these patients and the efficacy of the treatements. For these reasons research in AD is currently considered as a priority. At this time, the pharmacological treatments available aim to enhance the cognitive impairments once the disease is diagnosed, only cholinesterase inhibitors and one NMDA receptor antagonist are commercialized. Despite these products can alleviate the symptomatology, they are far away to constitute an effective remedy to cure or prevent the deleterious effect of the disease. In line of these observations, methods for improving diagnosis are needed, the search of biomarkers and neuroimaging techniques might help to support clinical diagnosis and detect the disease in the earliest stages. The identification of potential genetic and environmental risk factors as well as protective ones may provide a new window of action even if interventions at this level are more complex and controversial (Ballard C et al., 2011).

The Microtubule-Dissociating Tau in Neurological Disorders 293

stereotyped, sequential and hierarchical pathway. The progression is categorized into ten stages according to the brain regions affected: transentorhinal cortex (S1), entorhinal (S2), hippocampus (S3), anterior temporal cortex (S4), inferior temporal cortex (S5), medium temporal cortex (S6), polymodal-association areas (prefrontal, parietal inferior and temporal superior) (S7), unimodal areas (S8), primary motor (S9a) or sensory (S9b, S9c) areas and all

Fig. 1. NFD evolution in AD and cognitive decline. Watches represent the perception of the

Despite tau proteins are heat stable, acid stable and very soluble in its native unfolded form (Cleveland DW et al., 1997), numerous methods have been used in order to dissect tau aggregates. First, PHFs in AD were initially observed by electron microscopy in 1963 (Kidd M. 1963). Then, in chronological order, Selkoe and collaborators described in 1982 a partial purification of PHFs from human brain tissue. PHFs showed a small solubility in urea, guanidine and detergents as sodium dodecyl sulphate (SDS), representing an example in neurons of a rigid intracellular polymer maybe as a consequence of covalent bonds that avoid a molecular separation by gel electrophoresis (Selkoe DJ et al., 1982). The first commonly used PHF preparation is that described by Nukina N and Ihara Y in 1985 and consists to have PHF in Sarkosyl insoluble fractions. Further purification of Sarkosyl pellets was described by Hasegawa and collaborators in 1992. Pellets were suspended in a small volume of 50 mM Tris-HCI (pH 7.6), and dissolved with 6 M guanidine HCI for further purification. The guanidine HCI suspension was centrifuged at 500,000 X g for 30 min on a TL100.3 microcentrifuge (Heckman). The supernatants were treated with iodoacetate after

objects depending on the stage of the disease.

neocortical areas (S10). Up to stage 6, the disease can be asymptomatic (Figure 1).

Despite AD covers between 60 to 80% of the causes of dementia, there are many other causes: vascular dementia, mixed dementia, dementia with Lewy bodies, Parkinson's disease, frontotemporal dementia, Creutzfeldt-Jakob disease, Huntington's disease and Wernicke-Korsakoff syndrome are some of them (http://www.alz.org). Current available diagnosis of AD is based mainly on the severity of cognitive impairments. However, even with the help of several neuroimaging techniques it is not simple to discriminate among AD and other age-related cognitive impairments. Unfortunately only an accurate diagnosis of AD can be reached after autopsy examination. Nonetheless, it is necessary and desirable to incorporate new biomarkers that are more sentitive, specific and may facilitate the diagnosis not only among the different disorders but also to discern the clinical progression (Seshadri S et al., 2011).

As it is described along this chapter the field of proteomics provides a powerful tool, which might enable to identify new proteins for early diagnostic and potentially therapeutic targets in AD. It is also remarkable the mandatory use of animal models in order to elucidate new pathways involved in the pathogenesis. Transgenic mouse models provide biochemical modulable approches where in a dependent or independent way several parameters can be studied (Sowell RA et al., 2009).

#### **2. Historical input of proteomics to Alzheimer´s disease and other neurological disorders**

AD is a progressive neurodegenerative disorder that leads to dementia. This pathology is characterized by two histopathological features: senile plaques and neurofibrillary degeneration (NFD) (Alzheimer A et al., 1907). Senile plaques are an extracellular accumulation of amyloid deposits formed by Aβ peptide. Aβ is a small 39 to 43 amino acid peptide produced by the complex catabolism of a type I transmembrane glycoprotein precursor named amyloid precursor protein (APP). Despite in AD only 1% of the cases have a familial history or inherited, most of the mutations described are related to APP, presenilin 1 (PSEN1), PSEN2 and SORL1 genes. Indeed the amyloid hypothesis of AD is considered almost like a dogma regarding the number of therapeutical research focused on this event (Hardy J and Selkoe DJ 2002). NFD has been consistently found in many neurodegenerative diseases among which the most prevalent is AD. Others include corticobasal degeneration (CBD), dementia pugilistica, fronto-temporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), head trauma, Down syndrome, postencephalic parkinsonism, progressive supranuclear palsy (PSP), myotonic dystrophy (DM) and in Pick´s disease (Buee L et al, 2000). Nonetheless, the vast majority of studies have been performed in AD.

At the molecular level NFD corresponds to the aggregation of hyper- and abnormally phophorylated Tau proteins into filaments referred to paired helical filaments (PHFs) (Brion JP et al., 1985; Ihara Y et al., 1986). The spatiotemporal distribution of NFD in the diseased human nervous system is well correlated with the clinical expression of cognitive deficits (Delacourte A et al., 1999). However, there is a long and clinically silent period during which the lesions slowly developed and progress in several brain areas and are yet clinically silent. Neuropathological studies show that NFD is already detected in locus coeruleus of some people under 30. Moreover, the entorhinal cortex of non-demented individuals aged over 50 years, and the hippocampus are also often affected. During the earliest stages of AD with cognitive functions impairment, NFD is quite specific, spreading from the hippocampal formation to the anterior, inferior, and mid temporal cortex. NFD follows a

Despite AD covers between 60 to 80% of the causes of dementia, there are many other causes: vascular dementia, mixed dementia, dementia with Lewy bodies, Parkinson's disease, frontotemporal dementia, Creutzfeldt-Jakob disease, Huntington's disease and Wernicke-Korsakoff syndrome are some of them (http://www.alz.org). Current available diagnosis of AD is based mainly on the severity of cognitive impairments. However, even with the help of several neuroimaging techniques it is not simple to discriminate among AD and other age-related cognitive impairments. Unfortunately only an accurate diagnosis of AD can be reached after autopsy examination. Nonetheless, it is necessary and desirable to incorporate new biomarkers that are more sentitive, specific and may facilitate the diagnosis not only among the different disorders but also to discern the clinical progression (Seshadri

As it is described along this chapter the field of proteomics provides a powerful tool, which might enable to identify new proteins for early diagnostic and potentially therapeutic targets in AD. It is also remarkable the mandatory use of animal models in order to elucidate new pathways involved in the pathogenesis. Transgenic mouse models provide biochemical modulable approches where in a dependent or independent way several

AD is a progressive neurodegenerative disorder that leads to dementia. This pathology is characterized by two histopathological features: senile plaques and neurofibrillary degeneration (NFD) (Alzheimer A et al., 1907). Senile plaques are an extracellular accumulation of amyloid deposits formed by Aβ peptide. Aβ is a small 39 to 43 amino acid peptide produced by the complex catabolism of a type I transmembrane glycoprotein precursor named amyloid precursor protein (APP). Despite in AD only 1% of the cases have a familial history or inherited, most of the mutations described are related to APP, presenilin 1 (PSEN1), PSEN2 and SORL1 genes. Indeed the amyloid hypothesis of AD is considered almost like a dogma regarding the number of therapeutical research focused on this event (Hardy J and Selkoe DJ 2002). NFD has been consistently found in many neurodegenerative diseases among which the most prevalent is AD. Others include corticobasal degeneration (CBD), dementia pugilistica, fronto-temporal dementia with parkinsonism linked to chromosome 17 (FTDP-17), head trauma, Down syndrome, postencephalic parkinsonism, progressive supranuclear palsy (PSP), myotonic dystrophy (DM) and in Pick´s disease (Buee

**2. Historical input of proteomics to Alzheimer´s disease and other** 

L et al, 2000). Nonetheless, the vast majority of studies have been performed in AD.

At the molecular level NFD corresponds to the aggregation of hyper- and abnormally phophorylated Tau proteins into filaments referred to paired helical filaments (PHFs) (Brion JP et al., 1985; Ihara Y et al., 1986). The spatiotemporal distribution of NFD in the diseased human nervous system is well correlated with the clinical expression of cognitive deficits (Delacourte A et al., 1999). However, there is a long and clinically silent period during which the lesions slowly developed and progress in several brain areas and are yet clinically silent. Neuropathological studies show that NFD is already detected in locus coeruleus of some people under 30. Moreover, the entorhinal cortex of non-demented individuals aged over 50 years, and the hippocampus are also often affected. During the earliest stages of AD with cognitive functions impairment, NFD is quite specific, spreading from the hippocampal formation to the anterior, inferior, and mid temporal cortex. NFD follows a

S et al., 2011).

**neurological disorders** 

parameters can be studied (Sowell RA et al., 2009).

stereotyped, sequential and hierarchical pathway. The progression is categorized into ten stages according to the brain regions affected: transentorhinal cortex (S1), entorhinal (S2), hippocampus (S3), anterior temporal cortex (S4), inferior temporal cortex (S5), medium temporal cortex (S6), polymodal-association areas (prefrontal, parietal inferior and temporal superior) (S7), unimodal areas (S8), primary motor (S9a) or sensory (S9b, S9c) areas and all neocortical areas (S10). Up to stage 6, the disease can be asymptomatic (Figure 1).

Fig. 1. NFD evolution in AD and cognitive decline. Watches represent the perception of the objects depending on the stage of the disease.

Despite tau proteins are heat stable, acid stable and very soluble in its native unfolded form (Cleveland DW et al., 1997), numerous methods have been used in order to dissect tau aggregates. First, PHFs in AD were initially observed by electron microscopy in 1963 (Kidd M. 1963). Then, in chronological order, Selkoe and collaborators described in 1982 a partial purification of PHFs from human brain tissue. PHFs showed a small solubility in urea, guanidine and detergents as sodium dodecyl sulphate (SDS), representing an example in neurons of a rigid intracellular polymer maybe as a consequence of covalent bonds that avoid a molecular separation by gel electrophoresis (Selkoe DJ et al., 1982). The first commonly used PHF preparation is that described by Nukina N and Ihara Y in 1985 and consists to have PHF in Sarkosyl insoluble fractions. Further purification of Sarkosyl pellets was described by Hasegawa and collaborators in 1992. Pellets were suspended in a small volume of 50 mM Tris-HCI (pH 7.6), and dissolved with 6 M guanidine HCI for further purification. The guanidine HCI suspension was centrifuged at 500,000 X g for 30 min on a TL100.3 microcentrifuge (Heckman). The supernatants were treated with iodoacetate after

The Microtubule-Dissociating Tau in Neurological Disorders 295

Proteomics field may be divided into two main areas: protein profiling and functional proteomics. Profiling proteomics provides all the proteins of a sample, level of expression and global profile. At a functional level proteomics afford a lot of new and challenge pathways that may be related to disease aetiology and development of the symptoms. Identification of theses pathways and protein changes in expression or post-translational modifications might lead to a novel window of therapeutical targets. A better knowledge of the evolution in these proteins during the pathological process may also increase the accuracy for an early clinical diagnosis. In that sense, the most challenging discovery would be to find characteristic biomarkers of each disease and their modifications concerning the worsening of the symptoms during the progress of the illness. The study of the human brain proteome is one of the most challenging aspects in science during the last decades. Brain functions and their involvement in process like memory, behavior, and emotions in

physiological as well as in pathological orchestration remain far from understood.

extraction protocols and the quality of the sample after autopsy.

**3.1 Identification methods** 

Independently where samples come from tissue, cells or body fluids as cerebrospinal fluid (CSF), the extraction of proteins is the *caput anguli* in all experiments. It is mandatory to establish the brain area, neuronal population or affected region, which is object of study. Moreover, thanks to the enormous protocols available for protein isolation, it is possible to achieve material enough from subcellular regions such as mitochondria or lipid rafts. Nowadays it is very useful and worldwide use the microdissection that enables to select a homogenous tissue or neuronal population, using a laser-dissecting microscope. Noteworthy that proteome analysis is not always reliable, not only because of changes in the expression profile as a consequence of genomic modifications, but also due to variability in

Proteomics analyses include two key steps, on one hand the separation and isolation of the protein to study and on the other hand the identification of proteins by mass spectrometry. In addition to separation and identification methods, there are also many well characterized technology to quantify protein as 2D differential gel electrophoresis (2D-DIGE), iTRAQ-Isobaric Tags for Relative and Absolute Quantification or SILAC-Stable Isotope Labeling by Amino Acids. Proteomics and bioinformatic developing technologies run in pararell since it is not possible to achieve hight standards in protein quantification and reliable identification if softwares do not allow discriminating among the possible variants and erasing the background that all the experimental conditions generate. Filters and integrators constitutes a general paradigm for signal detection in biology (Ideker T et al., 2011). In any case the researcher owns the most powerful weapon that is the capacity to assume the feasibility of a biological data, it means how the system is constructed and the functions carried out. Software enables to have update database easily accessible on internet including genome, transcriptome, metabolome, interactome and of course proteome (Brewis IA and Brennan P, 2010). There are several databases available for the research community dedicated to the analysis of protein sequences and structures, some of them are NCBI Peptidome, Expert Protein Analysis System (ExPASy), PeptideAtlas, the PRoteomics IDEntifications database

(PRIDE) and Global Proteome Machine Database (GPMDB) (Vizcaíno JA et al., 2010).

Mass spectrometry (MS) is one of the most widespread developed analytical technique in biological sciences. Analysis of the amino acid sequence, tridimensional structure and characterization of post-translational modifications has allowed elucidating protein functions. Despite it is not the aim of this chapter it is useful to say that MS is also used in

reduction and fractionated on a TSK gel G-3000 SW column (7.8 X 600 mm, Tosoh) equilibrated with 6 M guanidine HCI in 10 mM phosphate buffer (pH 6.0), at a flow rate of 1.0 ml/min. The TSK fractions contain full-length tau with unusually slow mobilities in SDS-PAGE. The second commonly used preparation is that of Greenberg and Davies: about 50% of PHF immunoreactivity can be obtained in 27,200 x g supernatants following homogenization in buffers containing 0.8 M NaCl. Further enrichment was made by taking advantage of PHF insolubility in the presence of zwitterionic detergents and 2 mercaptoethanol, then removal of aggregates by filtration through 0.45-microns filters, and sucrose density centrifugation. PHF-enriched fractions contained proteins of 57-68 kDa that displayed the same antigenic properties as PHFs. The next stept was to develop an amino acid sequencing technique for PHFs combining a purification and solubilization procedure. After electrophoresis the insoluble fraction presented identical amino acid composition despite successive electrophoresis. Electron microscopy confirmed no changes in PHFs structures for the insoluble fraction even after electrophoresis. Moreover, this insoluble fraction displayed immunoreactivity against purified PHFs antibodies. Almost totally solubilization for the insoluble part was achieved by increasing the time of electrophoresis till almost 35 h showing one predominant band at 66 kDa and three additional bands between 50 and 70 kDa (Vogelsang GD et al., 1990).

Further studies based on the soluble and insoluble fractions after sucrose density gradient showed tau amino-terminal epitopes were more abundant in the soluble part and almost nonexistent in the insoluble one, in the other way around carboxy-terminal epitopes were observed in both fractions. These last observations pointed out the proteolytic degradation involved tau amino-terminal region and not in the carboxy-terminal part in the formation of PHFs in NFD (Ksiezak-Reding H et al., 1994).

Apart from characterization of PHFs from the solubility point of view, the development of additional approaches as electronic microscopy has definitely contributed to elucidate their ultrastructure. For instance, scanning transmission electron microscopy (STEM) provides accurate measurements of samples purified from human tissue and allows quantitave comparison between aggregated and dispersed population (Ksiezak-Reding H et al., 2005). Information regarding the filamentous conformation contributes to uncover the phosphorylation role in their formation. PHFs display ultrastructural different characteristics in AD and other neurological disorders. One possible classification is according to the straight or twisted filaments, based on the width of them along the length. Particularly twisted filaments are more abundant in AD and straight ones in PSP and both can be easily differentiated in CBD.

Along this section it has been described the main attempts to solubilize PHFs in order to clarify their composition, structure and their role in the aetiology in neurodegenerative disorders, mainly focused on AD. It can be considered that these were the first proteomics contribution to uncover the NFD progress involved in the cognitive impairments and loss of memory. In the next section we will discuss about the more modern and current proteomics methods and their application in the field of neurodegeneration.

#### **3. Proteomic methods**

Proteomics is the study of proteome, which are the whole set of proteins expressed by a genome of a cell, tissue or organism. So the analysis of a proteome is any study directed to level expression, degradation or post-translational modifications of proteins. Proteomics methods enable the identification and composition of these proteins from diverse biological samples.

reduction and fractionated on a TSK gel G-3000 SW column (7.8 X 600 mm, Tosoh) equilibrated with 6 M guanidine HCI in 10 mM phosphate buffer (pH 6.0), at a flow rate of 1.0 ml/min. The TSK fractions contain full-length tau with unusually slow mobilities in SDS-PAGE. The second commonly used preparation is that of Greenberg and Davies: about 50% of PHF immunoreactivity can be obtained in 27,200 x g supernatants following homogenization in buffers containing 0.8 M NaCl. Further enrichment was made by taking advantage of PHF insolubility in the presence of zwitterionic detergents and 2 mercaptoethanol, then removal of aggregates by filtration through 0.45-microns filters, and sucrose density centrifugation. PHF-enriched fractions contained proteins of 57-68 kDa that displayed the same antigenic properties as PHFs. The next stept was to develop an amino acid sequencing technique for PHFs combining a purification and solubilization procedure. After electrophoresis the insoluble fraction presented identical amino acid composition despite successive electrophoresis. Electron microscopy confirmed no changes in PHFs structures for the insoluble fraction even after electrophoresis. Moreover, this insoluble fraction displayed immunoreactivity against purified PHFs antibodies. Almost totally solubilization for the insoluble part was achieved by increasing the time of electrophoresis till almost 35 h showing one predominant band at 66 kDa and three additional bands

Further studies based on the soluble and insoluble fractions after sucrose density gradient showed tau amino-terminal epitopes were more abundant in the soluble part and almost nonexistent in the insoluble one, in the other way around carboxy-terminal epitopes were observed in both fractions. These last observations pointed out the proteolytic degradation involved tau amino-terminal region and not in the carboxy-terminal part in the formation of

Apart from characterization of PHFs from the solubility point of view, the development of additional approaches as electronic microscopy has definitely contributed to elucidate their ultrastructure. For instance, scanning transmission electron microscopy (STEM) provides accurate measurements of samples purified from human tissue and allows quantitave comparison between aggregated and dispersed population (Ksiezak-Reding H et al., 2005). Information regarding the filamentous conformation contributes to uncover the phosphorylation role in their formation. PHFs display ultrastructural different characteristics in AD and other neurological disorders. One possible classification is according to the straight or twisted filaments, based on the width of them along the length. Particularly twisted filaments are more abundant in AD and straight ones in PSP and both

Along this section it has been described the main attempts to solubilize PHFs in order to clarify their composition, structure and their role in the aetiology in neurodegenerative disorders, mainly focused on AD. It can be considered that these were the first proteomics contribution to uncover the NFD progress involved in the cognitive impairments and loss of memory. In the next section we will discuss about the more modern and current proteomics

Proteomics is the study of proteome, which are the whole set of proteins expressed by a genome of a cell, tissue or organism. So the analysis of a proteome is any study directed to level expression, degradation or post-translational modifications of proteins. Proteomics methods enable the identification and composition of these proteins from diverse biological samples.

methods and their application in the field of neurodegeneration.

between 50 and 70 kDa (Vogelsang GD et al., 1990).

PHFs in NFD (Ksiezak-Reding H et al., 1994).

can be easily differentiated in CBD.

**3. Proteomic methods** 

Proteomics field may be divided into two main areas: protein profiling and functional proteomics. Profiling proteomics provides all the proteins of a sample, level of expression and global profile. At a functional level proteomics afford a lot of new and challenge pathways that may be related to disease aetiology and development of the symptoms. Identification of theses pathways and protein changes in expression or post-translational modifications might lead to a novel window of therapeutical targets. A better knowledge of the evolution in these proteins during the pathological process may also increase the accuracy for an early clinical diagnosis. In that sense, the most challenging discovery would be to find characteristic biomarkers of each disease and their modifications concerning the worsening of the symptoms during the progress of the illness. The study of the human brain proteome is one of the most challenging aspects in science during the last decades. Brain functions and their involvement in process like memory, behavior, and emotions in physiological as well as in pathological orchestration remain far from understood.

Independently where samples come from tissue, cells or body fluids as cerebrospinal fluid (CSF), the extraction of proteins is the *caput anguli* in all experiments. It is mandatory to establish the brain area, neuronal population or affected region, which is object of study. Moreover, thanks to the enormous protocols available for protein isolation, it is possible to achieve material enough from subcellular regions such as mitochondria or lipid rafts. Nowadays it is very useful and worldwide use the microdissection that enables to select a homogenous tissue or neuronal population, using a laser-dissecting microscope. Noteworthy that proteome analysis is not always reliable, not only because of changes in the expression profile as a consequence of genomic modifications, but also due to variability in extraction protocols and the quality of the sample after autopsy.

Proteomics analyses include two key steps, on one hand the separation and isolation of the protein to study and on the other hand the identification of proteins by mass spectrometry. In addition to separation and identification methods, there are also many well characterized technology to quantify protein as 2D differential gel electrophoresis (2D-DIGE), iTRAQ-Isobaric Tags for Relative and Absolute Quantification or SILAC-Stable Isotope Labeling by Amino Acids. Proteomics and bioinformatic developing technologies run in pararell since it is not possible to achieve hight standards in protein quantification and reliable identification if softwares do not allow discriminating among the possible variants and erasing the background that all the experimental conditions generate. Filters and integrators constitutes a general paradigm for signal detection in biology (Ideker T et al., 2011). In any case the researcher owns the most powerful weapon that is the capacity to assume the feasibility of a biological data, it means how the system is constructed and the functions carried out. Software enables to have update database easily accessible on internet including genome, transcriptome, metabolome, interactome and of course proteome (Brewis IA and Brennan P, 2010). There are several databases available for the research community dedicated to the analysis of protein sequences and structures, some of them are NCBI Peptidome, Expert Protein Analysis System (ExPASy), PeptideAtlas, the PRoteomics IDEntifications database (PRIDE) and Global Proteome Machine Database (GPMDB) (Vizcaíno JA et al., 2010).

#### **3.1 Identification methods**

Mass spectrometry (MS) is one of the most widespread developed analytical technique in biological sciences. Analysis of the amino acid sequence, tridimensional structure and characterization of post-translational modifications has allowed elucidating protein functions. Despite it is not the aim of this chapter it is useful to say that MS is also used in

The Microtubule-Dissociating Tau in Neurological Disorders 297

MALDI was introduced by Hillenkamp and Karas and currently is like ESI a suitable technique to the study of complex biological samples (Hillenkamp F and Karas M, 1990). MALDI produces mostly singly charged ions by a pulsed-laser irradiation. Moreover, MALDI owns a really high sensibility with almost no sample wasting and no desalting process is necessary since it works at physiological concentration of salts. In addition, MALDI requires relatively cheap equipment and quite easy to handle. MALDI TOF mass spectrometry is the technique of choice for protein identification separated by twodimensional gel electrophoresis. MALDI TOF is widely used in the study of AD in different cellular compartment as synaptosomes proteins (Yang H et al., 2011), Aβ isoforms and their effect on tau phosphorylation in transgenic mouse model overexpressing Aβ1-40 and Aβ1- 42 (Mustafiz T et al., 2011), evalutation of a vaccine specifically targeting the pathological amino-truncated species of Aβ42 that induces the production of specific antibodies against pathological Aβ products (Sergeant N et al., 2003), the possible role of heavy metal as copper (II) in the formation of PHFs (Zhou LX et al., 2007), identification of lipids containing in the PHFs from human brain as phosphatidylcholine, cholesterol, galactocerebrosides and sphingomyelin (Gellermann GP, et al 2006), identification of post-translational changes of proteins involved in AD as JNK-interacting protein 1 that is hyperphosphorylated following activation of stress-activated and MAP kinases (D'Ambrosio C et al., 2006), enrichment of more truncated glycans in PHFs (Sato Y et al., 2001) and decrease in the expression of M2

Once ions have been originated they are transported to the mass analyser region and separated according to their *m/z*. The election of one o the type of analyser will depend on their resolution, when more resolutive high capacity to defferentiate two close signals. Mass analysers available in the market are electric- and magnetic-field, depending on the way to separate the ions. The choise among them will depend on the application needed and the budget since each analyzer type has its strengths and weaknesses. Mass analysers systems are Quadrupoles, Sectors, Fourier transform cyclotrons and TOF. Quadrupole analysers are normally coupled to ESI ion sources and TOF analysers are often used with MALDI ion sources. Anyway, hybrid systems are also employed as ESI–TOF and MALDI–QTOF. TOF spectrometer separates ions based on their velocity with a theorical mass gap unlimitated. TOF consists basically of a flight tube in high vacuum where ions are accelerated with equal energies and fly along the tube with different velocities. The flight time is related to the m/z values of the ions. The combination of high m/z range and compatibility with pulsed-ionization methods has made TOF the most commonly used

In Peptide Mass Fingerprinting approach gel-separated proteins are digested in the gel with a site-specific proteinase as trypsin (Hellman U et al., 1995). Then MS measurement of the cleavaged proteins is performed generally by MALDI TOF equipment. Finally Fingerprint peptides are compared to databases in which protein sequences have been already digested with the same proteinase. This is the method of choise for highthroughput identification of numerous samples. Moreover, robotic systems launched onto the market make possible the automation from detection spot in the gel till MS identification (Henzel WJ et al., 1993). Tandem Mass Spectrometry (MS/MS) is another identification method predominantly suitable for analysing complex samples and a routine method used in research. This technique permits the identification of unkown proteins by sequencing their peptides.

acetylcholine receptor (Zuchner T et al., 2005) are some examples.

**Mass analyser** 

analyser for MALDI experiments.

DNA studies (Murray KK, 1996). MS is nowadays used in a large number of fields including from biochemistry to genome studies (Pandey A and Mann M, 2000). In combination with separation techniques, MS due to its sensitivity and speed may have an important role in identifying and monitoring biomarkers in physiological fluids as well as in drug discovery. This approach enables to identify therapeutic targets present at low concentrations in complex biological samples.

From the theorical point of view MS is not a measure of the mass, indeed it is a mass-tocharge (m/z) ratio of gas-phase ion. The values should be represented in terms of Daltons (Da) per unit of charge and the unit in the International System are Kilograms per Columb. In spite of the information obtained with this analysis is directly associated with the molecular weight and amount of protein, the results offered the possibility to acquire additional information as structural disposition (Zellner M et al., 2009).

MS are composed by three different parts: an ionization source, a mass analyser and a detector. The development of this technique is strengthly linked to the introduction of new and more sensitive components in these equipments.

#### **Ionization source**

Ionization can be defined as any process by which electrically neutral compounds are converted into ions (electrically charged atoms or molecules). Samples must be ionised and transferred to the gas phase, as a consequence of this step sample is destroyed. Classically ionization takes places in two separate steps, one in which the sample is volatilized and another one where it is ionized. The improvement in ionization methods permits to ionise large, non-volatile and thermally labile biomolecules and convert them into a gas phase without dissociation (Chait BT and Kent SB, 1992). The importance of these improvements was awarded in 2002 by the Nobel Prize in Chemistry "for the development of methods for identification and structure analyses of biological macromolecules" with one half jointly to John B. Fenn and Koichi Tanaka "for their development of soft desorption ionisation methods for mass spectrometric analyses of biological macromolecules" and the other half to Kurt Wüthrich "for his development of nuclear magnetic resonance spectroscopy for determining the three-dimensional structure of biological macromolecules in solution". Electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) are the most worldwide ionization sources used nowadays.

In ESI the ion transfer from the solution to the gas phase ocurrs at atmospheric pressure (Zellner M et al., 2009). It is a process by which an aerosol is generated between two electrodes through a capillary held at a high potential (classically 3–4 kV), ions are separated of the solvent and get into the mass analyser. This method does not present a limit of size of the molecule to ionize and it can be easily coupled to MS and liquid separation techniques. Another variation of ESI is nanospray that owns a higher ionisation efficacy and it is less sensible to salt contamination. ESI might be the technique of choice for the design and development of quenchers against α,β-unsaturated aldehydes that are strongly associated with the oxidative stress (Beretta G et al., 2008), it has been used for instance to identify a human T-cell activation RhoGTPase-activating protein in a high frequency electromagnetic field irradiation model to induce AD features (Chang IF and Hsiao HY, 2005), to identify phosphorylation sites on tau (Reynolds CH et al., 2008) and analysis of phospholipids in CSF of AD patients (Kosicek M et al., 2010).

MALDI is maybe the most common ionization source used at the present time in proteomics era. Above all because it can be easily coupled to time-of-flight (TOF) mass analysers. MALDI was introduced by Hillenkamp and Karas and currently is like ESI a suitable technique to the study of complex biological samples (Hillenkamp F and Karas M, 1990). MALDI produces mostly singly charged ions by a pulsed-laser irradiation. Moreover, MALDI owns a really high sensibility with almost no sample wasting and no desalting process is necessary since it works at physiological concentration of salts. In addition, MALDI requires relatively cheap equipment and quite easy to handle. MALDI TOF mass spectrometry is the technique of choice for protein identification separated by twodimensional gel electrophoresis. MALDI TOF is widely used in the study of AD in different cellular compartment as synaptosomes proteins (Yang H et al., 2011), Aβ isoforms and their effect on tau phosphorylation in transgenic mouse model overexpressing Aβ1-40 and Aβ1- 42 (Mustafiz T et al., 2011), evalutation of a vaccine specifically targeting the pathological amino-truncated species of Aβ42 that induces the production of specific antibodies against pathological Aβ products (Sergeant N et al., 2003), the possible role of heavy metal as copper (II) in the formation of PHFs (Zhou LX et al., 2007), identification of lipids containing in the PHFs from human brain as phosphatidylcholine, cholesterol, galactocerebrosides and sphingomyelin (Gellermann GP, et al 2006), identification of post-translational changes of proteins involved in AD as JNK-interacting protein 1 that is hyperphosphorylated following activation of stress-activated and MAP kinases (D'Ambrosio C et al., 2006), enrichment of more truncated glycans in PHFs (Sato Y et al., 2001) and decrease in the expression of M2 acetylcholine receptor (Zuchner T et al., 2005) are some examples.

#### **Mass analyser**

296 Proteomics – Human Diseases and Protein Functions

DNA studies (Murray KK, 1996). MS is nowadays used in a large number of fields including from biochemistry to genome studies (Pandey A and Mann M, 2000). In combination with separation techniques, MS due to its sensitivity and speed may have an important role in identifying and monitoring biomarkers in physiological fluids as well as in drug discovery. This approach enables to identify therapeutic targets present at low concentrations in

From the theorical point of view MS is not a measure of the mass, indeed it is a mass-tocharge (m/z) ratio of gas-phase ion. The values should be represented in terms of Daltons (Da) per unit of charge and the unit in the International System are Kilograms per Columb. In spite of the information obtained with this analysis is directly associated with the molecular weight and amount of protein, the results offered the possibility to acquire

MS are composed by three different parts: an ionization source, a mass analyser and a detector. The development of this technique is strengthly linked to the introduction of new

Ionization can be defined as any process by which electrically neutral compounds are converted into ions (electrically charged atoms or molecules). Samples must be ionised and transferred to the gas phase, as a consequence of this step sample is destroyed. Classically ionization takes places in two separate steps, one in which the sample is volatilized and another one where it is ionized. The improvement in ionization methods permits to ionise large, non-volatile and thermally labile biomolecules and convert them into a gas phase without dissociation (Chait BT and Kent SB, 1992). The importance of these improvements was awarded in 2002 by the Nobel Prize in Chemistry "for the development of methods for identification and structure analyses of biological macromolecules" with one half jointly to John B. Fenn and Koichi Tanaka "for their development of soft desorption ionisation methods for mass spectrometric analyses of biological macromolecules" and the other half to Kurt Wüthrich "for his development of nuclear magnetic resonance spectroscopy for determining the three-dimensional structure of biological macromolecules in solution". Electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) are

In ESI the ion transfer from the solution to the gas phase ocurrs at atmospheric pressure (Zellner M et al., 2009). It is a process by which an aerosol is generated between two electrodes through a capillary held at a high potential (classically 3–4 kV), ions are separated of the solvent and get into the mass analyser. This method does not present a limit of size of the molecule to ionize and it can be easily coupled to MS and liquid separation techniques. Another variation of ESI is nanospray that owns a higher ionisation efficacy and it is less sensible to salt contamination. ESI might be the technique of choice for the design and development of quenchers against α,β-unsaturated aldehydes that are strongly associated with the oxidative stress (Beretta G et al., 2008), it has been used for instance to identify a human T-cell activation RhoGTPase-activating protein in a high frequency electromagnetic field irradiation model to induce AD features (Chang IF and Hsiao HY, 2005), to identify phosphorylation sites on tau (Reynolds CH et al., 2008) and analysis of phospholipids in

MALDI is maybe the most common ionization source used at the present time in proteomics era. Above all because it can be easily coupled to time-of-flight (TOF) mass analysers.

additional information as structural disposition (Zellner M et al., 2009).

and more sensitive components in these equipments.

the most worldwide ionization sources used nowadays.

CSF of AD patients (Kosicek M et al., 2010).

complex biological samples.

**Ionization source** 

Once ions have been originated they are transported to the mass analyser region and separated according to their *m/z*. The election of one o the type of analyser will depend on their resolution, when more resolutive high capacity to defferentiate two close signals. Mass analysers available in the market are electric- and magnetic-field, depending on the way to separate the ions. The choise among them will depend on the application needed and the budget since each analyzer type has its strengths and weaknesses. Mass analysers systems are Quadrupoles, Sectors, Fourier transform cyclotrons and TOF. Quadrupole analysers are normally coupled to ESI ion sources and TOF analysers are often used with MALDI ion sources. Anyway, hybrid systems are also employed as ESI–TOF and MALDI–QTOF.

TOF spectrometer separates ions based on their velocity with a theorical mass gap unlimitated. TOF consists basically of a flight tube in high vacuum where ions are accelerated with equal energies and fly along the tube with different velocities. The flight time is related to the m/z values of the ions. The combination of high m/z range and compatibility with pulsed-ionization methods has made TOF the most commonly used analyser for MALDI experiments.

In Peptide Mass Fingerprinting approach gel-separated proteins are digested in the gel with a site-specific proteinase as trypsin (Hellman U et al., 1995). Then MS measurement of the cleavaged proteins is performed generally by MALDI TOF equipment. Finally Fingerprint peptides are compared to databases in which protein sequences have been already digested with the same proteinase. This is the method of choise for highthroughput identification of numerous samples. Moreover, robotic systems launched onto the market make possible the automation from detection spot in the gel till MS identification (Henzel WJ et al., 1993).

Tandem Mass Spectrometry (MS/MS) is another identification method predominantly suitable for analysing complex samples and a routine method used in research. This technique permits the identification of unkown proteins by sequencing their peptides.

The Microtubule-Dissociating Tau in Neurological Disorders 299

Two-dimensional gel electrophoresis (2D) is one of the most often-used separation methods in proteomics since first description by O´Farrell PH in 1975. This approach combines two electrophoretic methods: in the first dimension proteins are separated on an immobilized pH gradient strip with isoelectric focusing and migrate to the point on the strip at which their net charge is zero or pI, and in the second dimension or SDS-PAGE, proteins are separated according to their MW and thus isolating isoforms and isovariants of a certain

This approache provides two kinds of information depending on the aim of the study. On one hand it can offer the global proteome profile with a high resolution containing nearly one thousand protein spots. However, the main limitation of the 2D is that several replicates of the same gel should be performed in order to reach statistically differences. The lack of a loading control makes complicated to rule out between differences in protein expression and loading variability among gels (Molloy MP et al., 2003). In addition, absence of an internal control for loading makes this approche very hand variable. On the other hand, this method is quite indicated if qualitative analysis is pointed out, ie if post-translational modifications are searched, the performance of a 2D western blot for two different conditions may supply changes in pI and /or MW. More specifically in the case of the tau protein, this method might give interesting data about the acidification or alkalinization as a consequence of phosphorylation process, which is the most common post-translational modification. For instance in figure 2 is shown 2D western blots for human total tau and phospho dependent AD2 antibodies in AD brain sample. Remarkably in the acidic part of the membrane it can be observed the characteristic triplet of phosphorylated tau (2A) in AD (60,64,69 kDa), while in the basic region all the tau isovariants dephosphorylated with postmorten delay are revealed (2B). Interestingly, in a recent study of our group it has been shown that the use of 2D may provide evidence that tau mutations dysregulate tau phosphorylation status. This event could

Fig. 2. 2D profiles of phospho-tau (A) and total tau (B) antibodies. Number 1 represents the hyperphosphorylated isovariants of tau while number 2 shows the low phosphorylated ones. Number 3 displays the native form of tau (Fernandez-Gomez FJ et al., personal

**3.2.2 Quantitative proteomics by Two-Dimensional Differential Gel Electrophoresis** 

2D-DIGE method is based on the same principle as "classical" 2D. The main differences rely on the fact that proteins are labeled with fluorescent dyes and all the samples are separated at

**3.2.1 Two-dimensional gel electrophoresis in AD brain** 

be one of the first steps in the NFD cascade (Bretteville A et al., 2009).

protein.

unpublished data).

**(2D-DIGE)** 

MS/MS involved two steps of MS. In the first analyser ions with a desired m/z are separated (product ions) from the rest of the ions coming from the ionization source, and in the second type of analyser the mass spectrum is measured. Furthermore, MS/MS experiments improve the ratio signal/noise facilitating the resolution.

The product ions can be used to find out the primary structure of the peptide but nowadays most efforts are directed towards identification of post-translational modifications. In the case of tau protein is particulary special, since phosphorylation provides an additional negative charge to the sample. This fact complicates the analysis by MS because of detection of phosphopeptides is highly dependant on the equipment used as well as the software applied to analyze the spectra. Moreover, the existence of several adjacent serine or threonine residues allows MS/MS not to attribute the exact position of a phophate group as a result of the fragmentation of the peptide data.

The team of Hasegawa performed the earliest application for identification of Tau into PHFs. They used different fractions: purified PHF-tau, AD-soluble tau, or normal tau treated or not with alkaline phosphatase. The digests were applied to a Superspher Select B column (2.1 X 125 mm, Merck) and eluted with a linear gradient of 4-48% acetonitrile in 0.1% trifluoroacetic acid in 20 min at a flow rate of 0.2 ml/min. Amino Acid Sequence and Mass Spectrometric Analyses of the API Peptides-Fractionated peptides were sequenced on an Applied Biosystems 477A Protein Sequencer equipped with an on-line 120A PTH Analyzer or on an Applied Biosystems 473A Protein Sequencer. Mass spectral analysis was performed on a PE-SCIEX API 111 Hiomolecular Mass Analyzer (triple-stage quadrupole mass spectrometer) equipped with a standard atmospheric pressure ion source. Detailed comparison of peptide maps of PHF-tau and normal tau before and after dephosphorylation pointed to three anomalously eluted peaks which contained abnormally phosphorylated peptides, residues 191-225,226-240,260-267, and 386-438, according to the numbering of the longest tau isoform. Protein sequence and mass spectrometric analyses localized Thr-231 and ser-235 as the abnormal phosphorylation sites and further indicated that each tau 1 site (residues 191-225) and the most carboxyl-terminal portion of the protein (residues 386-438) carries more than two abnormal phosphates. Ser-262 was also phosphorylated in a fraction of PHF-tau. Modifications other than phosphorylation, removal of the initiator methionine, and Nu-acetylation at the amino terminus and deamidation at 2 asparaginyl residues were found in PHF-tau, but these modifications were also present in normal tau (Hasegawa et al., 1992).

NMR spectroscopy is an alternative to MS and it has been used to uncover physiological and pathological roles of tau protein. However, this is challenging since tau protein has 441 amino acids and an unfavorable amino acid composition. Quantification of phosphorylated tau samples is complex and studies are being performed in vitro using recombinant kisases (Landrieu I et al., 2010).

#### **3.2 Separation methods**

Analysis of a sample is always a challenge, it depends on the origin and of the aim of the experiment. Separation of the components of a sample offers the possiblility to establish a pre-selection and to perform a study concerning parameters as molecular weight (MW) and isoelectric point (pI). The separation methods available today have the enormous advantage that they can be coupled to other quantification techniques, including in this way not only the identification of the protein of interest, but also it relative amount compared to the control conditions. During this section we will converse abouth two separation approaches such as bidimensional electrophoresis and liquid chromatography.

MS/MS involved two steps of MS. In the first analyser ions with a desired m/z are separated (product ions) from the rest of the ions coming from the ionization source, and in the second type of analyser the mass spectrum is measured. Furthermore, MS/MS

The product ions can be used to find out the primary structure of the peptide but nowadays most efforts are directed towards identification of post-translational modifications. In the case of tau protein is particulary special, since phosphorylation provides an additional negative charge to the sample. This fact complicates the analysis by MS because of detection of phosphopeptides is highly dependant on the equipment used as well as the software applied to analyze the spectra. Moreover, the existence of several adjacent serine or threonine residues allows MS/MS not to attribute the exact position of a phophate group as

The team of Hasegawa performed the earliest application for identification of Tau into PHFs. They used different fractions: purified PHF-tau, AD-soluble tau, or normal tau treated or not with alkaline phosphatase. The digests were applied to a Superspher Select B column (2.1 X 125 mm, Merck) and eluted with a linear gradient of 4-48% acetonitrile in 0.1% trifluoroacetic acid in 20 min at a flow rate of 0.2 ml/min. Amino Acid Sequence and Mass Spectrometric Analyses of the API Peptides-Fractionated peptides were sequenced on an Applied Biosystems 477A Protein Sequencer equipped with an on-line 120A PTH Analyzer or on an Applied Biosystems 473A Protein Sequencer. Mass spectral analysis was performed on a PE-SCIEX API 111 Hiomolecular Mass Analyzer (triple-stage quadrupole mass spectrometer) equipped with a standard atmospheric pressure ion source. Detailed comparison of peptide maps of PHF-tau and normal tau before and after dephosphorylation pointed to three anomalously eluted peaks which contained abnormally phosphorylated peptides, residues 191-225,226-240,260-267, and 386-438, according to the numbering of the longest tau isoform. Protein sequence and mass spectrometric analyses localized Thr-231 and ser-235 as the abnormal phosphorylation sites and further indicated that each tau 1 site (residues 191-225) and the most carboxyl-terminal portion of the protein (residues 386-438) carries more than two abnormal phosphates. Ser-262 was also phosphorylated in a fraction of PHF-tau. Modifications other than phosphorylation, removal of the initiator methionine, and Nu-acetylation at the amino terminus and deamidation at 2 asparaginyl residues were found in PHF-tau, but these

NMR spectroscopy is an alternative to MS and it has been used to uncover physiological and pathological roles of tau protein. However, this is challenging since tau protein has 441 amino acids and an unfavorable amino acid composition. Quantification of phosphorylated tau samples is complex and studies are being performed in vitro using recombinant kisases

Analysis of a sample is always a challenge, it depends on the origin and of the aim of the experiment. Separation of the components of a sample offers the possiblility to establish a pre-selection and to perform a study concerning parameters as molecular weight (MW) and isoelectric point (pI). The separation methods available today have the enormous advantage that they can be coupled to other quantification techniques, including in this way not only the identification of the protein of interest, but also it relative amount compared to the control conditions. During this section we will converse abouth two separation approaches

experiments improve the ratio signal/noise facilitating the resolution.

modifications were also present in normal tau (Hasegawa et al., 1992).

such as bidimensional electrophoresis and liquid chromatography.

(Landrieu I et al., 2010).

**3.2 Separation methods** 

a result of the fragmentation of the peptide data.

#### **3.2.1 Two-dimensional gel electrophoresis in AD brain**

Two-dimensional gel electrophoresis (2D) is one of the most often-used separation methods in proteomics since first description by O´Farrell PH in 1975. This approach combines two electrophoretic methods: in the first dimension proteins are separated on an immobilized pH gradient strip with isoelectric focusing and migrate to the point on the strip at which their net charge is zero or pI, and in the second dimension or SDS-PAGE, proteins are separated according to their MW and thus isolating isoforms and isovariants of a certain protein.

This approache provides two kinds of information depending on the aim of the study. On one hand it can offer the global proteome profile with a high resolution containing nearly one thousand protein spots. However, the main limitation of the 2D is that several replicates of the same gel should be performed in order to reach statistically differences. The lack of a loading control makes complicated to rule out between differences in protein expression and loading variability among gels (Molloy MP et al., 2003). In addition, absence of an internal control for loading makes this approche very hand variable. On the other hand, this method is quite indicated if qualitative analysis is pointed out, ie if post-translational modifications are searched, the performance of a 2D western blot for two different conditions may supply changes in pI and /or MW. More specifically in the case of the tau protein, this method might give interesting data about the acidification or alkalinization as a consequence of phosphorylation process, which is the most common post-translational modification. For instance in figure 2 is shown 2D western blots for human total tau and phospho dependent AD2 antibodies in AD brain sample. Remarkably in the acidic part of the membrane it can be observed the characteristic triplet of phosphorylated tau (2A) in AD (60,64,69 kDa), while in the basic region all the tau isovariants dephosphorylated with postmorten delay are revealed (2B). Interestingly, in a recent study of our group it has been shown that the use of 2D may provide evidence that tau mutations dysregulate tau phosphorylation status. This event could be one of the first steps in the NFD cascade (Bretteville A et al., 2009).

Fig. 2. 2D profiles of phospho-tau (A) and total tau (B) antibodies. Number 1 represents the hyperphosphorylated isovariants of tau while number 2 shows the low phosphorylated ones. Number 3 displays the native form of tau (Fernandez-Gomez FJ et al., personal unpublished data).

#### **3.2.2 Quantitative proteomics by Two-Dimensional Differential Gel Electrophoresis (2D-DIGE)**

2D-DIGE method is based on the same principle as "classical" 2D. The main differences rely on the fact that proteins are labeled with fluorescent dyes and all the samples are separated at

The Microtubule-Dissociating Tau in Neurological Disorders 301

constitute the IS and Cy5 is the sample object of study. In this technique saturation dyes have a maleimide reactive group, which is designed to form a covalent bond with the thiol group of cysteine residues on proteins via a thioether linkage, and a high dye-to-protein labeling ratio is required. This type of labeling approach tries to label all available cysteines on every protein. This method has the main inconvenient that only one sample can be loaded in a gel apart from the IS and not two sample like in the minimal labelling. The big adventage is that cyanines offer great sensitivity with detection over 5 orders of magnitude

The main limitation inherent to 2D method is that the gap of separation is among pH 3-10. As a consequence of this, poor solubilisation of highly acidic and basic proteins is reached. Proteins strongly attached to the biological membranes and samples with high concentration of salt own difficulty to be separated by isoelectric focusing, for this reason it is strong recommended to perform a purification step previous to the first dimension. 2D-DIGE application accomplishes one of the new perpectives in the medical research. This approach is been widely used for many studys in neurodegenerative disorders including AD. 2D-DIGE has been utilized in the search for biomarkers in CSF in amyotrophic lateral sclerosis (Brettschneider J et al., 2008), in Creutzfeldt-Jakob disease (Brechlin P et al., 2008) in AD patients (Maarouf CL et al., 2009), in frontal cortex brain samples of AD (Müller T et al.,

**3.2.3 Quantitative proteomics by Liquid Chromatography linked to Mass Spectrometry** 

Liquid chromatography (LC) consist in separating proteins eluted from a LC column after the peptides are enzimatically digested, then they can be measured by MS. LC separation takes place when the sample components interact to a different extent with a mobile or stationary phase and elute at different times from this system. Normally several chromatographic systems are used in order to achieve a high resolution separation since only one system may not separate the complex mixture of peptides successfully. Then eluted fractions are undertaken to MS. The biggest adventage of LC coupled with MS is that this system presents a high-speed identification of the sample in an automatically way avoiding interindividual variability (Zellner et al., 2009). LC-MS is not a quantitative method *per se*, the peptide products coming from the proteolytic cleavage may alter the intensity of the signal in MS analysis due to their physicochemical characteristics. In order to discard this problem the use of stable isotopes has had a wide acceptance in the science community to achive accurancy in the quantification. The approach is based on the idea that a stable isotope-labeled peptide is chemically identical to its native counterpart and behaves identically during fractionation, digestion, chromatographic and MS analysis, but is distinguishable in a MS due to the mass diference. The ratio of signal intensities for the labeled and unlabeled peptide pairs provides an accurate measure of relative abundance of peptides from different samples. Stable isotopic tags can be introduced onto selective sites on peptides via metabolically, chemically, enzymatically, or provided by adding synthetic peptide standards to the sample. Strategies for isotope-based quantitative proteomics can be divided into two groups, depending on whether the isotopic tag is incorporated *in vitro* during sample preparation (iTRAQ, ICAT) or *in vivo* (SILAC) (Colucci-D´Amato et al., 2011). Isotope Coded Affinity Tagging (ICAT) reagents consist of an affinity biotin tag for selective purification, a linker that incorporates stable isotopes and an iodoacetamide group that specifically reacts with free thiol of cysteines. Proteins from two different samples are

(Shaw J et al., 2003).

2008) and in animal models.

**(LC-MS)** 

the same time in the same gel reducing spot pattern variability and the number of gels in an experiment. The reduction in number of gels during the manipulation increases the cost effectiveness and accurate spot matching. 2D-DIGE presents also the advantage that it is a quantitative approche since each protein spot has its own internal standard (IS), which ensure that the differences found are real and not due to a gel-to gel variation. Moreover, 2D-DIGE is a very sensitive technique with a detection threshold of around 1 femtomole of protein (Gong L et al., 2004). In the minimal labeling proteins are stained by cyanines, these dyes has a Nhydroxysuccinimidyl ester reactive group which forms a covalent bond with the epsilon amino group of the lysine in proteins via an amide connection. The single positive charge of the cyanine replaces the single positive charge of the lysine and the pI of the protein is not altered. This labeling reaction is minimal since only affects between 1-3% of the lysine residues. Using different cyanines dyes as Cy2, Cy3 and Cy5 covalently coupled to one protein sample each, then they can be mixed and loaded in the same gel (Viswanathan S et al., 2006) as it is shown in figure 3. A pool of all the samples is labeled with Cy2 and in this way the loading variability among gel is reduced to about 7% (Tannu NS et al., 2006). Differences will be observed after measurement of the intensity of the fluorescence for each cyanine. The 2D analysis software using the IS achieves a fast detection of less than 10% of differences between samples with more than 95% of statistical confidence (Gharbi S et al., 2002).

Fig. 3. Cy2, Cy3 and Cy5 merged (A) Cy2 labels IS (B) Cy3 pool of control (C) and Cy5 pool of AD samples (D). The software overlaps Cy2, Cy3 and Cy5 in order to establish the statistical differences among the replicates of the gels for each spot (Fernandez-Gomez FJ et al., personal unpublished data).

Despite the fact that it is far less used, there is in the market another 2D-DIGE method called saturation labeling where only two cyanines are used. Cy3 is the pool of samples and it

the same time in the same gel reducing spot pattern variability and the number of gels in an experiment. The reduction in number of gels during the manipulation increases the cost effectiveness and accurate spot matching. 2D-DIGE presents also the advantage that it is a quantitative approche since each protein spot has its own internal standard (IS), which ensure that the differences found are real and not due to a gel-to gel variation. Moreover, 2D-DIGE is a very sensitive technique with a detection threshold of around 1 femtomole of protein (Gong L et al., 2004). In the minimal labeling proteins are stained by cyanines, these dyes has a Nhydroxysuccinimidyl ester reactive group which forms a covalent bond with the epsilon amino group of the lysine in proteins via an amide connection. The single positive charge of the cyanine replaces the single positive charge of the lysine and the pI of the protein is not altered. This labeling reaction is minimal since only affects between 1-3% of the lysine residues. Using different cyanines dyes as Cy2, Cy3 and Cy5 covalently coupled to one protein sample each, then they can be mixed and loaded in the same gel (Viswanathan S et al., 2006) as it is shown in figure 3. A pool of all the samples is labeled with Cy2 and in this way the loading variability among gel is reduced to about 7% (Tannu NS et al., 2006). Differences will be observed after measurement of the intensity of the fluorescence for each cyanine. The 2D analysis software using the IS achieves a fast detection of less than 10% of differences between

samples with more than 95% of statistical confidence (Gharbi S et al., 2002).

Fig. 3. Cy2, Cy3 and Cy5 merged (A) Cy2 labels IS (B) Cy3 pool of control (C) and Cy5 pool of AD samples (D). The software overlaps Cy2, Cy3 and Cy5 in order to establish the statistical differences among the replicates of the gels for each spot (Fernandez-Gomez FJ et

Despite the fact that it is far less used, there is in the market another 2D-DIGE method called saturation labeling where only two cyanines are used. Cy3 is the pool of samples and it

al., personal unpublished data).

constitute the IS and Cy5 is the sample object of study. In this technique saturation dyes have a maleimide reactive group, which is designed to form a covalent bond with the thiol group of cysteine residues on proteins via a thioether linkage, and a high dye-to-protein labeling ratio is required. This type of labeling approach tries to label all available cysteines on every protein. This method has the main inconvenient that only one sample can be loaded in a gel apart from the IS and not two sample like in the minimal labelling. The big adventage is that cyanines offer great sensitivity with detection over 5 orders of magnitude (Shaw J et al., 2003).

The main limitation inherent to 2D method is that the gap of separation is among pH 3-10. As a consequence of this, poor solubilisation of highly acidic and basic proteins is reached. Proteins strongly attached to the biological membranes and samples with high concentration of salt own difficulty to be separated by isoelectric focusing, for this reason it is strong recommended to perform a purification step previous to the first dimension.

2D-DIGE application accomplishes one of the new perpectives in the medical research. This approach is been widely used for many studys in neurodegenerative disorders including AD. 2D-DIGE has been utilized in the search for biomarkers in CSF in amyotrophic lateral sclerosis (Brettschneider J et al., 2008), in Creutzfeldt-Jakob disease (Brechlin P et al., 2008) in AD patients (Maarouf CL et al., 2009), in frontal cortex brain samples of AD (Müller T et al., 2008) and in animal models.

#### **3.2.3 Quantitative proteomics by Liquid Chromatography linked to Mass Spectrometry (LC-MS)**

Liquid chromatography (LC) consist in separating proteins eluted from a LC column after the peptides are enzimatically digested, then they can be measured by MS. LC separation takes place when the sample components interact to a different extent with a mobile or stationary phase and elute at different times from this system. Normally several chromatographic systems are used in order to achieve a high resolution separation since only one system may not separate the complex mixture of peptides successfully. Then eluted fractions are undertaken to MS. The biggest adventage of LC coupled with MS is that this system presents a high-speed identification of the sample in an automatically way avoiding interindividual variability (Zellner et al., 2009). LC-MS is not a quantitative method *per se*, the peptide products coming from the proteolytic cleavage may alter the intensity of the signal in MS analysis due to their physicochemical characteristics. In order to discard this problem the use of stable isotopes has had a wide acceptance in the science community to achive accurancy in the quantification. The approach is based on the idea that a stable isotope-labeled peptide is chemically identical to its native counterpart and behaves identically during fractionation, digestion, chromatographic and MS analysis, but is distinguishable in a MS due to the mass diference. The ratio of signal intensities for the labeled and unlabeled peptide pairs provides an accurate measure of relative abundance of peptides from different samples. Stable isotopic tags can be introduced onto selective sites on peptides via metabolically, chemically, enzymatically, or provided by adding synthetic peptide standards to the sample. Strategies for isotope-based quantitative proteomics can be divided into two groups, depending on whether the isotopic tag is incorporated *in vitro* during sample preparation (iTRAQ, ICAT) or *in vivo* (SILAC) (Colucci-D´Amato et al., 2011). Isotope Coded Affinity Tagging (ICAT) reagents consist of an affinity biotin tag for selective purification, a linker that incorporates stable isotopes and an iodoacetamide group that specifically reacts with free thiol of cysteines. Proteins from two different samples are

The Microtubule-Dissociating Tau in Neurological Disorders 303

as cancer cells, CSF and tissue lysates. A few microliters of a sample of interest are deposited on the chromatographic surface. The protein chip arrays are incubated and then washed with a suitable buffer. SELDI protein chip surfaces are uniquely designed to retain proteins from complex mixtures according to their specific properties using chromatographic-based selectivity. The proteins of interest are captured on the chromatographic surface by adsorption, partition, electrostatic interaction or affinity chromatography depending on their properties, and analyzed by MS. SELDI is frequently coupled to MALDI-TOF and possess the significative advantage that minimal amount of sample is consuming and

The main application of this technique is in the search of biomarker in cancer as well as in neurodegenerative disorders. In the field of AD, SELDI has been used to find significantly higher levels of amyloid-beta peptides monomer and dimer in the blood of AD subjects compare to controls (Villemagne VL et al., 2010) and in CSF the enrichment in Aβ10-40 paralleled by depletion of the fragment Aβ1-42 seems to be a common event in familial AD

Classification and characterization of neurodegenerative disorders have been one of the biggest achievements in proteomic field. Proteomics enable to separate, identify and study protein-protein interactions within the different pathologies. Nowadays the term tauopathies includes more than twenty well-characterized diseases. The high resolution separations of tau proteins in electrophoretic profiles as well as the immunoreactivity with a wide range of antibodies provide substantial information to discriminate among the different diseases. Major post-translational modification in tau proteins is phophorylation. For this reason vast of studies are focused on the role of this modification in the structure, function, pI and signalling pathways of tau proteins during the progression of the diseases.

Tau (tubulin associated unit) is the major component of PHFs. Weingarten MD et al. described this protein for the first time in 1975 as an essential factor for the organization, stabilization, and dynamics of microtubules (Weingarten MD et al., 1975). Tau is essentially a neuronal phosphoprotein located within the axonal compartment (Butler M and Shelanski ML, 1986). Tau is prone to modulate the axonal transport and neuronal plasticity (Sergeant N et al., 2005). Recently, it has been established that tau regulates the motility of dynein and kinesin motors proteins by an isoform-dependent mechanism. Indeed, the shortest tau isoform lacking exon 2, 3 and 10 impedes the motility of both kinesin and dynein whereas the longest tau isoforms with all exons less affects motor protein motility (Dixit R et al., 2008). Therefore, a modified pattern of tau isoform expression/ratio, due to tau aggregation for instance, may profoundly affect the axonal transport and could possibly lead to neurodegeneration (Crosby AH, 2003). Besides its known role as a microtubule-stabilizer and organizer, tau may exert several other functions as signalling pathway in neurons (Ittner LM et al., 2010 and Leugers CJ, 2010) and

A unique human tau (MAPT) gene is located on chromosome 17 at the band position 17q21. The restriction analysis and sequencing of the gene shows that it contains two CpG islands, one associated with the promoter region and the other with the exon 9 (Andriadis A et al., 1992). The human tau primary transcript contains 16 exons and in the adult human brain,

**4. Contribution of proteomics to Tauopathies classification** 

DNA protection under stress stimuli (Sultan A et al., 2011).

consequently not destroyed.

(Ghidoni R et al., 2009).

**4.1 Tau proteins** 

labeled with either light or heavy ICAT reagents obtaining a distinctive mass (eight or nine Da). To minimise the error, the labeled mixture of protein samples are combined, digested with protease to peptides and fractionated by multidimensional chromatography and analysed by LC-MS. The ratios of signal intensities of differentially mass-tagged peptide pairs are quantified to determine the relative levels of proteins in the two samples. An interesting application of this technique is for the redox proteomic since ICAT labels cysteine residues (Sethuraman M et al., 2004). However, this method is not suitable for quantifying proteins that do not contain enough residues of cysteine and it presents the limitation that only two samples can be done at once (Shiio Y and Aebersold R, R 2006). For this reason this approach is limited for studying of post-translational modifications and splice isoforms.

Another amino group-based isotope labeling approach is isobaric tagging for relative and absolute protein quantification (iTRAQ). Unlike ICAT this method allows identification and quantification as well as comparison of up to eight conditions at the same time. This strategy has been developed in order to overcome the limitations of the previous one, so this method targets the peptide N-terminus of the residues (Ross PL et al., 2004). The iTRAQ reagent consists of a reporter group that is a tag with a specific mass in each individual reagent and a balance group to ensure that the reporter and balanced groups remain invariant without changing the mass. After collision-induced dissociation reporter ions spectra is correlating with the protein-sequence database and relative quantification of proteins with high accuracy is reached (Gevaert K et al., 2008).

Stable Isotope Labeling by Amino Acids (SILAC) is a metabolic stable isotope labeling during cell growth and division in bacteria and afterwards was adapted to amino acids in cell cultures (Ong SE et al., 2002). SILAC is a simple procedure in which natural variants of essential amino acids are replaced by deuterated, carbon-13 or more currently by nitrogen-15. Using nitrogen-15 the number of incorporate labels is defined and not dependent of the number of carbons that constitute the peptide sequence, this facilitates the analysis of the results. The advantage of this method relies on it accurate quantification since stable isotopes are incorporated very early in the sample. The main inconvenient of this technique is that isotopes can only be incorporated during protein synthesis. This is a huge limitation for the study of CSF and human brain tissue taking into account that neurons are postmitotic cells (Bantscheff M et al., 2007). Despite this handicap SILAC is a powerful tool to study cellular pathways as polyubiquitin involment in the aetiology of AD (Dammer EB et al., 2011), neuroinflamation (McGeer EG and McGeer PL, 2010), reactive microglia (Klegeris A et al., 2008), neurotrophin signaling (Zhang G et al., 2011), oxidative stress (Akude E et al., 2011), TDP-43 proteinopathy in frontotemporal lobar degeneration and amyotrophic lateral sclerosis (Seyfried NT et al., 2010) mitochondrial alterations in dopaminergic cells (Jin J et al., 2007) and modulation of ion channels by phosphorylation (Park KS et al., 2006).

Other methods for protein quantification are multiple reaction monitoring (MRM) that has been successfully used for low abundant proteins in plasma (Anderson L and Hunter CL, 2006) and phosphopeptides quantification (Lange V et al., 2008). The absolute quantification of proteins (AQUA) technology uses a known quantity of heavy isotope labeled peptides as IS added as soon as possible in the analytical process (Kettenbach AN et al., 2011).

#### **3.2.4 Surface-enhanced laser desorption/ionization mass spectrometry**

Surface-enhanced laser desorption/ionization mass spectrometry (SELDI) method combines retention chromatography with MS detection, and it can be used in biological samples such as cancer cells, CSF and tissue lysates. A few microliters of a sample of interest are deposited on the chromatographic surface. The protein chip arrays are incubated and then washed with a suitable buffer. SELDI protein chip surfaces are uniquely designed to retain proteins from complex mixtures according to their specific properties using chromatographic-based selectivity. The proteins of interest are captured on the chromatographic surface by adsorption, partition, electrostatic interaction or affinity chromatography depending on their properties, and analyzed by MS. SELDI is frequently coupled to MALDI-TOF and possess the significative advantage that minimal amount of sample is consuming and consequently not destroyed.

The main application of this technique is in the search of biomarker in cancer as well as in neurodegenerative disorders. In the field of AD, SELDI has been used to find significantly higher levels of amyloid-beta peptides monomer and dimer in the blood of AD subjects compare to controls (Villemagne VL et al., 2010) and in CSF the enrichment in Aβ10-40 paralleled by depletion of the fragment Aβ1-42 seems to be a common event in familial AD (Ghidoni R et al., 2009).

#### **4. Contribution of proteomics to Tauopathies classification**

Classification and characterization of neurodegenerative disorders have been one of the biggest achievements in proteomic field. Proteomics enable to separate, identify and study protein-protein interactions within the different pathologies. Nowadays the term tauopathies includes more than twenty well-characterized diseases. The high resolution separations of tau proteins in electrophoretic profiles as well as the immunoreactivity with a wide range of antibodies provide substantial information to discriminate among the different diseases. Major post-translational modification in tau proteins is phophorylation. For this reason vast of studies are focused on the role of this modification in the structure, function, pI and signalling pathways of tau proteins during the progression of the diseases.

#### **4.1 Tau proteins**

302 Proteomics – Human Diseases and Protein Functions

labeled with either light or heavy ICAT reagents obtaining a distinctive mass (eight or nine Da). To minimise the error, the labeled mixture of protein samples are combined, digested with protease to peptides and fractionated by multidimensional chromatography and analysed by LC-MS. The ratios of signal intensities of differentially mass-tagged peptide pairs are quantified to determine the relative levels of proteins in the two samples. An interesting application of this technique is for the redox proteomic since ICAT labels cysteine residues (Sethuraman M et al., 2004). However, this method is not suitable for quantifying proteins that do not contain enough residues of cysteine and it presents the limitation that only two samples can be done at once (Shiio Y and Aebersold R, R 2006). For this reason this approach is limited for studying of post-translational modifications and

Another amino group-based isotope labeling approach is isobaric tagging for relative and absolute protein quantification (iTRAQ). Unlike ICAT this method allows identification and quantification as well as comparison of up to eight conditions at the same time. This strategy has been developed in order to overcome the limitations of the previous one, so this method targets the peptide N-terminus of the residues (Ross PL et al., 2004). The iTRAQ reagent consists of a reporter group that is a tag with a specific mass in each individual reagent and a balance group to ensure that the reporter and balanced groups remain invariant without changing the mass. After collision-induced dissociation reporter ions spectra is correlating with the protein-sequence database and relative quantification of proteins with high

Stable Isotope Labeling by Amino Acids (SILAC) is a metabolic stable isotope labeling during cell growth and division in bacteria and afterwards was adapted to amino acids in cell cultures (Ong SE et al., 2002). SILAC is a simple procedure in which natural variants of essential amino acids are replaced by deuterated, carbon-13 or more currently by nitrogen-15. Using nitrogen-15 the number of incorporate labels is defined and not dependent of the number of carbons that constitute the peptide sequence, this facilitates the analysis of the results. The advantage of this method relies on it accurate quantification since stable isotopes are incorporated very early in the sample. The main inconvenient of this technique is that isotopes can only be incorporated during protein synthesis. This is a huge limitation for the study of CSF and human brain tissue taking into account that neurons are postmitotic cells (Bantscheff M et al., 2007). Despite this handicap SILAC is a powerful tool to study cellular pathways as polyubiquitin involment in the aetiology of AD (Dammer EB et al., 2011), neuroinflamation (McGeer EG and McGeer PL, 2010), reactive microglia (Klegeris A et al., 2008), neurotrophin signaling (Zhang G et al., 2011), oxidative stress (Akude E et al., 2011), TDP-43 proteinopathy in frontotemporal lobar degeneration and amyotrophic lateral sclerosis (Seyfried NT et al., 2010) mitochondrial alterations in dopaminergic cells (Jin J et

al., 2007) and modulation of ion channels by phosphorylation (Park KS et al., 2006).

IS added as soon as possible in the analytical process (Kettenbach AN et al., 2011).

**3.2.4 Surface-enhanced laser desorption/ionization mass spectrometry** 

Other methods for protein quantification are multiple reaction monitoring (MRM) that has been successfully used for low abundant proteins in plasma (Anderson L and Hunter CL, 2006) and phosphopeptides quantification (Lange V et al., 2008). The absolute quantification of proteins (AQUA) technology uses a known quantity of heavy isotope labeled peptides as

Surface-enhanced laser desorption/ionization mass spectrometry (SELDI) method combines retention chromatography with MS detection, and it can be used in biological samples such

splice isoforms.

accuracy is reached (Gevaert K et al., 2008).

Tau (tubulin associated unit) is the major component of PHFs. Weingarten MD et al. described this protein for the first time in 1975 as an essential factor for the organization, stabilization, and dynamics of microtubules (Weingarten MD et al., 1975). Tau is essentially a neuronal phosphoprotein located within the axonal compartment (Butler M and Shelanski ML, 1986). Tau is prone to modulate the axonal transport and neuronal plasticity (Sergeant N et al., 2005). Recently, it has been established that tau regulates the motility of dynein and kinesin motors proteins by an isoform-dependent mechanism. Indeed, the shortest tau isoform lacking exon 2, 3 and 10 impedes the motility of both kinesin and dynein whereas the longest tau isoforms with all exons less affects motor protein motility (Dixit R et al., 2008). Therefore, a modified pattern of tau isoform expression/ratio, due to tau aggregation for instance, may profoundly affect the axonal transport and could possibly lead to neurodegeneration (Crosby AH, 2003). Besides its known role as a microtubule-stabilizer and organizer, tau may exert several other functions as signalling pathway in neurons (Ittner LM et al., 2010 and Leugers CJ, 2010) and DNA protection under stress stimuli (Sultan A et al., 2011).

A unique human tau (MAPT) gene is located on chromosome 17 at the band position 17q21. The restriction analysis and sequencing of the gene shows that it contains two CpG islands, one associated with the promoter region and the other with the exon 9 (Andriadis A et al., 1992). The human tau primary transcript contains 16 exons and in the adult human brain,

The Microtubule-Dissociating Tau in Neurological Disorders 305

The amino-terminal region together with the proline-rich domain is referred to as the "projection domain". This unstructured and negatively charged region detaches from the surface microtubules (Hirokawa N et al., 1988) and can interact with the plasma membrane or cytoskeletal proteins (Brandt R et al., 1995). Tau may therefore contribute to spacing in between microtubule lattice and to the parallel ordered organization of microtubules in axons (Chen J et al., 1992). Amino-terminal region of tau also interacts with a growing panel of polypeptides including motor proteins such as kinesin-1 (Utton MA et al., 2005) and dynactin/dynein complex (Magnani E et al., 2007). All interacting polypeptides constitute the interactome of tau and indicate the functions in which tau may be implicated. The application of 2D gel electrophoresis method has been used to study tau (Janke C et al., 1996). The six main isoforms of tau are separated as several isovariants with isoelectric points comprised between 9.5 and 6.5 due to the alternative splicing and to posttranslational modifications. The amino-terminal region has a pI of 3.8, proline domain has a pI of 11.4 and carboxy-terminal has a pI of 10.8. Regarding to the primary structure, the polypeptide sequences encoded by exons 2/3 add to tau acidity, whereas exon 10 encodes a positively charged sequence that adds to the basic character of tau. Thus tau is rather a dipole with two domains with opposite charge modulated either by post-translational

Tau stabilizes oligomers of tubulins, it is partially folded while interacting with microtubules and it was shown to link laterally protofilaments made of tubulin (Santarella RA et al., 2004). NMR investigations showed that residues between Val226 to Glu372 are binding to microtubule surface involving the all four repeat binding motifs showing that amino- and carboxy-terminal domains do not participate in the binding properties of tau to microtubules (Sillen A et al., 2007). Tau mutations like in FTDP may impair the binding of tau to microtubules (Delobel P et al., 2002). Regarding the physic-chemical properties of tau protein it has been addressed that tau protein owns pro-aggregative motifs called PHF6 and PHF6\* in its carboxi-terminal region at the level of R2 and R3. The amino acids sequence of these motifs (306)VQIVYK(311) and (275)VQIINK(280) are prone to promote aggregation by the formation of beta-structure (von Bergen M et al., 2001). This aggregation and accumulation of misfolded proteins might have a common cause and pathological pathway in several neurodegenerative disorders resulting in neuronal loss (Tyedmers J et al., 2010). Several studies have revealed that truncated tau drive NFD *in vivo* (Zilka N et al., 2006) and caspase activation lead to tangles formation (de Calignon et

Phosphorylation of tau is instrumental to NFD and it is the main post-translational modification in tau isovariants as it was shown by 2D immunoblots (Butler M and Shelanski ML, 1986). These data shed light to the impact of tau protein for tau biology. There are 85 potential phosphorylation sites on the longest brain tau isoform. Phosphorylation sites were identified with proteomic approaches as MS, NMR, phospho-peptide mapping and the use of site-specific phosphorylation dependent tau antibodies (Hanger et al., 2007). Among them around 71 correspond to putative phosphorylation sites in physiological and pathological conditions. It is worthy to remark that most of the phosphorylation sites surround the microtubule-binding domains in the proline-rich region and carboxi-terminal region of tau. Phosphorylation regulates several functions of tau such as its binding to microtubules, the axonal transport of tau as well as its interactions with amino-terminal partners' particularly

modifications or tau proteolysis (Wischik CM et al., 1988).

**4.2 Post-translational changes of Tau proteins** 

al., 2010).

alternative splicing of exons 2, 3 and 10 gives rise to six tau isoforms where exon 3 never appears independently of exon 2. Alternative splicing is regulated during development and differentially between tissues. A single isoform lacking the 3 alternative exons 2, 3 and 10 is expressed in the foetal brain. Exon 10 encodes an additional microtubule-binding motif numbered R1 to R4. Half of tau proteins contain three microtubule-binding motifs and the other halves have four microtubule-binding motifs (figure 4A). Constitutive exons are 1, 4, 5, 7, 9, 11, 12 and 13 and the start codon is located in exon 1. There are two alternate stop codons located either following exon 13 or inside exon 14 (Andreadis A, 2005 and Sergeant N et al., 2008). Human brain tau isoforms have a range from 352 to 441 amino acids and a molecular weigth between 45 to 65 kDa in polyacrylamide gel electrophoresis (figure 4B). Primary sequence analysis of tau protein shows that it can be subdivided in four structural regions. The amino-terminal region is acidic and variable, depending on the presence or absence of exons 2/3 and a proline-rich domain follows it. The latter is followed by 3 or 4 imperfect repeat motifs (R1 to R4; see figure 4A) - depending on the presence or absence of exon 10 - and corresponding to the microtubule-binding domain of tau. Finally, a short carboxy-terminal region is found and it is the basic region of the protein (figure 4C).

Fig. 4. Six tau isoforms are presented in human brain. These isoforms differ by the absence or presence of one or two 29 amino acids inserts encoded by exon 2 (green box) and 3 (violet box) in the amino-terminal part. Exon 3 is always incorporated with exon 2. R2 corresponds to the presence of exon 10 (orange box) that encodes an additional microtubule-binding motif numbered R1 to R4 in the carboxy-terminal part and they are represented as black boxes. (A). Molecular weight in mono-dimensional electrophoresis for the six isoforms of tau (B) and tau protein regions corresponding to the full-length isoform (C).

alternative splicing of exons 2, 3 and 10 gives rise to six tau isoforms where exon 3 never appears independently of exon 2. Alternative splicing is regulated during development and differentially between tissues. A single isoform lacking the 3 alternative exons 2, 3 and 10 is expressed in the foetal brain. Exon 10 encodes an additional microtubule-binding motif numbered R1 to R4. Half of tau proteins contain three microtubule-binding motifs and the other halves have four microtubule-binding motifs (figure 4A). Constitutive exons are 1, 4, 5, 7, 9, 11, 12 and 13 and the start codon is located in exon 1. There are two alternate stop codons located either following exon 13 or inside exon 14 (Andreadis A, 2005 and Sergeant N et al., 2008). Human brain tau isoforms have a range from 352 to 441 amino acids and a molecular weigth between 45 to 65 kDa in polyacrylamide gel electrophoresis (figure 4B). Primary sequence analysis of tau protein shows that it can be subdivided in four structural regions. The amino-terminal region is acidic and variable, depending on the presence or absence of exons 2/3 and a proline-rich domain follows it. The latter is followed by 3 or 4 imperfect repeat motifs (R1 to R4; see figure 4A) - depending on the presence or absence of exon 10 - and corresponding to the microtubule-binding domain of tau. Finally, a short

carboxy-terminal region is found and it is the basic region of the protein (figure 4C).

Fig. 4. Six tau isoforms are presented in human brain. These isoforms differ by the absence or presence of one or two 29 amino acids inserts encoded by exon 2 (green box) and 3 (violet box) in the amino-terminal part. Exon 3 is always incorporated with exon 2. R2 corresponds to the presence of exon 10 (orange box) that encodes an additional microtubule-binding motif numbered R1 to R4 in the carboxy-terminal part and they are represented as black boxes. (A). Molecular weight in mono-dimensional electrophoresis for the six isoforms of

tau (B) and tau protein regions corresponding to the full-length isoform (C).

The amino-terminal region together with the proline-rich domain is referred to as the "projection domain". This unstructured and negatively charged region detaches from the surface microtubules (Hirokawa N et al., 1988) and can interact with the plasma membrane or cytoskeletal proteins (Brandt R et al., 1995). Tau may therefore contribute to spacing in between microtubule lattice and to the parallel ordered organization of microtubules in axons (Chen J et al., 1992). Amino-terminal region of tau also interacts with a growing panel of polypeptides including motor proteins such as kinesin-1 (Utton MA et al., 2005) and dynactin/dynein complex (Magnani E et al., 2007). All interacting polypeptides constitute the interactome of tau and indicate the functions in which tau may be implicated. The application of 2D gel electrophoresis method has been used to study tau (Janke C et al., 1996). The six main isoforms of tau are separated as several isovariants with isoelectric points comprised between 9.5 and 6.5 due to the alternative splicing and to posttranslational modifications. The amino-terminal region has a pI of 3.8, proline domain has a pI of 11.4 and carboxy-terminal has a pI of 10.8. Regarding to the primary structure, the polypeptide sequences encoded by exons 2/3 add to tau acidity, whereas exon 10 encodes a positively charged sequence that adds to the basic character of tau. Thus tau is rather a dipole with two domains with opposite charge modulated either by post-translational modifications or tau proteolysis (Wischik CM et al., 1988).

Tau stabilizes oligomers of tubulins, it is partially folded while interacting with microtubules and it was shown to link laterally protofilaments made of tubulin (Santarella RA et al., 2004). NMR investigations showed that residues between Val226 to Glu372 are binding to microtubule surface involving the all four repeat binding motifs showing that amino- and carboxy-terminal domains do not participate in the binding properties of tau to microtubules (Sillen A et al., 2007). Tau mutations like in FTDP may impair the binding of tau to microtubules (Delobel P et al., 2002). Regarding the physic-chemical properties of tau protein it has been addressed that tau protein owns pro-aggregative motifs called PHF6 and PHF6\* in its carboxi-terminal region at the level of R2 and R3. The amino acids sequence of these motifs (306)VQIVYK(311) and (275)VQIINK(280) are prone to promote aggregation by the formation of beta-structure (von Bergen M et al., 2001). This aggregation and accumulation of misfolded proteins might have a common cause and pathological pathway in several neurodegenerative disorders resulting in neuronal loss (Tyedmers J et al., 2010). Several studies have revealed that truncated tau drive NFD *in vivo* (Zilka N et al., 2006) and caspase activation lead to tangles formation (de Calignon et al., 2010).

#### **4.2 Post-translational changes of Tau proteins**

Phosphorylation of tau is instrumental to NFD and it is the main post-translational modification in tau isovariants as it was shown by 2D immunoblots (Butler M and Shelanski ML, 1986). These data shed light to the impact of tau protein for tau biology. There are 85 potential phosphorylation sites on the longest brain tau isoform. Phosphorylation sites were identified with proteomic approaches as MS, NMR, phospho-peptide mapping and the use of site-specific phosphorylation dependent tau antibodies (Hanger et al., 2007). Among them around 71 correspond to putative phosphorylation sites in physiological and pathological conditions. It is worthy to remark that most of the phosphorylation sites surround the microtubule-binding domains in the proline-rich region and carboxi-terminal region of tau. Phosphorylation regulates several functions of tau such as its binding to microtubules, the axonal transport of tau as well as its interactions with amino-terminal partners' particularly

The Microtubule-Dissociating Tau in Neurological Disorders 307

glycosylation for the same sites. In fact, tau proteins from AD brains present abnormally glycosylation in comparison with controls. Using a recombinant O-GlcNAc modified tau, MS has mapped O-GlcNAc on tau at Thr-123, Ser-400 sites and a third one on either Ser-409, Ser-412, or Ser-413 (Yuzwa SA et al., 2011). The identification of these sites may provide

The microtubule-associated protein tau is known to be post-translationally modified also by acetylayion. Recent studies reported that tau is acetylated and this acetylation avoids its degradation. Tau acetylation impares tau-microtubules interactions and facilitates tau aggregation. In fact, specific antibodies for acetylated tau showed an increase in acetylation in several Braak stages with the involment of histone acetyltransferase p300 and the deacetylase SIRT1 (Min SW et al., 2010). MS provides specific lysines within the microtubule-binding domain including lysine 280 (K280) that are main sites of tau acetylation. One model shows

The most obvious pathological event in tauopathies is the presence of aggregates of tau isoforms into intraneuronal filamentous inclusions. The evolution in the proteomics era allows to establish different physiological and pathological electrophoretical patterns to distinguish among the diversity of tauopathies. Comparative biochemistry of tau aggregates differs in both isoform phosphorylation and content, which enables a molecular classification of tauopathies. In postmorten brain tissue tau proteins are resolved as six bands (figure 4B) whereas more acidic hyperphosphorylated isoforms present four bands between 60 and 74 kDa depending on the disorder (figure 5). The classification presented here is composed by five classes of tauopathies, depending on the type of tau aggregates

Frontal lobe degeneration is the second more common presinile disorder that leads to dementia after AD. This class is genetically linked to mutations in the progranulin gene (Baker M et al., 2006 and Cruts M et al., 2006). Frontal lobe degeneration presents no specific neuropathological hallmarks, no tau aggregation and a loss of expression in tau proteins. The transactive response (TAR)-DNA-binding protein with a molecular weight of 43 kDa (TDP-43), encoded by the TARDBP gene, has been recently identified as a major pathological protein of frontotemporal lobar degeneration with ubiquitin-positive and taunegative inclusions. It is the most common underlying pathology in frontotemporal dementias with and without motor neuron disease. In fact TDP-43 pathology is identified till the 50% of AD cases and it is the main component in the amyotrophic lateral sclerosis (Wilson AC et al., 2011). This pathology from the clinical point of view is quite similar to Pick´s disease. It is characterized by a frontal distribution of morphologic changes involves neuronal cell loss, spongliosis and gliosis mainly in the superficial cortical layers of the

Class I is characterized by a pathological tau quartet at 60, 64 and 69 kDa, and a minor pathological tau at 72/74 kDa (figure 5). This pathological tau quartet corresponds to the aggregation of the six tau isoforms (Sergeant N et al., 1997b and Goedert M et al., 1992). The pathological tau 60 is composed of the shortest tau isoform (2–3-10-). The pathological tau 64

that K280 is exclusively acetylated in pathological conditions (Cohen TJ et al., 2011).

that constitute the bar code for neurodegenerative diseases (Sergeant et al., 2005).

evidence to elucidate the role of glycosylation in tau function.

**4.3 Tau as a bar code for neurodegenerative diseases** 

*Class 0: frontal lobe degeneration non-Alzheimer non-Pick* 

frontal and temporal cortex (Delacourte A et al., 1977).

*Class I: all brain Tau isoforms are aggregated* 

SH3-containing proteins (Rosenberg KJ et al., 2008). For instance, tau transport along the axon is negatively regulated by its phosphorylation by GSK3β leading to a reduced binding to kinesin-1 (Cuchillo-Ibanez I et al., 2008). By phosphorylating amino-terminal serines 212 and 217, GSK3β also reduces the binding of SH3-containing proteins, such as Fyn, PLC-γ1, p85α (Reynolds CH et al., 2008). Once tau proteins are phosphorylated they cannot polymerize tubulin into microtubules and do not stabilize the latter.

Tau phosphorylation is mainly regulated through kinases and phosphatases, but other enzymes are also involved, such as Pin1 isomerase (Buee L et al., 2000). A total of more than 20 protein kinases can phosphorylate tau proteins (Sergeant N et al., 2008). This includes four groups of protein kinases. (a) Proline-directed protein kinases (PDPKs), which phosphorylate tau on serines or threonines that are followed by a proline residue. This group includes CDK1 and 5 (Hamdane M et al., 2003), MAPK and several SAPKs (Ferrer I et al., 2005). (b) The non-PDPK group includes tau-tubulin kinases 1 and 2, casein kinases 1 and 2, DYRK1A (dual-specificity tyrosine-phosphorylated and –regulated kinase 1A), phosphorylase kinase, Rho kinase, PKA, PKB/Akt, PKC and PKN (Sergeant N 2005). (c) The third group includes protein kinases that phosphorylate tau on serine or threonine residues followed or not by a proline. GSK (glycogen synthase kinase) 3*α* and GSK3*β* and AGC kinases (such as MSK1 (mitogen- and stressactivated protein kinase) belong to this group and have recognition motifs SXXXS or SXXXD/E and RXRXXS/T respectively (Buée L et al., 2010). (d) The fourth group corresponds to tyrosine protein kinases such as Src kinases, c-Abl and c-Met (http://cnr.iop.kcl.ac.uk/hangerlab/tautable). The principal role of tau phosphorylation is related to microtubule binding. However, phosphorylation or dephosphorylation of tau may also contribute to the cell localization of tau. For instance, phosphorylation of tau by GSK3*β* regulates its axonal transport by reducing its interaction with kinesin. In sharp contrast, dephosphorylated tau is located to the cell nucleus and is suggested to contribute to nucleolar organization and/or contribute to chromosome stability. Mutations in TAU gene lead to a change in the affinity of kinases that phosphorylate tau near the site of the mutation. Some mutations like R406W may reduce the phosphorylation of tau at Ser404, which is necessary for GSK3-β to phosphorylate tau at Ser396 afterwards (Tatebayashi Y et al., 2006). However, this priming putative phosphorylation site is not a prerequisite for JNK3 to phosphorylate tau at Ser396. These data provide evidence that tau mutations may potentially modify the global phophorylation state of tau.

Abnormal phospho sites on PHF-tau were identified on constitutive exons, such as Ser212– 214 together and Ser422. These three new sites were identified on the alternative sequence encoded by exon 2. As tau isoforms expression may be different in subneuronal populations, these phospho epitopes would be of interest in identifying such subneuronal populations or the laminar distribution of NDF in AD (Delacourte A et al., 1996).

In normal brains the phospho-epitopes are rapidly dephosphorylate during postmorten delay, this effect may be due to the drop in ATP and inactivation of phophatases. However, in AD brains this dephosphorylatyon does not occur. Some of the hypotheses are that aggregation of tau proteins into filaments render them inaccessible to phosphatases, phosphatases are not activated any more or their activity is suddenly decreased.

Other post-translational modification of tau proteins is O-glycosylation. O-glycosylation results from the attachment of a sugar on the hydroxyl radical of serine or threonine residue in the vicinity of the proline-rich domain. Glycosylation decreases tau phosphorylation by CDK5, PKA and and GSKβ, probably due to a competition between phosphorylation and

SH3-containing proteins (Rosenberg KJ et al., 2008). For instance, tau transport along the axon is negatively regulated by its phosphorylation by GSK3β leading to a reduced binding to kinesin-1 (Cuchillo-Ibanez I et al., 2008). By phosphorylating amino-terminal serines 212 and 217, GSK3β also reduces the binding of SH3-containing proteins, such as Fyn, PLC-γ1, p85α (Reynolds CH et al., 2008). Once tau proteins are phosphorylated they cannot

Tau phosphorylation is mainly regulated through kinases and phosphatases, but other enzymes are also involved, such as Pin1 isomerase (Buee L et al., 2000). A total of more than 20 protein kinases can phosphorylate tau proteins (Sergeant N et al., 2008). This includes four groups of protein kinases. (a) Proline-directed protein kinases (PDPKs), which phosphorylate tau on serines or threonines that are followed by a proline residue. This group includes CDK1 and 5 (Hamdane M et al., 2003), MAPK and several SAPKs (Ferrer I et al., 2005). (b) The non-PDPK group includes tau-tubulin kinases 1 and 2, casein kinases 1 and 2, DYRK1A (dual-specificity tyrosine-phosphorylated and –regulated kinase 1A), phosphorylase kinase, Rho kinase, PKA, PKB/Akt, PKC and PKN (Sergeant N 2005). (c) The third group includes protein kinases that phosphorylate tau on serine or threonine residues followed or not by a proline. GSK (glycogen synthase kinase) 3*α* and GSK3*β* and AGC kinases (such as MSK1 (mitogen- and stressactivated protein kinase) belong to this group and have recognition motifs SXXXS or SXXXD/E and RXRXXS/T respectively (Buée L et al., 2010). (d) The fourth group corresponds to tyrosine protein kinases such as Src kinases, c-Abl and c-Met (http://cnr.iop.kcl.ac.uk/hangerlab/tautable). The principal role of tau phosphorylation is related to microtubule binding. However, phosphorylation or dephosphorylation of tau may also contribute to the cell localization of tau. For instance, phosphorylation of tau by GSK3*β* regulates its axonal transport by reducing its interaction with kinesin. In sharp contrast, dephosphorylated tau is located to the cell nucleus and is suggested to contribute to nucleolar organization and/or contribute to chromosome stability. Mutations in TAU gene lead to a change in the affinity of kinases that phosphorylate tau near the site of the mutation. Some mutations like R406W may reduce the phosphorylation of tau at Ser404, which is necessary for GSK3-β to phosphorylate tau at Ser396 afterwards (Tatebayashi Y et al., 2006). However, this priming putative phosphorylation site is not a prerequisite for JNK3 to phosphorylate tau at Ser396. These data provide evidence that tau mutations may potentially modify the global phophorylation

Abnormal phospho sites on PHF-tau were identified on constitutive exons, such as Ser212– 214 together and Ser422. These three new sites were identified on the alternative sequence encoded by exon 2. As tau isoforms expression may be different in subneuronal populations, these phospho epitopes would be of interest in identifying such subneuronal

In normal brains the phospho-epitopes are rapidly dephosphorylate during postmorten delay, this effect may be due to the drop in ATP and inactivation of phophatases. However, in AD brains this dephosphorylatyon does not occur. Some of the hypotheses are that aggregation of tau proteins into filaments render them inaccessible to phosphatases,

Other post-translational modification of tau proteins is O-glycosylation. O-glycosylation results from the attachment of a sugar on the hydroxyl radical of serine or threonine residue in the vicinity of the proline-rich domain. Glycosylation decreases tau phosphorylation by CDK5, PKA and and GSKβ, probably due to a competition between phosphorylation and

populations or the laminar distribution of NDF in AD (Delacourte A et al., 1996).

phosphatases are not activated any more or their activity is suddenly decreased.

polymerize tubulin into microtubules and do not stabilize the latter.

state of tau.

glycosylation for the same sites. In fact, tau proteins from AD brains present abnormally glycosylation in comparison with controls. Using a recombinant O-GlcNAc modified tau, MS has mapped O-GlcNAc on tau at Thr-123, Ser-400 sites and a third one on either Ser-409, Ser-412, or Ser-413 (Yuzwa SA et al., 2011). The identification of these sites may provide evidence to elucidate the role of glycosylation in tau function.

The microtubule-associated protein tau is known to be post-translationally modified also by acetylayion. Recent studies reported that tau is acetylated and this acetylation avoids its degradation. Tau acetylation impares tau-microtubules interactions and facilitates tau aggregation. In fact, specific antibodies for acetylated tau showed an increase in acetylation in several Braak stages with the involment of histone acetyltransferase p300 and the deacetylase SIRT1 (Min SW et al., 2010). MS provides specific lysines within the microtubule-binding domain including lysine 280 (K280) that are main sites of tau acetylation. One model shows that K280 is exclusively acetylated in pathological conditions (Cohen TJ et al., 2011).

#### **4.3 Tau as a bar code for neurodegenerative diseases**

The most obvious pathological event in tauopathies is the presence of aggregates of tau isoforms into intraneuronal filamentous inclusions. The evolution in the proteomics era allows to establish different physiological and pathological electrophoretical patterns to distinguish among the diversity of tauopathies. Comparative biochemistry of tau aggregates differs in both isoform phosphorylation and content, which enables a molecular classification of tauopathies. In postmorten brain tissue tau proteins are resolved as six bands (figure 4B) whereas more acidic hyperphosphorylated isoforms present four bands between 60 and 74 kDa depending on the disorder (figure 5). The classification presented here is composed by five classes of tauopathies, depending on the type of tau aggregates that constitute the bar code for neurodegenerative diseases (Sergeant et al., 2005).

#### *Class 0: frontal lobe degeneration non-Alzheimer non-Pick*

Frontal lobe degeneration is the second more common presinile disorder that leads to dementia after AD. This class is genetically linked to mutations in the progranulin gene (Baker M et al., 2006 and Cruts M et al., 2006). Frontal lobe degeneration presents no specific neuropathological hallmarks, no tau aggregation and a loss of expression in tau proteins. The transactive response (TAR)-DNA-binding protein with a molecular weight of 43 kDa (TDP-43), encoded by the TARDBP gene, has been recently identified as a major pathological protein of frontotemporal lobar degeneration with ubiquitin-positive and taunegative inclusions. It is the most common underlying pathology in frontotemporal dementias with and without motor neuron disease. In fact TDP-43 pathology is identified till the 50% of AD cases and it is the main component in the amyotrophic lateral sclerosis (Wilson AC et al., 2011). This pathology from the clinical point of view is quite similar to Pick´s disease. It is characterized by a frontal distribution of morphologic changes involves neuronal cell loss, spongliosis and gliosis mainly in the superficial cortical layers of the frontal and temporal cortex (Delacourte A et al., 1977).

#### *Class I: all brain Tau isoforms are aggregated*

Class I is characterized by a pathological tau quartet at 60, 64 and 69 kDa, and a minor pathological tau at 72/74 kDa (figure 5). This pathological tau quartet corresponds to the aggregation of the six tau isoforms (Sergeant N et al., 1997b and Goedert M et al., 1992). The pathological tau 60 is composed of the shortest tau isoform (2–3-10-). The pathological tau 64

The Microtubule-Dissociating Tau in Neurological Disorders 309

Aggregation of tau proteins with four microtubule-binding domains is the characteristic of class II (figure 5). This pathological tau profile is observed in CBD, argyrophilic grain dementia, PSP and FTDP-17 due to tau gene mutations (Sergeant N et al., 1999 and Tolnay M et al., 2002). PSP, CBD and argyrophilic grain dementia are rare atypical parkinsonism

This class of tauopathies includes Pick's disease and autosomal dominant inherited FDTP-17 (figure 5). Pick's disease is a rare form of neurodegenerative disorder characterized by a progressive dementing process. Early in the clinical course, patients show signs of frontal disinhibition. Neuropathologically, Pick's disease is characterized by the presence of typical spheroid inclusions in the soma of neurons called Pick bodies. Pick bodies are labeled by tau antibodies, with a higher density in neurons of the dentate gyrus of the hippocampal formation than in the temporal and frontal cortices. The pathological tau profile of Pick's disease contrasts with that of class II tauopathies, with the pathological tau isoforms

Immunohistologic staining of these aggregates is positive for AD2 and exon 2 antibodies but negative for exon 10 antibodies. In addition, aggregated tau proteins in Pick's disease are not detected by the monoclonal antibody 12E8 raised against the phosphorylated residue Ser262/Ser356, whereas this phosphorylation site is detected in other neurodegenerative disorders. The lack of phosphorylation at Ser262 and Ser356 sites is likely to be related to either a kinase is not active in neurons that degenerate in Pick's disease or those neurons do not constitutively express these kinases within degenerating neurons (Mailliot C et al., 1998).

This group is represented by a single neurological disorder: myotonic dystrophy (DM) of types I and II (figure 5). DM is the commonest form of adult-onset muscular dystrophy. Genetically it is an inherited autosomal dominant disorder caused by a single gene mutation consisting of expansion of a CTG trinucleotide motif in the 3V untranslated of the myotonic dystrophy protein kinase gene (DMPK), located on chromosome 19q. It is a multisystemic disease affecting many systems as the central nervous system (cognitive and neuropsychiatric impairments), the heart, the genital tract, the eyes, the ears, gastrointestinal tract, endocrine system, thus leading to a wide and variable complex panel of symptoms (Meola G, 2000). Cognitive impairments, as memory, visuo-spatial recall and verbal scale, cortical atrophy essentially of the frontal and the temporal lobe and white matter lesions are

Neuropathological lesions, as neurofibrillary tangles (NFTs), have been observed in adult DM1 individuals aged over 50 years. The pathological tau profile of DM1 is characterized by a strong pathological tau band at 60 kDa and, to a lesser extent, a pathological tau component at 64 and 69 kDa. This typical pathological tau profile is reflected by a reduced number of tau isoforms expressed in the brain of individuals with DM1, both at the protein and mRNA levels (Sergeant N et al., 2001). In addition, tau protein expression is also demonstrated to be altered in transgenic mice with human DM1 locus (Gomes-Pereira M et al., 2007). Using specific immunological probes against exon 2 and exon 3 corresponding amino acid sequences, the neurofibrillary lesions were shown to be devoid of tau isoforms with amino-terminal inserts (Maurage CA et al., 2005). An altered splicing of tau

*Class II: Tau isoforms containing the exon 10 encoding sequence aggregate* 

*Class III: Tau isoforms lacking the exon 10 encoding sequence aggregate* 

*Class IV: Tau isoform lacking exon 2, 3 and 10 principally aggregate* 

often described in both DM1 and DM2 (Sansone V et al., 2007).

consisting essentially of the 3R tau isoforms.

disorders.

and 69 are each composed of two tau isoforms: tau isoforms with either the exon 2 or exon 10 alone compose the pathological tau 64, while the pathological tau 69 is made of tau isoforms with either exon 2 + 10 or 2 + 3. The longest tau isoform containing exons 2, 3 and 10 (2 + 3 + 10) constitutes the 72/74-kDa pathological component, as determined by 2D gel electrophoresis coupled to western blotting using exon-specific tau antibodies (Sergeant N et al., 1997a). This typical tau profile was first characterized in AD, but now includes nine additional neurological disorders AD as cerebral aging (over 75 years), ALS/parkinsonism– dementia complex of Guam, Parkinson with dementia of Guadeloupe, Niemann–Pick disease type C, Postencephalitic parkinsonism, Familial British dementia, Dementia pugilisticia, Down's syndrome and FTDP-17. Using histochemistry, aggregates of this class can be observed with AD2 and antibodies against exon 2 and exon 10 (Buee L et al., 2000 and Sergeant N et al., 2008).

Fig. 5. Bar Code for neurodegenerative diseases. Schematic representation of the modifications leading to tau proteins aggregation in Tauopathies. Native tau proteins are detected as a triplet of bands ranging between 60 and 74 kDa by numerous phosphorylation-dependent antibodies. Tau proteins are shown by western blotting as three major bands between 60 and 69 kDa, and a minor band at 74 kDa. AD pattern is also found in Down's syndrome, post-encephalitic parkinsonism, ALS/parkinsonism–dementia complex of Guam among others (class I). The doublet tau 64, 69 represent the aggregation of hyperphosphorylated tau isoforms with exon 10 (orange box) typical for CBP and CBD (class II), the exclusion of exon 10 (only black boxes) in hyperphosphorylated tau aggregation lead to tau 60, 64 doublet characteristic for Pick's disease (class III). The aggregation of Tau isoforms lacking exons 2 (green box) and 3 (violet box) is found in myotonic dystrophy (class IV).

and 69 are each composed of two tau isoforms: tau isoforms with either the exon 2 or exon 10 alone compose the pathological tau 64, while the pathological tau 69 is made of tau isoforms with either exon 2 + 10 or 2 + 3. The longest tau isoform containing exons 2, 3 and 10 (2 + 3 + 10) constitutes the 72/74-kDa pathological component, as determined by 2D gel electrophoresis coupled to western blotting using exon-specific tau antibodies (Sergeant N et al., 1997a). This typical tau profile was first characterized in AD, but now includes nine additional neurological disorders AD as cerebral aging (over 75 years), ALS/parkinsonism– dementia complex of Guam, Parkinson with dementia of Guadeloupe, Niemann–Pick disease type C, Postencephalitic parkinsonism, Familial British dementia, Dementia pugilisticia, Down's syndrome and FTDP-17. Using histochemistry, aggregates of this class can be observed with AD2 and antibodies against exon 2 and exon 10 (Buee L et al., 2000

Fig. 5. Bar Code for neurodegenerative diseases. Schematic representation of the

detected as a triplet of bands ranging between 60 and 74 kDa by numerous

modifications leading to tau proteins aggregation in Tauopathies. Native tau proteins are

phosphorylation-dependent antibodies. Tau proteins are shown by western blotting as three major bands between 60 and 69 kDa, and a minor band at 74 kDa. AD pattern is also found in Down's syndrome, post-encephalitic parkinsonism, ALS/parkinsonism–dementia complex of Guam among others (class I). The doublet tau 64, 69 represent the aggregation of hyperphosphorylated tau isoforms with exon 10 (orange box) typical for CBP and CBD (class II), the exclusion of exon 10 (only black boxes) in hyperphosphorylated tau aggregation lead to tau 60, 64 doublet characteristic for Pick's disease (class III). The aggregation of Tau isoforms lacking exons 2 (green box) and 3 (violet box) is found in

and Sergeant N et al., 2008).

myotonic dystrophy (class IV).

#### *Class II: Tau isoforms containing the exon 10 encoding sequence aggregate*

Aggregation of tau proteins with four microtubule-binding domains is the characteristic of class II (figure 5). This pathological tau profile is observed in CBD, argyrophilic grain dementia, PSP and FTDP-17 due to tau gene mutations (Sergeant N et al., 1999 and Tolnay M et al., 2002). PSP, CBD and argyrophilic grain dementia are rare atypical parkinsonism disorders.

#### *Class III: Tau isoforms lacking the exon 10 encoding sequence aggregate*

This class of tauopathies includes Pick's disease and autosomal dominant inherited FDTP-17 (figure 5). Pick's disease is a rare form of neurodegenerative disorder characterized by a progressive dementing process. Early in the clinical course, patients show signs of frontal disinhibition. Neuropathologically, Pick's disease is characterized by the presence of typical spheroid inclusions in the soma of neurons called Pick bodies. Pick bodies are labeled by tau antibodies, with a higher density in neurons of the dentate gyrus of the hippocampal formation than in the temporal and frontal cortices. The pathological tau profile of Pick's disease contrasts with that of class II tauopathies, with the pathological tau isoforms consisting essentially of the 3R tau isoforms.

Immunohistologic staining of these aggregates is positive for AD2 and exon 2 antibodies but negative for exon 10 antibodies. In addition, aggregated tau proteins in Pick's disease are not detected by the monoclonal antibody 12E8 raised against the phosphorylated residue Ser262/Ser356, whereas this phosphorylation site is detected in other neurodegenerative disorders. The lack of phosphorylation at Ser262 and Ser356 sites is likely to be related to either a kinase is not active in neurons that degenerate in Pick's disease or those neurons do not constitutively express these kinases within degenerating neurons (Mailliot C et al., 1998).

#### *Class IV: Tau isoform lacking exon 2, 3 and 10 principally aggregate*

This group is represented by a single neurological disorder: myotonic dystrophy (DM) of types I and II (figure 5). DM is the commonest form of adult-onset muscular dystrophy. Genetically it is an inherited autosomal dominant disorder caused by a single gene mutation consisting of expansion of a CTG trinucleotide motif in the 3V untranslated of the myotonic dystrophy protein kinase gene (DMPK), located on chromosome 19q. It is a multisystemic disease affecting many systems as the central nervous system (cognitive and neuropsychiatric impairments), the heart, the genital tract, the eyes, the ears, gastrointestinal tract, endocrine system, thus leading to a wide and variable complex panel of symptoms (Meola G, 2000). Cognitive impairments, as memory, visuo-spatial recall and verbal scale, cortical atrophy essentially of the frontal and the temporal lobe and white matter lesions are often described in both DM1 and DM2 (Sansone V et al., 2007).

Neuropathological lesions, as neurofibrillary tangles (NFTs), have been observed in adult DM1 individuals aged over 50 years. The pathological tau profile of DM1 is characterized by a strong pathological tau band at 60 kDa and, to a lesser extent, a pathological tau component at 64 and 69 kDa. This typical pathological tau profile is reflected by a reduced number of tau isoforms expressed in the brain of individuals with DM1, both at the protein and mRNA levels (Sergeant N et al., 2001). In addition, tau protein expression is also demonstrated to be altered in transgenic mice with human DM1 locus (Gomes-Pereira M et al., 2007). Using specific immunological probes against exon 2 and exon 3 corresponding amino acid sequences, the neurofibrillary lesions were shown to be devoid of tau isoforms with amino-terminal inserts (Maurage CA et al., 2005). An altered splicing of tau

The Microtubule-Dissociating Tau in Neurological Disorders 311

tau. Transgenic fruitflies showed key features of tauopathies as tissue- and temporal-specific effects as adult onset, progressive neurodegeneration, early death, enhanced toxicity of mutant tau, accumulation of abnormal tau and relative anatomic selectivity coupled with differential effects of distinct tau isoforms (Papanikolopoulou K and Skoulakis EM, 2011).

The novel use of the vertebrate zebrafish as a model system for AD research offers a powerful platform for genetic and chemical screens as well as developmental studies (Tomasiewicz HG et al., 2002). The transgenic expression of the human tau mutation P301L in zebrafish neurons by Gal4/UAS–based vector system recapitulates most pathological features of tauopathies as abnormally phosphorylated reactivity with the epitopes AT180, AT270, 12E8, PHF1, 422, and AT8 in spinal cord neurons, aggregation and behavioral impairments (Paquet D et al., 2010). Application of inhibitors of human GSK3β reduced tau phosphorylation showing that zebrafish kinases are sufficiently conserved with respect to their human orthologues. Current evidence point out that zebrafish tau models recapitulate pathological and biochemical events that occur in tauopathies and therefore may be useful

tools for further studies in the aetiology of dementia (Bai Q and Burton EA, 2011).

Tau mouse models where tau expression is suppressed by MAPT deletion or invalidation present no major changes and animals are physiologically normal (Harada A et al., 1994). It seems other microtubule-associated proteins such as MAP1A probably compensate tau deficiency. Among the mice models available with wild-type human tau it is remarkable to note that overexpression of 3R tau isoforms lead to an accumulation of hyperphosphorylated tau proteins in spinal cord neurons and axonal degeneration as well as a reduction in axonal transport (Brion JP et al., 1999). Similar data were observed in transgenic mice expressing the longest human brain tau isoform under the control of the human Thy-1 promoter. Hyperphosphorylated human tau protein was present in nerve cell bodies, axons and dendrites (Gotz J et al., 1995). Furthermore, recent studies in transgenic mouse models that express the entire human MAPT gene in the presence and absence of the mouse Mapt gene show differences between mouse and human tau in the regulation of exon 10 inclusion during development and in the young adult. In addition, it was observed species-specific variations in the expression of 3R- and 4R-tau within the frontal cortex and hippocampus during the development as well as in cell distribution of the isoforms

Mutated tau transgenes have been used under various promoters (2',3'-cyclic nucleotide 3' phosphodiesterase, CaMKII, PDGF, Prion, or Thy1.2) with or without inducible systems. The most common phenotype of transgene tau animal is the motor alterations. Tau transgenic mice rTg4510 present P301L mutation in an inductible way and develop NFTs, neuronal loss and behavioural impairments (Santacruz K et al., 2005). Nonetheless the suppression of the expression of this mutated tau revers behavioural impairements despite the NFTs formation keeps on, indeed it seems soluble tau rather than NFTs may be deleterious. These observations are in agreement with a recent report in which brain extract injection from mutant P301S tau expressing mice into brain of transgenic wild-type tau-

**5.1.4 Tau knock out mice and transgenic mice with wild-type human Tau** 

**5.1.3 Zebrafish** 

(McMillan P et al., 2008).

**5.1.5 Transgenic mice with mutated human Tau** 

characterized by a reduced expression of tau isoforms containing the amino-terminal inserts characterizes both DM1 and DM2. Overall, it demonstrates that the central nervous system is affected and that DMs are real tauopathies (Dhaenens CM et al., 2011). The direct relationship between the altered splicing of tau and NFD in DM remains to be established. Indeed, such an altered splicing of tau is commonly observed in FTDP- 17 and considered as reminiscent to NFD and tauopathies.

#### **5. Use of proteomics to investigate the mechanisms leading to Tauopathies**

Induction of tau fibrillization in cells remain unsatisfactory, this is a limiting factor since NFD cannot be totally reproduced *in vitro* (Sibille N et al., 2006). The development of *in vivo* models has provided an important tool to precise sequence of molecular events leading to tau aggregation. The use of proteomics in these transgenic animals has permitted to go further in the uncovering of the cellular and molecular pathways involved in NFD spreading within the brain and its relationship with the clinical expression of neurological disorders. In this section we will focus on the overexpresion either several isoforms of tau protein or mutated forms in animal models.

#### **5.1 Tau models**

Several animal models have been created to recapitulate the two main hallmarks of AD, refearing as amyloid plaques and PHFs. Despite the numerous models existing to mimic the features of this disease, none of them cover all the neuropathological, biochemical and behaviour alterations so far. There are models focus on overexpression of APP and/or presenilin containing one or more mutations linked to familial AD but they do not present NFD. Inspite tau mutations have not been described in AD patients, mutations in tau result in NFTs in an inherited form of FTDP and this dysfunction can lead to neurodegeneration and dementia. Taking into account that AD is a complex disorder and the perfect model does not exist, the large number of tau transgenic models with their strengths and weaknesses may allow for both understanding tau pathology and developing innovative therapeutic strategies. Nowadays there are several transgenic models which own combination of mutant APP, presenilin and tau (Chin J 2011). However, this triple model presents the "limitation" that tau pathology cannot be studied independently of the amyloid effects (Sergeant N and Buée L 2011).

#### **5.1.1** *Caenorhabditis elegans*

The nematode *Caenorhabditis elegans* is widely being used to study neurodegenerative disorders despite the evolutionary difference. *C. elegans* has a short lifespan and it is easy to manipulate genetically. Modelling tauopathies is achieved through pan-neuronal overexpression either wild-type or mutated tau leading to a progressive uncoordinated locomotion which is directly correlated with the nervous system alterations in worms. This model is very useful to identify new genetic targets (Wolozin B et al., 2011). Recent data point out that tau pathology may lead to specific interference with intracellular mechanisms of axonal outgrowth and pathfinding (Brandt R et al., 2009).

#### **5.1.2** *Drosophila melanogaster*

Another model used is the fruitfly *Drosophila melanogaster*. Regarding tauopathies, many groups developed fruitfly models by overexpressing wild-type and mutant forms of human tau. Transgenic fruitflies showed key features of tauopathies as tissue- and temporal-specific effects as adult onset, progressive neurodegeneration, early death, enhanced toxicity of mutant tau, accumulation of abnormal tau and relative anatomic selectivity coupled with differential effects of distinct tau isoforms (Papanikolopoulou K and Skoulakis EM, 2011).

#### **5.1.3 Zebrafish**

310 Proteomics – Human Diseases and Protein Functions

characterized by a reduced expression of tau isoforms containing the amino-terminal inserts characterizes both DM1 and DM2. Overall, it demonstrates that the central nervous system is affected and that DMs are real tauopathies (Dhaenens CM et al., 2011). The direct relationship between the altered splicing of tau and NFD in DM remains to be established. Indeed, such an altered splicing of tau is commonly observed in FTDP- 17 and considered as

**5. Use of proteomics to investigate the mechanisms leading to Tauopathies**  Induction of tau fibrillization in cells remain unsatisfactory, this is a limiting factor since NFD cannot be totally reproduced *in vitro* (Sibille N et al., 2006). The development of *in vivo* models has provided an important tool to precise sequence of molecular events leading to tau aggregation. The use of proteomics in these transgenic animals has permitted to go further in the uncovering of the cellular and molecular pathways involved in NFD spreading within the brain and its relationship with the clinical expression of neurological disorders. In this section we will focus on the overexpresion either several isoforms of tau

Several animal models have been created to recapitulate the two main hallmarks of AD, refearing as amyloid plaques and PHFs. Despite the numerous models existing to mimic the features of this disease, none of them cover all the neuropathological, biochemical and behaviour alterations so far. There are models focus on overexpression of APP and/or presenilin containing one or more mutations linked to familial AD but they do not present NFD. Inspite tau mutations have not been described in AD patients, mutations in tau result in NFTs in an inherited form of FTDP and this dysfunction can lead to neurodegeneration and dementia. Taking into account that AD is a complex disorder and the perfect model does not exist, the large number of tau transgenic models with their strengths and weaknesses may allow for both understanding tau pathology and developing innovative therapeutic strategies. Nowadays there are several transgenic models which own combination of mutant APP, presenilin and tau (Chin J 2011). However, this triple model presents the "limitation" that tau pathology cannot be studied independently of the amyloid

The nematode *Caenorhabditis elegans* is widely being used to study neurodegenerative disorders despite the evolutionary difference. *C. elegans* has a short lifespan and it is easy to manipulate genetically. Modelling tauopathies is achieved through pan-neuronal overexpression either wild-type or mutated tau leading to a progressive uncoordinated locomotion which is directly correlated with the nervous system alterations in worms. This model is very useful to identify new genetic targets (Wolozin B et al., 2011). Recent data point out that tau pathology may lead to specific interference with intracellular mechanisms

Another model used is the fruitfly *Drosophila melanogaster*. Regarding tauopathies, many groups developed fruitfly models by overexpressing wild-type and mutant forms of human

reminiscent to NFD and tauopathies.

protein or mutated forms in animal models.

effects (Sergeant N and Buée L 2011).

of axonal outgrowth and pathfinding (Brandt R et al., 2009).

**5.1.1** *Caenorhabditis elegans*

**5.1.2** *Drosophila melanogaster*

**5.1 Tau models** 

The novel use of the vertebrate zebrafish as a model system for AD research offers a powerful platform for genetic and chemical screens as well as developmental studies (Tomasiewicz HG et al., 2002). The transgenic expression of the human tau mutation P301L in zebrafish neurons by Gal4/UAS–based vector system recapitulates most pathological features of tauopathies as abnormally phosphorylated reactivity with the epitopes AT180, AT270, 12E8, PHF1, 422, and AT8 in spinal cord neurons, aggregation and behavioral impairments (Paquet D et al., 2010). Application of inhibitors of human GSK3β reduced tau phosphorylation showing that zebrafish kinases are sufficiently conserved with respect to their human orthologues. Current evidence point out that zebrafish tau models recapitulate pathological and biochemical events that occur in tauopathies and therefore may be useful tools for further studies in the aetiology of dementia (Bai Q and Burton EA, 2011).

#### **5.1.4 Tau knock out mice and transgenic mice with wild-type human Tau**

Tau mouse models where tau expression is suppressed by MAPT deletion or invalidation present no major changes and animals are physiologically normal (Harada A et al., 1994). It seems other microtubule-associated proteins such as MAP1A probably compensate tau deficiency. Among the mice models available with wild-type human tau it is remarkable to note that overexpression of 3R tau isoforms lead to an accumulation of hyperphosphorylated tau proteins in spinal cord neurons and axonal degeneration as well as a reduction in axonal transport (Brion JP et al., 1999). Similar data were observed in transgenic mice expressing the longest human brain tau isoform under the control of the human Thy-1 promoter. Hyperphosphorylated human tau protein was present in nerve cell bodies, axons and dendrites (Gotz J et al., 1995). Furthermore, recent studies in transgenic mouse models that express the entire human MAPT gene in the presence and absence of the mouse Mapt gene show differences between mouse and human tau in the regulation of exon 10 inclusion during development and in the young adult. In addition, it was observed species-specific variations in the expression of 3R- and 4R-tau within the frontal cortex and hippocampus during the development as well as in cell distribution of the isoforms (McMillan P et al., 2008).

#### **5.1.5 Transgenic mice with mutated human Tau**

Mutated tau transgenes have been used under various promoters (2',3'-cyclic nucleotide 3' phosphodiesterase, CaMKII, PDGF, Prion, or Thy1.2) with or without inducible systems. The most common phenotype of transgene tau animal is the motor alterations. Tau transgenic mice rTg4510 present P301L mutation in an inductible way and develop NFTs, neuronal loss and behavioural impairments (Santacruz K et al., 2005). Nonetheless the suppression of the expression of this mutated tau revers behavioural impairements despite the NFTs formation keeps on, indeed it seems soluble tau rather than NFTs may be deleterious. These observations are in agreement with a recent report in which brain extract injection from mutant P301S tau expressing mice into brain of transgenic wild-type tau-

The Microtubule-Dissociating Tau in Neurological Disorders 313

(Mayeux R et al., 2011). This is extremely complex in diseases concerning elderly since many symptoms are common and indistinguishable among them as the dementia sign. It is compulsory to find proteins and their post-translational modifications that may provide accuracy on the early diagnosis of the disease and eventually could serve as a therapeutic target. Successfully the development in neuroimaging techniques enables to facilitate and

Focusing on tauopathies, the presence of tau in CSF was first described in 1993. In AD, tau inclusions in the brain associated with neuronal damages lead to the leakage of abnormal forms of tau in the CSF resulting in quantitative and qualitative changes in CSF-tau composition. Numerous studies demonstrated increased CSF total tau and phosphorylated tau levels in AD, with mean levels 2-3 times higher compared to healthy controls. Tau is now a validated biomarker for AD, it improves the clinical diagnostic accuracy and its assessment for AD diagnosis is now proposed (Dubois B et al., 2010). As the brain lesions develop very early during the disease course even before the first clinical symptoms appear, CSF tau is not only a useful diagnostic marker in the advanced stages of the disease but also a usefull predictive marker in the earliest stages when clinical expression is weak (Hertze J et al., 2010). However, for differential diagnosis of dementia, the actually available tests measuring tau and phosphorylated tau levels in CSF are not sufficient and the identification of more specific postranslational modifications of tau in AD by proteomic approaches is needed. In the future, for the use of tau as biomarker in large clinical trials or in clinical practice, one important goal will be to develop sensitive methods to detect the very low concentration of tau in the blood (<1 pMol). Therefore, sample pre-treatment and handling

Tau is a neuronal protein that promotes neuronal survival, it is essentially located within the axonal and indispensable for the organization, stabilization, and dynamics of microtubules. The interaction between tau and microtubules is regulated by phosphorylation. It is widely reported that abnormally and hyperphosphorylated tau proteins lead to insoluble aggregates. The presence of these aggregates is clinically correlated with cognitive decline in a process called NFD; this event common to more than twenty diseases is referred as

The development of the proteomics era has achieved to go further in the characterization of tauopathies and shed light to the mechanism involved in their aetiology. Proteomics approaches as chromatography, mono- and bi-dimensional gel electrophoresis have reached to separate proteins with a quite high resolution after fractioning precedures, selecting a concrete population of cells or organelle isolation. The use of additional reagents to the extraction buffer such as detergents and the evolution of concomitant technologies as microscopy have provided a broad spectrum to characterize the structure and size of a large number of biological complex samples. The combination of protein separation methods with fluorescence dyes and radioactive isotopes (ICAT, iTRAQ, SILAC) makes possible not only more sensitive and reproducible results but also provides a quantitative analysis among

The previous hallmark is extremely linked to the identification of the separated or isolated proteins. MS has provided the composition of the molecules and also their post-translational modifications since changes in amino acid residues may be identified and characterized by

establish a preliminary diagnosis of different neurodegenerative disorders.

will be crucial in developing a reliable tau assay in blood/plasma.

**7. Conclusion** 

tauopathies.

samples (2D-DIGE, LC-MS, SELDI).

expressing animals induces assembly of wild-type human tau into filaments and spreading of pathology from the site of injection to neighbouring brain regions (Clavaguera et al., 2009).

Another transgenic mice model is TauRD/ΔK280 that expresses only the 4R tau domains and carry the ΔK280 mutation with a deletion of the amino- and carboxy terminal regions of tau protein. This mutation leads to tau aggregation followed by astrogliosis and neuronal loss. When the transgene is switched off the aggregation of the exogenous tau disappears within around one month and a haf and only aggregated murine tau proteins remain acting as a nucleation factor for tau aggregation (Mocanu MM et al., 2008). Other study suggest a "prion –like" propagation since aggregation continues even if the original tau species have disappeared (Sydow A and Mandelkov EM, 2010).

The K3 transgenic mouse strain expresses human tau carrying the K369I mutation under the Thy1 promoter (Ittner LM et al., 2008). This tau mutation was found in a family of patients presenting with Pick's disease without parkinsonism and amyotrophy (Neumann M et al., 2001). The transgenic mice present early-onset memory impairment and amyotrophy in the absence of overt neurodegeneration. Tau transgene is mainly expressed in the substantia nigra and such expression leads to an early-onset parkinsonism phenotype. Interestingly, motor performance of young, but not old K3 mice improves upon L-dopa treatment. Amyotrophy is probable to be related to tau expression in the sciatic nerve in the same way as in Tg30tau model where pathogenic mutations (P301S and G272V) are expressed in the forebrain and the spinal cord showing progressive motor impairment with neurogenic muscle atrophy besides the hippocampal atrophy (Leroy K et al., 2007). Moreover, transgenic mouse model overexpressing human 1N4R double-mutant tau (P301S and G272V) and invalidated endogenous TAU gene show an accelerated human mutant tau aggregation (Ando K et al., 2011) suggesting that murine tau proteins may act as inhibitors of tau aggregation.

Thy-Tau22 mouse transgenic line exhibits progressive neuron-specific AD-like tau pathology devoid of any motor deficits (Schindowski K et al., 2006). In addition to neurofibrillary tangle-like inclusions and mild astrogliosis, this model shows hyper- and abnormally phosphorylated tau on several Alzheimer's disease-relevant tau epitopes that accumulates within the somato-dendritic area in the hippocampus (Schindowski K et al., 2008). A progressive development of NFTs is observed in the hippocampus and amygdala, which parallels behavioural impairments as well as electrophysiological alterations (Van der Jeugd et al., 2011). These latter changes are observed despite any striking loss of neuronal/synaptic markers until 12 months of age in the hippocampus. Interestingly, at that time point, THY-Tau22 mice exhibit septo-hippocampal tau pathology accompanied by altered retrograde transport from hippocampus to medial septum (Belarbi K et al., 2009) with an accumulation of the nerve growth factor (NGF) levels in the hippocampus consistent with a decrease of its uptake or retrograde transport by cholinergic terminals (Belarbi K et al., in press). Recent data indicate that voluntary exercise prevented memory alterations in these transgenic mice and increased mRNA levels of genes involved in cholesterol trafficking such as NPC1 and NPC2 (Belarbi K et al., 2011).

#### **6. Tau proteins as biomarkers of Tauopathies**

Searching for biomarkers is one of the most challenges in current medicine. Biomarkers must be not only specific for a single pathology but also indicative of its progression (Mayeux R et al., 2011). This is extremely complex in diseases concerning elderly since many symptoms are common and indistinguishable among them as the dementia sign. It is compulsory to find proteins and their post-translational modifications that may provide accuracy on the early diagnosis of the disease and eventually could serve as a therapeutic target. Successfully the development in neuroimaging techniques enables to facilitate and establish a preliminary diagnosis of different neurodegenerative disorders.

Focusing on tauopathies, the presence of tau in CSF was first described in 1993. In AD, tau inclusions in the brain associated with neuronal damages lead to the leakage of abnormal forms of tau in the CSF resulting in quantitative and qualitative changes in CSF-tau composition. Numerous studies demonstrated increased CSF total tau and phosphorylated tau levels in AD, with mean levels 2-3 times higher compared to healthy controls. Tau is now a validated biomarker for AD, it improves the clinical diagnostic accuracy and its assessment for AD diagnosis is now proposed (Dubois B et al., 2010). As the brain lesions develop very early during the disease course even before the first clinical symptoms appear, CSF tau is not only a useful diagnostic marker in the advanced stages of the disease but also a usefull predictive marker in the earliest stages when clinical expression is weak (Hertze J et al., 2010). However, for differential diagnosis of dementia, the actually available tests measuring tau and phosphorylated tau levels in CSF are not sufficient and the identification of more specific postranslational modifications of tau in AD by proteomic approaches is needed. In the future, for the use of tau as biomarker in large clinical trials or in clinical practice, one important goal will be to develop sensitive methods to detect the very low concentration of tau in the blood (<1 pMol). Therefore, sample pre-treatment and handling will be crucial in developing a reliable tau assay in blood/plasma.

#### **7. Conclusion**

312 Proteomics – Human Diseases and Protein Functions

expressing animals induces assembly of wild-type human tau into filaments and spreading of pathology from the site of injection to neighbouring brain regions (Clavaguera et al.,

Another transgenic mice model is TauRD/ΔK280 that expresses only the 4R tau domains and carry the ΔK280 mutation with a deletion of the amino- and carboxy terminal regions of tau protein. This mutation leads to tau aggregation followed by astrogliosis and neuronal loss. When the transgene is switched off the aggregation of the exogenous tau disappears within around one month and a haf and only aggregated murine tau proteins remain acting as a nucleation factor for tau aggregation (Mocanu MM et al., 2008). Other study suggest a "prion –like" propagation since aggregation continues even if the original tau species have

The K3 transgenic mouse strain expresses human tau carrying the K369I mutation under the Thy1 promoter (Ittner LM et al., 2008). This tau mutation was found in a family of patients presenting with Pick's disease without parkinsonism and amyotrophy (Neumann M et al., 2001). The transgenic mice present early-onset memory impairment and amyotrophy in the absence of overt neurodegeneration. Tau transgene is mainly expressed in the substantia nigra and such expression leads to an early-onset parkinsonism phenotype. Interestingly, motor performance of young, but not old K3 mice improves upon L-dopa treatment. Amyotrophy is probable to be related to tau expression in the sciatic nerve in the same way as in Tg30tau model where pathogenic mutations (P301S and G272V) are expressed in the forebrain and the spinal cord showing progressive motor impairment with neurogenic muscle atrophy besides the hippocampal atrophy (Leroy K et al., 2007). Moreover, transgenic mouse model overexpressing human 1N4R double-mutant tau (P301S and G272V) and invalidated endogenous TAU gene show an accelerated human mutant tau aggregation (Ando K et al., 2011) suggesting that murine tau proteins may act as inhibitors

Thy-Tau22 mouse transgenic line exhibits progressive neuron-specific AD-like tau pathology devoid of any motor deficits (Schindowski K et al., 2006). In addition to neurofibrillary tangle-like inclusions and mild astrogliosis, this model shows hyper- and abnormally phosphorylated tau on several Alzheimer's disease-relevant tau epitopes that accumulates within the somato-dendritic area in the hippocampus (Schindowski K et al., 2008). A progressive development of NFTs is observed in the hippocampus and amygdala, which parallels behavioural impairments as well as electrophysiological alterations (Van der Jeugd et al., 2011). These latter changes are observed despite any striking loss of neuronal/synaptic markers until 12 months of age in the hippocampus. Interestingly, at that time point, THY-Tau22 mice exhibit septo-hippocampal tau pathology accompanied by altered retrograde transport from hippocampus to medial septum (Belarbi K et al., 2009) with an accumulation of the nerve growth factor (NGF) levels in the hippocampus consistent with a decrease of its uptake or retrograde transport by cholinergic terminals (Belarbi K et al., in press). Recent data indicate that voluntary exercise prevented memory alterations in these transgenic mice and increased mRNA levels of genes involved in

Searching for biomarkers is one of the most challenges in current medicine. Biomarkers must be not only specific for a single pathology but also indicative of its progression

cholesterol trafficking such as NPC1 and NPC2 (Belarbi K et al., 2011).

**6. Tau proteins as biomarkers of Tauopathies** 

disappeared (Sydow A and Mandelkov EM, 2010).

2009).

of tau aggregation.

Tau is a neuronal protein that promotes neuronal survival, it is essentially located within the axonal and indispensable for the organization, stabilization, and dynamics of microtubules. The interaction between tau and microtubules is regulated by phosphorylation. It is widely reported that abnormally and hyperphosphorylated tau proteins lead to insoluble aggregates. The presence of these aggregates is clinically correlated with cognitive decline in a process called NFD; this event common to more than twenty diseases is referred as tauopathies.

The development of the proteomics era has achieved to go further in the characterization of tauopathies and shed light to the mechanism involved in their aetiology. Proteomics approaches as chromatography, mono- and bi-dimensional gel electrophoresis have reached to separate proteins with a quite high resolution after fractioning precedures, selecting a concrete population of cells or organelle isolation. The use of additional reagents to the extraction buffer such as detergents and the evolution of concomitant technologies as microscopy have provided a broad spectrum to characterize the structure and size of a large number of biological complex samples. The combination of protein separation methods with fluorescence dyes and radioactive isotopes (ICAT, iTRAQ, SILAC) makes possible not only more sensitive and reproducible results but also provides a quantitative analysis among samples (2D-DIGE, LC-MS, SELDI).

The previous hallmark is extremely linked to the identification of the separated or isolated proteins. MS has provided the composition of the molecules and also their post-translational modifications since changes in amino acid residues may be identified and characterized by

The Microtubule-Dissociating Tau in Neurological Disorders 315

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The generation of animal models helps to elucidate the genetic and proteomics aspects involved during the origin and development of tauopathies. Only by the knowledge of the different components of the disease and their contribution, it will be possible to proceed in the right way.

In summary, numerous proteomics approaches are available in order to accomplish new perspectives in the neurodegenerative disorders field. At the moment there are many studies focused on finding out the functions of tau protein thoughtout proteomics approaches. Proteomics methods allow to uncover the different signaling pathways involved in tau biology, proteomics data are intimately related to the protein state: posttranslational modifications, cleavage, conformation, synthesis, degradation and activity (if it is known). The selection of the different techniques depends on the aim of the research: protein identification, de novo peptide sequencing, and identification of post-translational modifications or determination of protein-protein interactions. Understanding the human proteome and its variations in physiological and pathological conditions will be intimately related to uncover cellular and molecular pathways involved in the aetiology and progression of the tauopathies as well as to identify potential targets for drug design.

#### **8. Acknowledgment**

The authors thank Dr. Nicolas Sergeant, Dr Malika Hamdane and Dr. David Blum for careful reading and comments on the manuscript. This work was supported by Inserm and Lille2 University (France). F.J.F-G has a post-doctoral contract from the French National Research Agency (ANR).

#### **9. References**


MS/MS, Peptide Mass Fingerprinting and NMR. The utilization of the current available ionization sources as ESI and MALDI coupled to mass analysers mainly TOF allows almost any compound to be analysed by MS at low levels in complex mixtures. Furthermore, there are a large number of software tools dedicated to facilitate raw data processing, databasedependent search, statistical evaluation of the search result, quantitative algorithms and

The generation of animal models helps to elucidate the genetic and proteomics aspects involved during the origin and development of tauopathies. Only by the knowledge of the different components of the disease and their contribution, it will be possible to proceed in

In summary, numerous proteomics approaches are available in order to accomplish new perspectives in the neurodegenerative disorders field. At the moment there are many studies focused on finding out the functions of tau protein thoughtout proteomics approaches. Proteomics methods allow to uncover the different signaling pathways involved in tau biology, proteomics data are intimately related to the protein state: posttranslational modifications, cleavage, conformation, synthesis, degradation and activity (if it is known). The selection of the different techniques depends on the aim of the research: protein identification, de novo peptide sequencing, and identification of post-translational modifications or determination of protein-protein interactions. Understanding the human proteome and its variations in physiological and pathological conditions will be intimately related to uncover cellular and molecular pathways involved in the aetiology and

progression of the tauopathies as well as to identify potential targets for drug design.

The authors thank Dr. Nicolas Sergeant, Dr Malika Hamdane and Dr. David Blum for careful reading and comments on the manuscript. This work was supported by Inserm and Lille2 University (France). F.J.F-G has a post-doctoral contract from the French National

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**15** 

**Identification of Factors** 

*3INSERM, U967, F-92265 Fontenay-aux-Roses* 

*Institut de Radiobiologie Cellulaire et Moléculaire,* 

*Fontenay-aux-Roses* 

*France* 

**Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse** 

François Chevalier1\*, Alexandra Chicheportiche2,3,4,5\*,

*5Université Paris Sud, UMR 967, F-92265 Fontenay-aux-Roses,* 

Mathieu Daynac2,3,4,5, Jordane Depagne1, Pascale Bertrand1, François D. Boussin2,3,4,5 and Marc-André Mouthon2,3,4,5

**Subventricular Zone: A Preliminary Study** 

*1CEA DSV iRCM, Plateforme de Protéomique, F-92265 Fontenay-aux-Roses* 

*2CEA DSV iRCM SCSR, Laboratoire de Radiopathologie, F-92265 Fontenay-aux-Roses* 

*4Université Paris Diderot, Sorbonne Paris Cité, UMR 967, F-92265 Fontenay-aux-Roses* 

Neurogenesis insures the production of functional neurons throughout life and occurs in two narrow regions of the adult mammalian brain, the subventricular zone (SVZ) lining the lateral ventricles and the subgranular zone in the hippocampus (Alvarez-Buylla&Lim,2004). The adult SVZ, which is separated from the lateral ventricle by a layer of epithelial cells known as ependymal cell, contains three main populations of neural progenitors: neural stem cells (NSCs), transit-amplifying cells (TAPs) and neuroblasts. NSCs are undifferentiated cells generally characterized by their functional capacities to both selfrenew and to generate a large number of differentiated progeny cells (Song, et al.,2011). Adult NSCs have an astrocyte-like phenotype (type B cells) and represent only 0.2-0.4 % of the SVZ cells, they are relatively quiescent and divide very slowly *in vivo* with a cell cycle length of 14 days (Morshead, et al.,1994). These cells are the precursors of rapidly dividing TAPs, or type C cells, which have the capacity to differentiate into neuroblasts (type A). Neuroblasts are organized as migratory chains along the rostral migratory stream (RMS)

The highly organised cytoarchitecture of the SVZ constitutes a niche for NSCs (Ihrie&Alvarez-Buylla, 2011). Cells localized in this specific microenvironment secrete a variety of factors involved in NSC proliferation, migration and/or differentiation

and integrate the Olfactory bulb (OB) to become interneurons (Fig. 1).

**1. Introduction** 

 \*

Contributed equally to this work


### **Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study**

François Chevalier1\*, Alexandra Chicheportiche2,3,4,5\*, Mathieu Daynac2,3,4,5, Jordane Depagne1, Pascale Bertrand1, François D. Boussin2,3,4,5 and Marc-André Mouthon2,3,4,5 *1CEA DSV iRCM, Plateforme de Protéomique, F-92265 Fontenay-aux-Roses 2CEA DSV iRCM SCSR, Laboratoire de Radiopathologie, F-92265 Fontenay-aux-Roses 3INSERM, U967, F-92265 Fontenay-aux-Roses 4Université Paris Diderot, Sorbonne Paris Cité, UMR 967, F-92265 Fontenay-aux-Roses 5Université Paris Sud, UMR 967, F-92265 Fontenay-aux-Roses, Institut de Radiobiologie Cellulaire et Moléculaire, Fontenay-aux-Roses France* 

#### **1. Introduction**

326 Proteomics – Human Diseases and Protein Functions

Zilka, N.; Filipcik, P.; Koson, P.; Fialova, L.; Skrabana, R.; Zilkova, M.; Rolkova, G.;

Zuchner, T.; Schliebs, R. & Perez-Polo, JR. (2005). Down-regulation of muscarinic

Alzheimer's Association. Copyright © 2011 Alzheimer's Association®. All rights reserved.

Diane Hanger, MRC Centre for Neurodegeneration Research, King's College London, UK

relevant genes and proteins. *J Neurochem*, Vol. 95(1), pp. 20-32.

http://cnr.iop.kcl.ac.uk/hangerlab/tautable

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Kontsekova, E. & Novak, M. (2006). Truncated tau from sporadic Alzheimer's disease suffices to drive neurofibrillary degeneration in vivo. *FEBS Lett*, Vol.

acetylcholine receptor M2 adversely affects the expression of Alzheimer's disease-

Neurogenesis insures the production of functional neurons throughout life and occurs in two narrow regions of the adult mammalian brain, the subventricular zone (SVZ) lining the lateral ventricles and the subgranular zone in the hippocampus (Alvarez-Buylla&Lim,2004). The adult SVZ, which is separated from the lateral ventricle by a layer of epithelial cells known as ependymal cell, contains three main populations of neural progenitors: neural stem cells (NSCs), transit-amplifying cells (TAPs) and neuroblasts. NSCs are undifferentiated cells generally characterized by their functional capacities to both selfrenew and to generate a large number of differentiated progeny cells (Song, et al.,2011). Adult NSCs have an astrocyte-like phenotype (type B cells) and represent only 0.2-0.4 % of the SVZ cells, they are relatively quiescent and divide very slowly *in vivo* with a cell cycle length of 14 days (Morshead, et al.,1994). These cells are the precursors of rapidly dividing TAPs, or type C cells, which have the capacity to differentiate into neuroblasts (type A). Neuroblasts are organized as migratory chains along the rostral migratory stream (RMS) and integrate the Olfactory bulb (OB) to become interneurons (Fig. 1).

The highly organised cytoarchitecture of the SVZ constitutes a niche for NSCs (Ihrie&Alvarez-Buylla, 2011). Cells localized in this specific microenvironment secrete a variety of factors involved in NSC proliferation, migration and/or differentiation

<sup>\*</sup> Contributed equally to this work

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 329

antagonist of bone morphogenetic proteins, antagonizes Bone morphogenetic protein signalling and stimulates neurogenesis (Lim, et al.,2000). Brain endothelial cells are thought to be crucial for the NSC niche because they lie in close proximity to NSCs (Tavazoie, et al.,2008), where they probably regulate self-renewal of NSCs and their differentiation into neurons (Ramirez-Castillejo, et al.,2006). Brain endothelial cells may also balance proliferation/quiescence of NSCs by secreting Bone morphogenetic proteins (Mathieu, et

A number of studies indicate that brain injury can induced SVZ cells to migrate towards non-OB areas especially towards lesions and participate in neuronal and glial repair (Alonso,1999, Cayre, et al.,2006, Goings, et al.,2004, Jankovski, et al.,1998, Macas, et al.,2006, Picard-Riera, et al.,2002, Yamashita, et al.,2006). Adult NSCs may therefore promise hopes in

Adult NSCs survive anti-mitotic cytosine arabinoside treatment *in vivo* unlike actively dividing TAPs and neuroblasts, which rapidly disappear. This assay has been used by Doetsch et al. to demonstrate that quiescent NSCs are able regenerate to the neurogenic SVZ niches (Doetsch, et al.,1999). Exposition of the brain to ionizing radiation induces apoptosis of proliferating cells in the SVZ (Shinohara, et al.,1997). For high doses of irradiation an incomplete repopulation of the SVZ occurred and neurogenesis is collapsed for long term

However, exposition to low doses allows SVZ to be replenished. A recovery of proliferating cells is observed starting 3 days after radiation and peaking at day 7 (Hopewell and

In light of the recovery capacity of SVZ, we developed a model of low dose irradiation (2 Gy) of adult mouse brain, which provokes a transient collapse of neurogenesis followed by a rapid recovery of the SVZ. The repopulation of the SVZ was most probably due to the stimulation of relatively quiescent NSCs and their proliferation (Morshead, et al.,1994,

Seek for factors involved in the stimulation of neurogenesis and production of new neurons remains a challenging task. We assume that our *in vivo* irradiation model will be helpful to identify proteins coinciding with SVZ reconstitution, i.e. those involved in NSC

Only few global proteomic analyses on rodent brains have been reported, attempting to identify proteins that are involved in brain injury such as middle cerebral artery occlusion ischemia (Sung, et al. 2010), or to gain further insight into the molecular mechanisms of neurodegenerative diseases (Broadwater, et al., Castegna, et al.,2002). Others proteomic studies aimed to identify crucial proteins by comparison between different brain developmental stages and during brain aging (Shoemaker, et al. 2010, Yang, et al.,2008). Therefore, we performed a global proteomic analysis based on 2 dimensional-gels and identification by mass spectroscopy in our SVZ reconstitution model. We decided to work with proteins extracted from SVZ, instead of cells isolated using a specific marker. Indeed, with all SVZ extract, including NSC, progenitor cells and the SVZ extracellular matrix, it was possible to search for protein and secreted factors potentially involved in NSC

In this preliminary study, an accurate analysis of 2D-gel revealed that several proteins from SVZ appeared as modulated following brain radiation. Important issues of this study are the

identification of candidates possibly involved in the stimulation of quiescent NSCs.

the repair of damaged central nervous system (Dubois-Dalcq,2005).

al.,2008).

(Tada, et al., 1999).

Pastrana, et al.,2009).

proliferation.

Cavanagh 1972; Tada, et al.,1999).

proliferation, migration and/or differentiation.

(A) In adult mouse brain, neurons are continuously produced in two restricted regions: the subgranular zone (SGZ) of the dentate gyrus in the hippocampus and the subventricular zone (SVZ) lining the lateral ventricles (LV). The former produces neurons that functionally integrate into the granular cell layer of the hippocampus, whereas the SVZ produces neuroblasts, which migrate along the rostral migratory stream (RMS) to integrate the olfactory bulbs (OB) and differentiate into interneurons. (B) The SVZ is an highly organized neurogenic area which contains three cell types of neural cells: neural stem cells (NSCs), transit-amplifying progenitors (TAPs) and neuroblasts. Ependymal cells that contact the NSCs are organized as a single layer of epithelial cells separating the SVZ from the ventricles.

(C) NSCs are defined as slow dividing cells having capacities to self-renew and to generate multiple cell types (neurons, astrocytes and oligodendrocytes). NSCs give rise to highly proliferating TAPs, which differentiate mostly in neuroblasts. NSCs are also able to produce oligodendrocytes and astrocytes according to development stages, in response to brain injury or *in vitro* conditions. The length of the cell cycle for NSCs and TAPs lasts 22 days and 12 h, respectively (Doetsch, et al.,1999, Morshead, et al.,1994). BV (blood vessel).

Fig. 1. Neurogenesis in the adult mouse brain

(Doetsch,2003). For example, GABA released from neuroblasts provides a feedback mechanism to control proliferation of NSCs in the SVZ (Liu, et al.,2005). Bone morphogenetic proteins signalling can direct neural progenitors to glial fate in the adult brain (Lim, et al.,2000), whereas the secretion by ependymal cells of Noggin, a polypeptide

(A) In adult mouse brain, neurons are continuously produced in two restricted regions: the subgranular zone (SGZ) of the dentate gyrus in the hippocampus and the subventricular zone (SVZ) lining the lateral ventricles (LV). The former produces neurons that functionally integrate into the granular cell layer of the hippocampus, whereas the SVZ produces neuroblasts, which migrate along the rostral migratory stream (RMS) to integrate the olfactory bulbs (OB) and differentiate into interneurons. (B) The SVZ is an highly organized neurogenic area which contains three cell types of neural cells: neural stem cells (NSCs), transit-amplifying progenitors (TAPs) and neuroblasts. Ependymal cells that contact the NSCs are organized as a single layer of epithelial cells separating the SVZ from the

(C) NSCs are defined as slow dividing cells having capacities to self-renew and to generate multiple cell types (neurons, astrocytes and oligodendrocytes). NSCs give rise to highly proliferating TAPs, which differentiate mostly in neuroblasts. NSCs are also able to produce oligodendrocytes and astrocytes according to development stages, in response to brain injury or *in vitro* conditions. The length of the cell cycle for NSCs and TAPs lasts 22 days and 12 h, respectively (Doetsch, et al.,1999, Morshead, et

(Doetsch,2003). For example, GABA released from neuroblasts provides a feedback mechanism to control proliferation of NSCs in the SVZ (Liu, et al.,2005). Bone morphogenetic proteins signalling can direct neural progenitors to glial fate in the adult brain (Lim, et al.,2000), whereas the secretion by ependymal cells of Noggin, a polypeptide

ventricles.

al.,1994). BV (blood vessel).

Fig. 1. Neurogenesis in the adult mouse brain

antagonist of bone morphogenetic proteins, antagonizes Bone morphogenetic protein signalling and stimulates neurogenesis (Lim, et al.,2000). Brain endothelial cells are thought to be crucial for the NSC niche because they lie in close proximity to NSCs (Tavazoie, et al.,2008), where they probably regulate self-renewal of NSCs and their differentiation into neurons (Ramirez-Castillejo, et al.,2006). Brain endothelial cells may also balance proliferation/quiescence of NSCs by secreting Bone morphogenetic proteins (Mathieu, et al.,2008).

A number of studies indicate that brain injury can induced SVZ cells to migrate towards non-OB areas especially towards lesions and participate in neuronal and glial repair (Alonso,1999, Cayre, et al.,2006, Goings, et al.,2004, Jankovski, et al.,1998, Macas, et al.,2006, Picard-Riera, et al.,2002, Yamashita, et al.,2006). Adult NSCs may therefore promise hopes in the repair of damaged central nervous system (Dubois-Dalcq,2005).

Adult NSCs survive anti-mitotic cytosine arabinoside treatment *in vivo* unlike actively dividing TAPs and neuroblasts, which rapidly disappear. This assay has been used by Doetsch et al. to demonstrate that quiescent NSCs are able regenerate to the neurogenic SVZ niches (Doetsch, et al.,1999). Exposition of the brain to ionizing radiation induces apoptosis of proliferating cells in the SVZ (Shinohara, et al.,1997). For high doses of irradiation an incomplete repopulation of the SVZ occurred and neurogenesis is collapsed for long term (Tada, et al., 1999).

However, exposition to low doses allows SVZ to be replenished. A recovery of proliferating cells is observed starting 3 days after radiation and peaking at day 7 (Hopewell and Cavanagh 1972; Tada, et al.,1999).

In light of the recovery capacity of SVZ, we developed a model of low dose irradiation (2 Gy) of adult mouse brain, which provokes a transient collapse of neurogenesis followed by a rapid recovery of the SVZ. The repopulation of the SVZ was most probably due to the stimulation of relatively quiescent NSCs and their proliferation (Morshead, et al.,1994, Pastrana, et al.,2009).

Seek for factors involved in the stimulation of neurogenesis and production of new neurons remains a challenging task. We assume that our *in vivo* irradiation model will be helpful to identify proteins coinciding with SVZ reconstitution, i.e. those involved in NSC proliferation.

Only few global proteomic analyses on rodent brains have been reported, attempting to identify proteins that are involved in brain injury such as middle cerebral artery occlusion ischemia (Sung, et al. 2010), or to gain further insight into the molecular mechanisms of neurodegenerative diseases (Broadwater, et al., Castegna, et al.,2002). Others proteomic studies aimed to identify crucial proteins by comparison between different brain developmental stages and during brain aging (Shoemaker, et al. 2010, Yang, et al.,2008). Therefore, we performed a global proteomic analysis based on 2 dimensional-gels and identification by mass spectroscopy in our SVZ reconstitution model. We decided to work with proteins extracted from SVZ, instead of cells isolated using a specific marker. Indeed, with all SVZ extract, including NSC, progenitor cells and the SVZ extracellular matrix, it was possible to search for protein and secreted factors potentially involved in NSC proliferation, migration and/or differentiation.

In this preliminary study, an accurate analysis of 2D-gel revealed that several proteins from SVZ appeared as modulated following brain radiation. Important issues of this study are the identification of candidates possibly involved in the stimulation of quiescent NSCs.

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 331

(A) A schematic representation of ventral face of the brain with the two cuts: one at the front just behind the OB and one at the level of optic chiasma. (B) Representative photographs of dorsal face and (C, D) ventral face of the mouse brain. (E, F) Coronal views of brain slices after cuts. (E) Lateral ventricles are visible from each part the septum. The ventricular walls to be dissected out containing the SVZ are

content was estimated in the supernatant using the Bradford assay. To limit variability, tissue pieces from mice with the same treatment were mixed together in proteomic sample

Two-dimensional electrophoresis was performed with at least 5 technical replicates. Briefly, precast 18 cm strips, pH range 3-10 NL (GE), were rehydrated in the presence of 100 µg of protein extract. Isoelectric focusing was carried out using a Protean IEF Cell (Bio-Rad, Hercules, CA, USA) isoelectric focusing system until 80 KV h-1. The strips were then incubated in the first equilibration solution (50 mM Tris–HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM DTT and then in the second equilibration solution (50 mM Tris–HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM iodoacetamide. Strips were then embedded using 1% (w/v) low-melting agarose on the top of the acrylamide gel. SDS-PAGE was carried out on a 12% acrylamide SDS-polyacrylamide

Gels were stained with Sypro-Ruby and scanned to images, which were digitized with a Typhoon 9400 fluorescent scanner (Typhoon 9400 GE) using the 532 nm excitation laser and the 610BP emission filter. Image were acquired at a 100 µm resolution with a 550 voltage

indicated by arrow and light blue lines.

**2.4 Two-dimensional electrophoresis** 

applied to the photomultiplier tube.

buffer.

Cx: cortex; cc: corpus callosum; St: striatum; Sp: septum. Fig. 3. Process for micro-dissection of the SVZ

gel, using the Dodeca Cell electrophoresis unit (Bio-Rad).

### **2. Material and methods**

#### **2.1 Mouse irradiation**

We used eight-week old male C57BL/6J mice (Janvier, Le Genest-Saint-Isle, France). All experimental procedures complied with the European Communities Council Directive of 24 November 1986 (86/609/EEC) and European Union guidelines.

Mice were irradiated with a medical Alcyon irradiator (γ-rays 60Co) (Fig. 2). Prior to radiation, mice were anesthetized with ketamine (75 mg/kg, Merial, Lyon, France) and medetomidine (1 mg/kg, Pfizer, Paris, France) by intraperitoneal (i.p.) route. Immobilized mice were placed under a lead shield in order to expose the head and to protect the rest of the body. A total dose of 2 Gy was delivered with a dose rate of 1 Gy/mn. After exposure, mice were woken up by i.p. injection of antipamezole (1 mg/kg, Pfizer, Paris, France).

Irradiation was performed with a medical irradiator (Alcyon, 60Co source). Irradiation window was focused at the level of the head of the anesthetized mouse. The rest of the body was protected by a lead shield.

Fig. 2. Schematic representation of brain irradiation

#### **2.2 Micro-dissection of the brain**

At different time points after radiation, mice were euthanatized and SVZ and striatum (STR) were micro-dissected (Fig. 3). The micro-dissection method used to isolate SVZ is very tricky since SVZ is a very tiny part of brain tissue. Briefly, skull was cut with scissors at the midline and carefully removed with forceps. The brain was transferred into a Petri dish containing phosphate buffered saline and 6g/L of glucose. OBs were removed and a coronal cut was made at the optical chiasma. Then, under a stereomicroscope, septum was removed from the fore part of the brain with small forceps. Lateral walls of ventricle, containing the SVZ, were microdissected using small forceps and cleared out of contaminating corpus callosum and STR. A piece of STR adjacent to SVZ was taken. Tissue pieces were immediately frozen in liquid nitrogen.

#### **2.3 Protein extraction**

Proteins were extracted from SVZ and STR of control (Ctr) and irradiated mice. Tissues were homogenized in buffer containing 9M urea, 4% CHAPS, 0.05% Triton X100, 65 mM DTT and a protease inhibitor cocktail (Roche) with a small Teflon pestle and cell debris were removed by ultra-centrifugation at 100 000g for 1 hour (TL100, Beckman). The protein

We used eight-week old male C57BL/6J mice (Janvier, Le Genest-Saint-Isle, France). All experimental procedures complied with the European Communities Council Directive of 24

Mice were irradiated with a medical Alcyon irradiator (γ-rays 60Co) (Fig. 2). Prior to radiation, mice were anesthetized with ketamine (75 mg/kg, Merial, Lyon, France) and medetomidine (1 mg/kg, Pfizer, Paris, France) by intraperitoneal (i.p.) route. Immobilized mice were placed under a lead shield in order to expose the head and to protect the rest of the body. A total dose of 2 Gy was delivered with a dose rate of 1 Gy/mn. After exposure, mice were woken up by i.p. injection of antipamezole (1 mg/kg, Pfizer, Paris, France).

Irradiation was performed with a medical irradiator (Alcyon, 60Co source). Irradiation window was focused at the level of the head of the anesthetized mouse. The rest of the body was protected by a lead

At different time points after radiation, mice were euthanatized and SVZ and striatum (STR) were micro-dissected (Fig. 3). The micro-dissection method used to isolate SVZ is very tricky since SVZ is a very tiny part of brain tissue. Briefly, skull was cut with scissors at the midline and carefully removed with forceps. The brain was transferred into a Petri dish containing phosphate buffered saline and 6g/L of glucose. OBs were removed and a coronal cut was made at the optical chiasma. Then, under a stereomicroscope, septum was removed from the fore part of the brain with small forceps. Lateral walls of ventricle, containing the SVZ, were microdissected using small forceps and cleared out of contaminating corpus callosum and STR. A piece of STR adjacent to SVZ was taken. Tissue pieces were

Proteins were extracted from SVZ and STR of control (Ctr) and irradiated mice. Tissues were homogenized in buffer containing 9M urea, 4% CHAPS, 0.05% Triton X100, 65 mM DTT and a protease inhibitor cocktail (Roche) with a small Teflon pestle and cell debris were removed by ultra-centrifugation at 100 000g for 1 hour (TL100, Beckman). The protein

November 1986 (86/609/EEC) and European Union guidelines.

Fig. 2. Schematic representation of brain irradiation

**2.2 Micro-dissection of the brain** 

immediately frozen in liquid nitrogen.

**2.3 Protein extraction** 

**2. Material and methods** 

**2.1 Mouse irradiation** 

shield.

(A) A schematic representation of ventral face of the brain with the two cuts: one at the front just behind the OB and one at the level of optic chiasma. (B) Representative photographs of dorsal face and (C, D) ventral face of the mouse brain. (E, F) Coronal views of brain slices after cuts. (E) Lateral ventricles are visible from each part the septum. The ventricular walls to be dissected out containing the SVZ are indicated by arrow and light blue lines.

Cx: cortex; cc: corpus callosum; St: striatum; Sp: septum.

Fig. 3. Process for micro-dissection of the SVZ

content was estimated in the supernatant using the Bradford assay. To limit variability, tissue pieces from mice with the same treatment were mixed together in proteomic sample buffer.

#### **2.4 Two-dimensional electrophoresis**

Two-dimensional electrophoresis was performed with at least 5 technical replicates. Briefly, precast 18 cm strips, pH range 3-10 NL (GE), were rehydrated in the presence of 100 µg of protein extract. Isoelectric focusing was carried out using a Protean IEF Cell (Bio-Rad, Hercules, CA, USA) isoelectric focusing system until 80 KV h-1. The strips were then incubated in the first equilibration solution (50 mM Tris–HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM DTT and then in the second equilibration solution (50 mM Tris–HCl pH 8.8, 6 M urea, 30% (v/v) glycerol, 2% (w/v) SDS) with 130 mM iodoacetamide. Strips were then embedded using 1% (w/v) low-melting agarose on the top of the acrylamide gel. SDS-PAGE was carried out on a 12% acrylamide SDS-polyacrylamide gel, using the Dodeca Cell electrophoresis unit (Bio-Rad).

Gels were stained with Sypro-Ruby and scanned to images, which were digitized with a Typhoon 9400 fluorescent scanner (Typhoon 9400 GE) using the 532 nm excitation laser and the 610BP emission filter. Image were acquired at a 100 µm resolution with a 550 voltage applied to the photomultiplier tube.

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 333

technical 2-D gel replicates (Fig. 4 B). In this case, a constant coefficient of variation was observed (about 20%) whatever spot abundance (Fig. 4 B) and sample treatment (Fig. 4 C). This analysis of 2-D gel reproducibility clearly showed the advantage to perform technical

Spots were excised from preparative two-dimensional electrophoresis gels by hand, and processed using a Packard Multiprobe II liquid-handling robot (Perkin Elmer, Courtaboeuf, France). After washing successively with water, 25 mM ammonium bicarbonate, acetonitrile / 25 mM ammonium bicarbonate (1:1, v/v) and acetonitrile, gel fragments were dried at 37°C. Protein digestion was carried out at 37°C for 5 hours following addition of 0.125 μg trypsin (sequencing grade, modified, Promega, Charbonières, France), and resulting fragments were extracted twice with 50 μL acetonitrile / water (1:1, v/v) containing 0.1 % trifluoroacetic acid for 15 min. Pooled supernatants were concentrated with a speedvac to a final volume of 20 μL. Peptides were simultaneously desalted and concentrated with C18 Zip-Tip micro-columns to a final volume of 3 μL, an aliquot of each sample was mixed (1/1) with the alpha-cyano-4- hydroxycinnamic acid matrix at half saturation in acetonitrile/water (1:1, v/v) and the mixture was immediately spotted on the MALDI target by the Multiprobe II robot. Mass spectra were recorded in the reflector mode on a UltraFlex II MALDI-TOF/TOF mass spectrometer (Bruker Daltonics, Bremen, Germany). Automatic annotation of monoisotopic masses was performed using Bruker's SNAPTM procedure. The MASCOT search engine software (Matrix Science, London, UK) was used to search the

The following parameters were used: mass tolerance of 30 to 100 ppm, a minimum of five peptides matching to the protein, carbamidomethylation of cysteine as fixed modification,

When low abundant spots could not be identified by PMF, LC-MS/MS analysis was conducted. Stained protein spots were excised manually, washed, digested with trypsin and extracted using formic acid. Protein digests were analysed using an ion trap mass spectrometer (Esquire HCT plus; Bruker, Billerica, MA, USA) coupled to a nanochromatography system (HPLC 1200, Agilent, Santa Clara, CA, USA) interfaced with an HPLC-Chip system (Chip Cube, Agilent). MS/MS data were searched against NCBI (National center for Biotechnology information) and MSDB databases using Mascot

We have developed a model of SVZ reconstitution after low dose irradiation (2 Gy) of adult mouse brain. Radiation exposure provoked a transient collapse of neurogenesis followed by

Cell proliferation was assessed in the SVZ by injection with the thymidine analog 5-bromo-2'-deoxyuridine (BrdU). We observed a sharp decrease in BrdU incorporation 72h after radiation, however, BrdU labelling subsequently recovered within 7 days after exposure (Fig. 6A). BrdU positive cells were scarce 72h after irradiation but most of them expressed

oxidation of methionine as variable modification, and one missed cleavage allowed.

replicates with mixed biological samples.

**2.7 MALDI-TOF MS analysis** 

NCBInr database.

software.

**2.8 Nano LC MS analysis** 

**3. Results and discussion** 

a rapid recovery of the SVZ (Fig. 5).

**3.1 Regeneration of the SVZ after radiation** 

#### **2.5 Image analysis**

Images from stained gels were analyzed using the Samespots software v4.1 (Non-linear Dynamics, UK). All pictures were first aligned and gel replicates were then grouped to create a global analysis with all conditions. Spots of each sample were compared between control and irradiated conditions. A multivariate statistic analysis was performed using the statistic mode of the Samespots software v4.1 (Non-linear Dynamics, UK). Spots with significant differences between control and irradiated cells (Anova t-test p<0.05) were first extracted. Then, only spots with a P value < 0.05 and a power > 0.8 were finally selected. Spots of interest were selected for subsequent protein identification by mass spectrometry analysis.

#### **2.6 Two-D gel reproductibility**

Using gel-based proteomics analysis, it is important to estimate the contribution of biological and technical variations. We assessed the degree of biological variation inherent to the 2-DE process. SVZ were extracted separately from 9 mice. For each mouse, about 150 µg proteins were extracted in buffer containing 9M urea, 4% CHAPS, 0.05% Triton X100, 65 mM DTT and a protease inhibitor cocktail (Roche) then were used to produce a corresponding 2D-gel. Protein spot volume was determined for all spots matched in an experimental set using the Samespots software packages with default settings, as used for all the study. Coefficients of variation (CV) were calculated for each protein sample, or as a function of spot intensity. CV was calculated as a percentage of standard variation as related to mean: (SD/mean) x100 (Anderson, et al., 1985).

The SVZ of each animal were used to perform a single 2-D gel. All spots of the corresponding 9 gels were compared as biological replicates (Fig. 4 A).

(A) coefficient of variation of SVZ Ctr samples between 9 biological replicates, as a function of spot intensity (HAS: high abundant spots; MHAS: medium high abundant spots; MLAS: medium low abundant spots; LAS: low abundant spots).

(B) coefficient of variation of all technical replicates, as a function of spot intensity.

(C) comparison between coefficients of variation of biological replicates (BR) and technical replicates (TR) of SVZ and STR samples.

Fig. 4. Biological and technical variability of proteomes as measured by 2-D gel electrophoresis.

Coefficient of variation grew up from 20% for high abundant spots to about 40% for low abundant spots. As a comparison, the degree of technical variation inherent to the 2-DE process was estimated for each SVZ and STR samples. Biological samples corresponding to the same treatment were mixed and the resulting protein samples were used to perform technical 2-D gel replicates (Fig. 4 B). In this case, a constant coefficient of variation was observed (about 20%) whatever spot abundance (Fig. 4 B) and sample treatment (Fig. 4 C). This analysis of 2-D gel reproducibility clearly showed the advantage to perform technical replicates with mixed biological samples.

#### **2.7 MALDI-TOF MS analysis**

332 Proteomics – Human Diseases and Protein Functions

Images from stained gels were analyzed using the Samespots software v4.1 (Non-linear Dynamics, UK). All pictures were first aligned and gel replicates were then grouped to create a global analysis with all conditions. Spots of each sample were compared between control and irradiated conditions. A multivariate statistic analysis was performed using the statistic mode of the Samespots software v4.1 (Non-linear Dynamics, UK). Spots with significant differences between control and irradiated cells (Anova t-test p<0.05) were first extracted. Then, only spots with a P value < 0.05 and a power > 0.8 were finally selected. Spots of interest were selected for

Using gel-based proteomics analysis, it is important to estimate the contribution of biological and technical variations. We assessed the degree of biological variation inherent to the 2-DE process. SVZ were extracted separately from 9 mice. For each mouse, about 150 µg proteins were extracted in buffer containing 9M urea, 4% CHAPS, 0.05% Triton X100, 65 mM DTT and a protease inhibitor cocktail (Roche) then were used to produce a corresponding 2D-gel. Protein spot volume was determined for all spots matched in an experimental set using the Samespots software packages with default settings, as used for all the study. Coefficients of variation (CV) were calculated for each protein sample, or as a function of spot intensity. CV was calculated as a percentage of standard variation as related

The SVZ of each animal were used to perform a single 2-D gel. All spots of the

(A) coefficient of variation of SVZ Ctr samples between 9 biological replicates, as a function of spot intensity (HAS: high abundant spots; MHAS: medium high abundant spots; MLAS: medium low

(C) comparison between coefficients of variation of biological replicates (BR) and technical replicates

Coefficient of variation grew up from 20% for high abundant spots to about 40% for low abundant spots. As a comparison, the degree of technical variation inherent to the 2-DE process was estimated for each SVZ and STR samples. Biological samples corresponding to the same treatment were mixed and the resulting protein samples were used to perform

(B) coefficient of variation of all technical replicates, as a function of spot intensity.

Fig. 4. Biological and technical variability of proteomes as measured by 2-D gel

subsequent protein identification by mass spectrometry analysis.

to mean: (SD/mean) x100 (Anderson, et al., 1985).

abundant spots; LAS: low abundant spots).

(TR) of SVZ and STR samples.

electrophoresis.

corresponding 9 gels were compared as biological replicates (Fig. 4 A).

**2.5 Image analysis** 

**2.6 Two-D gel reproductibility** 

Spots were excised from preparative two-dimensional electrophoresis gels by hand, and processed using a Packard Multiprobe II liquid-handling robot (Perkin Elmer, Courtaboeuf, France). After washing successively with water, 25 mM ammonium bicarbonate, acetonitrile / 25 mM ammonium bicarbonate (1:1, v/v) and acetonitrile, gel fragments were dried at 37°C. Protein digestion was carried out at 37°C for 5 hours following addition of 0.125 μg trypsin (sequencing grade, modified, Promega, Charbonières, France), and resulting fragments were extracted twice with 50 μL acetonitrile / water (1:1, v/v) containing 0.1 % trifluoroacetic acid for 15 min. Pooled supernatants were concentrated with a speedvac to a final volume of 20 μL. Peptides were simultaneously desalted and concentrated with C18 Zip-Tip micro-columns to a final volume of 3 μL, an aliquot of each sample was mixed (1/1) with the alpha-cyano-4- hydroxycinnamic acid matrix at half saturation in acetonitrile/water (1:1, v/v) and the mixture was immediately spotted on the MALDI target by the Multiprobe II robot. Mass spectra were recorded in the reflector mode on a UltraFlex II MALDI-TOF/TOF mass spectrometer (Bruker Daltonics, Bremen, Germany). Automatic annotation of monoisotopic masses was performed using Bruker's SNAPTM procedure. The MASCOT search engine software (Matrix Science, London, UK) was used to search the NCBInr database.

The following parameters were used: mass tolerance of 30 to 100 ppm, a minimum of five peptides matching to the protein, carbamidomethylation of cysteine as fixed modification, oxidation of methionine as variable modification, and one missed cleavage allowed.

#### **2.8 Nano LC MS analysis**

When low abundant spots could not be identified by PMF, LC-MS/MS analysis was conducted. Stained protein spots were excised manually, washed, digested with trypsin and extracted using formic acid. Protein digests were analysed using an ion trap mass spectrometer (Esquire HCT plus; Bruker, Billerica, MA, USA) coupled to a nanochromatography system (HPLC 1200, Agilent, Santa Clara, CA, USA) interfaced with an HPLC-Chip system (Chip Cube, Agilent). MS/MS data were searched against NCBI (National center for Biotechnology information) and MSDB databases using Mascot software.

#### **3. Results and discussion**

#### **3.1 Regeneration of the SVZ after radiation**

We have developed a model of SVZ reconstitution after low dose irradiation (2 Gy) of adult mouse brain. Radiation exposure provoked a transient collapse of neurogenesis followed by a rapid recovery of the SVZ (Fig. 5).

Cell proliferation was assessed in the SVZ by injection with the thymidine analog 5-bromo-2'-deoxyuridine (BrdU). We observed a sharp decrease in BrdU incorporation 72h after radiation, however, BrdU labelling subsequently recovered within 7 days after exposure (Fig. 6A). BrdU positive cells were scarce 72h after irradiation but most of them expressed

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 335

Mice were injected with BrdU 3 hours before sacrifice to label proliferating cells in control mice and after 2Gy-irradiation. Brain slices were counterstained with 4',6-diamidino-2-phenylindole (DAPI). (A) BrdU positive cells almost completely disappeared 72h post irradiation then they subsequently recovered in the SVZ niches.(B) 72 hours post-irradiation, some GFAP positive cells (pink), corresponding to candidate NSCs, have incorporated BrdU positive (green). At that time after irradiation, essentially NSCs proliferated in the SVZ niche. This result might underlie an activation of relatively quiescent NSCs 72h after irradiation. (C) 7 days after irradiation, the SVZ regeneration is evidenced by numerous neuroblasts PSA-NCAM postivie (green) and BrdU positive (red) in the SVZ.

Fig. 6. Regeneration of the SVZ after irradiation

Radiation exposure of mice at 2Gy provoked a transient disappearance of progenitors of the subventricular zone (SVZ) such as transit-amplifying cells (Type C cell) and neuroblasts (Type A cell) at 72 hours. Four days later, those cells had almost completely replenished the SVZ.

Fig. 5. Schematic representation of SVZ reconstitution model after adult mouse brain irradiation.

Glial Fibrillary acidic protein (GFAP), a marker for NSCs, suggesting that quiescent NSCs are activated to re-enter cell cycle. Seven days after radiation, more numerous BrdU positive cells were observed and expressed the neuroblast marker PSA-NCAM arguing that neurogenesis recovered (Fig. 6B).

On the basis of these data and with the aim of finding proteins, such as growth factors secreted in the microenvironment involved in regeneration of the cells of the SVZ after radiation, we extracted proteins of non-irradiated SVZ and 2 Gy-irradiated SVZ at 3 and 7 days after exposure.

Radiation exposure of mice at 2Gy provoked a transient disappearance of progenitors of the

Fig. 5. Schematic representation of SVZ reconstitution model after adult mouse brain

72 hours. Four days later, those cells had almost completely replenished the SVZ.

irradiation.

neurogenesis recovered (Fig. 6B).

days after exposure.

subventricular zone (SVZ) such as transit-amplifying cells (Type C cell) and neuroblasts (Type A cell) at

Glial Fibrillary acidic protein (GFAP), a marker for NSCs, suggesting that quiescent NSCs are activated to re-enter cell cycle. Seven days after radiation, more numerous BrdU positive cells were observed and expressed the neuroblast marker PSA-NCAM arguing that

On the basis of these data and with the aim of finding proteins, such as growth factors secreted in the microenvironment involved in regeneration of the cells of the SVZ after radiation, we extracted proteins of non-irradiated SVZ and 2 Gy-irradiated SVZ at 3 and 7

Mice were injected with BrdU 3 hours before sacrifice to label proliferating cells in control mice and after 2Gy-irradiation. Brain slices were counterstained with 4',6-diamidino-2-phenylindole (DAPI). (A) BrdU positive cells almost completely disappeared 72h post irradiation then they subsequently recovered in the SVZ niches.(B) 72 hours post-irradiation, some GFAP positive cells (pink), corresponding to candidate NSCs, have incorporated BrdU positive (green). At that time after irradiation, essentially NSCs proliferated in the SVZ niche. This result might underlie an activation of relatively quiescent NSCs 72h after irradiation. (C) 7 days after irradiation, the SVZ regeneration is evidenced by numerous neuroblasts PSA-NCAM postivie (green) and BrdU positive (red) in the SVZ.

Fig. 6. Regeneration of the SVZ after irradiation

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 337

Fig. 8. Two-dimensional electrophoresis of SVZ proteins, separated under reducing

the second dimension.

conditions using 18-cm pH 3-10 strips for the first dimension, and a 12% acrylamide gel for

#### **3.2 Proteomic strategy**

We combined 2D-gel electrophoresis and MS analyses of SVZ samples to determine proteins that are altered following radiation in adult neurogenic niches in comparison to a nonneurogenic brain region, i.e. STR. Protein samples were separated using two-dimensional electrophoresis in 3-10 non-linear pH gradient strips and 12% acrylamide gels.

SVZ samples obtained from irradiated mice at 3 and 7 days after radiation were compared with SVZ samples from non-irradiated control mice (Fig. 7). Along with micro-dissection procedures, SVZ can be contaminated with STR. For that reason and because the striatum is not a neurogenic zone, non-irradiated and 7 days-irradiated striatum samples were also taken.

Fig. 7. Proteomic strategy of SVZ and STR samples comparison with no radiation (Ctr) and 3 days (IR 3) or 7 days (IR 7) after radiation.

#### **3.3 Proteomic map analysis**

As it can be observed in Fig. 7, pictures corresponding to the different samples analysed were very close and only minor differences can be observed, using a dedicated image analysis software.

From the 871 spots observed, a total of 36 spots were significantly modified for all the comparisons (Fig. 8). Thirty-two spots were modulated after irradiation and 4 spots were different between SVZ and STR. From these last spots, only one spot was more abundant in the SVZ than in STR suggesting a protein specifically expressed in the SVZ.

Using non-irradiated SVZ as control, 5 spots were modulated 3 days after radiation, 16 spots were modulated at 7 days (Table 1). Sixteen spots were altered at day 7 after radiation when compared with day 3. In addition, SVZ samples were also compared with STR and 4 spots appeared as modulated 7 days following radiation. The variation in spot intensity was ranged between 1.2 and 1.9 that was in agreement with previously published data using similar 2D-gel approach (Broadwater, et al. 2011, Gasperini, et al. 2011).

These spots, according to their localisation on the 2D reference map (Fig. 8), displayed an heterogeneous repartition on the gel, with a large range of iso-electric points (from 4 to 8) and of molecular weights (from 13 to 46 kDa).

Different profiles were established according to spot modifications, as illustrated in the Fig. 9.

We combined 2D-gel electrophoresis and MS analyses of SVZ samples to determine proteins that are altered following radiation in adult neurogenic niches in comparison to a nonneurogenic brain region, i.e. STR. Protein samples were separated using two-dimensional

SVZ samples obtained from irradiated mice at 3 and 7 days after radiation were compared with SVZ samples from non-irradiated control mice (Fig. 7). Along with micro-dissection procedures, SVZ can be contaminated with STR. For that reason and because the striatum is not a neurogenic zone, non-irradiated and 7 days-irradiated striatum samples were also

Fig. 7. Proteomic strategy of SVZ and STR samples comparison with no radiation (Ctr) and 3

As it can be observed in Fig. 7, pictures corresponding to the different samples analysed were very close and only minor differences can be observed, using a dedicated image

From the 871 spots observed, a total of 36 spots were significantly modified for all the comparisons (Fig. 8). Thirty-two spots were modulated after irradiation and 4 spots were different between SVZ and STR. From these last spots, only one spot was more abundant in

Using non-irradiated SVZ as control, 5 spots were modulated 3 days after radiation, 16 spots were modulated at 7 days (Table 1). Sixteen spots were altered at day 7 after radiation when compared with day 3. In addition, SVZ samples were also compared with STR and 4 spots appeared as modulated 7 days following radiation. The variation in spot intensity was ranged between 1.2 and 1.9 that was in agreement with previously published data using

These spots, according to their localisation on the 2D reference map (Fig. 8), displayed an heterogeneous repartition on the gel, with a large range of iso-electric points (from 4 to 8)

Different profiles were established according to spot modifications, as illustrated in the Fig. 9.

the SVZ than in STR suggesting a protein specifically expressed in the SVZ.

similar 2D-gel approach (Broadwater, et al. 2011, Gasperini, et al. 2011).

electrophoresis in 3-10 non-linear pH gradient strips and 12% acrylamide gels.

**3.2 Proteomic strategy** 

days (IR 3) or 7 days (IR 7) after radiation.

and of molecular weights (from 13 to 46 kDa).

**3.3 Proteomic map analysis** 

analysis software.

taken.

Fig. 8. Two-dimensional electrophoresis of SVZ proteins, separated under reducing conditions using 18-cm pH 3-10 strips for the first dimension, and a 12% acrylamide gel for the second dimension.

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 339

Interestingly, 79% of the spot variations were obtained from comparison between nonirradiated SVZ and irradiated SVZ and appeared to be specific of the SVZ. Forty three percent of these spots were found modified between 3 and 7 days post-irradiation that might underlie functions of these proteins during the intense proliferation phase. Thirteen percent of them were modified 3 days after radiation as compared with control SVZ

The rest of them (21%) corresponded to spots altered in both regions after radiation in comparison to respective region controls, suggesting a role of these proteins in the response

Among the 32 spots identified with a significant response to radiation, we eliminated 9 abundant spots (more than 0.1% of the total volume of spots) because they might not correspond to proteins involved in proliferation processes but rather have a cytoarchitecture function in the tissue. In a first round of analysis, among the 21 remaining spots, only 15 were abundant enough and well separated in the gel to be excised allowing identification by

We verified the expression of the genes corresponding to the identified proteins (Table1) using the Allen Brain Atlas database (Fig. 10). This database contained a thorough list of genes expression in the mouse brain by RNA hybridization (http://mouse.brain-map.org) (Lein, et al.,2007). We found that indeed 47% of our identified proteins have its gene expressed in the SVZ; the others being at least expressed in the adjacent regions, i.e. striatum

Identified proteins were classified according to their known biological activity (Fig. 11). Surprisingly, we did not identify proteins matching with growth factors or cell cycle regulation, as they may be present in too small quantity to be identified. Otherwise, we cannot exclude that gel resolution might not be optimized for the identification of this type

Myelin basic protein (MBP) that wraps axons has been identified in two adjacent spots (41, 42) that varied according to Pi but not in their MW indicating posttranslational modifications. Strikingly, the amount of these spots decreased after radiation, which suggested a degradation of myelin sheets by radiation. This is of importance because MBP modifications have never been reported with such a low radiation dose (Tian, et al.,2008). Otherwise, this current proteomic analysis of SVZ demonstrates that a 2 Gy-radiation exposure affected major cellular functions such as proteasome, energy production, vesicle

Thirteen percent of the spots belong to the proteasome system known to be involved in the degradation of unneeded or damaged proteins by proteolysis (Mcbride, et al.,2003). The intensity of these spots were decreased between 3 and 7 days (spot) 34 or only 7 days post irradiation (spot 3) that might underscore a decrease of proteasome activity that has already been reported after irradiation in a variety of cell types (Mcbride, et al.,2002, Pajonk&Mcbride,2001). This reduction of proteasome activity has been proposed to be related, at least in part, to an increased expression of proteasome inhibitors

Twenty percent of the spots corresponded to proteins having functions in metabolism pathways. Two of them (5, 44) were modified within 3 days after radiation. Another was increased 7 days after radiation and the identified protein have been implicated in

suggesting a role for the proteins in the activation of quiescent NSCs.

to radiation.

MS (Table 1).

of proteins.

**3.4 Biological functions of the proteins** 

or in the corpus callosum (**Fig. 10**).

trafficking and cytoskeletal maintenance.

(Conconi&Friguet, 1997, Zaiss, et al., 2002).

In comparison to non-irradiated SVZ: (A,A') early modified from 3 days after radiation, (B) downregulated by irradiation from 3 days to 7days or (C,C') late response to radiation at 7 days. In comparison to STR (D): no response to radiation. Numbers of spots corresponded to those listed in Fig. 3 and Table 1. Results are representative of volume intensity of the spot ± standard deviation from 5 technical replicates.

Fig. 9. Intensity profiles of spots specifically modified in the SVZ

Interestingly, 79% of the spot variations were obtained from comparison between nonirradiated SVZ and irradiated SVZ and appeared to be specific of the SVZ. Forty three percent of these spots were found modified between 3 and 7 days post-irradiation that might underlie functions of these proteins during the intense proliferation phase. Thirteen percent of them were modified 3 days after radiation as compared with control SVZ suggesting a role for the proteins in the activation of quiescent NSCs.

The rest of them (21%) corresponded to spots altered in both regions after radiation in comparison to respective region controls, suggesting a role of these proteins in the response to radiation.

Among the 32 spots identified with a significant response to radiation, we eliminated 9 abundant spots (more than 0.1% of the total volume of spots) because they might not correspond to proteins involved in proliferation processes but rather have a cytoarchitecture function in the tissue. In a first round of analysis, among the 21 remaining spots, only 15 were abundant enough and well separated in the gel to be excised allowing identification by MS (Table 1).

#### **3.4 Biological functions of the proteins**

338 Proteomics – Human Diseases and Protein Functions

In comparison to non-irradiated SVZ: (A,A') early modified from 3 days after radiation, (B) downregulated by irradiation from 3 days to 7days or (C,C') late response to radiation at 7 days. In comparison to STR (D): no response to radiation. Numbers of spots corresponded to those listed in Fig. 3 and Table 1. Results are

representative of volume intensity of the spot ± standard deviation from 5 technical replicates.

Fig. 9. Intensity profiles of spots specifically modified in the SVZ

We verified the expression of the genes corresponding to the identified proteins (Table1) using the Allen Brain Atlas database (Fig. 10). This database contained a thorough list of genes expression in the mouse brain by RNA hybridization (http://mouse.brain-map.org) (Lein, et al.,2007). We found that indeed 47% of our identified proteins have its gene expressed in the SVZ; the others being at least expressed in the adjacent regions, i.e. striatum or in the corpus callosum (**Fig. 10**).

Identified proteins were classified according to their known biological activity (Fig. 11). Surprisingly, we did not identify proteins matching with growth factors or cell cycle regulation, as they may be present in too small quantity to be identified. Otherwise, we cannot exclude that gel resolution might not be optimized for the identification of this type of proteins.

Myelin basic protein (MBP) that wraps axons has been identified in two adjacent spots (41, 42) that varied according to Pi but not in their MW indicating posttranslational modifications. Strikingly, the amount of these spots decreased after radiation, which suggested a degradation of myelin sheets by radiation. This is of importance because MBP modifications have never been reported with such a low radiation dose (Tian, et al.,2008).

Otherwise, this current proteomic analysis of SVZ demonstrates that a 2 Gy-radiation exposure affected major cellular functions such as proteasome, energy production, vesicle trafficking and cytoskeletal maintenance.

Thirteen percent of the spots belong to the proteasome system known to be involved in the degradation of unneeded or damaged proteins by proteolysis (Mcbride, et al.,2003). The intensity of these spots were decreased between 3 and 7 days (spot) 34 or only 7 days post irradiation (spot 3) that might underscore a decrease of proteasome activity that has already been reported after irradiation in a variety of cell types (Mcbride, et al.,2002, Pajonk&Mcbride,2001). This reduction of proteasome activity has been proposed to be related, at least in part, to an increased expression of proteasome inhibitors (Conconi&Friguet, 1997, Zaiss, et al., 2002).

Twenty percent of the spots corresponded to proteins having functions in metabolism pathways. Two of them (5, 44) were modified within 3 days after radiation. Another was increased 7 days after radiation and the identified protein have been implicated in

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 341

Fig. 10. An example of the expression of one gene corresponding to an identified protein using the Allen Brain Atlas database. This gene is expressed along the SVZ, a part of the

**Biological functions Spot number Response to radiation** 

3 15 45

30

Proteasome 31 Late

Cytoskeleton 1 Mid

25 41 42

Fig. 11. Distribution of the proteins identified by MS according to their biological function.

Late Late Early

No response Late

> Early Mid Late

34 Mid

5 Early 16 Late 44 Early

19 Early

RMS and also in the cortex (cx) but not in the corpus callosum (cc).

Mitochondrial respiratory chain

Metabolism

Axogenesis/myelination

Intracellular transport <sup>20</sup>


Table 1. List of spots significantly modified in: control SVZ; 3 days following radiation SVZ, 7 days following radiation SVZ, control STR; 7 days following radiation STR. Spot number; experimental protein molecular weight and pI; variation level, under-expressed (-) or overexpressed (+) in the corresponding sample, *vs* reference sample; and ANOVA, (significant when p<0.05). Spots indicated in bold were excised from the gels and analysed by MS to allow the identification the proteins. The abundance corresponds to the ratio (%) of the volume one spot on the total volume of spots.

**Spot Observed pI MW Abundance Variations reference sample ANOVA (p)** 1 5.28 16 0.03899 -1.9 in SVZ IR 7 SVZ CT 0.008

**2 6.53 49 0.09853 -1.3 in SVZ IR 7 SVZ IR 3 0.022**  3 5.95 25 0.05079 +1.3 in SVZ IR 7 SVZ CT 0.033 **4 6.4 62 0.06269 +1.3 in SVZ IR 7 SVZ IR 3 0.004**  5 6.41 36 0.04725 -1.3 in SVZ IR 3 SVZ CT 0.012 **7 6.68 60 0.27306 +1.3 in SVZ IR 7 SVZ IR 3 0.043 8 4.3 68 0.19432 +1.4 in STR CT SVZ CT 0.05 9 6.44 68 0.01876 +1.3 in SVZ IR 7 SVZ IR 3 0.019 11 6.12 57 0.04227 -1.3 in STR IR 7 STR CT 0.031 12 6.61 49 0.10724 +1.2 in SVZ IR 7 SVZ CT 0.05** 

**13 6.43 49 0.06881 +1.2 in SVZ IR 7 SVZ IR 3 0.028** 

**14 5.74 48 0.08356 +1.2 in SVZ IR 7 SVZ IR 3 0.004**  15 5.82 46 0.08980 +1.3 in SVZ IR 7 SVZ CT 0.02 16 5.98 44 0.06190 +1.4 in SVZ IR 7 SVZ CT 0.021 **17 6.53 43 0.10764 +1.3 in SVZ IR 7 SVZ IR 3 0.003 18 7.18 41 0.25750 +1.2 in STR CT SVZ CT 0.018**  19 5.58 40 0.07969 -1.2 in SVZ IR 3 SVZ CT 0.012 20 5.72 37 0.09955 -1.2 in STR CT SVZ CT 0.012 **21 7.28 36 0.03971 -1.4 in SVZ IR 7 SVZ CT 0.038 22 7.29 34 0.12789 +1.2 in SVZ IR 3 SVZ CT 0.021** 

**23 6.47 33 0.38047 +1.2 in STR CT SVZ CT 0.011**  25 6.47 30 0.03257 -1.7 in SVZ IR 7 SVZ CT 0.027

**26 7.04 30 0.05344 -1.5 in SVZ IR 7 SVZ CT 0.04 27 6.35 30 0.02708 -1.6 in SVZ IR 7 SVZ CT 0.01** 

**29 7.33 27 0.10550 -1.3 in SVZ IR 7 SVZ IR 3 0.041** 

30 6.88 27 0.05109 -1.3 in SVZ IR 7 SVZ CT 0.038 31 6.37 27 0.04457 -1.4 in SVZ IR 7 SVZ CT 0.01 34 6.24 24 0.05691 -1.3 in SVZ IR 7 SVZ CT 0.017

**35 6.45 22 0.03776 -1.4 in SVZ IR 7 SVZ CT 0.016**  36 7.49 20 0.06499 -1.6 in SVZ IR 7 SVZ IR 3 0.046 **39 5.92 17 0.02929 -1.4 in SVZ IR 7 SVZ CT 0.015** 

41 5.74 16 0.07440 -1.9 in SVZ IR 7 SVZ CT 0.001

**42 5.5 16 0.04760 -1.6 in SVZ IR 7 SVZ CT 0.002** 

44 7.09 13 0.02003 +1.5 in SVZ IR 3 SVZ CT 0.018 45 4.63 13 0.06425 -1.4 in SVZ IR 3 SVZ CT 0.028 **46 8.48 12 0.94660 +1.4 in STR IR 7 STR CT 0.002**  Table 1. List of spots significantly modified in: control SVZ; 3 days following radiation SVZ, 7 days following radiation SVZ, control STR; 7 days following radiation STR. Spot number; experimental protein molecular weight and pI; variation level, under-expressed (-) or overexpressed (+) in the corresponding sample, *vs* reference sample; and ANOVA, (significant when p<0.05). Spots indicated in bold were excised from the gels and analysed by MS to allow the identification the proteins. The abundance corresponds to the ratio (%) of the

volume one spot on the total volume of spots.


**+1.2 in SVZ IR 7 SVZ IR 3 0.010** 

**+1.3 in STR CT SVZ CT 0.007** 

**+1.4 in STR IR 7 STR CT 0.001** 


**-1.6 in SVZ IR 7 SVZ IR 3 0.015** 

**+1.4 in STR IR 7 STR CT 0.004** 


**-1.4 in SVZ IR 7 SVZ IR 3 0.018** 


**-1.5 in STR IR 7 STR CT 0.013** 

Fig. 10. An example of the expression of one gene corresponding to an identified protein using the Allen Brain Atlas database. This gene is expressed along the SVZ, a part of the RMS and also in the cortex (cx) but not in the corpus callosum (cc).


Fig. 11. Distribution of the proteins identified by MS according to their biological function.

Identification of Factors Involved in Neurogenesis Recovery After Irradiation of the Adult Mouse Subventricular Zone: A Preliminary Study 343

increased 7 days after radiation probably as a consequence of an increased metabolic activity

The approach of the current proteomic analysis and the main results are represented in the Fig. 12. The next step of this work will be the validation of the variation of identified proteins and a deep analysis to demonstrate their involvement in neurogenesis stimulation following SVZ radiation and their potential use to stimulate neurogenesis in aged brains

This preliminary study demonstrates that 2D-gel electrophoresis is an accurate global proteomic analysis to analyse modifications in very small brain regions. In our model of neurogenic niche regeneration after 2Gy irradiation, which is associated with stem cell proliferation, we have identified proteins, which have major cellular functions such as energy production, cytoskeletal maintenance and vesicle trafficking. Interestingly, identified proteins have functions known to be involved in pathways previously reported to be altered by radiation which underscores the reliability of our proteomic approach. Especially, proteins involved in mitochondrial respiratory chain have been identified in our study; they produce energy and generate reactive oxygen species. Acharya et al. have shown that human NSCs significantly increased their oxidative and nitrosative stresses after radiation (Acharya, et al. 2010). The importance of endogenous oxygen reactive species to control NSC proliferation has been reported as well (Le Belle, et al. 2010). Moreover, oxidative stress is implicated in the progression of aging and neurodegenerative disorders (Berlett&Stadtman, 1997, Butterfield, et al.,1997, Lauderback, et al.,2002, Richardson, 2009). In light of theses studies, our data concerning mitochondrial respiratory chain are very promising. This chapter describes the first part of our project, including a validation of the technical strategy, with examples of protein functions, associated with SVZ regeneration following 2Gy irradiation. The characterization of

identified proteins is still under investigation, and needs further biological validations.

This work was supported by grants of ANR-SEST (Neurorad) and Electricité de France.

Acharya, M. M., Lan, M. L., Kan, V. H., Patel, N. H., Giedzinski, E., Tseng, B. P. & Limoli, C. L.,

spinal cord. J Comp Neurol, Vol. 414, No.2, pp. 149-166, 0021-9967 (Print) Anderson, N. L., Nance, S. L., Tollasken S. L., Giere F. A., Anderson N. G., (1985), Quantitative

Alvarez-Buylla, A. & Lim, D. A., (2004). For the long run: maintaining germinal niches in the

Berlett, B. S. & Stadtman, E. R., (1997). Protein oxidation in aging, disease, and oxidative stress. *J Biol Chem*, Vol. 272, No.33, pp. 20313-20316, 0021-9258 (Print)

strains. *Electrophoresis*, Vol. 6; No12, pp. 592-599, 0173-0835 (Print).

adult brain. *Neuron*, Vol. 41, No.5, pp. 683-686, 0896-6273 (Print)

(2010), Consequences of ionizing radiation-induced damage in human neural stem cells. Free Radic Biol Med, Vol. 49, No.12, pp. 1846-1855, 1873-4596 (Electronic) Alonso, G., (1999). Neuronal progenitor-like cells expressing polysialylated neural cell

adhesion molecule are present on the ventricular surface of the adult rat brain and

reproducibility of measurements from coomassie blue-stained two-dimensional gelsanalysis of mouse-liver protein-patterns and compmarison of BALC/C and C57

of proliferating SVZ cells.

**4. Conclusion** 

**5. Acknowledgment** 

**6. References** 

and/or neurodegenerative diseases.

phospholipid biosynthesis that might correspond to proteins involved in the organisation of membrane structure during cell proliferation. In our context, these identified proteins might interfere with the proliferation wave during SVZ regeneration.

Thirteen percent of the modulated proteins (spots 34, 30, 20) are central effectors in intracellular signal transduction pathways like GTPases mediating the formation of vesicles in neural cells, a fundamental process of neurotransmitter release (De Camilli, et al.,1995).

Thirteen percent of the spots referred to cytoskeleton proteins (spots 1, 19) showing alteration from day 3 to 7. Thus, this proteomic analysis revealed that radiation exposure influence the expression of proteins involved the reorganisation of actin cytoskeleton probably associated with proliferation and/or migration.

It appeared that 20% of the modulated proteins were known for being implicated in oxidative metabolism. One of them (spot 45) decreased early after radiation that might reflect a radio-induced defect of mitochondrial metabolism. The two other spots (3, 15) were

From 871 spots, 32 spots showed variations of intensity after irradiation. Among the last spots, we analysed and identified 17 spots. Twenty-six percents of the identified proteins matched with proteins already described in oxidative metabolism. The next step of this work will consist firstly, in confirming these variations of intensity by other technical approaches such as western blot and immunohistochemistry and secondly, in demonstrating their involvement in the neurogenesis stimulation after SVZ irradiation.

Fig. 12. Schematic representation of the current comparative proteomic analysis from control SVZ and 2Gy-irradiated SVZ at 3 and 7 days after exposure.

increased 7 days after radiation probably as a consequence of an increased metabolic activity of proliferating SVZ cells.

The approach of the current proteomic analysis and the main results are represented in the Fig. 12. The next step of this work will be the validation of the variation of identified proteins and a deep analysis to demonstrate their involvement in neurogenesis stimulation following SVZ radiation and their potential use to stimulate neurogenesis in aged brains and/or neurodegenerative diseases.

#### **4. Conclusion**

342 Proteomics – Human Diseases and Protein Functions

phospholipid biosynthesis that might correspond to proteins involved in the organisation of membrane structure during cell proliferation. In our context, these identified proteins might

Thirteen percent of the modulated proteins (spots 34, 30, 20) are central effectors in intracellular signal transduction pathways like GTPases mediating the formation of vesicles in neural cells, a fundamental process of neurotransmitter release (De Camilli, et

Thirteen percent of the spots referred to cytoskeleton proteins (spots 1, 19) showing alteration from day 3 to 7. Thus, this proteomic analysis revealed that radiation exposure influence the expression of proteins involved the reorganisation of actin cytoskeleton

It appeared that 20% of the modulated proteins were known for being implicated in oxidative metabolism. One of them (spot 45) decreased early after radiation that might reflect a radio-induced defect of mitochondrial metabolism. The two other spots (3, 15) were

From 871 spots, 32 spots showed variations of intensity after irradiation. Among the last spots, we analysed and identified 17 spots. Twenty-six percents of the identified proteins matched with proteins already described in oxidative metabolism. The next step of this work will consist firstly, in confirming

immunohistochemistry and secondly, in demonstrating their involvement in the neurogenesis

Fig. 12. Schematic representation of the current comparative proteomic analysis from control

these variations of intensity by other technical approaches such as western blot and

SVZ and 2Gy-irradiated SVZ at 3 and 7 days after exposure.

stimulation after SVZ irradiation.

interfere with the proliferation wave during SVZ regeneration.

probably associated with proliferation and/or migration.

al.,1995).

This preliminary study demonstrates that 2D-gel electrophoresis is an accurate global proteomic analysis to analyse modifications in very small brain regions. In our model of neurogenic niche regeneration after 2Gy irradiation, which is associated with stem cell proliferation, we have identified proteins, which have major cellular functions such as energy production, cytoskeletal maintenance and vesicle trafficking. Interestingly, identified proteins have functions known to be involved in pathways previously reported to be altered by radiation which underscores the reliability of our proteomic approach. Especially, proteins involved in mitochondrial respiratory chain have been identified in our study; they produce energy and generate reactive oxygen species. Acharya et al. have shown that human NSCs significantly increased their oxidative and nitrosative stresses after radiation (Acharya, et al. 2010). The importance of endogenous oxygen reactive species to control NSC proliferation has been reported as well (Le Belle, et al. 2010). Moreover, oxidative stress is implicated in the progression of aging and neurodegenerative disorders (Berlett&Stadtman, 1997, Butterfield, et al.,1997, Lauderback, et al.,2002, Richardson, 2009). In light of theses studies, our data concerning mitochondrial respiratory chain are very promising. This chapter describes the first part of our project, including a validation of the technical strategy, with examples of protein functions, associated with SVZ regeneration following 2Gy irradiation. The characterization of identified proteins is still under investigation, and needs further biological validations.

#### **5. Acknowledgment**

This work was supported by grants of ANR-SEST (Neurorad) and Electricité de France.

#### **6. References**


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**Part 4** 

**Organelles and Secretome Proteomics** 


### **Part 4**

### **Organelles and Secretome Proteomics**

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Garcia-Verdugo, J. M. & Doetsch, F., (2008). A specialized vascular niche for adult neural stem cells. *Cell Stem Cell*, Vol. 3, No.3, pp. 279-288, 1875-9777 (Electronic) Tian, Y., Shi, Z., Yang, S., Chen, Y. & Bao, S., (2008). Changes in myelin basic protein and

demyelination in the rat brain within 3 months of single 2-, 10-, or 30-Gy whole-brain radiation treatments. *J Neurosurg*, Vol. 109, No.5, pp. 881-888, 0022-3085 (Print) Yamashita, T., Ninomiya, M., Hernandez Acosta, P., Garcia-Verdugo, J. M., Sunabori, T.,

Sakaguchi, M., Adachi, K., Kojima, T., Hirota, Y., Kawase, T., Araki, N., Abe, K., Okano, H. & Sawamoto, K., (2006). Subventricular zone-derived neuroblasts migrate and differentiate into mature neurons in the post-stroke adult striatum. *J* 

Comparative proteomic analysis of brains of naturally aging mice. *Neuroscience*,

proteasome formation and antigen processing. *Proc Natl Acad Sci U S A*, Vol. 99,

**16** 

*USA* 

**Analysis of Organelle** 

**Dynamics by Quantitative** 

**Mass Spectrometry Based Proteomics** 

Florian Fröhlich, Tobias C. Walther and Romain Christiano

A major goal of cell biology is to understand the dynamic interplay between different reactions in the cell. In eukaryotes, compartmentalization of the cytoplasm into organelles facilitates the coordinated execution of many cellular functions. To understand how this is achieved, it is important to know the protein composition of the different organelles, and to determine how it may change over time. In addition, the activity of many proteins is regulated by often reversible and dynamic **p**ost-**t**ranslational **m**odifications (PTMs). In recent years, proteomics has matured into a staple technique for cell biology. Modern approaches of proteomics rely on mass-spectrometry. Here, we build on several reviews (Aebersold and Mann, 2003; Choudhary and Mann, 2010; Walther and Mann, 2010) to summarize and highlight contemporary applications of MS-based proteomics to the analysis

**2. MS-based quantitative proteomics approaches for proteins and their post-**

Most proteomics studies aim to not just identify proteins but to quantitate their abundance in different samples. In the past few years, several quantitative proteomics approaches have

The most commonly used method to quantitate proteins is **s**table **i**sotope **l**abeling by **a**mino acids in cell **c**ulture (SILAC; (Ong et al., 2002)) in combination with **l**iquid **c**hromatography (LC) and tandem high resolution mass spectrometry (LC-MS/MS). Cells are labeled with non-radioactive heavy labeled amino acids, typically arginine and /or lysine. After cell lysis, extracts from differently labeled and differently treated cells are mixed and digested with a protease that cuts after the labeled amino acids, such as trypsin for the case of arginine/lysine. The resulting peptide mixture is fractionated by LC on a C18 column and analyzed in a high resolution mass spectrometer. The mass shift between labeled and unlabeled peptides allows the quantification of intensities of peptides, and based on that of

**1. Introduction** 

of organelle dynamics.

**translational modifications** 

been developed to accomplish this task.

**2.1 MS-based quantitative proteomics approaches** 

**2.1.1 Stable isotope labeling by amino acids in cell culture (SILAC)** 

*Yale University School of Medicine, Department of Cell Biology* 

### **Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics**

Florian Fröhlich, Tobias C. Walther and Romain Christiano *Yale University School of Medicine, Department of Cell Biology USA* 

#### **1. Introduction**

A major goal of cell biology is to understand the dynamic interplay between different reactions in the cell. In eukaryotes, compartmentalization of the cytoplasm into organelles facilitates the coordinated execution of many cellular functions. To understand how this is achieved, it is important to know the protein composition of the different organelles, and to determine how it may change over time. In addition, the activity of many proteins is regulated by often reversible and dynamic **p**ost-**t**ranslational **m**odifications (PTMs). In recent years, proteomics has matured into a staple technique for cell biology. Modern approaches of proteomics rely on mass-spectrometry. Here, we build on several reviews (Aebersold and Mann, 2003; Choudhary and Mann, 2010; Walther and Mann, 2010) to summarize and highlight contemporary applications of MS-based proteomics to the analysis of organelle dynamics.

#### **2. MS-based quantitative proteomics approaches for proteins and their posttranslational modifications**

Most proteomics studies aim to not just identify proteins but to quantitate their abundance in different samples. In the past few years, several quantitative proteomics approaches have been developed to accomplish this task.

#### **2.1 MS-based quantitative proteomics approaches**

#### **2.1.1 Stable isotope labeling by amino acids in cell culture (SILAC)**

The most commonly used method to quantitate proteins is **s**table **i**sotope **l**abeling by **a**mino acids in cell **c**ulture (SILAC; (Ong et al., 2002)) in combination with **l**iquid **c**hromatography (LC) and tandem high resolution mass spectrometry (LC-MS/MS). Cells are labeled with non-radioactive heavy labeled amino acids, typically arginine and /or lysine. After cell lysis, extracts from differently labeled and differently treated cells are mixed and digested with a protease that cuts after the labeled amino acids, such as trypsin for the case of arginine/lysine. The resulting peptide mixture is fractionated by LC on a C18 column and analyzed in a high resolution mass spectrometer. The mass shift between labeled and unlabeled peptides allows the quantification of intensities of peptides, and based on that of

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 351

translational modified peptides are generally of low abundance and even small changes can

Sometimes only a few modified peptides are of interest for the question under consideration. In these cases, proteomics can be targeted to sequencing to a subset of previously identified peptides (Schmidt et al., 2009). One method to achieve this, called **m**ultiple **r**eaction **m**onitoring (MRM), is performed on so called triple quadrupole mass analyzers. In the first quadrupole, peptides of interest are isolated by their mass. The second quadrupole is a collision cell where the peptides are fragmented. The last quadrupole is set to some specific fragments that are characteristic for the peptide. The advantage of MRMs over unbiased approaches is the high sensitivity and speed (Kitteringham et al., 2009; Malmstrom et al., 2009; Wolf-Yadlin et al., 2007). However, false positive rates in these experiments can be high due to limited resolution of the quadrupole instruments compared to orbitraps. In alternative approaches using high resolution orbitrap instruments, the specific m/z of peptides of interest are written in an "inclusion list" (Jaffe et al., 2008). Whenever a peptide of this m/z is found in the MS spectrum it is selected for fragmentation and MS/MS analysis. If higher sensitivity of the instrument is required, **s**elected **i**on **m**onitoring (SIM) scans can be used to survey one or several pre-defined ranges of the m/z spectrum rather than a full spectrum during the chromatography run (Michalski et al.,

Quantitative proteomics can detect changes of the abundance of proteins in a whole proteome from cells in different conditions. In addition biological activity of proteins varies and is often regulated by PTMs. Therefore, it is important to measure changes of PTMs spatially and temporarily. MS is ideal to study PTMs because it can detect specific mass shifts due to the modification and assign its exact position in the amino acid

The most studied PTM is phosphorylation of the amino acids serine, threonine or tyrosine. Phosphorylation is important for many cellular processes. Protein phosphorylation conventionally is analyzed by 32P labeling, band shifts on SDS-PAGE gels or the detection of phosphorylated residues by site-specific antibodies. While these techniques yielded great insights, they focus usually on just one or a few proteins at a time. To study the complexity of signaling networks, systematic methods to study phosphorylation of many proteins at the same time are required. MS-based methods analyzing phosphorylation of proteins by detecting the phosphorylated peptides resulting from their digestion require enrichment of phosphorylated peptides to overcome their low abundance in cells. Several different methods have been established to enrich phosphorylated peptides. Antibodies specific for a specific phosphorylated amino acid, e.g., phospho-tyrosine, are used. These enrichment methods are very useful to study a specific phosphorylation species. A different enrichment method is **i**mmobilized **m**etal (e.g., Fe3+) **a**ffinity **c**hromatography (IMAC) (Corthals et al., 2005; Muszynska et al., 1992). With IMAC, all phosphopeptides are enriched due to the interaction between negatively charged phosphate groups and the immobilized positive metal ions. A similar technique uses TiO2 to complex phosphorylated peptides on a resin by their charge (Pinkse et al.,

have important effects on the cell.

**2.2 Posttranslational modifications** 

2011).

sequence.

**2.2.1 Phosphorylation** 

proteins, derived from cells differentially labeled and subjected to different conditions. Since chemically identical peptides are quantitated in the same spectrum, the accuracy of this methodology is very high. In addition, mixing samples directly after lysis limits the chance for experimental errors. However, in its simplest rendition, SILAC-based proteomics is limited to samples that can be metabolic labeled, including cells and model organisms (ranging from yeast to mice) (de Godoy et al., 2008; Kruger et al., 2008). Recently developed "spike-in" approaches that use isotope labeled cell extracts as standards for analysis of samples of interests from sources that cannot be labeled, such as patient samples, are compared. In case a single cell extract does not adequately represent a particular tissue or sample, several extracts can be mixed to obtain a "super-SILAC" standard (Geiger et al., 2010). This approach of SILAC reference standards is not limited to quantitation of protein abundance but can also be applied to quantify changes in PTMs, such as phosphorylation. In an example of such an analysis, a phosphopeptide standard combining untreated or insulin treated mouse liver cell lines were spiked into samples derived from the liver of insulin treated or untreated mice. This method led to the identification of over 15,000 and quantitation of 10,000 phosphosites (Monetti et al., 2011).

#### **2.1.2 Isobaric tags for relative and absolute quantification (iTRAQ)**

Chemically labeling of proteins in different samples can also be used for their quantitation. One such technique uses **i**sobaric **t**ags for **r**elative and **a**bsolute **q**uantification (iTRAQ). In iTRAQ experiments, different chemical groups modify the primary amino group of either the N-terminus or lysine side chains of peptides in different samples (Ross et al., 2004). These differentially labeled peptides are pooled and analyzed by LC-MS/MS setup. Each of the labels has the same mass and therefore each peptide is visible in a single peak in the MS spectrum. However, fragmentation of that peak leads to formation of a low molecular mass reporter ion characteristic for each tag in the MS/MS spectrum that is used to quantify the relative amounts of the corresponding peptides and proteins. It is very important for this technique to distinguish peptides which have a similar mass and elute at the same time because this would lead to false ratios as both peptides contribute to the abundance of the same reporter ions (Ow et al., 2009; Zhang et al., 2010). In addition, it is crucial to ensure complete labeling of the sample. Moreover, side reactions of chemical labeling may be unavoidable, but can lead to false positive identifications of PTMs. For example, alkylation of a peptide mixture with iodoacetamide can produce a 2-acetamidoacetamide covalent adduct to lysine. This has the same atomic composition as a diglycine adduct of a ubiquitinated peptide after tryptic digest (Nielsen et al., 2008).

#### **2.1.3 Label free approaches**

In addition to the described labeling methods, so called "label-free" approaches that directly compare the abundance of peptides and proteins between samples are very attractive to analyze complex protein mixtures. Such approaches enable the analysis of samples which cannot be easily labeled. One type of label free quantitation approaches uses alignments of separate LC-MS/MS runs to compare peptide intensities between different samples. Recent advances in computational proteomics enable quantification of peptides from less complex samples by this approach (Mueller et al., 2008; Wong and Cagney, 2010). However, the accuracy of this approach is still somewhat lower compared to measurements from metabolic labeled samples. The analysis is particularly challenging for PTMs. Post translational modified peptides are generally of low abundance and even small changes can have important effects on the cell.

Sometimes only a few modified peptides are of interest for the question under consideration. In these cases, proteomics can be targeted to sequencing to a subset of previously identified peptides (Schmidt et al., 2009). One method to achieve this, called **m**ultiple **r**eaction **m**onitoring (MRM), is performed on so called triple quadrupole mass analyzers. In the first quadrupole, peptides of interest are isolated by their mass. The second quadrupole is a collision cell where the peptides are fragmented. The last quadrupole is set to some specific fragments that are characteristic for the peptide. The advantage of MRMs over unbiased approaches is the high sensitivity and speed (Kitteringham et al., 2009; Malmstrom et al., 2009; Wolf-Yadlin et al., 2007). However, false positive rates in these experiments can be high due to limited resolution of the quadrupole instruments compared to orbitraps. In alternative approaches using high resolution orbitrap instruments, the specific m/z of peptides of interest are written in an "inclusion list" (Jaffe et al., 2008). Whenever a peptide of this m/z is found in the MS spectrum it is selected for fragmentation and MS/MS analysis. If higher sensitivity of the instrument is required, **s**elected **i**on **m**onitoring (SIM) scans can be used to survey one or several pre-defined ranges of the m/z spectrum rather than a full spectrum during the chromatography run (Michalski et al., 2011).

#### **2.2 Posttranslational modifications**

Quantitative proteomics can detect changes of the abundance of proteins in a whole proteome from cells in different conditions. In addition biological activity of proteins varies and is often regulated by PTMs. Therefore, it is important to measure changes of PTMs spatially and temporarily. MS is ideal to study PTMs because it can detect specific mass shifts due to the modification and assign its exact position in the amino acid sequence.

#### **2.2.1 Phosphorylation**

350 Proteomics – Human Diseases and Protein Functions

proteins, derived from cells differentially labeled and subjected to different conditions. Since chemically identical peptides are quantitated in the same spectrum, the accuracy of this methodology is very high. In addition, mixing samples directly after lysis limits the chance for experimental errors. However, in its simplest rendition, SILAC-based proteomics is limited to samples that can be metabolic labeled, including cells and model organisms (ranging from yeast to mice) (de Godoy et al., 2008; Kruger et al., 2008). Recently developed "spike-in" approaches that use isotope labeled cell extracts as standards for analysis of samples of interests from sources that cannot be labeled, such as patient samples, are compared. In case a single cell extract does not adequately represent a particular tissue or sample, several extracts can be mixed to obtain a "super-SILAC" standard (Geiger et al., 2010). This approach of SILAC reference standards is not limited to quantitation of protein abundance but can also be applied to quantify changes in PTMs, such as phosphorylation. In an example of such an analysis, a phosphopeptide standard combining untreated or insulin treated mouse liver cell lines were spiked into samples derived from the liver of insulin treated or untreated mice. This method led to the identification of over 15,000 and

Chemically labeling of proteins in different samples can also be used for their quantitation. One such technique uses **i**sobaric **t**ags for **r**elative and **a**bsolute **q**uantification (iTRAQ). In iTRAQ experiments, different chemical groups modify the primary amino group of either the N-terminus or lysine side chains of peptides in different samples (Ross et al., 2004). These differentially labeled peptides are pooled and analyzed by LC-MS/MS setup. Each of the labels has the same mass and therefore each peptide is visible in a single peak in the MS spectrum. However, fragmentation of that peak leads to formation of a low molecular mass reporter ion characteristic for each tag in the MS/MS spectrum that is used to quantify the relative amounts of the corresponding peptides and proteins. It is very important for this technique to distinguish peptides which have a similar mass and elute at the same time because this would lead to false ratios as both peptides contribute to the abundance of the same reporter ions (Ow et al., 2009; Zhang et al., 2010). In addition, it is crucial to ensure complete labeling of the sample. Moreover, side reactions of chemical labeling may be unavoidable, but can lead to false positive identifications of PTMs. For example, alkylation of a peptide mixture with iodoacetamide can produce a 2-acetamidoacetamide covalent adduct to lysine. This has the same atomic composition as a diglycine adduct of a

In addition to the described labeling methods, so called "label-free" approaches that directly compare the abundance of peptides and proteins between samples are very attractive to analyze complex protein mixtures. Such approaches enable the analysis of samples which cannot be easily labeled. One type of label free quantitation approaches uses alignments of separate LC-MS/MS runs to compare peptide intensities between different samples. Recent advances in computational proteomics enable quantification of peptides from less complex samples by this approach (Mueller et al., 2008; Wong and Cagney, 2010). However, the accuracy of this approach is still somewhat lower compared to measurements from metabolic labeled samples. The analysis is particularly challenging for PTMs. Post

quantitation of 10,000 phosphosites (Monetti et al., 2011).

ubiquitinated peptide after tryptic digest (Nielsen et al., 2008).

**2.1.3 Label free approaches** 

**2.1.2 Isobaric tags for relative and absolute quantification (iTRAQ)** 

The most studied PTM is phosphorylation of the amino acids serine, threonine or tyrosine. Phosphorylation is important for many cellular processes. Protein phosphorylation conventionally is analyzed by 32P labeling, band shifts on SDS-PAGE gels or the detection of phosphorylated residues by site-specific antibodies. While these techniques yielded great insights, they focus usually on just one or a few proteins at a time. To study the complexity of signaling networks, systematic methods to study phosphorylation of many proteins at the same time are required. MS-based methods analyzing phosphorylation of proteins by detecting the phosphorylated peptides resulting from their digestion require enrichment of phosphorylated peptides to overcome their low abundance in cells. Several different methods have been established to enrich phosphorylated peptides. Antibodies specific for a specific phosphorylated amino acid, e.g., phospho-tyrosine, are used. These enrichment methods are very useful to study a specific phosphorylation species. A different enrichment method is **i**mmobilized **m**etal (e.g., Fe3+) **a**ffinity **c**hromatography (IMAC) (Corthals et al., 2005; Muszynska et al., 1992). With IMAC, all phosphopeptides are enriched due to the interaction between negatively charged phosphate groups and the immobilized positive metal ions. A similar technique uses TiO2 to complex phosphorylated peptides on a resin by their charge (Pinkse et al.,

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 353

their unmodified counterparts. Thus, N-glycosylated proteins are enriched for analysis by affinity purification using lectins (Bunkenborg et al., 2004) or by chemical linkage of the

After enrichment, samples are treated with a global deglycosylating enzyme leading to the deamidation of the asparagine residue to aspartic acid. The resulting mass increase of 0.9848 Da can be detected in the precursor scan as well as in the peptide fragments (Kuster and Mann, 1999). Previously, N-glycosylated peptides were identified with low resolution of the peptide precursor mass. A recent study, using high resolution MS instead, mapped roughly 6,400 N-glycosylation sites in different murine cell lines quantitatively (Zielinska et al.,

Other common PTMs are acetylation or methylation of lysines and arginines. These are reversible PTMs that change the charge of the amino acid thereby possibly regulating protein function. The most prominent example is histone acetylation or methylation, which regulates transcription. Acetylated or methylated peptides are relatively low abundant and specific enrichment is required, similar as in the case of phosphorylation analysis. Enrichment with antibodies which recognize the modified amino acid is generally performed prior to analysis by MS. A recent study identified an unexpected large number of acetylation sites. By using high resolution MS in combination with SILAC, 3600 acetylation sites were identified on 1750 proteins. Due to the low abundance of acetylated peptides, Choudhary et al used isoelectric focusing after enrichment of acetylated peptides to further reduce sample complexity (Choudhary et al., 2009). A previous study revealed an unexpected role of acetylation in mitochondrial function (Kim et al., 2006). A method to measure methylation quantitatively is heavy methyl- SILAC. In this approach, heavy methionine serves as the sole donor for the methyl group. The mass shift of the peptide containing the heavy versus light methyl groups is detected by high resolution mass

Ubiquitination of proteins is another PTM amenable to MS based proteomics. The protein ubiquitin is crosslinked to lysine residues on target proteins. Ubiquitination plays an important role in proteasomal degradation and endocytosis of plasma membrane proteins. Since ubiquitin is a protein, it can be tagged for subsequent affinity enrichment. HIS tagged versions of ubiquitin can be used to isolate ubiquitinated proteins by affinity purification, leading in one example to the identification of 110 ubiquitination sites on 72 proteins in *Saccharomyces cerevisiae* (Peng et al., 2003)*.* Instead of isolating ubiquitinated proteins, a site specific antibody that detects the diGly motif of ubiquitinated peptides, yielded 374 diglycine modified lysines on 236 ubiquitinated proteins from HEK293 cells (Xu et al., 2010). One problem analyzing ubiquitinated proteins are their very complex fragmentation spectra. In addition it is difficult to distinguish by MS analysis modification by ubiquitin or by other ubiquitin-like molecules, such as interferon-induced 17kDA protein (ISG15) that leave the same diGly tag after digestion. Furthermore, it is not possible to distinguish if a protein is mono- or polyubiquitinated by LC MS/MS since digestion of the proteins with a protease is necessary and cleaves polyubiquitin chains. For a representative analysis of post

sugar moiety to a solid phase (Zhang et al., 2003).

**2.2.3 Acetylation and methylation** 

spectrometry (Ong et al., 2004).

translational modified proteins see Figure 1.

**2.2.4 Ubiquitination** 

2010).

2004). In contrast to IMAC, phosphopeptide enrichment by TiO2 requires a competitor for the binding sites, such as **d**i**h**ydro**b**enzoic acid (DHB) or lactic acid, to exclude unphosphorylated negatively charged peptides (Larsen et al., 2005). A drawback of these competitors is possible contamination of the MS instruments by the competitor. In organisms of relatively low proteome complexity, such as *Saccharomyces cerevisiae,* this setup was sufficient to identify 5534 phosphosites (Soufi et al., 2009). However, due to the much higher complexity of the mammalian phosphoproteome, some studies use a prefractionation step of peptides before phosphopeptides enrichment, e.g., by **s**trong **c**ation e**x**change chromatography (SCX) (Villen and Gygi, 2008).

After enrichment of phosphorylated peptides, samples are analyzed by LC-MS/MS. To identify phosphorylated peptides and assign the localization of the phosphorylation site with high confidence, distinct fragmentation methods have been used. **C**ollision **i**nduced **d**issociation (CID) fragmentation often results in a neutral loss because the phosphoester bond is relatively fragile. The resulting lost ions of 98 or 80 Da were used to scan specifically for phosphorylated peptides. However, this phenomenon often dominates the MS/MS scans (Tholey et al., 1999) and leads to reduced backbone fragmentation. For efficient identification of phosphorylated peptides in ion traps, the neutral loss signal can be isolated after MS/MS and subjected to additional CID to yield a MS3 spectrum (Jin et al., 2004). Multistage activation virtually combines MS2 and MS3 by parallel excitation and fragmentation (Schroeder et al., 2004). Even with multistage activation, fragmentation of phosphopeptides can be insufficient to identify the peptide sequence or to assign the phosphorylation site correctly with high confidence. Recent technical developments allow for different fragmentation techniques. **E**lectron **c**apture **d**issociation (ECD) or **e**lectron **t**ransfer **d**issociation (ETD) lead to sole backbone fragmentation between N and C bonds thereby generating c and z ions (Syka et al., 2004; Zubarev et al., 2000). PTMs unstable during CID fragmentation therefore stay intact with ETD fragmentation and make site specific assignment easier. **H**igher **c**ollision **e**nergy dissociation (HCD) uses the same principle of CID but higher collision energies, thus efficiently fragmenting the peptide backbone even in the presence of a low energy bond to a PTM. A recent study used a LTQ OrbitrapVelos to analyze both the precursor ion and its peptide fragments after HCD with high resolution in an orbitrap. This so called "high-high" technique yielded up to 16,000 identified phosphorylation sites with high confident assignments (Nagaraj et al., 2010). Data quality is a particularly important issue for large scale PTM studies. For example, it is

possible to identify a phosphorylated peptide with high confidence (>99%), but it is sometimes impossible to assign its site with high confidence between two adjacent serines. Therefore large scale datasets should always contain a peptide identification score and a PTM localization score (Beausoleil et al., 2006; Gnad et al., 2011; Olsen et al., 2006).

#### **2.2.2 Glycosylation**

Although phosphorylation is by far the most studied PTM, MS can also be used to study other PTMs. Another example for a prominent PTM is N-glycosylation of asparagine residues occurring in the endoplasmic reticulum. N-glycosylation plays an important role in the assembly of complex organelles and is involved in many cellular processes, such as apoptosis and the immune response (Varki, 2009). Due to the complexity of sugar moieties it is very challenging to analyze N-glycosylated proteins or peptides by MS approaches. Additionally the abundance of N-glycosylated proteins is usually very low in comparison to their unmodified counterparts. Thus, N-glycosylated proteins are enriched for analysis by affinity purification using lectins (Bunkenborg et al., 2004) or by chemical linkage of the sugar moiety to a solid phase (Zhang et al., 2003).

After enrichment, samples are treated with a global deglycosylating enzyme leading to the deamidation of the asparagine residue to aspartic acid. The resulting mass increase of 0.9848 Da can be detected in the precursor scan as well as in the peptide fragments (Kuster and Mann, 1999). Previously, N-glycosylated peptides were identified with low resolution of the peptide precursor mass. A recent study, using high resolution MS instead, mapped roughly 6,400 N-glycosylation sites in different murine cell lines quantitatively (Zielinska et al., 2010).

#### **2.2.3 Acetylation and methylation**

352 Proteomics – Human Diseases and Protein Functions

2004). In contrast to IMAC, phosphopeptide enrichment by TiO2 requires a competitor for the binding sites, such as **d**i**h**ydro**b**enzoic acid (DHB) or lactic acid, to exclude unphosphorylated negatively charged peptides (Larsen et al., 2005). A drawback of these competitors is possible contamination of the MS instruments by the competitor. In organisms of relatively low proteome complexity, such as *Saccharomyces cerevisiae,* this setup was sufficient to identify 5534 phosphosites (Soufi et al., 2009). However, due to the much higher complexity of the mammalian phosphoproteome, some studies use a prefractionation step of peptides before phosphopeptides enrichment, e.g., by **s**trong **c**ation

After enrichment of phosphorylated peptides, samples are analyzed by LC-MS/MS. To identify phosphorylated peptides and assign the localization of the phosphorylation site with high confidence, distinct fragmentation methods have been used. **C**ollision **i**nduced **d**issociation (CID) fragmentation often results in a neutral loss because the phosphoester bond is relatively fragile. The resulting lost ions of 98 or 80 Da were used to scan specifically for phosphorylated peptides. However, this phenomenon often dominates the MS/MS scans (Tholey et al., 1999) and leads to reduced backbone fragmentation. For efficient identification of phosphorylated peptides in ion traps, the neutral loss signal can be isolated after MS/MS and subjected to additional CID to yield a MS3 spectrum (Jin et al., 2004). Multistage activation virtually combines MS2 and MS3 by parallel excitation and fragmentation (Schroeder et al., 2004). Even with multistage activation, fragmentation of phosphopeptides can be insufficient to identify the peptide sequence or to assign the phosphorylation site correctly with high confidence. Recent technical developments allow for different fragmentation techniques. **E**lectron **c**apture **d**issociation (ECD) or **e**lectron **t**ransfer **d**issociation (ETD) lead to sole backbone fragmentation between N and C bonds thereby generating c and z ions (Syka et al., 2004; Zubarev et al., 2000). PTMs unstable during CID fragmentation therefore stay intact with ETD fragmentation and make site specific assignment easier. **H**igher **c**ollision **e**nergy dissociation (HCD) uses the same principle of CID but higher collision energies, thus efficiently fragmenting the peptide backbone even in the presence of a low energy bond to a PTM. A recent study used a LTQ OrbitrapVelos to analyze both the precursor ion and its peptide fragments after HCD with high resolution in an orbitrap. This so called "high-high" technique yielded up to 16,000 identified phosphorylation sites with high confident assignments (Nagaraj et al., 2010). Data quality is a particularly important issue for large scale PTM studies. For example, it is possible to identify a phosphorylated peptide with high confidence (>99%), but it is sometimes impossible to assign its site with high confidence between two adjacent serines. Therefore large scale datasets should always contain a peptide identification score and a

PTM localization score (Beausoleil et al., 2006; Gnad et al., 2011; Olsen et al., 2006).

Although phosphorylation is by far the most studied PTM, MS can also be used to study other PTMs. Another example for a prominent PTM is N-glycosylation of asparagine residues occurring in the endoplasmic reticulum. N-glycosylation plays an important role in the assembly of complex organelles and is involved in many cellular processes, such as apoptosis and the immune response (Varki, 2009). Due to the complexity of sugar moieties it is very challenging to analyze N-glycosylated proteins or peptides by MS approaches. Additionally the abundance of N-glycosylated proteins is usually very low in comparison to

**2.2.2 Glycosylation** 

e**x**change chromatography (SCX) (Villen and Gygi, 2008).

Other common PTMs are acetylation or methylation of lysines and arginines. These are reversible PTMs that change the charge of the amino acid thereby possibly regulating protein function. The most prominent example is histone acetylation or methylation, which regulates transcription. Acetylated or methylated peptides are relatively low abundant and specific enrichment is required, similar as in the case of phosphorylation analysis. Enrichment with antibodies which recognize the modified amino acid is generally performed prior to analysis by MS. A recent study identified an unexpected large number of acetylation sites. By using high resolution MS in combination with SILAC, 3600 acetylation sites were identified on 1750 proteins. Due to the low abundance of acetylated peptides, Choudhary et al used isoelectric focusing after enrichment of acetylated peptides to further reduce sample complexity (Choudhary et al., 2009). A previous study revealed an unexpected role of acetylation in mitochondrial function (Kim et al., 2006). A method to measure methylation quantitatively is heavy methyl- SILAC. In this approach, heavy methionine serves as the sole donor for the methyl group. The mass shift of the peptide containing the heavy versus light methyl groups is detected by high resolution mass spectrometry (Ong et al., 2004).

#### **2.2.4 Ubiquitination**

Ubiquitination of proteins is another PTM amenable to MS based proteomics. The protein ubiquitin is crosslinked to lysine residues on target proteins. Ubiquitination plays an important role in proteasomal degradation and endocytosis of plasma membrane proteins. Since ubiquitin is a protein, it can be tagged for subsequent affinity enrichment. HIS tagged versions of ubiquitin can be used to isolate ubiquitinated proteins by affinity purification, leading in one example to the identification of 110 ubiquitination sites on 72 proteins in *Saccharomyces cerevisiae* (Peng et al., 2003)*.* Instead of isolating ubiquitinated proteins, a site specific antibody that detects the diGly motif of ubiquitinated peptides, yielded 374 diglycine modified lysines on 236 ubiquitinated proteins from HEK293 cells (Xu et al., 2010). One problem analyzing ubiquitinated proteins are their very complex fragmentation spectra. In addition it is difficult to distinguish by MS analysis modification by ubiquitin or by other ubiquitin-like molecules, such as interferon-induced 17kDA protein (ISG15) that leave the same diGly tag after digestion. Furthermore, it is not possible to distinguish if a protein is mono- or polyubiquitinated by LC MS/MS since digestion of the proteins with a protease is necessary and cleaves polyubiquitin chains. For a representative analysis of post translational modified proteins see Figure 1.

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 355

For few compartments, such as nucleus or mitochondrion, that are rather easy to isolate with high purity, MS-based proteomics has yielded protein inventories (Andersen et al., 2002; Taylor et al., 2003). These and all other organellar proteomics experiments usually start with cell disruption under mild conditions designed to maintain organelle integrity. Organelles are then purified from crude cell extracts by differential centrifugations and are then processed for MS analysis. In this scheme, separation of organelles depends on their sedimentation velocity, a function of their size and density. Generally purification is improved by combining velocity sedimentation centrifugation with density gradient centrifugation (Michelsen and von Hagen, 2009; Wiederhold et al., 2010). Alternatively, higher purity is obtained by affinity purification using e.g., an antibody directed against a surface protein of the organelle, free flow electrophoresis (Islinger et al., 2010) or by modification of organelles density. An example for the latter is the report of phagosomes or endosomes inventories isolated after flotation gradient. In these cases, latex beads of different diameters can be internalized either by phagocytosis (Duclos and Desjardins, 2011; Jutras et al., 2008) or endocytosis (Duclos et al., 2011) and the resulting organelles are

purified by a single step flotation gradient centrifugation before analysis by MS.

For some organelles, no efficient purification schemes exist based solemnly on differences of density or sedimentation. Therefore, in some instances, chemical modification is used to facilitate the purification. For example, cell surface modification by cell impermeable chemicals is commonly used to modify plasma membrane proteins from the extracellular environment. In such strategy, plasma membrane proteins are covalently linked to biotin, which in turn is used as an affinity tag for subsequent purification on a column carrying streptavidin. Cell surface modification relying on different chemical properties of proteins such as the reactivity of primary amines (N-termini of proteins and side chains of lysines, (Elia, 2008; Scheurer et al., 2005), thiols (Laragione et al., 2003) or of carbohydrates (Teckchandani et al., 2009) have been developed to analyze the proteome of plasma membranes in normal (Zhao et al., 2004) and in pathological conditions (Hubbard et al.,

**3.1 MS-based proteomics approaches to map protein composition of organelles**  Each organelle is composed of a specific set of proteins that contribute to its function and that allow for communication with the rest of the cell. The content of organelles is highly dynamic and includes resident as well as transient proteins coming from and going to other compartments in the cell. Traditionally, to identify protein localization, cell biologists employ protein tagging by fluorescent labels or subcellular fractionation of cells in combination with detection of proteins by immunolabeling. These techniques have been successfully applied over many years and were adapted to high throughput studies, for example to tag nearly the complete proteome of budding (Huh et al., 2003) or of fission yeast (Matsuyama et al., 2006). However, technical limitations (e.g., absence of homologous recombination in many systems) and the enormous effort required for these approaches impede its application to more complex systems, such as mammalian cells. Therefore, organelle proteomics based on LS-MS was developed into a complementary approach that has made valuable contributions to the elucidation of organelles' inventory in many systems. In this approach, organelles or parts thereof, including protein complexes, are

**3. Spatial analysis of the cellular proteome** 

purified biochemically and analyzed by MS-based techniques.

**3.1.1 MS based proteomics of purified organelle** 

Cells are grown (label free or SILAC) and lysed for protein extraction. Extracted proteins are digested with an endoproteinase (Trypsin; LysC) and resulting peptides are fractionated (strong cation exachange (SCX) or strong anion exchange (SAX) chromatography). Peptides are enriched by their PTMs (specific antibodies, IMAC, TiO2, lectin affinity). Enriched peptides are fractionated by nano-flow LC and directly injected by electrospray ionization into the MS. Relative abundances of peptides are quantified by the first, full-scan MS event (MS). Peptide identification and determination of the PTM is achieved by fragmentation (CID, HCD, ETD, ECD) in the second stage of MS (MS/MS).

Fig. 1. Workflow for quantitative proteomics of post translational modified proteins.

#### **3. Spatial analysis of the cellular proteome**

354 Proteomics – Human Diseases and Protein Functions

Cells are grown (label free or SILAC) and lysed for protein extraction. Extracted proteins are digested with an endoproteinase (Trypsin; LysC) and resulting peptides are fractionated (strong cation exachange (SCX) or strong anion exchange (SAX) chromatography). Peptides are enriched by their PTMs (specific antibodies, IMAC, TiO2, lectin affinity). Enriched peptides are fractionated by nano-flow LC and directly injected by electrospray ionization into the MS. Relative abundances of peptides are quantified by the first, full-scan MS event (MS). Peptide identification and determination of the PTM is

achieved by fragmentation (CID, HCD, ETD, ECD) in the second stage of MS (MS/MS).

Fig. 1. Workflow for quantitative proteomics of post translational modified proteins.

#### **3.1 MS-based proteomics approaches to map protein composition of organelles**

Each organelle is composed of a specific set of proteins that contribute to its function and that allow for communication with the rest of the cell. The content of organelles is highly dynamic and includes resident as well as transient proteins coming from and going to other compartments in the cell. Traditionally, to identify protein localization, cell biologists employ protein tagging by fluorescent labels or subcellular fractionation of cells in combination with detection of proteins by immunolabeling. These techniques have been successfully applied over many years and were adapted to high throughput studies, for example to tag nearly the complete proteome of budding (Huh et al., 2003) or of fission yeast (Matsuyama et al., 2006). However, technical limitations (e.g., absence of homologous recombination in many systems) and the enormous effort required for these approaches impede its application to more complex systems, such as mammalian cells. Therefore, organelle proteomics based on LS-MS was developed into a complementary approach that has made valuable contributions to the elucidation of organelles' inventory in many systems. In this approach, organelles or parts thereof, including protein complexes, are purified biochemically and analyzed by MS-based techniques.

#### **3.1.1 MS based proteomics of purified organelle**

For few compartments, such as nucleus or mitochondrion, that are rather easy to isolate with high purity, MS-based proteomics has yielded protein inventories (Andersen et al., 2002; Taylor et al., 2003). These and all other organellar proteomics experiments usually start with cell disruption under mild conditions designed to maintain organelle integrity. Organelles are then purified from crude cell extracts by differential centrifugations and are then processed for MS analysis. In this scheme, separation of organelles depends on their sedimentation velocity, a function of their size and density. Generally purification is improved by combining velocity sedimentation centrifugation with density gradient centrifugation (Michelsen and von Hagen, 2009; Wiederhold et al., 2010). Alternatively, higher purity is obtained by affinity purification using e.g., an antibody directed against a surface protein of the organelle, free flow electrophoresis (Islinger et al., 2010) or by modification of organelles density. An example for the latter is the report of phagosomes or endosomes inventories isolated after flotation gradient. In these cases, latex beads of different diameters can be internalized either by phagocytosis (Duclos and Desjardins, 2011; Jutras et al., 2008) or endocytosis (Duclos et al., 2011) and the resulting organelles are purified by a single step flotation gradient centrifugation before analysis by MS.

For some organelles, no efficient purification schemes exist based solemnly on differences of density or sedimentation. Therefore, in some instances, chemical modification is used to facilitate the purification. For example, cell surface modification by cell impermeable chemicals is commonly used to modify plasma membrane proteins from the extracellular environment. In such strategy, plasma membrane proteins are covalently linked to biotin, which in turn is used as an affinity tag for subsequent purification on a column carrying streptavidin. Cell surface modification relying on different chemical properties of proteins such as the reactivity of primary amines (N-termini of proteins and side chains of lysines, (Elia, 2008; Scheurer et al., 2005), thiols (Laragione et al., 2003) or of carbohydrates (Teckchandani et al., 2009) have been developed to analyze the proteome of plasma membranes in normal (Zhao et al., 2004) and in pathological conditions (Hubbard et al.,

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 357

have been performed using label free (Andersen et al., 2003) and SILAC quantitation (Dengjel et al., 2010). SILAC-PCP allows determination of accurate profiles. In that case, two independent gradients are obtained from two differently labeled cells. A fraction enriched for the organelle of interest is isolated from a gradient and spiked into each fraction of the other gradient (with a ratio 1 to 1). Profiles based on SILAC ratios can be extracted from each fraction of the gradient. Genuine proteins from the organelle of interest will show the highest ratio (close to one) in the organelle fraction, but have lower fractions elsewhere in the profile. In contrast, contaminants have higher ratios in other fractions that represent the organelles that they mostly purify with (Figure 2). Such approaches rely on the reproducibility of the gradient separation. An alternate gradient-based approach, **l**ocalization of **p**roteins by **i**sotope **t**agging (LOPIT) has been successfully applied to plant

PCP-SILAC: A light labeled (L) fraction enriched in a target organelle by gradient centrifugation is spiked into each fraction of a gradient of heavy (H) labeled cells Then, each combined fraction is analyzed independently. H/L ratios are extracted for proteins in all fractions and then abundance profiles are estimated. LOPIT: Specific organelle enriched fractions from a gradient are separated and labeled separately with different iTRAQ reagents. Fractions are pooled and processed into peptides. Then, the fraction is analyzed in a single MS run. Ratios from pair wise analyses are extracted and

submitted to multivariate data analyses to identify genuine proteins.

Fig. 2. Gradient Profiling based organelle proteomics.

2011; Yang et al., 2011) or to identify the dynamic proteome of the plasma membrane (Christiano et al., 2010).

#### **3.1.2 Differential proteomics**

Despite the remarkable success of these simple organelle proteomics approaches in some cases, formidable technical challenges to obtain pure organelles remain. Particularly in combination with highly sensitive mass spectrometers, simple purification schemes often result in the identification of large numbers of proteins. It is difficult to determine which ones among them are *bona fide* constituents of the target organelle and which ones are contaminants. One solution to overcome this limitation is the application of differential proteomics to subtract the proteins in the fraction in which the target organelle is found from a closely resembling fraction containing the most prominent contaminants. For this approach to work, the target organelle of interest should be enriched to the highest possible degree in one fraction, but be absent completely from the control fraction. This control sample could be as simple as a crude cell extract devoid of the target organelle, or better, reflect a similar purification in the absence of the organelle. After analysis of both samples, proteins that have been exclusively identified in the fraction enriched in the target organelle are considered as genuine proteins. In this approach, the accuracy of the obtained results strongly depends on the quality of two different MS analyses, as missing a protein in the control will lead its assignment to the target organelle. This point is critical considering the variability of analysis inherent to many MS approaches. For example, identification of a particular protein depends on different properties of the protein (such as abundance in the sample, ability of its peptide to be ionized in the mass spectrometer), as well as the MS methodology used. Failure to detect a protein does not necessarily mean it is absent from the sample. In differential proteomics approaches, this may lead to false negative or positive results, if a protein is randomly not detected in either the organelle enriched or the control fraction, respectively. Moreover, as sensitivity of MS analysis is improved and hence the number of identified proteins in each sample increases, the lists derived from each of the two fractions will more and more overlap, since proteins are likely to differ only in abundance and not strictly be absent from one sample. Thus, a large set of false negative proteins of the target organelle may be subtracted due to their identification in both samples.

#### **3.1.3 Quantitative organellar MS-based proteomics**

Quantitative proteomics is particularly useful in cases where sufficient enrichment for a specific organelle cannot be achieved. For example, organelles of the secretory pathway are similar to each other in their physical properties, with their content dynamically exchanging between them. Methods that only catalogue proteins in a sample cannot provide information on the dynamic behavior of proteins and are prone to detect contamination from co-purifying organelles. Intensive efforts have focused on sample preparation prior to MS. Successful strategies combine gradient profiling and quantitative MS-based proteomics. Such strategies rely on partial separation and distribution of subcellular compartments along density gradients. In **p**rotein **c**orrelation **p**rofiling (PCP) protein abundance profiles along the gradient are obtained by MS analysis and matched to profiles of known organelle markers. In this strategy, the accuracy of the quantification is a critical parameter to obtain reliable mapping of the organelle constituents. To date MS-based quantitative PCP analyses

2011; Yang et al., 2011) or to identify the dynamic proteome of the plasma membrane

Despite the remarkable success of these simple organelle proteomics approaches in some cases, formidable technical challenges to obtain pure organelles remain. Particularly in combination with highly sensitive mass spectrometers, simple purification schemes often result in the identification of large numbers of proteins. It is difficult to determine which ones among them are *bona fide* constituents of the target organelle and which ones are contaminants. One solution to overcome this limitation is the application of differential proteomics to subtract the proteins in the fraction in which the target organelle is found from a closely resembling fraction containing the most prominent contaminants. For this approach to work, the target organelle of interest should be enriched to the highest possible degree in one fraction, but be absent completely from the control fraction. This control sample could be as simple as a crude cell extract devoid of the target organelle, or better, reflect a similar purification in the absence of the organelle. After analysis of both samples, proteins that have been exclusively identified in the fraction enriched in the target organelle are considered as genuine proteins. In this approach, the accuracy of the obtained results strongly depends on the quality of two different MS analyses, as missing a protein in the control will lead its assignment to the target organelle. This point is critical considering the variability of analysis inherent to many MS approaches. For example, identification of a particular protein depends on different properties of the protein (such as abundance in the sample, ability of its peptide to be ionized in the mass spectrometer), as well as the MS methodology used. Failure to detect a protein does not necessarily mean it is absent from the sample. In differential proteomics approaches, this may lead to false negative or positive results, if a protein is randomly not detected in either the organelle enriched or the control fraction, respectively. Moreover, as sensitivity of MS analysis is improved and hence the number of identified proteins in each sample increases, the lists derived from each of the two fractions will more and more overlap, since proteins are likely to differ only in abundance and not strictly be absent from one sample. Thus, a large set of false negative proteins of the target organelle may be subtracted due to their identification in both

Quantitative proteomics is particularly useful in cases where sufficient enrichment for a specific organelle cannot be achieved. For example, organelles of the secretory pathway are similar to each other in their physical properties, with their content dynamically exchanging between them. Methods that only catalogue proteins in a sample cannot provide information on the dynamic behavior of proteins and are prone to detect contamination from co-purifying organelles. Intensive efforts have focused on sample preparation prior to MS. Successful strategies combine gradient profiling and quantitative MS-based proteomics. Such strategies rely on partial separation and distribution of subcellular compartments along density gradients. In **p**rotein **c**orrelation **p**rofiling (PCP) protein abundance profiles along the gradient are obtained by MS analysis and matched to profiles of known organelle markers. In this strategy, the accuracy of the quantification is a critical parameter to obtain reliable mapping of the organelle constituents. To date MS-based quantitative PCP analyses

(Christiano et al., 2010).

samples.

**3.1.3 Quantitative organellar MS-based proteomics** 

**3.1.2 Differential proteomics** 

have been performed using label free (Andersen et al., 2003) and SILAC quantitation (Dengjel et al., 2010). SILAC-PCP allows determination of accurate profiles. In that case, two independent gradients are obtained from two differently labeled cells. A fraction enriched for the organelle of interest is isolated from a gradient and spiked into each fraction of the other gradient (with a ratio 1 to 1). Profiles based on SILAC ratios can be extracted from each fraction of the gradient. Genuine proteins from the organelle of interest will show the highest ratio (close to one) in the organelle fraction, but have lower fractions elsewhere in the profile. In contrast, contaminants have higher ratios in other fractions that represent the organelles that they mostly purify with (Figure 2). Such approaches rely on the reproducibility of the gradient separation. An alternate gradient-based approach, **l**ocalization of **p**roteins by **i**sotope **t**agging (LOPIT) has been successfully applied to plant

PCP-SILAC: A light labeled (L) fraction enriched in a target organelle by gradient centrifugation is spiked into each fraction of a gradient of heavy (H) labeled cells Then, each combined fraction is analyzed independently. H/L ratios are extracted for proteins in all fractions and then abundance profiles are estimated. LOPIT: Specific organelle enriched fractions from a gradient are separated and labeled separately with different iTRAQ reagents. Fractions are pooled and processed into peptides. Then, the fraction is analyzed in a single MS run. Ratios from pair wise analyses are extracted and submitted to multivariate data analyses to identify genuine proteins.

Fig. 2. Gradient Profiling based organelle proteomics.

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 359

A) Immunoprecipitation of endogenous proteins. Complexes from heavy (red) and light (green, control) labeled cells are purified using antibody-conjugated beads that target a specific subunit. Then the fractions are pooled before sample preparation and subsequent MS analysis. Control fraction can be obtained by using a cell lysate devoid of the target subunit by siRNA (QUICK) or by incubating a total lysate with the beads alone. B) One step purification of tagged proteins. A tagged subunit is expressed in heavy labeled cells (red). Using affinity columns, protein complexes are affinity purified using the tag and then eluted before being pooled with the control light fraction (green). After sample preparation the combined fraction is analyzed by MS. The control light fraction is obtained by expressing the tag alone

cleavage site are fused to the protein of interest. In a first step the complex is purified with the fusion protein using the first tag, then the protein of interest (along with its interactors) is released from the affinity matrix by specific cleavage of the tag before performing a second round of purification with the remainder of the tag. Different combinations of tags

in the cells.

Fig. 3. Affinity purification based MS (AP-MS).

organelles (Dunkley et al., 2004; Lilley and Dunkley, 2008). In that case, several organelleenriched fractions from one gradient are separately labeled with different iTRAQ reagents and pooled. Ratios derived from pair-wise comparisons are analyzed by multivariate data analyses to assign organelle localization to the proteins identified. (Figure 2)

#### **3.2 Protein complexes**

Proteins interact with each other to form complexes that contribute to cellular functions. Therefore, identifying protein interactions is of critical importance to unravel mechanisms underlying cellular functions. To identify complexes, **a**ffinity **p**urification-based **MS** (AP-MS) is the method of choice: Prior to MS analysis, protein complexes are purified with affinity matrices that bind to one subunit of the complex. The confident identification of true binders to the bait protein relies on the comparison with a good control fraction of the affinity purification. The emergence of affinity tags and robust antibodies greatly contributed to the success of AP-MS.

The use of affinity tags is particularly attractive because it allows standardizing procedures that can be applicable to any system or protein. To date, a wide variety of tags has been used in AP-MS ranging from small peptides to proteins of several kDa. Fluorescent tags, such as **g**reen **f**luorescent **p**rotein (GFP), are common because they can also be used for live cell imaging. Furthermore, GFP purification allows recovery of most of the GFP-fused protein from a complex mixture in a single step. In this approach, proteins co-purifying with the bait are compared to a control condition where only the GFP protein is expressed (Figure 3 B). Nevertheless, tag insertion might affect protein functions or prevent/trigger interactions with other proteins. Moreover, expression levels of fusion protein can be significantly different compared to their native counterparts and can affect binding capacities. Therefore, when efficient and specific antibodies are available, it is preferable to rely on purification of the native protein subunits to purify complexes. In that case, the control fraction can be obtained by incubating lysates with the beads alone (Trinkle-Mulcahy et al., 2008) or by depleting the protein of interest by RNA interference (**qu**antitative **i**mmunoprecipitation **c**ombined with **k**nockdown, QUICK) (Selbach and Mann, 2006) (Figure 3 A). Recently, Hubner et al. (Hubner et al., 2010) described **qu**antitative **b**acterial artificial chromosome **i**ntera**c**tomics (QUBIC), using tagged proteins expressed under endogenous control in mammalian cells. Identification of interacting partners is achieved by robust and efficient affinity purification based on the GFP tag (Hubner and Mann, 2011; Vermeulen et al., 2010). However, most protein-protein interactions are dynamic and are hardly assessed by single AP-MS experiments. Depending on internal or external cues protein binding specificities can be modulated, e.g., by post-translational modification. This can result in their release from or recruitment to specific protein complexes thus potentially affecting their function(s) or their localization.

At steady state, different forms of a protein are present in the cell which can hinder the discrimination among its interactors. A solution to this problem can be to use exogenous baits corresponding to a specific state of the protein of interest or of a peptide to identify interactions specific to a particular state. Different strategies have been developed for the identification of genuine interactors with high confidence. Generally, single step purifications are associated with a large number of contaminants. Lower background signal is achieved using more stringent purification, for instance by sequential purification using two affinity tags (**t**andem **a**ffinity **p**urification; TAP). In TAP, two tags separated by a

organelles (Dunkley et al., 2004; Lilley and Dunkley, 2008). In that case, several organelleenriched fractions from one gradient are separately labeled with different iTRAQ reagents and pooled. Ratios derived from pair-wise comparisons are analyzed by multivariate data

Proteins interact with each other to form complexes that contribute to cellular functions. Therefore, identifying protein interactions is of critical importance to unravel mechanisms underlying cellular functions. To identify complexes, **a**ffinity **p**urification-based **MS** (AP-MS) is the method of choice: Prior to MS analysis, protein complexes are purified with affinity matrices that bind to one subunit of the complex. The confident identification of true binders to the bait protein relies on the comparison with a good control fraction of the affinity purification. The emergence of affinity tags and robust antibodies greatly

The use of affinity tags is particularly attractive because it allows standardizing procedures that can be applicable to any system or protein. To date, a wide variety of tags has been used in AP-MS ranging from small peptides to proteins of several kDa. Fluorescent tags, such as **g**reen **f**luorescent **p**rotein (GFP), are common because they can also be used for live cell imaging. Furthermore, GFP purification allows recovery of most of the GFP-fused protein from a complex mixture in a single step. In this approach, proteins co-purifying with the bait are compared to a control condition where only the GFP protein is expressed (Figure 3 B). Nevertheless, tag insertion might affect protein functions or prevent/trigger interactions with other proteins. Moreover, expression levels of fusion protein can be significantly different compared to their native counterparts and can affect binding capacities. Therefore, when efficient and specific antibodies are available, it is preferable to rely on purification of the native protein subunits to purify complexes. In that case, the control fraction can be obtained by incubating lysates with the beads alone (Trinkle-Mulcahy et al., 2008) or by depleting the protein of interest by RNA interference (**qu**antitative **i**mmunoprecipitation **c**ombined with **k**nockdown, QUICK) (Selbach and Mann, 2006) (Figure 3 A). Recently, Hubner et al. (Hubner et al., 2010) described **qu**antitative **b**acterial artificial chromosome **i**ntera**c**tomics (QUBIC), using tagged proteins expressed under endogenous control in mammalian cells. Identification of interacting partners is achieved by robust and efficient affinity purification based on the GFP tag (Hubner and Mann, 2011; Vermeulen et al., 2010). However, most protein-protein interactions are dynamic and are hardly assessed by single AP-MS experiments. Depending on internal or external cues protein binding specificities can be modulated, e.g., by post-translational modification. This can result in their release from or recruitment to specific protein complexes thus potentially affecting their function(s)

At steady state, different forms of a protein are present in the cell which can hinder the discrimination among its interactors. A solution to this problem can be to use exogenous baits corresponding to a specific state of the protein of interest or of a peptide to identify interactions specific to a particular state. Different strategies have been developed for the identification of genuine interactors with high confidence. Generally, single step purifications are associated with a large number of contaminants. Lower background signal is achieved using more stringent purification, for instance by sequential purification using two affinity tags (**t**andem **a**ffinity **p**urification; TAP). In TAP, two tags separated by a

analyses to assign organelle localization to the proteins identified. (Figure 2)

**3.2 Protein complexes** 

or their localization.

contributed to the success of AP-MS.

A) Immunoprecipitation of endogenous proteins. Complexes from heavy (red) and light (green, control) labeled cells are purified using antibody-conjugated beads that target a specific subunit. Then the fractions are pooled before sample preparation and subsequent MS analysis. Control fraction can be obtained by using a cell lysate devoid of the target subunit by siRNA (QUICK) or by incubating a total lysate with the beads alone. B) One step purification of tagged proteins. A tagged subunit is expressed in heavy labeled cells (red). Using affinity columns, protein complexes are affinity purified using the tag and then eluted before being pooled with the control light fraction (green). After sample preparation the combined fraction is analyzed by MS. The control light fraction is obtained by expressing the tag alone in the cells.

#### Fig. 3. Affinity purification based MS (AP-MS).

cleavage site are fused to the protein of interest. In a first step the complex is purified with the fusion protein using the first tag, then the protein of interest (along with its interactors) is released from the affinity matrix by specific cleavage of the tag before performing a second round of purification with the remainder of the tag. Different combinations of tags

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 361

Light labeled cells (green) are pulsed with heavy amino acid containing medium (red). Cells are harvested at different time points, lysed, processed into peptides and analyzed independently. Ratios H/L are derived from intensities of heavy (H, red) and light (L, green) peptide peaks in the MS spectra. Note, at protein half life (T1/2) intensity of light and heavy peptides are equal in the mass spectra. Then loss of light

To date, pSILAC has been mostly applied to cells in culture: *Saccharomyces cerevisiae* (Pratt et al., 2002), *Streptomyces coelicolor* (Jayapal et al., 2010) and human cells (Doherty et al., 2009; Schwanhäusser et al., 2011; Zee et al., 2010). Schwanhäusser et al. measured protein abundances and turnover of more than 5,000 proteins in HeLa cells. Combination of pSILAC with metabolic pulse labeling of mRNA in the same cells enabled comparison of proteins

At steady state, protein levels are constant. Therefore synthesis and degradation rates are equal. This feature is critical when comparing proteome turnover, but this assumption may not always be fulfilled when comparing different conditions. An alternative approach overcoming this limitation is to follow synthesis and degradation independently from each other. To follow regulation of protein synthesis during changes of cellular iron, Schwannäusser et al. combined control (non-treated) and iron-treated HeLa cells that have been differently SILAC pulsed (Schwanhäusser et al., 2009). At the beginning of the experiment, cells in both conditions were identically light labeled (L). Concomitantly with iron treatment, cells were differently pulsed with medium (M) or heavy (H) amino-acids. Then cell lysates were combined and analyzed by MS. The relative abundance of newly synthesized proteins in both conditions was then extracted from intensities of the M and H peaks in the MS spectrum. As expected, most of the 1311 proteins identified in this study do not present

label can be calculated over time and turnover rates are extracted from non-linear curve fitting.

Fig. 4. pSILAC to analyze protein turnover.

and mRNAs turnover at an unprecedented depth.

have been combined and TAP strategies have been extensively used for AP-MS purification in many model systems (Gavin et al., 2006; Krogan et al., 2006). However, one drawback is the loss of weak interactors during stringent purification. A compromise is achieved by inserting a cleavage site between a tag and the protein of interest thus reducing the number of treatments and decreasing significantly the number of non-specific binders (Aguilar et al., 2010). In case of weak interactions, complexes can be fixed by treating with cross-linking chemicals either in live cells (Stingl et al., 2008; Yong et al., 2010) or cell lysates (Sinz, 2010).

In general, quantitation greatly helps to discriminate false positive from true interactors. SILAC quantitation is more accurate and has advantages for protein complexes that are difficult to purify and where enrichment over background is smaller. As in all SILAC experiments, two differently labeled samples, here a control and a sample enriched in the target complex, are mixed and relative abundances of protein ratios are extracted from the MS spectra. In case of AP-MS, background proteins have a ratio of one as they bind as efficiently in both conditions. In contrast, true interactors have a high ratio as they are preferable purified from the labeled sample enriched in the complex of interest. Samples can be mixed either before (**p**urification **a**fter **m**ixing, PAM, (Wang and Huang, 2008)) or after purification (**m**ixed **a**fter **p**urification, MAP). The main drawback of PAM strategies is that dynamic interactions result in equilibrium between the light and heavy form during the purification, thus decreasing the observed ratio even for true interactors.

#### **4. Temporal analysis of proteomes**

#### **4.1 Quantification of protein turnover by pulsed SILAC**

Protein turnover and protein degradation are critical for numerous biological processes, including the cell cycle, signal transduction and apoptosis. Organelle proteomes change over time and can be quickly adapted to respond to changing conditions. Therefore, unraveling mechanisms that determine protein abundances is important for understanding organelle dynamics and regulation.

In general, quantitative approaches described before can be employed to gather snapshots of protein repertoires over time or in different conditions. However, such approaches quantify abundance changes of a given protein but fail to address what are the mechanisms underlying protein dynamics (decrease/increase turnover). Pulse-chase isotope tracer-based methods have been the method of choice to study protein degradation for decades. In such approaches, cells or animals are first metabolically pulse-labeled with radioisotope tracers (most commonly 3H, 15N, 35S are used). After a chase period concomitant with the beginning of the experiment, loss of the radiolabel from the protein of interest is followed by scintillation or autoradiography as a readout of protein degradation. Such classical pulse chase experiments have been successfully translated to protein turnover analyses by MS. In MS-based turnover analyses, cells are only pulsed. For cells in culture, the most common approach is a modification of SILAC. Instead of combining two differently labeled cell populations, cells are pulsed with stable isotope containing amino acids (pSILAC). The replacement of amino-acids in proteins is followed by MS analysis over time to extract turnover rates. Similarly to results from a standard SILAC experiment, pSILAC yields two forms of the same peptide. At the time point corresponding to the half-life (t1/2) of a specific protein, the intensities of the light and heavy peaks for a peptide pair derived from this protein are equal (Figure 4).

Light labeled cells (green) are pulsed with heavy amino acid containing medium (red). Cells are harvested at different time points, lysed, processed into peptides and analyzed independently. Ratios H/L are derived from intensities of heavy (H, red) and light (L, green) peptide peaks in the MS spectra. Note, at protein half life (T1/2) intensity of light and heavy peptides are equal in the mass spectra. Then loss of light label can be calculated over time and turnover rates are extracted from non-linear curve fitting.

Fig. 4. pSILAC to analyze protein turnover.

360 Proteomics – Human Diseases and Protein Functions

have been combined and TAP strategies have been extensively used for AP-MS purification in many model systems (Gavin et al., 2006; Krogan et al., 2006). However, one drawback is the loss of weak interactors during stringent purification. A compromise is achieved by inserting a cleavage site between a tag and the protein of interest thus reducing the number of treatments and decreasing significantly the number of non-specific binders (Aguilar et al., 2010). In case of weak interactions, complexes can be fixed by treating with cross-linking chemicals either in live cells (Stingl et al., 2008; Yong et al., 2010) or cell

In general, quantitation greatly helps to discriminate false positive from true interactors. SILAC quantitation is more accurate and has advantages for protein complexes that are difficult to purify and where enrichment over background is smaller. As in all SILAC experiments, two differently labeled samples, here a control and a sample enriched in the target complex, are mixed and relative abundances of protein ratios are extracted from the MS spectra. In case of AP-MS, background proteins have a ratio of one as they bind as efficiently in both conditions. In contrast, true interactors have a high ratio as they are preferable purified from the labeled sample enriched in the complex of interest. Samples can be mixed either before (**p**urification **a**fter **m**ixing, PAM, (Wang and Huang, 2008)) or after purification (**m**ixed **a**fter **p**urification, MAP). The main drawback of PAM strategies is that dynamic interactions result in equilibrium between the light and heavy form during the

Protein turnover and protein degradation are critical for numerous biological processes, including the cell cycle, signal transduction and apoptosis. Organelle proteomes change over time and can be quickly adapted to respond to changing conditions. Therefore, unraveling mechanisms that determine protein abundances is important for understanding

In general, quantitative approaches described before can be employed to gather snapshots of protein repertoires over time or in different conditions. However, such approaches quantify abundance changes of a given protein but fail to address what are the mechanisms underlying protein dynamics (decrease/increase turnover). Pulse-chase isotope tracer-based methods have been the method of choice to study protein degradation for decades. In such approaches, cells or animals are first metabolically pulse-labeled with radioisotope tracers (most commonly 3H, 15N, 35S are used). After a chase period concomitant with the beginning of the experiment, loss of the radiolabel from the protein of interest is followed by scintillation or autoradiography as a readout of protein degradation. Such classical pulse chase experiments have been successfully translated to protein turnover analyses by MS. In MS-based turnover analyses, cells are only pulsed. For cells in culture, the most common approach is a modification of SILAC. Instead of combining two differently labeled cell populations, cells are pulsed with stable isotope containing amino acids (pSILAC). The replacement of amino-acids in proteins is followed by MS analysis over time to extract turnover rates. Similarly to results from a standard SILAC experiment, pSILAC yields two forms of the same peptide. At the time point corresponding to the half-life (t1/2) of a specific protein, the intensities of the light and heavy peaks for a peptide pair derived from this

purification, thus decreasing the observed ratio even for true interactors.

**4.1 Quantification of protein turnover by pulsed SILAC** 

**4. Temporal analysis of proteomes** 

organelle dynamics and regulation.

protein are equal (Figure 4).

lysates (Sinz, 2010).

To date, pSILAC has been mostly applied to cells in culture: *Saccharomyces cerevisiae* (Pratt et al., 2002), *Streptomyces coelicolor* (Jayapal et al., 2010) and human cells (Doherty et al., 2009; Schwanhäusser et al., 2011; Zee et al., 2010). Schwanhäusser et al. measured protein abundances and turnover of more than 5,000 proteins in HeLa cells. Combination of pSILAC with metabolic pulse labeling of mRNA in the same cells enabled comparison of proteins and mRNAs turnover at an unprecedented depth.

At steady state, protein levels are constant. Therefore synthesis and degradation rates are equal. This feature is critical when comparing proteome turnover, but this assumption may not always be fulfilled when comparing different conditions. An alternative approach overcoming this limitation is to follow synthesis and degradation independently from each other. To follow regulation of protein synthesis during changes of cellular iron, Schwannäusser et al. combined control (non-treated) and iron-treated HeLa cells that have been differently SILAC pulsed (Schwanhäusser et al., 2009). At the beginning of the experiment, cells in both conditions were identically light labeled (L). Concomitantly with iron treatment, cells were differently pulsed with medium (M) or heavy (H) amino-acids. Then cell lysates were combined and analyzed by MS. The relative abundance of newly synthesized proteins in both conditions was then extracted from intensities of the M and H peaks in the MS spectrum. As expected, most of the 1311 proteins identified in this study do not present

Analysis of Organelle Dynamics by Quantitative Mass Spectrometry Based Proteomics 363

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specific synthesis regulation upon iron treatment (ratio M/H of 1). However proteins such as ferritins show up to 13 fold synthesis upregulation compared to normal conditions.

#### **4.2 Analysis of cell signaling as an example of dynamic PTM analysis**

Unbiased, quantitative proteomics enables studying signaling networks in a time resolved manner. Protein abundance is often the output of complete signaling cascades. MS can detect PTM changes as an early response to system perturbations, as well as the changed protein abundance as the output of a signaling cascade.

Combination of phosphoproteomics with quantitative approaches such as SILAC enables the temporal analysis of signaling networks. In these experiments, cells are labeled in different SILAC states and stimulated for different periods of time. Applying this methodology, Olsen et al. quantified 6,600 phosphorylation sites in HeLa cells at 5 different time points after stimulation with **e**pidermal **g**rowth **f**actor (EGF) (Olsen et al., 2006). These experiments revealed insights into the early response of cells to EGF stimulation. Interestingly, this study showed that different phosphorylation sites on the same protein can react with completely different timing to a stimulus. Such results are easiest obtained by MS because it allows for detection of phosphorylation changes at particular amino acids of the protein sequence. Effects at different sites can otherwise easily be missed with classical methods that detect total phosphorylation of a protein. Other studies used a combination of SILAC labeling and different drug treatments to analyze the proteome and phosphoproteome of HeLa cells over the complete cell cycle (Olsen et al., 2010). A similar study in *Saccharomyces cerevisiae* used a mutant of the cell cycle kinase Cdk1 that allows inhibition of the kinase with an ATP analogue (Holt et al., 2009). Another recent phosphoproteome revealed great insights into the early differentiation of embryonic stem cells after stimulation with a diacylglycerol analogue. This study yielded roughly 20,000 phosphosites with almost 50% of them responding to the stimulus (Rigbolt et al., 2011). These experiments help to dissect complex signaling networks since proteins are clustered into certain groups according to their response. In addition, the high resolution datasets serve as a great resource for the scientific community and provide data for further analyses to generate models for signaling networks.

#### **5. Conclusion**

Quantitative mass spectrometry based proteomics emerged over the last few years as a crucial technique for cell biology and biochemistry research. The exciting developments in this field discussed in this chapter have provided unexpected aspects of organelle dynamics, protein turnover and PTMs. Future developments in methodology, computation and technical developments will make these technologies accessible for a larger group of scientists. This should help to generate more high quality datasets which will serve as a reference for the larger scientific community. In addition, integration of proteomics data with data from other system-wide approaches, such as genetic screens or transcriptome analysis, will help to understand complex biological processes.

#### **6. Acknowledgement**

We thank current and former members of the Walther laboratory for discussions of this book chapter. We apologize to colleagues whose contributions have not been cited due to space limitation. Research in the Walther laboratory is supported by the German Academic Research Council (DFG).

#### **7. References**

362 Proteomics – Human Diseases and Protein Functions

specific synthesis regulation upon iron treatment (ratio M/H of 1). However proteins such as

Unbiased, quantitative proteomics enables studying signaling networks in a time resolved manner. Protein abundance is often the output of complete signaling cascades. MS can detect PTM changes as an early response to system perturbations, as well as the changed

Combination of phosphoproteomics with quantitative approaches such as SILAC enables the temporal analysis of signaling networks. In these experiments, cells are labeled in different SILAC states and stimulated for different periods of time. Applying this methodology, Olsen et al. quantified 6,600 phosphorylation sites in HeLa cells at 5 different time points after stimulation with **e**pidermal **g**rowth **f**actor (EGF) (Olsen et al., 2006). These experiments revealed insights into the early response of cells to EGF stimulation. Interestingly, this study showed that different phosphorylation sites on the same protein can react with completely different timing to a stimulus. Such results are easiest obtained by MS because it allows for detection of phosphorylation changes at particular amino acids of the protein sequence. Effects at different sites can otherwise easily be missed with classical methods that detect total phosphorylation of a protein. Other studies used a combination of SILAC labeling and different drug treatments to analyze the proteome and phosphoproteome of HeLa cells over the complete cell cycle (Olsen et al., 2010). A similar study in *Saccharomyces cerevisiae* used a mutant of the cell cycle kinase Cdk1 that allows inhibition of the kinase with an ATP analogue (Holt et al., 2009). Another recent phosphoproteome revealed great insights into the early differentiation of embryonic stem cells after stimulation with a diacylglycerol analogue. This study yielded roughly 20,000 phosphosites with almost 50% of them responding to the stimulus (Rigbolt et al., 2011). These experiments help to dissect complex signaling networks since proteins are clustered into certain groups according to their response. In addition, the high resolution datasets serve as a great resource for the scientific community and provide data for further analyses

Quantitative mass spectrometry based proteomics emerged over the last few years as a crucial technique for cell biology and biochemistry research. The exciting developments in this field discussed in this chapter have provided unexpected aspects of organelle dynamics, protein turnover and PTMs. Future developments in methodology, computation and technical developments will make these technologies accessible for a larger group of scientists. This should help to generate more high quality datasets which will serve as a reference for the larger scientific community. In addition, integration of proteomics data with data from other system-wide approaches, such as genetic screens or transcriptome

We thank current and former members of the Walther laboratory for discussions of this book chapter. We apologize to colleagues whose contributions have not been cited due to

ferritins show up to 13 fold synthesis upregulation compared to normal conditions.

**4.2 Analysis of cell signaling as an example of dynamic PTM analysis** 

protein abundance as the output of a signaling cascade.

to generate models for signaling networks.

analysis, will help to understand complex biological processes.

**5. Conclusion** 

**6. Acknowledgement** 


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M. Kassem, M. Mann, J.V. Olsen, and B. Blagoev. 2011. System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem

Pillai, S. Dey, S. Daniels, S. Purkayastha, P. Juhasz, S. Martin, M. Bartlet-Jones, F. He, A. Jacobson, and D.J. Pappin. 2004. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. *Mol Cell Proteomics*. 3:1154-69.


**17** 

*Brazil* 

**Mitochondrial Proteomics:** 

**From Structure to Function** 

*Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, Brasília-DF* 

Bernardo A. Petriz, Jeeser A. Almeida, Mirna S. Freire,

*Center of Proteomic and Biochemical Analyses, Postgraduate* 

Luiz A. O. Rocha, Taia M. B. Rezende and Octavio L. Franco

Mitochondria may be considered an evolutionary product originating from the endosymbiotic process of an aerobic bacterium with a protoeukaryotic cell, which started about 2 billion years ago. This hypothesis was based on the similarities in DNA and protein synthesis machinery between prokaryotic and eukaryotic cells (Margulis, 1970). In 1890, Altmann, who termed this organelle the "bioblast", concluded that these were elementary organisms for performing vital functions (Ernster & Schatz, 1981). Furthermore, in 1898 Benda introduced the name "mitochondrion" from the Greek (*Mitos*: thread and *Chondrion*: granule). Other authors showed the appearance of these structures during spermatogenesis (Ernster & Schatz, 1981) and defined them as subcellular organelles commonly found in the cytoplasm, with essential functions of aerobic metabolism for eukaryotic cells. These organelles have a wide plasticity and mobility, allowing some modifications in shape. Mitochondrial movement into the cytosol has been associated with microtubules, which may determine organelle organization in different cell types (Yaffe, 1999). Mitochondria can form long filaments of mobile chains, as observed in cardiac muscle cells, or be fixed into a single position as studied in sperm flagella (Yaffe, 1999). The number of mitochondria per cell may also vary according to cell type, usually located in high ATP utilization regions such as cardiac muscle and liver (Frederick & Shaw, 2007; Scheffler, 2008). This organelle also occupies a considerable portion of the cytoplasmic volume, being essential for the

Mitochondria are also involved in several central metabolic pathways that are important for cell function, modulation of cytosolic Ca+2 signaling, determination of cell death (apoptosis) (Camara et al., 2011), aging and associated diseases (Kowald & Kirkwood, 2011), autophagous embryonic development (Ong & Hausenloy, 2010), as well as being a continuous source of superoxide and reactive oxygen species (Stowe & Camara, 2009; Koopman et al., 2010). Nevertheless, the main function of mitochondria is to generate chemical energy sources (ATP) through oxidative phosphorylation (Saraste, 1999), acting as the organism's "powerhouse". Without the presence of this particular organelle, animal cells would depend on anaerobic glycolysis for their energy production, which would be

**1. Introduction** 

evolution of a number of organisms.

interaction proteomics and genome-wide profiling of epigenetic histone marks and their readers. *Cell*. 142:967-80.


### **Mitochondrial Proteomics: From Structure to Function**

Bernardo A. Petriz, Jeeser A. Almeida, Mirna S. Freire, Luiz A. O. Rocha, Taia M. B. Rezende and Octavio L. Franco *Center of Proteomic and Biochemical Analyses, Postgraduate Program in Genomic Sciences and Biotechnology, Catholic University of Brasília, Brasília-DF Brazil* 

#### **1. Introduction**

368 Proteomics – Human Diseases and Protein Functions

Villen, J., and S.P. Gygi. 2008. The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. *Nat Protoc*. 3:1630-8. Walther, T.C., and M. Mann. 2010. Mass spectrometry–based proteomics in cell biology. *Cell*. Wang, X., and L. Huang. 2008. Identifying dynamic interactors of protein complexes by quantitative mass spectrometry. *Molecular & cellular proteomics : MCP*. 7:46-57. Wiederhold, E., L.M. Veenhoff, B. Poolman, and D.J. Slotboom. 2010. Proteomics of Saccharomyces cerevisiae Organelles. *Molecular & cellular proteomics : MCP*. 9:431-45. Wolf-Yadlin, A., S. Hautaniemi, D.A. Lauffenburger, and F.M. White. 2007. Multiple

their readers. *Cell*. 142:967-80.

networks. *Proc Natl Acad Sci U S A*. 104:5860-5.

*Molecular & cellular proteomics : MCP*. 10:M110.007294.

mass spectrometry. *Nat Biotechnol*. 21:660-6.

141:897-907.

membrane proteins. *Analytical chemistry*. 76:1817-23.

interaction proteomics and genome-wide profiling of epigenetic histone marks and

reaction monitoring for robust quantitative proteomic analysis of cellular signaling

of Metastasis-associated Cell-surface Sialoglycoproteins in Prostate Cancer.

translational modifications and variants in human cells. *Epigenetics & Chromatin*. 3:22.

N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and

error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia. *Mol Cell Proteomics*. 9:780-90. Zhao, Y., W. Zhang, Y. Kho, and Y. Zhao. 2004. Proteomic analysis of integral plasma

vivo N-glycoproteome reveals rigid topological and sequence constraints. *Cell*.

Carpenter, and F.W. McLafferty. 2000. Electron capture dissociation for structural

Wong, J.W., and G. Cagney. 2010. An overview of label-free quantitation methods in

Xu, G., J.S. Paige, and S.R. Jaffrey. 2010. Global analysis of lysine ubiquitination by ubiquitin

Yang, L., J.O. Nyalwidhe, S. Guo, R.R. Drake, and O.J. Semmes. 2011. Targeted Identification

Yong, J., M. Kasim, J.L. Bachorik, L. Wan, and G. Dreyfuss. 2010. Gemin5 delivers snRNA precursors to the SMN complex for snRNP biogenesis. *Molecular cell*. 38:551-62. Zee, B.M., R.S. Levin, P.A. Dimaggio, and B.A. Garcia. 2010. Global turnover of histone post-

Zhang, H., X.J. Li, D.B. Martin, and R. Aebersold. 2003. Identification and quantification of

Zhang, Y., M. Askenazi, J. Jiang, C.J. Luckey, J.D. Griffin, and J.A. Marto. 2010. A robust

Zielinska, D.F., F. Gnad, J.R. Wisniewski, and M. Mann. 2010. Precision mapping of an in

Zubarev, R.A., D.M. Horn, E.K. Fridriksson, N.L. Kelleher, N.A. Kruger, M.A. Lewis, B.K.

characterization of multiply charged protein cations. *Anal Chem*. 72:563-73.

proteomics by mass spectrometry. *Methods Mol Biol*. 604:273-83.

remnant immunoaffinity profiling. *Nature biotechnology*. 28:868-73.

Mitochondria may be considered an evolutionary product originating from the endosymbiotic process of an aerobic bacterium with a protoeukaryotic cell, which started about 2 billion years ago. This hypothesis was based on the similarities in DNA and protein synthesis machinery between prokaryotic and eukaryotic cells (Margulis, 1970). In 1890, Altmann, who termed this organelle the "bioblast", concluded that these were elementary organisms for performing vital functions (Ernster & Schatz, 1981). Furthermore, in 1898 Benda introduced the name "mitochondrion" from the Greek (*Mitos*: thread and *Chondrion*: granule). Other authors showed the appearance of these structures during spermatogenesis (Ernster & Schatz, 1981) and defined them as subcellular organelles commonly found in the cytoplasm, with essential functions of aerobic metabolism for eukaryotic cells. These organelles have a wide plasticity and mobility, allowing some modifications in shape. Mitochondrial movement into the cytosol has been associated with microtubules, which may determine organelle organization in different cell types (Yaffe, 1999). Mitochondria can form long filaments of mobile chains, as observed in cardiac muscle cells, or be fixed into a single position as studied in sperm flagella (Yaffe, 1999). The number of mitochondria per cell may also vary according to cell type, usually located in high ATP utilization regions such as cardiac muscle and liver (Frederick & Shaw, 2007; Scheffler, 2008). This organelle also occupies a considerable portion of the cytoplasmic volume, being essential for the evolution of a number of organisms.

Mitochondria are also involved in several central metabolic pathways that are important for cell function, modulation of cytosolic Ca+2 signaling, determination of cell death (apoptosis) (Camara et al., 2011), aging and associated diseases (Kowald & Kirkwood, 2011), autophagous embryonic development (Ong & Hausenloy, 2010), as well as being a continuous source of superoxide and reactive oxygen species (Stowe & Camara, 2009; Koopman et al., 2010). Nevertheless, the main function of mitochondria is to generate chemical energy sources (ATP) through oxidative phosphorylation (Saraste, 1999), acting as the organism's "powerhouse". Without the presence of this particular organelle, animal cells would depend on anaerobic glycolysis for their energy production, which would be

Mitochondrial Proteomics: From Structure to Function 371

diffusion of proteins into the cytosol, inducing possible cell death (Chipuk et al., 2006). The intermembrane determines the space between the outer and inner membrane. Some proteins enter the intermembrane space, but they do not show the ability to penetrate the inner

The inner membrane is characterized by a major phospholipid structure termed cardiolipin. This structure consists of four fatty acids, allowing the membrane potentially to become impervious to almost all molecules (McMillin & Dowhan, 2002). The membrane is convoluted in form, due to the presence of numerous cristae, which tend to be projected into the matrix, considerably increasing the inner membrane area. Interestingly, mitochondria from heart muscle cells have three times more cristae than those from liver cells (Alberts et al., 2002). Possibly this difference occurs due to ATP demand for different cell types. Inside the inner membrane there is a widely varying number of transporter proteins, which provide limited permeability and assist the entry and exit of ions and molecules that are required or metabolized by enzymes located in the mitochondrial matrix (Herrmann & Neupert, 2000). In summary, the inner membrane proteins have basically three categories and corresponding functions: ATP synthase which produces ATP in the matrix, carrier proteins to regulate the passage of metabolites from within the matrix to outside it, and

The matrix is considered the highly functional zone inside mitochondria, due to its responsibility for ATP production (Krebs, 1940). It also contains mitochondrial DNA, RNA and ribosome special carriers. The matrix contains a vast amount of the enzyme responsible for the tricarboxylic acid cycle (TAC), also known as the Krebs cycle. This produces NADH + H+ and FADH2, essential for ATP synthesis by electrons donated to the electron transport chain (Krebs, 1940). These electron pairs are transferred to an O2 molecule to form H2O molecules. In this way, protons are loaded from the intermembrane space to the mitochondrial matrix by ATP synthase enzyme, yielding ATP for each proton

**2. Classic and novel techniques applied to the mitochondrial proteome** 

Progress in mitochondrial research has been made with a range of molecular research tools that have been used to identify and compare several compounds. Proteins are one of the major molecules of interest within mitochondria, because their disturbance is directly responsible for several biologic modulations. Proteomics is the study of all proteins at a single given moment; it involves tools to identify and elicit the entire protein composition of a single tissue, cell or organelle (Wilkins et al., 1996). Proteomic tools have developed steadily, and each has been used in its time in mitochondrial proteome research, starting with the classic combination of IEF (isoelectric focusing) and SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) known as 2-DE (two-dimensional electrophoresis). Next came the DIGE (Differential Gel Electrophoresis) fluorescent method, and now tools include several gel-free (MudPIT) and novel peptide labeling techniques, in addition to complementary functional analysis methods (e.g. Blue Native

The analysis of cell compartments and specific organelles has become more common among proteomics researchers, now that subcellular research has provided in-depth knowledge of specific cellular signaling and other related functions (Scheffler, 2008). Up to now, mitochondria have been one of the main organelles to be investigated with a wide range of

membrane due to enhanced hydrophobicity (Yoon et al., 2010).

proteins involved in oxidation reactions in the respiratory chain.

loaded (Ernster & Schatz, 1981).

and Native DIGE).

completely insufficient to meet demand. Mitochondrial metabolism of sugars is considered highly efficient, producing more energy than the glycolysis pathway.

Each mitochondrion has its own DNA and RNA with a complete system of transcription and translation, which synthesizes only a few proteins (Yoon et al., 2010). The mitochondrial genome of different tissues differs in size and gene content (Gray et al., 1999). In mammals, the mitochondrial genome is composed of a circular DNA molecule that has been reduced to approximately 16.5kb in size, encoding genes for only 13 proteins, all of them indispensable for electron transport chain functioning. The human mitochondrial genome is composed of a single double-stranded DNA molecule that curiously does not have an intron, but contains 37 genes essential to the mitochondrial respiratory function (Wallace, 1999). Moreover, two ribosomal RNAs and 22 transfer RNAs are absolutely crucial for the mitochondria translation system (Yoon et al., 2010). All other genes responsible for normal mitochondrial operations are encoded in the cell nucleus (Wallace, 1999).

Mitochondria have a double membrane system and are structurally divided into four compartments: inner membrane, outer membrane, matrix and intermembrane space (Chan, 2006), as represented in Figure 1. Several porin molecules are present in the outer mitochondrial membrane. Porins are transporter proteins, which form aqueous channels through the lipid bilayer (Schirmer, 1998). Thus, the outer membrane functions as a filter, being impermeable to molecules above 5,000 kDa (Weissig et al., 2004). However, larger proteins can enter the mitochondrion through an N-terminus signal sequence connecting it to a specific protein (outer membrane translocase), located in the outer mitochondrial membrane (Herrmann & Neupert, 2000). An outer membrane disruption can lead to a

Fig. 1. Scheme of the structure and function of mitochondria by a diagrammatic representation of the Krebs cycle, electron transport chain and ATP synthesis.

completely insufficient to meet demand. Mitochondrial metabolism of sugars is considered

Each mitochondrion has its own DNA and RNA with a complete system of transcription and translation, which synthesizes only a few proteins (Yoon et al., 2010). The mitochondrial genome of different tissues differs in size and gene content (Gray et al., 1999). In mammals, the mitochondrial genome is composed of a circular DNA molecule that has been reduced to approximately 16.5kb in size, encoding genes for only 13 proteins, all of them indispensable for electron transport chain functioning. The human mitochondrial genome is composed of a single double-stranded DNA molecule that curiously does not have an intron, but contains 37 genes essential to the mitochondrial respiratory function (Wallace, 1999). Moreover, two ribosomal RNAs and 22 transfer RNAs are absolutely crucial for the mitochondria translation system (Yoon et al., 2010). All other genes responsible for normal mitochondrial operations are encoded in the cell

Mitochondria have a double membrane system and are structurally divided into four compartments: inner membrane, outer membrane, matrix and intermembrane space (Chan, 2006), as represented in Figure 1. Several porin molecules are present in the outer mitochondrial membrane. Porins are transporter proteins, which form aqueous channels through the lipid bilayer (Schirmer, 1998). Thus, the outer membrane functions as a filter, being impermeable to molecules above 5,000 kDa (Weissig et al., 2004). However, larger proteins can enter the mitochondrion through an N-terminus signal sequence connecting it to a specific protein (outer membrane translocase), located in the outer mitochondrial membrane (Herrmann & Neupert, 2000). An outer membrane disruption can lead to a

Fig. 1. Scheme of the structure and function of mitochondria by a diagrammatic representation of the Krebs cycle, electron transport chain and ATP synthesis.

highly efficient, producing more energy than the glycolysis pathway.

nucleus (Wallace, 1999).

diffusion of proteins into the cytosol, inducing possible cell death (Chipuk et al., 2006). The intermembrane determines the space between the outer and inner membrane. Some proteins enter the intermembrane space, but they do not show the ability to penetrate the inner membrane due to enhanced hydrophobicity (Yoon et al., 2010).

The inner membrane is characterized by a major phospholipid structure termed cardiolipin. This structure consists of four fatty acids, allowing the membrane potentially to become impervious to almost all molecules (McMillin & Dowhan, 2002). The membrane is convoluted in form, due to the presence of numerous cristae, which tend to be projected into the matrix, considerably increasing the inner membrane area. Interestingly, mitochondria from heart muscle cells have three times more cristae than those from liver cells (Alberts et al., 2002). Possibly this difference occurs due to ATP demand for different cell types. Inside the inner membrane there is a widely varying number of transporter proteins, which provide limited permeability and assist the entry and exit of ions and molecules that are required or metabolized by enzymes located in the mitochondrial matrix (Herrmann & Neupert, 2000). In summary, the inner membrane proteins have basically three categories and corresponding functions: ATP synthase which produces ATP in the matrix, carrier proteins to regulate the passage of metabolites from within the matrix to outside it, and proteins involved in oxidation reactions in the respiratory chain.

The matrix is considered the highly functional zone inside mitochondria, due to its responsibility for ATP production (Krebs, 1940). It also contains mitochondrial DNA, RNA and ribosome special carriers. The matrix contains a vast amount of the enzyme responsible for the tricarboxylic acid cycle (TAC), also known as the Krebs cycle. This produces NADH + H+ and FADH2, essential for ATP synthesis by electrons donated to the electron transport chain (Krebs, 1940). These electron pairs are transferred to an O2 molecule to form H2O molecules. In this way, protons are loaded from the intermembrane space to the mitochondrial matrix by ATP synthase enzyme, yielding ATP for each proton loaded (Ernster & Schatz, 1981).

#### **2. Classic and novel techniques applied to the mitochondrial proteome**

Progress in mitochondrial research has been made with a range of molecular research tools that have been used to identify and compare several compounds. Proteins are one of the major molecules of interest within mitochondria, because their disturbance is directly responsible for several biologic modulations. Proteomics is the study of all proteins at a single given moment; it involves tools to identify and elicit the entire protein composition of a single tissue, cell or organelle (Wilkins et al., 1996). Proteomic tools have developed steadily, and each has been used in its time in mitochondrial proteome research, starting with the classic combination of IEF (isoelectric focusing) and SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis) known as 2-DE (two-dimensional electrophoresis). Next came the DIGE (Differential Gel Electrophoresis) fluorescent method, and now tools include several gel-free (MudPIT) and novel peptide labeling techniques, in addition to complementary functional analysis methods (e.g. Blue Native and Native DIGE).

The analysis of cell compartments and specific organelles has become more common among proteomics researchers, now that subcellular research has provided in-depth knowledge of specific cellular signaling and other related functions (Scheffler, 2008). Up to now, mitochondria have been one of the main organelles to be investigated with a wide range of

Mitochondrial Proteomics: From Structure to Function 373

The bulk of mitochondria proteome research has been performed using 2-DE associated with MS (mass spectrometry) identification. By combining IEF and protein separation by molecular mass (SDS-PAGE), after the staining process 2-DE gels may reveal up to 3000 protein spots (Lopez & Melov, 2002). The capacity to simultaneously resolve thousands of proteins and by this technique compare different proteomes is possibly the main strength of this technique, which has undergone constant improvement since its invention in the middle of the 70´s (Klose, 1975; O'Farrell, 1975). To overcome its various limitations, such as sample complexity, poor protein solubilizing (e.g. membrane proteins) and low resolution (e.g. hydrophobic and extreme acid or basic proteins) observed in 2-DE analyses, several upgrades have been developed, from sample preparation (e.g. protein extraction and solubilizing process) to gel conduction. Given the precision needed in mitochondrial proteomic analysis, these limitations represent a considerable barrier. Marty and Sluse (2008) reported that mitochondrial proteins appear mostly within the pH range of 3 to 11, and within the 15 to 100 kDa molecular mass range. Furthermore, several proteins from the electron transporter chain, which represent about 40% of inner membrane proteins (Schwerzmann et al., 1986), have high hydrophobic properties, a property that clearly makes IEF processes more difficult (Santoni et al., 2000). An incomplete proteome map may reflect a series of misconducted analyses and slightly erroneous insights, leading overall to a very

Moving on from the classic gel-based technique, the limitations positively promoted a series of upgrades in proteomic methods, leading mitochondriomics research to provide more accurate, qualitative, quantitative and functional proteome data. Development of several fluorescent staining methods and protein labeling prior to 2-DE separation as performed by the Difference Gel Electrophoresis method, known as DIGE (Unlu et al., 1997), responded to specific concerns about the qualitative and quantitative limitations of protein spot detection. The DIGE method is based on the incorporation of different fluorescent cyanide dyes (Cydye3, Cydye5 and Cydye7 by GE Healthcare) into lysine residue present in the protein samples (Byrne et al., 2009). Once each sample is labeled with a distinct fluorescent dye, which includes an internal standard (a mixture of all labeled samples), samples are resolved together by the same 2-DE experiment. As the gel is scanned, fluorescent excitation from each distinct dye is captured and overlapped for spot expression comparison between the other dyes and the standard signal, enabling the results to overcome possible intra-gel variability errors (Lilley & Friedman, 2004). In comparison to classic 2-DE, the DIGE technique has been successfully used by various research groups to shed some light on the mitochondrial proteome in a wide variety of organisms and physiological modulations (Jacoby et al., 2010; Egan et al., 2011; Glancy & Balaban, 2011). Because it overcomes the problem of data reproducibility, DIGE is the ultimate gel-based method and strongly recommended when protein quantification and comparison is desirable, as has been well

Most of the gel-based methods (2-DE and DIGE) started with treatments using strong solubilizing detergents (e.g. SDS, CHAPS, Sulfobetaines, Triton-X) and chaotropic agents (e.g Urea, Thiourea) as reviewed by Petriz *et al.,* (2011). The use of such agents leads to a real limitation on gel-based analysis caused by denaturing processes, which prevent any functional and protein-cross-talk analysis. However, major protein complexes present in abundance in the mitochondrial membrane may be analyzed from a functional perspective,

**2.1 Proteomic gel-based techniques in mitochondrial research** 

poor understanding of mitochondrial processes.

reviewed (Mathy & Sluse, 2008).

research tools (Lopez & Melov, 2002), which have generated valuable molecular data (Scheffler, 2008). The main interest driving mitochondrial research stems from the wide spectrum of important molecular signaling that this organelle performs within the cell and also the entire organism, including its crucial role in energy production (Ernster & Schatz, 1981), calcium homeostasis (Deluca et al., 1962), programmed cell apoptosis (Liu et al., 1996) and several pathologies (Chan, 2006; Pagliarini et al., 2008). Furthermore, mitochondrial dysfunction may play a crucial role in neurological (e.g. Alzheimer's, Parkinson's) (Lovell et al., 2005; Jones, 2010), cardiovascular (e.g. Ischemia, heart failure) and muscular diseases (Kim et al., 2006; Finsterer & Stollberger, 2010), as well as in the development of cancer (Kamp et al., 2011) and aging processes (Dencher et al., 2007; O'Connell & Ohlendieck, 2009).

It is also known that the mitochondrion network within a multicellular organism may be coordinated by a proteome reservoir of thousands of polypeptides (~1500), which can be up or down-regulated and have not been fully covered until now (Meisinger et al., 2008). Therefore the cataloging of the entire organelle proteome is of inestimable value to biomedical research, and further understanding of molecular mitochondrial modulation of positive stimulus or pathological insults shows enormous pharmacological potential (Fearnley et al., 2007; Wang et al., 2009). By different proteomic methods, much progress has been made in this direction in order to target and catalogue mitochondrial proteins from different organisms, such as humans (Rabilloud et al., 1998; Lefort et al., 2009), rodents (Zhang et al., 2008; Doran et al., 2009), fungi (Grinyer et al., 2004) and plants (Taylor et al., 2011).

In addition to simple proteome cataloging, several studies have been carried out to compare mitochondrial proteomes from different tissues (Forner et al., 2006; Fang & Lee, 2009; Forner et al., 2009), as well as from organ structures (e.g. heart atria, ventricle) (Forner et al., 2009) and mitochondrial subpopulations (e.g. intermyofibrillar and subsarcolemmal) (Kavazis et al., 2009; Ferreira et al., 2010; Ferreira et al., 2010), with the aim of characterizing specific molecular profiles and functions. Using these tissues, specific proteome characterization during various biologic stimuli and disturbances, such as aging (Dencher et al., 2007; O'Connell & Ohlendieck, 2009), exercise (Kavazis et al., 2009; Egan et al., 2011) and oxidative stress (2008; Lee et al., 2008; Zhang et al., 2008) have also been investigated to attempt to answer the numerous questions concerning the disturbance and adaptation of mitochondrial homeostasis.

Concerning the delicate process of organelle isolation and some proteomic analysis limitations, much has been achieved in surveying the mitochondrial proteome, as reviewed elsewhere (Mathy & Sluse, 2008). Classic protein detection methods, such as 2- DE, have been improved. Moreover, other proteomical techniques have reached higher levels of accuracy; data collection has improved with sample labeling and gel-free analysis, such as shotgun proteomics performed by multidimensional chromatography (2D-LC-MS) directly coupled with high throughput mass spectrometry (Aebersold & Mann, 2003; Tao et al., 2009). By means of these classic and up-to-date techniques, mitochondrial proteins have also been confirmed by the functional proteomic approach using native electrophoresis (e.g. Blue Native PAGE and Native DIGE) to investigate protein-protein interactions and membrane protein complexes (Brookes et al., 2002), increasing insights into molecular expression and signaling within inner and outer mitochondrial compartments.

research tools (Lopez & Melov, 2002), which have generated valuable molecular data (Scheffler, 2008). The main interest driving mitochondrial research stems from the wide spectrum of important molecular signaling that this organelle performs within the cell and also the entire organism, including its crucial role in energy production (Ernster & Schatz, 1981), calcium homeostasis (Deluca et al., 1962), programmed cell apoptosis (Liu et al., 1996) and several pathologies (Chan, 2006; Pagliarini et al., 2008). Furthermore, mitochondrial dysfunction may play a crucial role in neurological (e.g. Alzheimer's, Parkinson's) (Lovell et al., 2005; Jones, 2010), cardiovascular (e.g. Ischemia, heart failure) and muscular diseases (Kim et al., 2006; Finsterer & Stollberger, 2010), as well as in the development of cancer (Kamp et al., 2011) and aging processes (Dencher et al., 2007; O'Connell & Ohlendieck,

It is also known that the mitochondrion network within a multicellular organism may be coordinated by a proteome reservoir of thousands of polypeptides (~1500), which can be up or down-regulated and have not been fully covered until now (Meisinger et al., 2008). Therefore the cataloging of the entire organelle proteome is of inestimable value to biomedical research, and further understanding of molecular mitochondrial modulation of positive stimulus or pathological insults shows enormous pharmacological potential (Fearnley et al., 2007; Wang et al., 2009). By different proteomic methods, much progress has been made in this direction in order to target and catalogue mitochondrial proteins from different organisms, such as humans (Rabilloud et al., 1998; Lefort et al., 2009), rodents (Zhang et al., 2008; Doran et al., 2009), fungi (Grinyer et al., 2004) and plants (Taylor et al.,

In addition to simple proteome cataloging, several studies have been carried out to compare mitochondrial proteomes from different tissues (Forner et al., 2006; Fang & Lee, 2009; Forner et al., 2009), as well as from organ structures (e.g. heart atria, ventricle) (Forner et al., 2009) and mitochondrial subpopulations (e.g. intermyofibrillar and subsarcolemmal) (Kavazis et al., 2009; Ferreira et al., 2010; Ferreira et al., 2010), with the aim of characterizing specific molecular profiles and functions. Using these tissues, specific proteome characterization during various biologic stimuli and disturbances, such as aging (Dencher et al., 2007; O'Connell & Ohlendieck, 2009), exercise (Kavazis et al., 2009; Egan et al., 2011) and oxidative stress (2008; Lee et al., 2008; Zhang et al., 2008) have also been investigated to attempt to answer the numerous questions concerning the disturbance and adaptation of

Concerning the delicate process of organelle isolation and some proteomic analysis limitations, much has been achieved in surveying the mitochondrial proteome, as reviewed elsewhere (Mathy & Sluse, 2008). Classic protein detection methods, such as 2- DE, have been improved. Moreover, other proteomical techniques have reached higher levels of accuracy; data collection has improved with sample labeling and gel-free analysis, such as shotgun proteomics performed by multidimensional chromatography (2D-LC-MS) directly coupled with high throughput mass spectrometry (Aebersold & Mann, 2003; Tao et al., 2009). By means of these classic and up-to-date techniques, mitochondrial proteins have also been confirmed by the functional proteomic approach using native electrophoresis (e.g. Blue Native PAGE and Native DIGE) to investigate protein-protein interactions and membrane protein complexes (Brookes et al., 2002), increasing insights into molecular expression and signaling within inner and outer

2009).

2011).

mitochondrial homeostasis.

mitochondrial compartments.

#### **2.1 Proteomic gel-based techniques in mitochondrial research**

The bulk of mitochondria proteome research has been performed using 2-DE associated with MS (mass spectrometry) identification. By combining IEF and protein separation by molecular mass (SDS-PAGE), after the staining process 2-DE gels may reveal up to 3000 protein spots (Lopez & Melov, 2002). The capacity to simultaneously resolve thousands of proteins and by this technique compare different proteomes is possibly the main strength of this technique, which has undergone constant improvement since its invention in the middle of the 70´s (Klose, 1975; O'Farrell, 1975). To overcome its various limitations, such as sample complexity, poor protein solubilizing (e.g. membrane proteins) and low resolution (e.g. hydrophobic and extreme acid or basic proteins) observed in 2-DE analyses, several upgrades have been developed, from sample preparation (e.g. protein extraction and solubilizing process) to gel conduction. Given the precision needed in mitochondrial proteomic analysis, these limitations represent a considerable barrier. Marty and Sluse (2008) reported that mitochondrial proteins appear mostly within the pH range of 3 to 11, and within the 15 to 100 kDa molecular mass range. Furthermore, several proteins from the electron transporter chain, which represent about 40% of inner membrane proteins (Schwerzmann et al., 1986), have high hydrophobic properties, a property that clearly makes IEF processes more difficult (Santoni et al., 2000). An incomplete proteome map may reflect a series of misconducted analyses and slightly erroneous insights, leading overall to a very poor understanding of mitochondrial processes.

Moving on from the classic gel-based technique, the limitations positively promoted a series of upgrades in proteomic methods, leading mitochondriomics research to provide more accurate, qualitative, quantitative and functional proteome data. Development of several fluorescent staining methods and protein labeling prior to 2-DE separation as performed by the Difference Gel Electrophoresis method, known as DIGE (Unlu et al., 1997), responded to specific concerns about the qualitative and quantitative limitations of protein spot detection. The DIGE method is based on the incorporation of different fluorescent cyanide dyes (Cydye3, Cydye5 and Cydye7 by GE Healthcare) into lysine residue present in the protein samples (Byrne et al., 2009). Once each sample is labeled with a distinct fluorescent dye, which includes an internal standard (a mixture of all labeled samples), samples are resolved together by the same 2-DE experiment. As the gel is scanned, fluorescent excitation from each distinct dye is captured and overlapped for spot expression comparison between the other dyes and the standard signal, enabling the results to overcome possible intra-gel variability errors (Lilley & Friedman, 2004). In comparison to classic 2-DE, the DIGE technique has been successfully used by various research groups to shed some light on the mitochondrial proteome in a wide variety of organisms and physiological modulations (Jacoby et al., 2010; Egan et al., 2011; Glancy & Balaban, 2011). Because it overcomes the problem of data reproducibility, DIGE is the ultimate gel-based method and strongly recommended when protein quantification and comparison is desirable, as has been well reviewed (Mathy & Sluse, 2008).

Most of the gel-based methods (2-DE and DIGE) started with treatments using strong solubilizing detergents (e.g. SDS, CHAPS, Sulfobetaines, Triton-X) and chaotropic agents (e.g Urea, Thiourea) as reviewed by Petriz *et al.,* (2011). The use of such agents leads to a real limitation on gel-based analysis caused by denaturing processes, which prevent any functional and protein-cross-talk analysis. However, major protein complexes present in abundance in the mitochondrial membrane may be analyzed from a functional perspective,

Mitochondrial Proteomics: From Structure to Function 375

described by Ong *et al.,* (2002) to quantify protein expression differences from mammalian cells through a differentiation process. This method is also known as metabolic stableisotope labeling and has been used to investigate thousands of mitochondrial proteins identified by a variety of studies in pathology, such as Parkinson's treatment (Jin et al., 2007), human cytomegalovirus infection (Zhang et al., 2011) and diabetic sensory

Another well-established labeling method, ICAT, was first described by Gygi *et al.,* (1999) to quantify proteome modulation of yeast metabolic function under glucose-repressed stimulus. The ICAT technique labels cysteine (Cys) side chains with "light" or "heavy" isotope tags, permitting intensity pair comparison from peptide ions from distinctly labeled protein samples. This labeling technique has also been well implemented in mitochondrial analysis (Jiang et al., 2005; Lovell et al., 2005), but ICAT fails to identify non-cysteine-

A vital property of gel-free methods (2D-MSMS) is the generation of a great number of MS spectra, which may be further quantified by several peptide tagging methods (e.g. ICAT, SILAC, iTRAQ). Nevertheless, the identification and analysis of proteins from marked peptide spectra is usually time-consuming. Overcoming this limitation, the ultimate labeling method is iTRAQ, which has made the entire process less laborious. Another major improvement brought by this method is the ability to analyze up to four different protein samples in the same experiment, leading to a single MS spectrum peak (Ross et al., 2004). By labeling the peptides' N-termini iTRAQ is a successful tool in mitochondrial proteome development, principally within quantitation research design (Jullig et al., 2007; Kavazis et al., 2009; Glancy & Balaban, 2011). Gel-based and gel-free methods for mitochondrial analysis are schematically represented in Figure 2. Undoubtedly, mitochondrial proteomic research still leaves several gaps. It is clear that the classic proteomic approach alone will not be enough to fill these gaps. The two strategies together are likely to be the most complete

**3. Proteomic tools applied to understanding mitochondria: The effects of** 

The mitochondrion plays a key role in normal and healthy cells. However, under abnormal conditions this organelle can also be involved in cell dysfunction and death. Mitochondrial dysfunction may be implicated in a large number of diseases, such as cancer, neurodegenerative diseases (such as Alzheimer's), diabetes, ischemia-reperfusion injury and aging (Wallace, 1999). The identification of mitochondrial proteins may therefore be a beneficial tool in drug development and also to diagnose targets for such diseases. The treatment of these illnesses involves the drug-mitochondrial interaction that acts in the three main mitochondrial functions: energy production, reactive oxygen species fabrication, and cell death control via both apoptotic and necrotic pathways (Green & Reed 1998; Kristal & Brown 1999). These organelles have a superoxide radical anion source, which arises from oxidative phosphorylation and can produce reactive oxygen species and also their precursors. Although protective mechanisms are known to regulate these molecular species, reversible and irreversible oxidative damage to proteins, nucleic acids and lipids may occur. Under elevated oxidative stress conditions, such as cell aging or disease, these oxidative lesions can be accumulated and have drastic consequences for cellular function, leading ultimately to senescence, environmental

neuropathy etiology (Akude et al., 2011).

containing proteins, which is its major technical limitation.

and secure path to successful mitochondrial proteome research.

**drugs** 

as proposed by differential analyses, such as Blue-Native Electrophoresis (BN-PAGE) developed by Schagger and Von Jagow (1991). The main idea of BN-PAGE in mitochondrial research is first to separate membrane and other functional protein complexes by preserving enzyme activity using a non-denaturing gel. These entire complexes are then separated within a denaturating SDS-PAGE gel in order to divide protein complexes subunits by their molecular weight (Schagger & von Jagow, 1991). Brookes *et al.* (2002) have shown the great potential that this technique has demonstrated in functional proteomics for mitochondrial molecular signaling, protein-to-protein interaction and post-translational modifications, especially in respiratory chain proteins from the mitochondrial membrane. A variation of 2D BN-PAGE is the Native DIGE (Difference Gel Electrophoresis) technique (Dani & Dencher, 2008), which couples the fluorescent dyes labeling technique to the previously described BN-PAGE (Dani & Dencher, 2008). DIGE analyses use fluorescent labeling and internal standards to enhance the accuracy of analyses. Thus, non-denaturing techniques have been well used (van den Ecker et al., 2010; Phillips et al., 2011), demonstrating that these techniques may contribute extensively to greater knowledge on the part that molecular signaling plays in mitochondrial functionality.

#### **2.2 Gel-free and proteomic straightforward methods**

Up-to-date gel-free proteomic techniques, such as LC/LC-MSMS (Reinders & Sickmann, 2007; Lefort et al., 2009) have complemented gel-based experiments, leading mitochondrial molecular investigation to higher levels of data production and accuracy. It is well known that protein quantitation and low abundant protein detection have been among the major limitations on proteomic research. Fortunately, a series of gel-free methods have empowered this research by overcoming some of the main in-gel limitations and functioning as complementary tools for proteomic data mining. Gel-free methods are based on the separation of complex protein samples by liquid chromatography (e.g. reversed-phase, strong cationic exchange) followed by direct mass spectrometry analysis. When performed by more than one LC column this entire process characterizes 2D-LC analysis, known as multidimensional protein identification technology (MudPIT) (Link et al., 1999). MudPIT has been recognized as a high throughput method with enhanced ability to identify thousands of proteins within a single experiment (Motoyama & Yates, 2008). To enhance multidimensional chromatography for MS analysis, a series of chemical molecular labeling methods were developed to facilitate proteomic mining and especially quantitative proteome comparisons. Basically, different proteome samples are mixed with isobaric or stable isotope chemical tags, digested with proteolytic enzymes such as trypsin and then loaded together into MudPIT, enabling quantitative comparisons (Aebersold & Mann, 2003).

ICAT (Isotope-Coded Affinity Tags), SILAC (Stable Isotope Labeling with Amino acids in Cell culture) and iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) are the main quantitative-labeling methods for gel-free methodology, permitting comparisons between different samples (2 to ~4) within the same experiment, as well as increasing the amount of quantitative proteome data that can be gathered by making the final process of peptide MS identification easier (Lovell et al., 2005; Jin et al., 2007; Meany et al., 2007). Each of these labeling techniques has its own peculiarity; for instance, the SILAC method is based on the incorporation of "light" or "heavy" isotope agents (2H, 13C, 15N) within distinct cell cultures, which will further synthetize labeled proteins for proteomic comparisons. SILAC was first

as proposed by differential analyses, such as Blue-Native Electrophoresis (BN-PAGE) developed by Schagger and Von Jagow (1991). The main idea of BN-PAGE in mitochondrial research is first to separate membrane and other functional protein complexes by preserving enzyme activity using a non-denaturing gel. These entire complexes are then separated within a denaturating SDS-PAGE gel in order to divide protein complexes subunits by their molecular weight (Schagger & von Jagow, 1991). Brookes *et al.* (2002) have shown the great potential that this technique has demonstrated in functional proteomics for mitochondrial molecular signaling, protein-to-protein interaction and post-translational modifications, especially in respiratory chain proteins from the mitochondrial membrane. A variation of 2D BN-PAGE is the Native DIGE (Difference Gel Electrophoresis) technique (Dani & Dencher, 2008), which couples the fluorescent dyes labeling technique to the previously described BN-PAGE (Dani & Dencher, 2008). DIGE analyses use fluorescent labeling and internal standards to enhance the accuracy of analyses. Thus, non-denaturing techniques have been well used (van den Ecker et al., 2010; Phillips et al., 2011), demonstrating that these techniques may contribute extensively to greater knowledge on the part that molecular

Up-to-date gel-free proteomic techniques, such as LC/LC-MSMS (Reinders & Sickmann, 2007; Lefort et al., 2009) have complemented gel-based experiments, leading mitochondrial molecular investigation to higher levels of data production and accuracy. It is well known that protein quantitation and low abundant protein detection have been among the major limitations on proteomic research. Fortunately, a series of gel-free methods have empowered this research by overcoming some of the main in-gel limitations and functioning as complementary tools for proteomic data mining. Gel-free methods are based on the separation of complex protein samples by liquid chromatography (e.g. reversed-phase, strong cationic exchange) followed by direct mass spectrometry analysis. When performed by more than one LC column this entire process characterizes 2D-LC analysis, known as multidimensional protein identification technology (MudPIT) (Link et al., 1999). MudPIT has been recognized as a high throughput method with enhanced ability to identify thousands of proteins within a single experiment (Motoyama & Yates, 2008). To enhance multidimensional chromatography for MS analysis, a series of chemical molecular labeling methods were developed to facilitate proteomic mining and especially quantitative proteome comparisons. Basically, different proteome samples are mixed with isobaric or stable isotope chemical tags, digested with proteolytic enzymes such as trypsin and then loaded together into MudPIT, enabling quantitative comparisons (Aebersold & Mann, 2003). ICAT (Isotope-Coded Affinity Tags), SILAC (Stable Isotope Labeling with Amino acids in Cell culture) and iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) are the main quantitative-labeling methods for gel-free methodology, permitting comparisons between different samples (2 to ~4) within the same experiment, as well as increasing the amount of quantitative proteome data that can be gathered by making the final process of peptide MS identification easier (Lovell et al., 2005; Jin et al., 2007; Meany et al., 2007). Each of these labeling techniques has its own peculiarity; for instance, the SILAC method is based on the incorporation of "light" or "heavy" isotope agents (2H, 13C, 15N) within distinct cell cultures, which will further synthetize labeled proteins for proteomic comparisons. SILAC was first

signaling plays in mitochondrial functionality.

**2.2 Gel-free and proteomic straightforward methods** 

described by Ong *et al.,* (2002) to quantify protein expression differences from mammalian cells through a differentiation process. This method is also known as metabolic stableisotope labeling and has been used to investigate thousands of mitochondrial proteins identified by a variety of studies in pathology, such as Parkinson's treatment (Jin et al., 2007), human cytomegalovirus infection (Zhang et al., 2011) and diabetic sensory neuropathy etiology (Akude et al., 2011).

Another well-established labeling method, ICAT, was first described by Gygi *et al.,* (1999) to quantify proteome modulation of yeast metabolic function under glucose-repressed stimulus. The ICAT technique labels cysteine (Cys) side chains with "light" or "heavy" isotope tags, permitting intensity pair comparison from peptide ions from distinctly labeled protein samples. This labeling technique has also been well implemented in mitochondrial analysis (Jiang et al., 2005; Lovell et al., 2005), but ICAT fails to identify non-cysteinecontaining proteins, which is its major technical limitation.

A vital property of gel-free methods (2D-MSMS) is the generation of a great number of MS spectra, which may be further quantified by several peptide tagging methods (e.g. ICAT, SILAC, iTRAQ). Nevertheless, the identification and analysis of proteins from marked peptide spectra is usually time-consuming. Overcoming this limitation, the ultimate labeling method is iTRAQ, which has made the entire process less laborious. Another major improvement brought by this method is the ability to analyze up to four different protein samples in the same experiment, leading to a single MS spectrum peak (Ross et al., 2004). By labeling the peptides' N-termini iTRAQ is a successful tool in mitochondrial proteome development, principally within quantitation research design (Jullig et al., 2007; Kavazis et al., 2009; Glancy & Balaban, 2011). Gel-based and gel-free methods for mitochondrial analysis are schematically represented in Figure 2. Undoubtedly, mitochondrial proteomic research still leaves several gaps. It is clear that the classic proteomic approach alone will not be enough to fill these gaps. The two strategies together are likely to be the most complete and secure path to successful mitochondrial proteome research.

#### **3. Proteomic tools applied to understanding mitochondria: The effects of drugs**

The mitochondrion plays a key role in normal and healthy cells. However, under abnormal conditions this organelle can also be involved in cell dysfunction and death. Mitochondrial dysfunction may be implicated in a large number of diseases, such as cancer, neurodegenerative diseases (such as Alzheimer's), diabetes, ischemia-reperfusion injury and aging (Wallace, 1999). The identification of mitochondrial proteins may therefore be a beneficial tool in drug development and also to diagnose targets for such diseases. The treatment of these illnesses involves the drug-mitochondrial interaction that acts in the three main mitochondrial functions: energy production, reactive oxygen species fabrication, and cell death control via both apoptotic and necrotic pathways (Green & Reed 1998; Kristal & Brown 1999). These organelles have a superoxide radical anion source, which arises from oxidative phosphorylation and can produce reactive oxygen species and also their precursors. Although protective mechanisms are known to regulate these molecular species, reversible and irreversible oxidative damage to proteins, nucleic acids and lipids may occur. Under elevated oxidative stress conditions, such as cell aging or disease, these oxidative lesions can be accumulated and have drastic consequences for cellular function, leading ultimately to senescence, environmental

Mitochondrial Proteomics: From Structure to Function 377

preconditioning. This pathology is characterized by resistance to ischemia reperfusion injury in response to previous short ischemic episodes. In this study, the authors mimicked preconditioning, treating isolated rabbit ventricular myocytes with adenosine or diazoxide. They observed a distinctive pattern of affected proteins consistent with specific perturbation of mitochondrial metabolism through changes to a selected number of mitochondrial protein complexes, with specificity and complexity of the drug response. Compared to the vehicle-treated controls, expression of 28 significantly altered proteins was observed and 19 of them were identified. The majority of these proteins are involved in mitochondrial energetics, including subunits of enzymes from the tricarboxylic acid cycle and oxidative phosphorylation complexes. Among these changed proteins, the -subunit of ATP synthase was adenosine-phosphorylated after 60 min of treatment. These results prove that adenosine and diazoxide treatment acts in different cell parts, especially in the mitochondria (Arrell et

Regarding the anti-inflammatory agents that target mitochondria, the proteome-related literature has reported some data about simvastatin. Simvastatin is a 3-hydroxy-3 methylglutaryl coenzyme A reductase inhibitor that provides neuroprotection, acting against cell loss resulting from strokes (Law et al., 2003), Alzheimer's disease (Vega et al., 2003), Parkinson's disease (Rajanikant et al., 2007) and traumatic brain injury (Lu et al., 2004), all of which are pathologies that target mitochondria. In the same line, Pienaar *et al.,* (2009) compared rat mitochondrial proteins pre-treated with simvastatin for 14 days, followed or not by a unihemispheric injection of a mitochondrial complex S inhibitor. The authors identified 24 different mitochondrial proteins by mass spectroscopy, which represented many facets of mitochondrial integrity, with most of them forming part of the electron transport chain machinery (Pienaar et al., 2009). These results demonstrated that the simvastatin-mitochondrial interaction may contribute to beneficial effects, especially when

Research with cancer has shown that cancer cells are adapted to fast growth and proliferation in acid pH conditions and low oxygen tension (Griffiths, 2001). Cancer cells have a mitochondrial defect in oxidative phosphorylation, and this can be reversible. Therefore, mitochondrial-drug interaction in cancer pathology should divert energy production from the anaerobic path to oxidative phosphorylation. The effect is to decrease nuclear ATP, leading to a consequent reduction in cell proliferation, so that the tumor cells begin cellular differentiation and subsequently undergo cell death and apoptosis (Harris et

Neoplastic drugs that target different mitochondrial proteins are already on the market for the treatment of various types of cancer. One of these, doxorubicin, also known by the trademark name Adriamycn, is an anthracycline antibiotic and extremely effective antineoplastic agent used in a wide variety of solid cancers and hematological malignancies since the early 1960's (Di Marco et al., 1969). Hammer *et al.,* (2010) analyzed proteomically the doxorubicin-induced changes in a hepatocellular carcinoma cell model, using 2D DIGE, liquid chromatography coupled with electrospray ionization and a hybrid quadrupole linear ion-trap and Fourier-transform ion-cyclotron-resonance mass spectrometry (LC-ESI-LTQ-FTICR-MS) and nano-LC coupled offline to MALDI-TOF/TOF-MS (LC-offline-MALDI-TOF-TOF-MS.) These methods identified 155 different proteins that could be assigned to a wide variety of biological processes, compared to non-treated control cells. Functional analysis revealed major influences of doxorubicin on proteins involved in

al., 2006).

al., 2000).

associated with statin use.

Fig. 2. Proteomic tools for mitochondrial analysis. Schematic workflow of the gel-based (red) and gel-free (green) proteomic tools used to compose the mitochondrial proteome after extraction process (blue).

modification, for example in the S-nitrosylation process or the oxidation of cysteine to sulfenic acid, and also to cell death (Gibson, 2004).

In this context, mitochondria are a pharmacological target for drugs that modulate oxidative stress (Murphy & Smith, 2000). There are various compounds that can target oxidative stress in mitochondria. Coenzyme Q and vitamin E are natural compounds that are both used as dietary supplements. MitoQ and MitoVitE are based on triphenylphosphorium modifications of coenzyme Q and vitamin E, respectively. They are more potent than their natural sources and have been suggested for treating Friedreich's ataxia, since they prevent cell death (Jauslin et al., 2003). Another pharmacological example is flupirtine, a nonopioid analgesic drug with mitochondrial antioxidant activity, which acts as a free radical scavenger (Schluter et al., 2000). Flupirtine has been shown to inhibit ischemic injury (Osborne et al., 1996) and apoptosis, and may be protective against Alzheimer's and prion disease (Perovic et al., 1998).

In spite of the role of mitochondria in different diseases and their potential target for some disease treatments, there is little information about the proteome associated with drugmitochondrial interaction and target biomarker drugs for treatment design. Based on proteome analysis, with 2D-gel analysis followed by MALDI TOF MS or ESI MS/MS for protein identification, Arrell *et al.,* (2006) analyzed pharmacologically mimicked ischemic

Fig. 2. Proteomic tools for mitochondrial analysis. Schematic workflow of the gel-based (red) and gel-free (green) proteomic tools used to compose the mitochondrial proteome after

modification, for example in the S-nitrosylation process or the oxidation of cysteine to

In this context, mitochondria are a pharmacological target for drugs that modulate oxidative stress (Murphy & Smith, 2000). There are various compounds that can target oxidative stress in mitochondria. Coenzyme Q and vitamin E are natural compounds that are both used as dietary supplements. MitoQ and MitoVitE are based on triphenylphosphorium modifications of coenzyme Q and vitamin E, respectively. They are more potent than their natural sources and have been suggested for treating Friedreich's ataxia, since they prevent cell death (Jauslin et al., 2003). Another pharmacological example is flupirtine, a nonopioid analgesic drug with mitochondrial antioxidant activity, which acts as a free radical scavenger (Schluter et al., 2000). Flupirtine has been shown to inhibit ischemic injury (Osborne et al., 1996) and apoptosis, and may be protective against Alzheimer's and prion

In spite of the role of mitochondria in different diseases and their potential target for some disease treatments, there is little information about the proteome associated with drugmitochondrial interaction and target biomarker drugs for treatment design. Based on proteome analysis, with 2D-gel analysis followed by MALDI TOF MS or ESI MS/MS for protein identification, Arrell *et al.,* (2006) analyzed pharmacologically mimicked ischemic

extraction process (blue).

disease (Perovic et al., 1998).

sulfenic acid, and also to cell death (Gibson, 2004).

preconditioning. This pathology is characterized by resistance to ischemia reperfusion injury in response to previous short ischemic episodes. In this study, the authors mimicked preconditioning, treating isolated rabbit ventricular myocytes with adenosine or diazoxide. They observed a distinctive pattern of affected proteins consistent with specific perturbation of mitochondrial metabolism through changes to a selected number of mitochondrial protein complexes, with specificity and complexity of the drug response. Compared to the vehicle-treated controls, expression of 28 significantly altered proteins was observed and 19 of them were identified. The majority of these proteins are involved in mitochondrial energetics, including subunits of enzymes from the tricarboxylic acid cycle and oxidative phosphorylation complexes. Among these changed proteins, the -subunit of ATP synthase was adenosine-phosphorylated after 60 min of treatment. These results prove that adenosine and diazoxide treatment acts in different cell parts, especially in the mitochondria (Arrell et al., 2006).

Regarding the anti-inflammatory agents that target mitochondria, the proteome-related literature has reported some data about simvastatin. Simvastatin is a 3-hydroxy-3 methylglutaryl coenzyme A reductase inhibitor that provides neuroprotection, acting against cell loss resulting from strokes (Law et al., 2003), Alzheimer's disease (Vega et al., 2003), Parkinson's disease (Rajanikant et al., 2007) and traumatic brain injury (Lu et al., 2004), all of which are pathologies that target mitochondria. In the same line, Pienaar *et al.,* (2009) compared rat mitochondrial proteins pre-treated with simvastatin for 14 days, followed or not by a unihemispheric injection of a mitochondrial complex S inhibitor. The authors identified 24 different mitochondrial proteins by mass spectroscopy, which represented many facets of mitochondrial integrity, with most of them forming part of the electron transport chain machinery (Pienaar et al., 2009). These results demonstrated that the simvastatin-mitochondrial interaction may contribute to beneficial effects, especially when associated with statin use.

Research with cancer has shown that cancer cells are adapted to fast growth and proliferation in acid pH conditions and low oxygen tension (Griffiths, 2001). Cancer cells have a mitochondrial defect in oxidative phosphorylation, and this can be reversible. Therefore, mitochondrial-drug interaction in cancer pathology should divert energy production from the anaerobic path to oxidative phosphorylation. The effect is to decrease nuclear ATP, leading to a consequent reduction in cell proliferation, so that the tumor cells begin cellular differentiation and subsequently undergo cell death and apoptosis (Harris et al., 2000).

Neoplastic drugs that target different mitochondrial proteins are already on the market for the treatment of various types of cancer. One of these, doxorubicin, also known by the trademark name Adriamycn, is an anthracycline antibiotic and extremely effective antineoplastic agent used in a wide variety of solid cancers and hematological malignancies since the early 1960's (Di Marco et al., 1969). Hammer *et al.,* (2010) analyzed proteomically the doxorubicin-induced changes in a hepatocellular carcinoma cell model, using 2D DIGE, liquid chromatography coupled with electrospray ionization and a hybrid quadrupole linear ion-trap and Fourier-transform ion-cyclotron-resonance mass spectrometry (LC-ESI-LTQ-FTICR-MS) and nano-LC coupled offline to MALDI-TOF/TOF-MS (LC-offline-MALDI-TOF-TOF-MS.) These methods identified 155 different proteins that could be assigned to a wide variety of biological processes, compared to non-treated control cells. Functional analysis revealed major influences of doxorubicin on proteins involved in

Mitochondrial Proteomics: From Structure to Function 379

Antipsychotic drugs are another kind of medication that target mitochondria. Psychotic brains present anomalies in their mitochondria, including mitochondrial dysfunction or abnormal cerebral energy metabolism, which may play an important role in the pathophysiology of schizophrenia. Together with this, mitochondrial energy metabolism might be disturbed by the antipsychotic drugs used (Modica-Napolitano et al., 2003). Various antipsychotic drugs have been available for treatment: chlorpromazine (CPZ), was in the first generation and presents serious side-effects (Freedman, 2003); clozapine (CLZ), the first atypical antipsychotic drug to present more antipsychotic effects without the adverse mobility effects of the first-generation drugs (Freedman, 2003); and quetiapine (QTP), an atypical antipsychotic drug that usually acts on both dopamine receptors and serotonin receptors (Martorell et al., 2006). Comparative proteomics of all mitochondrial proteins from the cerebral cortex and hippocampus of a rat model (Sprague-Dawley rats) in response to CPZ, CLZ and QTP antipsychotic medication demonstrated 14 differentially expressed different proteins. Six of them belong to the respiratory electron transport chain of oxidative phosphorylation, showing significant changes in protein quantity including NADH dehydrogenase 1 subcomplex 10, NADH dehydrogenase flavoprotein 2, NADH dehydrogenase Fe-S protein 3, F1-ATPase beta subunit, ATPase, H+ transporting, lysosomal, beta 56/58 kDa, isoform 2 and ATPase, H+ transporting, V1 subunit A, isoform 1; demonstrating antipsychotic drug-mitochondrial interaction (Ji et

Dietary supplements can also act on the mitochondrial proteome. Epidemiological studies suggested that the consumption of soy-containing foods has the ability to prevent or to slow down the development of cardiovascular syndromes (Zhang. X, 2003). A systematic review suggested that a diet supplemented with soy protein isolate containing isoflavones reduces low-density lipoprotein (LDL) and cholesterol (critical risk factor in the development of cardiovascular disease), but no effects on triglycerides or high-density lipoprotein cholesterol contents were observed (Cassidy & Hooper, 2006). Proteome analysis revealed that the LDL-induced alterations of numerous proteins were reversed by the soy extracts and also by the combination of genistein/daidzein soy isoflavones. However, both treatments regulated only three proteins functionally linked to mitochondrial dysfunction and were also connected to reducing the generation of oxidized-LDL-mediated

Several reports demonstrate the role of specific foods in mitochondrial function (Fuchs. D et al., 2007). A nutrient-sensing target of the rapamycin pathway appears to have a conserved role in regulating life span. It has been demonstrated that the reduced nutrient-sensing target of rapamycin signaling extends yeast's chronological life-span by increasing mitochondrial oxygen consumption, in part by up-regulating mtDNA-encoded oxidative phosphorylation subunit translation (Bonawitz et al., 2007). These data demonstrate that mitochondrial dysfunction could also be related to aging. Besides, it has been demonstrated by 2D DIGE and MALDI MS/MS that the nutrient-sensing target of rapamycin signaling has a global role in regulating mitochondrial proteome dynamics and function (Pan &

Another proteomic field that improved the understanding of drug-mitochondria interaction is in the elucidation of drug targets and the discovery of mechanisms of resistance to different pathogens. *Plasmodium falciparum* is responsible for approximately 247 million cases of malaria and one million deaths each year (WHO, 2011). The drug doxycycline is currently one of the recommended chemoprophylactic regimens for

mitochondrial reactive oxygen species, which could induce damages.

al., 2009).

Shadel, 2009).

protein synthesis, DNA damage control, electron transport/mitochondrial function and tumor growth.

Further, as regards mitochondrial proteins, the authors above observed that doxorubicin increases the Bax level (apoptosis regulator), which is involved in cytochrome C release from mitochondria and in turn caspase activation and decreased expression of Bcl-2. Oxidative stress induction was also shown by the enhanced levels of ferredoxin reductase and transferring receptor protein 1 and decreased levels of ERO1-like protein-. Proteins involved in -oxidation, such as acyl-CoA dehydrogenase, acetyl-CoA carboxylase 1 and hydroxymethylglutaryl-CoA synthase, were also increased in the doxorubicin-treated group. These results demonstrated that application of doxorubicin led to up-regulation of proteins involved in adaptation to oxidative stress and maintenance of cell integrity (Hammer et al., 2010).

Doxorubicin is widely used in drug combination strategies for non-Hodgkin lymphoma therapy (Multani et al., 2001). Jiang, Sun *et al.,* (2009) used 2D-DIGE in combination with ESI-MS/MS to analyze changes in mitochondrial protein expression in controlling Raji (lymphoblast-like cells) and doxorubicin-treated Raji cells. Defects in the mitochondrial antioxidant defense system, DNA repair, and oxidative phosphorylation may be the main mechanisms involved in the effect of doxorubicin on the mitochondria of Raji cells. Imperfections in the mitochondrial antioxidant defense system have dual effects on the anticancer mechanism and cardiac toxicity. The authors also found numerous proteins associated with significant chemo-resistance to doxorubicin, including heat shock protein (HSP) 70, prohibitin and ATP-binding cassette B6 transporter isoform. The reported results identified several biomarkers with the potential to enable prediction of anticancer therapy response (Jiang et al., 2009).

Another example of a highly potent chemotherapeutic drug that interacts with mitochondria is cisplatin, commonly used for a variety of human malignancies, such as testicular, prostate, ovarian, cervical, lung, and colon cancers (Wang & Lippard, 2005). Cisplatin cytotoxicity is primarily mediated by its ability to cause DNA damage and apoptotic cell death (Siddik, 2003). Zhang *et al.,* (2009) demonstrated, by different experimental methods, together with proteome analysis, that the induction of phospholipase A2-activating protein (PLAA) promoted cisplatin-associated apoptosis in cervical carcinoma HeLa cells by four pathways: activation of phospholipase A2 and accumulation of arachidonic acid, which causes mitochondrial damage; down-regulation of clusterin, a cytoprotective protein which promotes chemoresistance; upregulation of IL-32, which causes apoptosis in HeLa cells; and activation of JNK/c-jun signaling, which is an established inducer of Fas ligand expression and apoptosis mediated receptor (Zhang et al., 2009).

Analgesics also interact with mitochondria. The literature has reported the widely used analgesic acetaminophen and its mitochondrial interaction, including the fact that acetaminophen overdose may cause severe centrilobular hepatic necrosis in experimental animal models and humans (Davidson & Eastham, 1966). Ruepp *et al.,* (2002) explored acetaminophen overdose effects in mitochondria from animal model liver by genomic and proteomic tools. Proteomics showed that protein changes in mitochondria were present at 15 min post injection, thus preceding most of the gene regulations. The decrease in ATP synthase subunits and -oxidation pathway proteins indicated a loss of energy production. Since mitochondrial morphology was also affected very early at top dose, they concluded that acetaminophen overdose was a direct action of its known reactive metabolite N-acetyl*p*-benzoquinone imine, rather than a consequence of gene regulation (Ruepp et al., 2002).

protein synthesis, DNA damage control, electron transport/mitochondrial function and

Further, as regards mitochondrial proteins, the authors above observed that doxorubicin increases the Bax level (apoptosis regulator), which is involved in cytochrome C release from mitochondria and in turn caspase activation and decreased expression of Bcl-2. Oxidative stress induction was also shown by the enhanced levels of ferredoxin reductase and transferring receptor protein 1 and decreased levels of ERO1-like protein-. Proteins involved in -oxidation, such as acyl-CoA dehydrogenase, acetyl-CoA carboxylase 1 and hydroxymethylglutaryl-CoA synthase, were also increased in the doxorubicin-treated group. These results demonstrated that application of doxorubicin led to up-regulation of proteins involved in adaptation to oxidative stress and maintenance of cell integrity

Doxorubicin is widely used in drug combination strategies for non-Hodgkin lymphoma therapy (Multani et al., 2001). Jiang, Sun *et al.,* (2009) used 2D-DIGE in combination with ESI-MS/MS to analyze changes in mitochondrial protein expression in controlling Raji (lymphoblast-like cells) and doxorubicin-treated Raji cells. Defects in the mitochondrial antioxidant defense system, DNA repair, and oxidative phosphorylation may be the main mechanisms involved in the effect of doxorubicin on the mitochondria of Raji cells. Imperfections in the mitochondrial antioxidant defense system have dual effects on the anticancer mechanism and cardiac toxicity. The authors also found numerous proteins associated with significant chemo-resistance to doxorubicin, including heat shock protein (HSP) 70, prohibitin and ATP-binding cassette B6 transporter isoform. The reported results identified several biomarkers with the potential to enable prediction of anticancer therapy

Another example of a highly potent chemotherapeutic drug that interacts with mitochondria is cisplatin, commonly used for a variety of human malignancies, such as testicular, prostate, ovarian, cervical, lung, and colon cancers (Wang & Lippard, 2005). Cisplatin cytotoxicity is primarily mediated by its ability to cause DNA damage and apoptotic cell death (Siddik, 2003). Zhang *et al.,* (2009) demonstrated, by different experimental methods, together with proteome analysis, that the induction of phospholipase A2-activating protein (PLAA) promoted cisplatin-associated apoptosis in cervical carcinoma HeLa cells by four pathways: activation of phospholipase A2 and accumulation of arachidonic acid, which causes mitochondrial damage; down-regulation of clusterin, a cytoprotective protein which promotes chemoresistance; upregulation of IL-32, which causes apoptosis in HeLa cells; and activation of JNK/c-jun signaling, which is an established inducer of Fas ligand expression

Analgesics also interact with mitochondria. The literature has reported the widely used analgesic acetaminophen and its mitochondrial interaction, including the fact that acetaminophen overdose may cause severe centrilobular hepatic necrosis in experimental animal models and humans (Davidson & Eastham, 1966). Ruepp *et al.,* (2002) explored acetaminophen overdose effects in mitochondria from animal model liver by genomic and proteomic tools. Proteomics showed that protein changes in mitochondria were present at 15 min post injection, thus preceding most of the gene regulations. The decrease in ATP synthase subunits and -oxidation pathway proteins indicated a loss of energy production. Since mitochondrial morphology was also affected very early at top dose, they concluded that acetaminophen overdose was a direct action of its known reactive metabolite N-acetyl*p*-benzoquinone imine, rather than a consequence of gene regulation (Ruepp et al., 2002).

tumor growth.

(Hammer et al., 2010).

response (Jiang et al., 2009).

and apoptosis mediated receptor (Zhang et al., 2009).

Antipsychotic drugs are another kind of medication that target mitochondria. Psychotic brains present anomalies in their mitochondria, including mitochondrial dysfunction or abnormal cerebral energy metabolism, which may play an important role in the pathophysiology of schizophrenia. Together with this, mitochondrial energy metabolism might be disturbed by the antipsychotic drugs used (Modica-Napolitano et al., 2003). Various antipsychotic drugs have been available for treatment: chlorpromazine (CPZ), was in the first generation and presents serious side-effects (Freedman, 2003); clozapine (CLZ), the first atypical antipsychotic drug to present more antipsychotic effects without the adverse mobility effects of the first-generation drugs (Freedman, 2003); and quetiapine (QTP), an atypical antipsychotic drug that usually acts on both dopamine receptors and serotonin receptors (Martorell et al., 2006). Comparative proteomics of all mitochondrial proteins from the cerebral cortex and hippocampus of a rat model (Sprague-Dawley rats) in response to CPZ, CLZ and QTP antipsychotic medication demonstrated 14 differentially expressed different proteins. Six of them belong to the respiratory electron transport chain of oxidative phosphorylation, showing significant changes in protein quantity including NADH dehydrogenase 1 subcomplex 10, NADH dehydrogenase flavoprotein 2, NADH dehydrogenase Fe-S protein 3, F1-ATPase beta subunit, ATPase, H+ transporting, lysosomal, beta 56/58 kDa, isoform 2 and ATPase, H+ transporting, V1 subunit A, isoform 1; demonstrating antipsychotic drug-mitochondrial interaction (Ji et al., 2009).

Dietary supplements can also act on the mitochondrial proteome. Epidemiological studies suggested that the consumption of soy-containing foods has the ability to prevent or to slow down the development of cardiovascular syndromes (Zhang. X, 2003). A systematic review suggested that a diet supplemented with soy protein isolate containing isoflavones reduces low-density lipoprotein (LDL) and cholesterol (critical risk factor in the development of cardiovascular disease), but no effects on triglycerides or high-density lipoprotein cholesterol contents were observed (Cassidy & Hooper, 2006). Proteome analysis revealed that the LDL-induced alterations of numerous proteins were reversed by the soy extracts and also by the combination of genistein/daidzein soy isoflavones. However, both treatments regulated only three proteins functionally linked to mitochondrial dysfunction and were also connected to reducing the generation of oxidized-LDL-mediated mitochondrial reactive oxygen species, which could induce damages.

Several reports demonstrate the role of specific foods in mitochondrial function (Fuchs. D et al., 2007). A nutrient-sensing target of the rapamycin pathway appears to have a conserved role in regulating life span. It has been demonstrated that the reduced nutrient-sensing target of rapamycin signaling extends yeast's chronological life-span by increasing mitochondrial oxygen consumption, in part by up-regulating mtDNA-encoded oxidative phosphorylation subunit translation (Bonawitz et al., 2007). These data demonstrate that mitochondrial dysfunction could also be related to aging. Besides, it has been demonstrated by 2D DIGE and MALDI MS/MS that the nutrient-sensing target of rapamycin signaling has a global role in regulating mitochondrial proteome dynamics and function (Pan & Shadel, 2009).

Another proteomic field that improved the understanding of drug-mitochondria interaction is in the elucidation of drug targets and the discovery of mechanisms of resistance to different pathogens. *Plasmodium falciparum* is responsible for approximately 247 million cases of malaria and one million deaths each year (WHO, 2011). The drug doxycycline is currently one of the recommended chemoprophylactic regimens for

Mitochondrial Proteomics: From Structure to Function 381

As reviewed by Lopez and Melov (2002), several studies have shown the characterization of complex mixtures of mitochondria proteins expressed in diseased and healthy samples, leading to a further understanding of their functions in metabolic or physiological processes. This approach has reinforced protein research, leading to important progress in the study of mitochondrial functions and cognate molecular signaling. It is well known that exercise leads to biological adaptations that are highly specific and directly dependent on duration, frequency, intensity and stimulus nature, leading this process to require intensified oxygen demand (Hawley et al., 2011). As is recognized, one of the most important contributions of mitochondria is ATP synthesis, through oxidative phosphorylation (OXPHOS). Therefore, it seems obvious that a large range of decisive phenotype modulations associated with exercise occur within mitochondria. Despite the great importance of the mitochondrial proteome in several physiologic adaptations, there are few data and many questions remaining vis-à-vis molecular signaling, interaction and

In this perspective, Bo, Zhang and Ji (2010) indicated that exercise not only raises mitochondrial OXPHOS, but also exerts profound influences on mitochondrial morphology and biogenesis, being one of the most important influences on the fusion-fission process, whose action is responsible for continuous mitochondrial morphology remodeling. The mitochondrion has been seen as a dynamic networking organelle in which fusion and fission are intrinsic coupled processes. This process supports deletions of animals' damaged mitochondria according to organelle size (Kowald & Kirkwood, 2011). The complete process of mitochondrial fusion-fission has not been thoroughly elucidated, however, because there are many proteins involved in execution or modulation that remain to be described. Nevertheless, the mitochondrial dynamic appears to be specifically regulated depending on cell type or by the given function of certain tissues (Liesa et al., 2009). Mitochondrial fusion is a mechanical two-step process, where the outer and inner mitochondrial membranes are fused. This process depends on membrane potential and also the presence of GTP molecules, and is coordinated by optic atrophy gene 1 (OPA1) and mitofusins (Mfns), which promote the fusion of the lipid membrane (Liesa et al., 2009; Zorzano, 2009; Otera & Mihara, 2011). These proteins carry out various activities in promoting fusion and modulating the mitochondrial membrane (Zorzano, 2009). Moreover, according to the same author, fission is coordinated by dynamin-related protein 1 (Drp1) and Fission 1 homologue

Mitochondrial dynamics plays an important role in organelle functionality, contributing to an efficient bioenergy supply. The interruption of such processes leads to loss of mitochondrial activity and further diminished OXPHOS, suggesting its essentiality for mitochondrial function (Misko et al., 2010; Kowald & Kirkwood, 2011) and its contribution to the development of some neurodegenerative diseases (Westermann, 2010). Dynamic modifications in mitochondrial fusion-fission proteins during a session of extended exercise with incremental duration leads to a decrease in mitofusin Mfn1/2 expression and also an increase in Fis1 expression (Bo et al., 2010). According to the same authors, these alterations are related to exercise intensity, suggesting that fission may play a compensatory role for OXPHOS injury through improving glucoses and pyruvate uptake. This fact could maintain energy supply and prevent lactate accumulation, delaying the fatigue process associated with the enhancement of H+ concentration. So it is

**4. Mitochondrial proteomics applied to exercise research** 

activity following exercise stimulus.

protein (Fis1) that completes these events.

travellers visiting malaria-endemic areas in southeast Asia, Africa and South America (Gras et al., 1993). The emergence of *P. falciparum* resistance to most anti-malarial compounds has highlighted the urgency to develop novel drugs and to clarify the mechanisms of anti-malarial medications currently used. In this study, the authors analyzed protein expression changes by 2D-DIGE and iTRAQ methods in the schizont stage of the malarial parasite*,* following doxycycline treatment. Although some of these proteins have already been described as being deregulated by other drug treatments (Lasonder et al., 2008), numerous modifications in protein levels seem to be specific to doxycycline treatment, suggesting that apicoplasts and mitochondria are the main targets of doxycycline (Briolant et al., 2010).

Despite the progress made in these combined efforts, human mitochondrial databases have not yet been fully exploited to identify or target new candidates for drug development. In addition, the proteome of different pathologies and also the interaction between drugpathology proteins related to pharmacological action and side effects should be further studied. On the other hand, proteomic tools can help to understand this important protein relation better, with a view to designing mitochondrial biomarkers that may be useful in drug screening, clinical diagnosis, treatment follow-up and in discovering mechanisms of drug resistance (Figure 3).

Fig. 3. Summary of main pathologies and drugs related to mitochondria, together with the main proteomic tools described in the studies above, as well as the proposed targets of this triple association.

travellers visiting malaria-endemic areas in southeast Asia, Africa and South America (Gras et al., 1993). The emergence of *P. falciparum* resistance to most anti-malarial compounds has highlighted the urgency to develop novel drugs and to clarify the mechanisms of anti-malarial medications currently used. In this study, the authors analyzed protein expression changes by 2D-DIGE and iTRAQ methods in the schizont stage of the malarial parasite*,* following doxycycline treatment. Although some of these proteins have already been described as being deregulated by other drug treatments (Lasonder et al., 2008), numerous modifications in protein levels seem to be specific to doxycycline treatment, suggesting that apicoplasts and mitochondria are the main targets

Despite the progress made in these combined efforts, human mitochondrial databases have not yet been fully exploited to identify or target new candidates for drug development. In addition, the proteome of different pathologies and also the interaction between drugpathology proteins related to pharmacological action and side effects should be further studied. On the other hand, proteomic tools can help to understand this important protein relation better, with a view to designing mitochondrial biomarkers that may be useful in drug screening, clinical diagnosis, treatment follow-up and in discovering mechanisms of

Fig. 3. Summary of main pathologies and drugs related to mitochondria, together with the main proteomic tools described in the studies above, as well as the proposed targets of this

of doxycycline (Briolant et al., 2010).

drug resistance (Figure 3).

triple association.

#### **4. Mitochondrial proteomics applied to exercise research**

As reviewed by Lopez and Melov (2002), several studies have shown the characterization of complex mixtures of mitochondria proteins expressed in diseased and healthy samples, leading to a further understanding of their functions in metabolic or physiological processes. This approach has reinforced protein research, leading to important progress in the study of mitochondrial functions and cognate molecular signaling. It is well known that exercise leads to biological adaptations that are highly specific and directly dependent on duration, frequency, intensity and stimulus nature, leading this process to require intensified oxygen demand (Hawley et al., 2011). As is recognized, one of the most important contributions of mitochondria is ATP synthesis, through oxidative phosphorylation (OXPHOS). Therefore, it seems obvious that a large range of decisive phenotype modulations associated with exercise occur within mitochondria. Despite the great importance of the mitochondrial proteome in several physiologic adaptations, there are few data and many questions remaining vis-à-vis molecular signaling, interaction and activity following exercise stimulus.

In this perspective, Bo, Zhang and Ji (2010) indicated that exercise not only raises mitochondrial OXPHOS, but also exerts profound influences on mitochondrial morphology and biogenesis, being one of the most important influences on the fusion-fission process, whose action is responsible for continuous mitochondrial morphology remodeling. The mitochondrion has been seen as a dynamic networking organelle in which fusion and fission are intrinsic coupled processes. This process supports deletions of animals' damaged mitochondria according to organelle size (Kowald & Kirkwood, 2011). The complete process of mitochondrial fusion-fission has not been thoroughly elucidated, however, because there are many proteins involved in execution or modulation that remain to be described. Nevertheless, the mitochondrial dynamic appears to be specifically regulated depending on cell type or by the given function of certain tissues (Liesa et al., 2009). Mitochondrial fusion is a mechanical two-step process, where the outer and inner mitochondrial membranes are fused. This process depends on membrane potential and also the presence of GTP molecules, and is coordinated by optic atrophy gene 1 (OPA1) and mitofusins (Mfns), which promote the fusion of the lipid membrane (Liesa et al., 2009; Zorzano, 2009; Otera & Mihara, 2011). These proteins carry out various activities in promoting fusion and modulating the mitochondrial membrane (Zorzano, 2009). Moreover, according to the same author, fission is coordinated by dynamin-related protein 1 (Drp1) and Fission 1 homologue protein (Fis1) that completes these events.

Mitochondrial dynamics plays an important role in organelle functionality, contributing to an efficient bioenergy supply. The interruption of such processes leads to loss of mitochondrial activity and further diminished OXPHOS, suggesting its essentiality for mitochondrial function (Misko et al., 2010; Kowald & Kirkwood, 2011) and its contribution to the development of some neurodegenerative diseases (Westermann, 2010). Dynamic modifications in mitochondrial fusion-fission proteins during a session of extended exercise with incremental duration leads to a decrease in mitofusin Mfn1/2 expression and also an increase in Fis1 expression (Bo et al., 2010). According to the same authors, these alterations are related to exercise intensity, suggesting that fission may play a compensatory role for OXPHOS injury through improving glucoses and pyruvate uptake. This fact could maintain energy supply and prevent lactate accumulation, delaying the fatigue process associated with the enhancement of H+ concentration. So it is

Mitochondrial Proteomics: From Structure to Function 383

In summary, data reported here clearly show the enormous importance of mitochondria in multiple processes such as drug metabolism and exercise. These processes could be better understood by the use of genomic and proteomic techniques, which are constantly improving. In this view, the use of such technologies could bring real benefits in physiological understanding and in the improvement of biotechnological research related to

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**5. Conclusion** 

**6. References** 

drug design and activity.

198-207.

possible that in severe exercise mitochondrial fission may be a powerful indicator of muscle damage (Bo et al., 2010).

The role of exercise in cardiac health promotion has been well described in literature (Ascensao et al., 2007). This cardioprotective phenotype, normally associated with endurance exercise, seems to be related to increased myocardial antioxidant capacity (Kavazis et al., 2009). Oxidative stress resulting from exercise seems to be part of physiological adaptation. Severe exercise intensities activate a cascade of intracellular sources for reactive oxygen species (ROS) and it is clear that muscle adaptation depends on this process. However, it is important to observe that excessive ROS production can negatively influence exercise performance and can also lead to long-term health consequences (Bailey et al., 2004; Sahlin et al., 2010; Sun et al., 2010). The mechanism of increased ROS production during exercise is not totally clear, but experimental evidence suggests that mitochondria are the main source of ROS production during exercise (Di Meo & Venditti, 2001; Fernstrom et al., 2007). ROS levels are also described as depending on oxygen concentrations, and an additional electron accepted during energy production is used to create superoxide, a more reactive form of oxygen, which can be converted to hydrogen peroxide (H2O2) (Sarsour et al., 2009). Animal cells have additional antioxidant enzymes (e.g. catalase, glutathione peroxidase), and the newly identified family of peroxidases (e.g. PRDX) to neutralize H2O2 (Chang et al., 2004). Peroxidases are a family with at least six isoforms in mammalian cells. One of these is the mitochondrion-specific isoform, PRDXIII, which acts in defense against oxidative stress caused by H2O2 produced during the mitochondrial respiratory chain process (Kavazis et al., 2009). By using the iTRAQ technique, Kavazis *et al.,* (2009) demonstrated a left ventricular remodeling after endurance exercise training in preconditioned animals. This study has shown the upregulation of PRDXIII in subsarcolemmal mitochondrial subfraction, demonstrating a possible role of PRDXIII in heart remodeling. Exercise has also been demonstrated to reduce oxidative stress and dysfunction of mitochondria after myocardial infarction.

ATP production is well described as declining with age (Drew & Leeuwenburgh, 2003; Drew et al., 2003; Short, 2005) leading to metabolic impairment, so endurance exercise seems to be an important agent in improving and preserving mitochondrial function. Using mass spectrometry methods and the iTRAQ approach, Lanza *et al.,* (2008) demonstrated that tricarboxylic cycle enzymes and electron transport protein expression were down-regulated in older sedentary people when compared to younger subjects. On the other hand, endurance-trained elderly men exhibited an up-regulation of those proteins, suggesting that the expression level of key mitochondrial proteins may be a primary determinant for ageing. Thus, diminished oxidative capacity and regular endurance exercise appear to be beneficial in improving ATP synthesis, partially reversing some metabolic impairment caused by aging. Recently, Egan *et al.,* (2011) used the 2-D DIGE technique to demonstrate that adaptation of skeletal muscle to endurance exercise occurs within only 7 days of training with an increase in ATP generation.

Exercise is therefore recognized as an important agent in improving health, and it is also an auxiliary in numerous medical treatments and therapy. These findings about exercise have led to a better perception of the plasticity of skeletal muscle, mainly by the modulation of the mitochondrial proteome, contributing to understand muscle sensitivity to exercise stimulus. All of these results obviously indicate that phenotype changes associated with exercise are linked directly to mitochondrial proteome modulation. This fact makes mitochondrial research a promising field in sports and medical science.

#### **5. Conclusion**

382 Proteomics – Human Diseases and Protein Functions

possible that in severe exercise mitochondrial fission may be a powerful indicator of

The role of exercise in cardiac health promotion has been well described in literature (Ascensao et al., 2007). This cardioprotective phenotype, normally associated with endurance exercise, seems to be related to increased myocardial antioxidant capacity (Kavazis et al., 2009). Oxidative stress resulting from exercise seems to be part of physiological adaptation. Severe exercise intensities activate a cascade of intracellular sources for reactive oxygen species (ROS) and it is clear that muscle adaptation depends on this process. However, it is important to observe that excessive ROS production can negatively influence exercise performance and can also lead to long-term health consequences (Bailey et al., 2004; Sahlin et al., 2010; Sun et al., 2010). The mechanism of increased ROS production during exercise is not totally clear, but experimental evidence suggests that mitochondria are the main source of ROS production during exercise (Di Meo & Venditti, 2001; Fernstrom et al., 2007). ROS levels are also described as depending on oxygen concentrations, and an additional electron accepted during energy production is used to create superoxide, a more reactive form of oxygen, which can be converted to hydrogen peroxide (H2O2) (Sarsour et al., 2009). Animal cells have additional antioxidant enzymes (e.g. catalase, glutathione peroxidase), and the newly identified family of peroxidases (e.g. PRDX) to neutralize H2O2 (Chang et al., 2004). Peroxidases are a family with at least six isoforms in mammalian cells. One of these is the mitochondrion-specific isoform, PRDXIII, which acts in defense against oxidative stress caused by H2O2 produced during the mitochondrial respiratory chain process (Kavazis et al., 2009). By using the iTRAQ technique, Kavazis *et al.,* (2009) demonstrated a left ventricular remodeling after endurance exercise training in preconditioned animals. This study has shown the upregulation of PRDXIII in subsarcolemmal mitochondrial subfraction, demonstrating a possible role of PRDXIII in heart remodeling. Exercise has also been demonstrated to reduce

oxidative stress and dysfunction of mitochondria after myocardial infarction.

ATP production is well described as declining with age (Drew & Leeuwenburgh, 2003; Drew et al., 2003; Short, 2005) leading to metabolic impairment, so endurance exercise seems to be an important agent in improving and preserving mitochondrial function. Using mass spectrometry methods and the iTRAQ approach, Lanza *et al.,* (2008) demonstrated that tricarboxylic cycle enzymes and electron transport protein expression were down-regulated in older sedentary people when compared to younger subjects. On the other hand, endurance-trained elderly men exhibited an up-regulation of those proteins, suggesting that the expression level of key mitochondrial proteins may be a primary determinant for ageing. Thus, diminished oxidative capacity and regular endurance exercise appear to be beneficial in improving ATP synthesis, partially reversing some metabolic impairment caused by aging. Recently, Egan *et al.,* (2011) used the 2-D DIGE technique to demonstrate that adaptation of skeletal muscle to endurance exercise occurs within only 7 days of training

Exercise is therefore recognized as an important agent in improving health, and it is also an auxiliary in numerous medical treatments and therapy. These findings about exercise have led to a better perception of the plasticity of skeletal muscle, mainly by the modulation of the mitochondrial proteome, contributing to understand muscle sensitivity to exercise stimulus. All of these results obviously indicate that phenotype changes associated with exercise are linked directly to mitochondrial proteome modulation. This fact makes

mitochondrial research a promising field in sports and medical science.

muscle damage (Bo et al., 2010).

with an increase in ATP generation.

In summary, data reported here clearly show the enormous importance of mitochondria in multiple processes such as drug metabolism and exercise. These processes could be better understood by the use of genomic and proteomic techniques, which are constantly improving. In this view, the use of such technologies could bring real benefits in physiological understanding and in the improvement of biotechnological research related to drug design and activity.

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**18** 

*France* 

*Université d'Artois, Lens* 

**Proteomic Analysis of Plasma Membrane** 

Sophie Duban-Deweer, Johan Hachani, Barbara Deracinois, Roméo Cecchelli, Christophe Flahaut and Yannis Karamanos  *Laboratoire de Physiopathologie de la Barrière Hémato-Encéphalique,* 

**Proteins in an** *In Vitro* **Blood-Brain Barrier Model** 

Although several cell types have important regulatory roles in the induction and maintenance of a properly functioning blood-brain barrier (BBB) [Abbott et al., 2006; Armulik et al., 2010], it is clear that brain capillary endothelial cells (BCECs) constitute the barrier *per se* in histological terms. In the central nervous system's blood vessels, BCECs are closely interconnected by tight junctions and form a continuous, circular tube lining the basal membrane in which pericytes are embedded. The basal membrane surface is itself covered by a continuous sleeve of astrocyte endfeet (Fig. 1). The BBB is one of the most

> Astrocyte end-foot

Fig. 1. Brain capillary endothelial cells constitute the core of the BBB. The endothelial cells are surrounded by a tubular sheath of astrocyte end-feet. Pericytes are embedded in the basal lamina (between the endothelium and the astrocyte end-feet). Reprinted from [Pottiez

Basal lamina

important physiological structures in the maintenance of brain homeostasis.

Endothelial cells

Capillary lumen

Pericyte

et al., 2009a], with permission from Elsevier).

Inter-neuron

**1. Introduction** 


### **Proteomic Analysis of Plasma Membrane Proteins in an** *In Vitro* **Blood-Brain Barrier Model**

Sophie Duban-Deweer, Johan Hachani, Barbara Deracinois, Roméo Cecchelli, Christophe Flahaut and Yannis Karamanos  *Laboratoire de Physiopathologie de la Barrière Hémato-Encéphalique, Université d'Artois, Lens France* 

#### **1. Introduction**

390 Proteomics – Human Diseases and Protein Functions

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Although several cell types have important regulatory roles in the induction and maintenance of a properly functioning blood-brain barrier (BBB) [Abbott et al., 2006; Armulik et al., 2010], it is clear that brain capillary endothelial cells (BCECs) constitute the barrier *per se* in histological terms. In the central nervous system's blood vessels, BCECs are closely interconnected by tight junctions and form a continuous, circular tube lining the basal membrane in which pericytes are embedded. The basal membrane surface is itself covered by a continuous sleeve of astrocyte endfeet (Fig. 1). The BBB is one of the most important physiological structures in the maintenance of brain homeostasis.

Fig. 1. Brain capillary endothelial cells constitute the core of the BBB. The endothelial cells are surrounded by a tubular sheath of astrocyte end-feet. Pericytes are embedded in the basal lamina (between the endothelium and the astrocyte end-feet). Reprinted from [Pottiez et al., 2009a], with permission from Elsevier).

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 393

different structure in which several β strands form a barrel-shaped structure with a central pore. These strands contain predominantly polar amino acids and no long hydrophobic segments. Nevertheless, the outward-facing side groups on each of the β-strands are hydrophobic and interact with the membrane lipids' fatty acyl groups, whereas the side chains facing the inside are mainly hydrophilic [Lodish et al., 2000]. Interestingly, several posttranslational modifications that do not occur in the cytosol (such as disulphide bond formation and glycosylation) enhance the stability of PM or secreted proteins prior to their exposure to the extracellular milieu. Overall, these particularities can dramatically decrease the PM proteins' sensitivity to trypsin digestion. Newly synthesized proteins can also be targeted to the PM via the covalent attachment of a lipid anchor. Indeed, some proteins bind to the PM's cytosolic surface via a covalently attached fatty acid (e.g. palmitate or myristate) or isoprene group (e.g. a farnesyl or geranyl group, whereas proteins from the PM's outer leaflet are tethered some distance out from the surface by a glycosylphosphatidylinositol

Traditionally, mass spectrometry (MS)-based identification methods, chromatography and common cell biology techniques can be combined to form powerful tools for the proteomic mapping of PM proteins. Although major technical progress in MS continues to be made [Savas et al., 2011], the extraction, purification, separation and analysis of PM proteins remains problematic due to the latter's low abundance, poor solubility in aqueous solution and micro-heterogeneity [Santoni et al., 2000]. It is now clear that the development of complementary approaches is a prerequisite for the comprehensive analysis of PM proteins, including protein isolation and enrichment strategies that best preserve certain functional states and minimize the loss of transient and/or peripherally associated nontransmembrane proteins [Helbig et al., 2010], (Fig. 2). Polarized cells are present in many different organs and so their PMs have heterogeneous morphological and functional domains. Conventionally, PM proteomics can be performed with either cells cultured in suspension or adherent cells. Fig. 2 illustrates the importance of choosing the right method for the isolation of PMs and membrane sub- and microdomains and summarizes the different methods used in PM proteome analysis. The analysis can be divided into three experimental steps, all of which are challenging: (i) PM protein enrichment, (ii) separation

Plasma membrane protein enrichment can be achieved either directly by extraction of membrane proteins or indirectly by pre-purification of the PM itself (or part of the PM) prior to proteome analysis. In view of the PM proteins' physicochemical properties, it is tempting to use of amphoteric agents (such as detergents) for enrichment. However, aqueous phase proteins will also be more soluble and may not necessarily be separated from the PM proteins. In contrast, the enrichment of membrane proteins based on two-phase partitioning (i.e. an aqueous phase and an organic phase) has been widely used and has proved its utility. The PM proteins can then be separated from aqueous proteins, due to the difference in hydrophobicity. Another way of directly studying the PM protein content involves its evaluation through its peptide fingerprinting. To this end, cell surface proteins undergo a "proteolytic shaving" procedure. The resulting peptides are purified, separated and then identified by liquid chromatography – tandem MS (LC-MS/MS). Although the proteolytic

and quantification and (iii) identification [Sprenger & Jensen, 2010].

(GPI) anchor [Paulick & Bertozzi, 2008].

**1.2 Proteomics of the plasma membrane** 

**1.3 Plasma membrane protein enrichment** 

The BBB is a dynamic, regulatory interface that controls the molecular and cellular exchanges between the bloodstream and the brain compartment [Abbott et al., 2010]. The BCECs' barrier function depends on the acquisition and maintenance of characteristic features (referred to as the "BBB phenotype"), such as the absence of endothelial fenestrae, decrease in the number of endocytosis vesicles, the reinforcement of tight junctions and changes in the expression pattern of certain proteins. Overall, these physiological characteristics condition cell polarisation and permeation, transendothelial electrical resistance and a number of metabolic, receptor-based and transport functions. The latter mainly rely on the properties of the BCECs' plasma membrane (PM). Relevant information regarding the lipid composition of the whole cell and of the apical and basolateral PMs has been reported [Tewes & Galla, 2001]. The latter authors demonstrated that each PM shows a unique lipid composition; the apical PM is enriched in phosphatidylcholine, whereas the basolateral PM is enriched in sphingomyelin and glucosylceramide. It has also been observed that co-culture with glioma C6 cells is able to induce a more *in vivo*-like fatty acid pattern in BCEC-based BBB models, although the intensity of these changes did not reach *in vivo* levels [Kramer et al., 2002]. Given the vital physiological functions performed by membrane lipids this aspect merits further investigation. In contrast, the PM's protein moieties have been extensively studied. The protein composition of the PM is determined by the balance between membrane protein sorting, internalization and recycling. Briefly, biosynthesized PM proteins are translocated from the endoplasmic reticulum to the Golgi apparatus, where they undergo posttranslational modifications. Proteins are then sorted to the apical or basal membrane of polarized cells. Some PM proteins are subsequently internalised and sequestrated in lysosomes and then degraded or recycled to the cell surface; endocytic adaptor proteins may have a pivotal role in this process [Howes et al., 2010; Kelly & Owen, 2011; O'Bryan, 2010; Reider & Wendland, 2011]. Plasma membrane proteins are involved in many BBB functions, including (i) cell-extracellular matrix interactions, (ii) the cell-cell junctions (especially tight junctions) that impede paracellular transport and polarise the cells, (iii) the molecular transport systems that regulate the exchange of nutrients and enable the passage of signalling molecules across the BBB and (iv) cell signalling via the expression of PM receptors [Leth-Larsen et al., 2010].

#### **1.1 Plasma membrane proteins**

Integral PM proteins are polypeptides whose particular physicochemical properties enable insertion into the lipid bilayer and interaction with both the extracellular environment and/or the intracellular compartment. In all transmembrane polypeptides examined to date, the membrane-spanning domains are -helices or multiple -strands. Most integral proteins span the entire phospholipid bilayer with one or more membrane domains. The domains may have as few as four amino acid residues or as many as several hundred. The integral insertion of proteins into the PM means that the side chains of buried amino acids have Van der Waals interactions with the fatty acyl chains and shield the peptide bond's polar carbonyl and imino groups. Indeed, integral proteins containing membrane-spanning αhelical domains are composed mainly of uncharged hydrophobic amino acids. These properties probably make spanning regions more resistant to proteolysis by the trypsin enzyme used in most proteomics protocols. However, hydrophobic helices are often flanked by positively charged amino acids (i.e. lysine and arginine) thought to stabilize the helix by neutralizing the helix's dipole moment and interacting with negatively charged phospholipid head groups. The second class of transmembrane proteins displays a radically different structure in which several β strands form a barrel-shaped structure with a central pore. These strands contain predominantly polar amino acids and no long hydrophobic segments. Nevertheless, the outward-facing side groups on each of the β-strands are hydrophobic and interact with the membrane lipids' fatty acyl groups, whereas the side chains facing the inside are mainly hydrophilic [Lodish et al., 2000]. Interestingly, several posttranslational modifications that do not occur in the cytosol (such as disulphide bond formation and glycosylation) enhance the stability of PM or secreted proteins prior to their exposure to the extracellular milieu. Overall, these particularities can dramatically decrease the PM proteins' sensitivity to trypsin digestion. Newly synthesized proteins can also be targeted to the PM via the covalent attachment of a lipid anchor. Indeed, some proteins bind to the PM's cytosolic surface via a covalently attached fatty acid (e.g. palmitate or myristate) or isoprene group (e.g. a farnesyl or geranyl group, whereas proteins from the PM's outer leaflet are tethered some distance out from the surface by a glycosylphosphatidylinositol (GPI) anchor [Paulick & Bertozzi, 2008].

#### **1.2 Proteomics of the plasma membrane**

392 Proteomics – Human Diseases and Protein Functions

The BBB is a dynamic, regulatory interface that controls the molecular and cellular exchanges between the bloodstream and the brain compartment [Abbott et al., 2010]. The BCECs' barrier function depends on the acquisition and maintenance of characteristic features (referred to as the "BBB phenotype"), such as the absence of endothelial fenestrae, decrease in the number of endocytosis vesicles, the reinforcement of tight junctions and changes in the expression pattern of certain proteins. Overall, these physiological characteristics condition cell polarisation and permeation, transendothelial electrical resistance and a number of metabolic, receptor-based and transport functions. The latter mainly rely on the properties of the BCECs' plasma membrane (PM). Relevant information regarding the lipid composition of the whole cell and of the apical and basolateral PMs has been reported [Tewes & Galla, 2001]. The latter authors demonstrated that each PM shows a unique lipid composition; the apical PM is enriched in phosphatidylcholine, whereas the basolateral PM is enriched in sphingomyelin and glucosylceramide. It has also been observed that co-culture with glioma C6 cells is able to induce a more *in vivo*-like fatty acid pattern in BCEC-based BBB models, although the intensity of these changes did not reach *in vivo* levels [Kramer et al., 2002]. Given the vital physiological functions performed by membrane lipids this aspect merits further investigation. In contrast, the PM's protein moieties have been extensively studied. The protein composition of the PM is determined by the balance between membrane protein sorting, internalization and recycling. Briefly, biosynthesized PM proteins are translocated from the endoplasmic reticulum to the Golgi apparatus, where they undergo posttranslational modifications. Proteins are then sorted to the apical or basal membrane of polarized cells. Some PM proteins are subsequently internalised and sequestrated in lysosomes and then degraded or recycled to the cell surface; endocytic adaptor proteins may have a pivotal role in this process [Howes et al., 2010; Kelly & Owen, 2011; O'Bryan, 2010; Reider & Wendland, 2011]. Plasma membrane proteins are involved in many BBB functions, including (i) cell-extracellular matrix interactions, (ii) the cell-cell junctions (especially tight junctions) that impede paracellular transport and polarise the cells, (iii) the molecular transport systems that regulate the exchange of nutrients and enable the passage of signalling molecules across the BBB and (iv)

cell signalling via the expression of PM receptors [Leth-Larsen et al., 2010].

Integral PM proteins are polypeptides whose particular physicochemical properties enable insertion into the lipid bilayer and interaction with both the extracellular environment and/or the intracellular compartment. In all transmembrane polypeptides examined to date, the membrane-spanning domains are -helices or multiple -strands. Most integral proteins span the entire phospholipid bilayer with one or more membrane domains. The domains may have as few as four amino acid residues or as many as several hundred. The integral insertion of proteins into the PM means that the side chains of buried amino acids have Van der Waals interactions with the fatty acyl chains and shield the peptide bond's polar carbonyl and imino groups. Indeed, integral proteins containing membrane-spanning αhelical domains are composed mainly of uncharged hydrophobic amino acids. These properties probably make spanning regions more resistant to proteolysis by the trypsin enzyme used in most proteomics protocols. However, hydrophobic helices are often flanked by positively charged amino acids (i.e. lysine and arginine) thought to stabilize the helix by neutralizing the helix's dipole moment and interacting with negatively charged phospholipid head groups. The second class of transmembrane proteins displays a radically

**1.1 Plasma membrane proteins** 

Traditionally, mass spectrometry (MS)-based identification methods, chromatography and common cell biology techniques can be combined to form powerful tools for the proteomic mapping of PM proteins. Although major technical progress in MS continues to be made [Savas et al., 2011], the extraction, purification, separation and analysis of PM proteins remains problematic due to the latter's low abundance, poor solubility in aqueous solution and micro-heterogeneity [Santoni et al., 2000]. It is now clear that the development of complementary approaches is a prerequisite for the comprehensive analysis of PM proteins, including protein isolation and enrichment strategies that best preserve certain functional states and minimize the loss of transient and/or peripherally associated nontransmembrane proteins [Helbig et al., 2010], (Fig. 2). Polarized cells are present in many different organs and so their PMs have heterogeneous morphological and functional domains. Conventionally, PM proteomics can be performed with either cells cultured in suspension or adherent cells. Fig. 2 illustrates the importance of choosing the right method for the isolation of PMs and membrane sub- and microdomains and summarizes the different methods used in PM proteome analysis. The analysis can be divided into three experimental steps, all of which are challenging: (i) PM protein enrichment, (ii) separation and quantification and (iii) identification [Sprenger & Jensen, 2010].

#### **1.3 Plasma membrane protein enrichment**

Plasma membrane protein enrichment can be achieved either directly by extraction of membrane proteins or indirectly by pre-purification of the PM itself (or part of the PM) prior to proteome analysis. In view of the PM proteins' physicochemical properties, it is tempting to use of amphoteric agents (such as detergents) for enrichment. However, aqueous phase proteins will also be more soluble and may not necessarily be separated from the PM proteins. In contrast, the enrichment of membrane proteins based on two-phase partitioning (i.e. an aqueous phase and an organic phase) has been widely used and has proved its utility. The PM proteins can then be separated from aqueous proteins, due to the difference in hydrophobicity. Another way of directly studying the PM protein content involves its evaluation through its peptide fingerprinting. To this end, cell surface proteins undergo a "proteolytic shaving" procedure. The resulting peptides are purified, separated and then identified by liquid chromatography – tandem MS (LC-MS/MS). Although the proteolytic

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 395

antibodies). The label serves as an anchor for silica bead- or magnetic bead-based separation. Loosely PM-associated proteins can always be removed by high-salt/high-pH washing [Josic & Clifton, 2007]. Similarly, the generally glycosylated PM proteins can be affinity-purified with

At a higher organizational level, the topological mapping of plasma protein complexes requires the use of chemical or photo- crosslinking prior to unavoidable cell lysis, to keep them in a close-to-native state. Crosslinkers are often homo- or hetero-bifunctional agents absorbed on the cell surface [Back et al., 2003]; after chemical or photonic triggering, polymerization leads to the formation of a network that entraps PM proteins [Cordwell & Thingholm, 2010]. The proteomic needs in this field are increasing. A recent review described a new strategy and recent progress in the field of chemical cross-linking coupled

Last but not least, membrane enrichment can be achieved by purifying microdomain components (e.g. caveolae, rafts and tetraspannin domains) enriched in the cholesterol and sphingolipids that give these cell surface structures their concave shape. This method exploits the poor solubility of membrane microstructure lipids vis-à-vis certain detergents [Zheng & Foster, 2009] (hence the term "detergent-resistant membranes"). Indeed, cholesterol- and sphingolipid-enriched membranes are insoluble in cold, non-ionic detergents (Triton X-family, NP-40, Tween, etc.) and their low buoyancy makes them amenable to purification by density gradient centrifugation. However, the main drawback of this method relates to the detergents' ability to break up protein-protein interactions. It is important to note that membrane surface labelling and affinity purification can also be used

Proteomics studies of the PM in human umbilical vein endothelial cells (HUVECs) [Karsan et al., 2005; Sprenger et al., 2004] and aortic endothelial cells [Dauly et al., 2006] have been initiated in the last decade. However, the phenotypic characteristics of these types of endothelial cell (EC) differ from those of BCECs. Hence, the use of non-brain ECs in *in vitro*

To date, the very few studies to have focused on BBB EC proteomics can be divided into two distinct categories. The first category is outside the scope of the present review but is mentioned here for the sake of completeness. It concerns mid- to high-throughput proteomics initiated with *in vivo* or *in vitro* cells and that seek to answer a well-defined question (e.g. to identify the broadest possible protein expression profile in the brain microvascular endothelium [Haseloff et al., 2003; Lu Q. et al., 2008; Pottiez et al., 2010]; investigate cerebral ischemia [Haqqani et al., 2007; Haqqani et al., 2005; Haseloff et al., 2006] or evaluate a differential solubility approach for the characterization of EC proteins [Lu L. et al., 2007; Murugesan et al., 2011; Pottiez et al., 2009b]. Nevertheless, some PM proteins have been identified in the course of these high-throughput studies. The second category of truly BBB-focused PM proteomic studies arose in 2008 with the work by Terasaki et al.. These researchers used the elegant principle of isotopic dilution (see [Brun et al., 2009] for a review) to achieve the absolute quantification of 34 proteins known to be of significant interest. This list of membrane transporter and receptor proteins has recently been expanded to 114, following a human brain microvessel study [Uchida et al., 2011]. In addition to studies focusing on known BBB PM proteins, an indirect method based on a multiplex expression cloning strategy after fluorescence activated cell sorting with a tissue-specific

BBB models is subject to debate [Cecchelli et al., 2007; Prieto et al., 2004].

lectin-based chromatography media [Cordwell & Thingholm, 2010].

to MS [Tang & Bruce, 2010].

to isolate this particular protein population.

**1.4 The state of the art in BBB PM proteomics** 

Fig. 2. A schematic drawing of complementary strategies for the comprehensive proteomic analysis of PM proteins. Approaches which best preserve certain functional states and minimize the loss of transient and/or peripherally associated non-transmembrane proteins are preferable [Helbig et al., 2010].

shaving offers many advantages in theory (because surface-exposed peptides are more watersoluble than their intrabilayer counterparts), the main drawback of this approach relates to its tendency to trigger cell lysis and thus the significant contamination of surface-exposed membrane peptides with cytosol-derived peptides. The glycosylation of PM proteins also prevents proteases from accessing the polypeptide moiety [Cordwell & Thingholm, 2010].

In view of the PM's lipid composition, membrane pre-purification and separation from soluble proteins is conventionally performed by zone centrifugation with a density gradient. Most of the PM-associated (peripheral) proteins are recovered with the integral PM protein fraction - which can constitute a drawback or an advantage. To overcome this problem, additional high-salt, high-pH washing steps can be used to form easily separable membrane sheets that lack peripheral proteins. Furthermore, plasma, mitochondrial and endoplasmic reticulum membranes all have similar densities and so membrane fractions prepared by ultracentrifugation often contain a mixture of the three [Chen et al., 2006].

In fact, the most frequently used methods for the enrichment of PMs are those based on affinity chromatography, cationic colloidal silica particles, cell biotinylation or a tissue-specific polyclonal antiserum [Agarwal & Shusta, 2009; Shusta et al., 2002]. The cell surface membrane proteins may be covalently labelled (e.g. in biotinylation) or not (e.g. with cationic silica and antibodies). The label serves as an anchor for silica bead- or magnetic bead-based separation. Loosely PM-associated proteins can always be removed by high-salt/high-pH washing [Josic & Clifton, 2007]. Similarly, the generally glycosylated PM proteins can be affinity-purified with lectin-based chromatography media [Cordwell & Thingholm, 2010].

At a higher organizational level, the topological mapping of plasma protein complexes requires the use of chemical or photo- crosslinking prior to unavoidable cell lysis, to keep them in a close-to-native state. Crosslinkers are often homo- or hetero-bifunctional agents absorbed on the cell surface [Back et al., 2003]; after chemical or photonic triggering, polymerization leads to the formation of a network that entraps PM proteins [Cordwell & Thingholm, 2010]. The proteomic needs in this field are increasing. A recent review described a new strategy and recent progress in the field of chemical cross-linking coupled to MS [Tang & Bruce, 2010].

Last but not least, membrane enrichment can be achieved by purifying microdomain components (e.g. caveolae, rafts and tetraspannin domains) enriched in the cholesterol and sphingolipids that give these cell surface structures their concave shape. This method exploits the poor solubility of membrane microstructure lipids vis-à-vis certain detergents [Zheng & Foster, 2009] (hence the term "detergent-resistant membranes"). Indeed, cholesterol- and sphingolipid-enriched membranes are insoluble in cold, non-ionic detergents (Triton X-family, NP-40, Tween, etc.) and their low buoyancy makes them amenable to purification by density gradient centrifugation. However, the main drawback of this method relates to the detergents' ability to break up protein-protein interactions. It is important to note that membrane surface labelling and affinity purification can also be used to isolate this particular protein population.

#### **1.4 The state of the art in BBB PM proteomics**

394 Proteomics – Human Diseases and Protein Functions

In suspension cells Adherent cells

Shaving Zonal centrifigation Affinity methods Triton-X100 isolation

Preparation of microdomains

Methods for tissue sampling are not discussed here

Proteomic proteolysis

Cationic silica particules, biotinylation, Immun-based and Lectin-based capture

Cross-linking

LC-MS/MS and protein identification

Fig. 2. A schematic drawing of complementary strategies for the comprehensive proteomic analysis of PM proteins. Approaches which best preserve certain functional states and minimize the loss of transient and/or peripherally associated non-transmembrane proteins

shaving offers many advantages in theory (because surface-exposed peptides are more watersoluble than their intrabilayer counterparts), the main drawback of this approach relates to its tendency to trigger cell lysis and thus the significant contamination of surface-exposed membrane peptides with cytosol-derived peptides. The glycosylation of PM proteins also prevents proteases from accessing the polypeptide moiety [Cordwell & Thingholm, 2010]. In view of the PM's lipid composition, membrane pre-purification and separation from soluble proteins is conventionally performed by zone centrifugation with a density gradient. Most of the PM-associated (peripheral) proteins are recovered with the integral PM protein fraction - which can constitute a drawback or an advantage. To overcome this problem, additional high-salt, high-pH washing steps can be used to form easily separable membrane sheets that lack peripheral proteins. Furthermore, plasma, mitochondrial and endoplasmic reticulum membranes all have similar densities and so membrane fractions prepared by

In fact, the most frequently used methods for the enrichment of PMs are those based on affinity chromatography, cationic colloidal silica particles, cell biotinylation or a tissue-specific polyclonal antiserum [Agarwal & Shusta, 2009; Shusta et al., 2002]. The cell surface membrane proteins may be covalently labelled (e.g. in biotinylation) or not (e.g. with cationic silica and

ultracentrifugation often contain a mixture of the three [Chen et al., 2006].

Two phase partitioning

are preferable [Helbig et al., 2010].

Preparation of proteins Preparation of membranes

Proteomics studies of the PM in human umbilical vein endothelial cells (HUVECs) [Karsan et al., 2005; Sprenger et al., 2004] and aortic endothelial cells [Dauly et al., 2006] have been initiated in the last decade. However, the phenotypic characteristics of these types of endothelial cell (EC) differ from those of BCECs. Hence, the use of non-brain ECs in *in vitro* BBB models is subject to debate [Cecchelli et al., 2007; Prieto et al., 2004].

To date, the very few studies to have focused on BBB EC proteomics can be divided into two distinct categories. The first category is outside the scope of the present review but is mentioned here for the sake of completeness. It concerns mid- to high-throughput proteomics initiated with *in vivo* or *in vitro* cells and that seek to answer a well-defined question (e.g. to identify the broadest possible protein expression profile in the brain microvascular endothelium [Haseloff et al., 2003; Lu Q. et al., 2008; Pottiez et al., 2010]; investigate cerebral ischemia [Haqqani et al., 2007; Haqqani et al., 2005; Haseloff et al., 2006] or evaluate a differential solubility approach for the characterization of EC proteins [Lu L. et al., 2007; Murugesan et al., 2011; Pottiez et al., 2009b]. Nevertheless, some PM proteins have been identified in the course of these high-throughput studies. The second category of truly BBB-focused PM proteomic studies arose in 2008 with the work by Terasaki et al.. These researchers used the elegant principle of isotopic dilution (see [Brun et al., 2009] for a review) to achieve the absolute quantification of 34 proteins known to be of significant interest. This list of membrane transporter and receptor proteins has recently been expanded to 114, following a human brain microvessel study [Uchida et al., 2011]. In addition to studies focusing on known BBB PM proteins, an indirect method based on a multiplex expression cloning strategy after fluorescence activated cell sorting with a tissue-specific

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 397

collagen (2 mg/mL) in ten-fold concentrated Dulbecco's Modified Eagle's Medium (DMEM) from GIBCO (Invitrogen Corporation, Carlsbad, CA, USA) and 0.4 M NaOH. The BCECs (4 x 105 cells/mL) were seeded and cultured in DMEM supplemented with 10% (v/v) heatinactivated foetal calf serum, 10% (v/v) heat-inactivated horse serum (Hyclone Laboratories, Logan, UT, USA), 2 mM glutamine, 50 mg/mL gentamicin (Biochrome Ltd, Cambridge, UK) and 1 ng/mL basic fibroblast growth factor (GIBCO). The culture medium was refreshed every 2 days until confluence (after around 6 days, typically). Co-cultures were set in Transwellcell culture inserts (diameter: 100 mm; pore size: 0.4 mm; Corning Inc., New York, NY, USA) coated on the upper side with rat tail collagen. Endothelial cells were then seeded onto the inserts and transferred to a 100 mm Petri dish containing glial cells prepared according to Booher and Sensenbrenner [Booher & Sensenbrenner, 1972]. After 12 days of co-culture (in the same medium as mentioned above), the re-induction of BBB properties in the BCECs was checked by measuring the paracellular permeability coefficient of Lucifer Yellow carbohydrazide (PeLY) and by immunostaining the main tight junction proteins (occludin and claudin-5) and the associated intracellular scaffolding protein zona occludens 1 (ZO-1). Endothelial cell biotinylation and harvesting were

Bovine BCEC biotinylation was performed by slightly modifying the previously reported method [Zhao et al., 2004]. Endothelial cells were washed three times with prewarmed (37°C) calcium- and magnesium-free PBS (CMF-PBS, pH 7.4) and gently shaken for 15 min at 37°C in CMF-PBS supplemented with 3 mg EZ-link sulfo-NHS-SS-biotin (Thermo Scientific, Cergy Pontoise, France) per Petri dish. The labelling reaction was quenched by adding 1 mL of 40 mM glycine in CMF-PBS, pH 8.0. Excess quenching buffer was removed

The cells were harvested by adding collagenase type XI (*Clostridium histolyticum*, Sigma, Lyon, France) as described previously [Pottiez et al., 2009b]. Briefly, bovine BCECs were incubated for 15 min with 1.5 mL of a 0.1% w/v collagenase solution. The cell suspension was harvested, washed three times in PBS and pelleted at 500 x *g* for 5 min at 4°C. The cell

Bovine BCEC pellets were lysed with 800 µL of ice-cold hypotonic buffer [10 Mm HEPES, pH 7.5, 1.5 mM MgCl2, 10 mM KCl, protease inhibitor cocktail) [Zhao et al., 2004] and incubated on ice for 30 min. The cells were lysed by dounce homogenization (50 passes) and then sonicated two times (30 W, 20 s). Unbroken cells and nuclei were pelleted from the cell homogenate by centrifugation at 1,000 x *g* for 10 min at 4 °C. Aliquots of supernatants and

The KCl concentration in the supernatants was adjusted to 150 mM. An aliquot (300 µL) of streptavidin magnetic beads (10 mg beads/mL, prewashed four times with hypotonic buffer) was added to supernatants. The supernatant/bead suspensions were rotated at room temperature (RT) for 90 min and then pelleted using a magnetic plate. To obtain the biotinylated protein fraction, the resulting preparations were washed three times with 500 µL of ice-cold 1 M KCl for 15 min, three times again with 500 µL of ice-cold 0.1 M Na2CO3, pH 11.5 and lastly once with ice-cold hypotonic buffer for 10 min. The trypsin digestion was

performed after 12 days of co-culture.

by washing the cells twice in CMF-PBS.

performed directly on the beads.

**2.2 Cell surface biotinylation and cell harvesting** 

pellets were stored at -80°C until protein extraction.

**2.3 Preparation of biotinylated cell surface proteins** 

entire pellets were stored at -20°C prior to dot blot biotinylation control.

polyclonal antiserum has been developed [Agarwal & Shusta, 2009; Shusta et al., 2002]. The latter researchers identified a total of 30 BBB membrane proteins at the transcript level. Even though the expression of the corresponding gene products remains to be confirmed, these results constitute a considerable advance. Given that most PM proteins are glycosylated, the leverage of this post-translational modification for addressing PM proteins is tempting. However, large-scale glycoproteomics studies have only recently been reported. Indeed, a methodology based on hydrazine capture of membrane and secreted glycoproteins [Haqqani et al., 2011] revealed an enrichment in glycoprotein content (over 90%) and led to the identification of 23 new glycoproteins (i.e. not referenced as such in the Uniprot database). The full study results will doubtless be published soon.

#### **1.5 Cell surface biotinylation**

Chemical labelling of cell surface proteins is a novel methodology for the isolation of new target proteins. One of the major advantages of this approach is that the labelling reagent's chemical properties can be chosen to suit the biological structures that are being targeted. Cell surface biotinylation is a selective technology for the capture of PM proteins. This technology comprises several steps: (i) the selective labelling of proteins with a biotinylating reagent, (ii) the capture of biotinylated proteins with avidin-coated magnetic beads, resins etc. and (iii) elution and digestion (or, for increased specificity, digestion and elution) of the biotinylated proteins [Scheurer et al., 2005].

Using our *in vitro* BBB co-culture model [Dehouck et al., 1990], we have initiated a differential PM proteome approach that selects, separates and identifies BCEC cell surface proteins that are expressed differently in bovine BCECs with limited BBB functions versus those with reinduced BBB functions. This method is based on biotinylation of bovine BCECs' cell surface proteins with the reagent sulfosuccinimidyl-2-[biotinamido]ethyl-1,3-dithiopropionate (sulfo-NHS-SS-biotin), in which biotin is coupled to a reactive ester group. The NHS group undergoes a nucleophilic substitution reaction with the primary amines of protein amino acids (mainly lysine residues, depending on the local pH). Due to the low dissociation constant for biotin and streptavidin, the use of a cleavable spacer arm containing a disulphide bond facilitates the release of biotinylated proteins after capture on immobilized streptavidin [Elia, 2008]. Moreover, the sulfo-NHS-ester derivatives of biotin are preferable for use in PM labelling because they are more soluble in water than NHS-esters alone. This enables reactions to be performed in the absence of polar aprotic solvents and membrane permeabilizing reagents like dimethylsulfoxyde and dimethylformamide. Furthermore, the sulfo-NHS-esters are membrane-impermeable reagents, which reduces interference from cytosolic components [Daniels & Amara, 1998; Elia, 2008]. After biotinylation and hypotonic cell lysis, biotin-labelled proteins can be captured on streptavidin-coated magnetic beads and on-bead digested by trypsin. The eluted peptides are separated with nano-liquid chromatography (nano-LC) coupled to a MALDI-TOF/TOF mass spectrometer. Proteins are then identified on the basis of the MS-fragmented peptide spectra via a protein-database search with Mascot software (Matrix Science Ltd, London, UK).

#### **2. Materials and methods**

#### **2.1 Cell culture**

Bovine BCECs were isolated and characterized as described previously [Meresse et al., 1989]. Petri dishes (diameter: 100 mm) were coated with an in-house preparation of rat tail collagen (2 mg/mL) in ten-fold concentrated Dulbecco's Modified Eagle's Medium (DMEM) from GIBCO (Invitrogen Corporation, Carlsbad, CA, USA) and 0.4 M NaOH. The BCECs (4 x 105 cells/mL) were seeded and cultured in DMEM supplemented with 10% (v/v) heatinactivated foetal calf serum, 10% (v/v) heat-inactivated horse serum (Hyclone Laboratories, Logan, UT, USA), 2 mM glutamine, 50 mg/mL gentamicin (Biochrome Ltd, Cambridge, UK) and 1 ng/mL basic fibroblast growth factor (GIBCO). The culture medium was refreshed every 2 days until confluence (after around 6 days, typically). Co-cultures were set in Transwellcell culture inserts (diameter: 100 mm; pore size: 0.4 mm; Corning Inc., New York, NY, USA) coated on the upper side with rat tail collagen. Endothelial cells were then seeded onto the inserts and transferred to a 100 mm Petri dish containing glial cells prepared according to Booher and Sensenbrenner [Booher & Sensenbrenner, 1972]. After 12 days of co-culture (in the same medium as mentioned above), the re-induction of BBB properties in the BCECs was checked by measuring the paracellular permeability coefficient of Lucifer Yellow carbohydrazide (PeLY) and by immunostaining the main tight junction proteins (occludin and claudin-5) and the associated intracellular scaffolding protein zona occludens 1 (ZO-1). Endothelial cell biotinylation and harvesting were performed after 12 days of co-culture.

#### **2.2 Cell surface biotinylation and cell harvesting**

396 Proteomics – Human Diseases and Protein Functions

polyclonal antiserum has been developed [Agarwal & Shusta, 2009; Shusta et al., 2002]. The latter researchers identified a total of 30 BBB membrane proteins at the transcript level. Even though the expression of the corresponding gene products remains to be confirmed, these results constitute a considerable advance. Given that most PM proteins are glycosylated, the leverage of this post-translational modification for addressing PM proteins is tempting. However, large-scale glycoproteomics studies have only recently been reported. Indeed, a methodology based on hydrazine capture of membrane and secreted glycoproteins [Haqqani et al., 2011] revealed an enrichment in glycoprotein content (over 90%) and led to the identification of 23 new glycoproteins (i.e. not referenced as such in the Uniprot

Chemical labelling of cell surface proteins is a novel methodology for the isolation of new target proteins. One of the major advantages of this approach is that the labelling reagent's chemical properties can be chosen to suit the biological structures that are being targeted. Cell surface biotinylation is a selective technology for the capture of PM proteins. This technology comprises several steps: (i) the selective labelling of proteins with a biotinylating reagent, (ii) the capture of biotinylated proteins with avidin-coated magnetic beads, resins etc. and (iii) elution and digestion (or, for increased specificity, digestion and elution) of the

Using our *in vitro* BBB co-culture model [Dehouck et al., 1990], we have initiated a differential PM proteome approach that selects, separates and identifies BCEC cell surface proteins that are expressed differently in bovine BCECs with limited BBB functions versus those with reinduced BBB functions. This method is based on biotinylation of bovine BCECs' cell surface proteins with the reagent sulfosuccinimidyl-2-[biotinamido]ethyl-1,3-dithiopropionate (sulfo-NHS-SS-biotin), in which biotin is coupled to a reactive ester group. The NHS group undergoes a nucleophilic substitution reaction with the primary amines of protein amino acids (mainly lysine residues, depending on the local pH). Due to the low dissociation constant for biotin and streptavidin, the use of a cleavable spacer arm containing a disulphide bond facilitates the release of biotinylated proteins after capture on immobilized streptavidin [Elia, 2008]. Moreover, the sulfo-NHS-ester derivatives of biotin are preferable for use in PM labelling because they are more soluble in water than NHS-esters alone. This enables reactions to be performed in the absence of polar aprotic solvents and membrane permeabilizing reagents like dimethylsulfoxyde and dimethylformamide. Furthermore, the sulfo-NHS-esters are membrane-impermeable reagents, which reduces interference from cytosolic components [Daniels & Amara, 1998; Elia, 2008]. After biotinylation and hypotonic cell lysis, biotin-labelled proteins can be captured on streptavidin-coated magnetic beads and on-bead digested by trypsin. The eluted peptides are separated with nano-liquid chromatography (nano-LC) coupled to a MALDI-TOF/TOF mass spectrometer. Proteins are then identified on the basis of the MS-fragmented peptide spectra via a protein-database search with Mascot software

Bovine BCECs were isolated and characterized as described previously [Meresse et al., 1989]. Petri dishes (diameter: 100 mm) were coated with an in-house preparation of rat tail

database). The full study results will doubtless be published soon.

**1.5 Cell surface biotinylation** 

biotinylated proteins [Scheurer et al., 2005].

(Matrix Science Ltd, London, UK).

**2. Materials and methods** 

**2.1 Cell culture** 

Bovine BCEC biotinylation was performed by slightly modifying the previously reported method [Zhao et al., 2004]. Endothelial cells were washed three times with prewarmed (37°C) calcium- and magnesium-free PBS (CMF-PBS, pH 7.4) and gently shaken for 15 min at 37°C in CMF-PBS supplemented with 3 mg EZ-link sulfo-NHS-SS-biotin (Thermo Scientific, Cergy Pontoise, France) per Petri dish. The labelling reaction was quenched by adding 1 mL of 40 mM glycine in CMF-PBS, pH 8.0. Excess quenching buffer was removed by washing the cells twice in CMF-PBS.

The cells were harvested by adding collagenase type XI (*Clostridium histolyticum*, Sigma, Lyon, France) as described previously [Pottiez et al., 2009b]. Briefly, bovine BCECs were incubated for 15 min with 1.5 mL of a 0.1% w/v collagenase solution. The cell suspension was harvested, washed three times in PBS and pelleted at 500 x *g* for 5 min at 4°C. The cell pellets were stored at -80°C until protein extraction.

#### **2.3 Preparation of biotinylated cell surface proteins**

Bovine BCEC pellets were lysed with 800 µL of ice-cold hypotonic buffer [10 Mm HEPES, pH 7.5, 1.5 mM MgCl2, 10 mM KCl, protease inhibitor cocktail) [Zhao et al., 2004] and incubated on ice for 30 min. The cells were lysed by dounce homogenization (50 passes) and then sonicated two times (30 W, 20 s). Unbroken cells and nuclei were pelleted from the cell homogenate by centrifugation at 1,000 x *g* for 10 min at 4 °C. Aliquots of supernatants and entire pellets were stored at -20°C prior to dot blot biotinylation control.

The KCl concentration in the supernatants was adjusted to 150 mM. An aliquot (300 µL) of streptavidin magnetic beads (10 mg beads/mL, prewashed four times with hypotonic buffer) was added to supernatants. The supernatant/bead suspensions were rotated at room temperature (RT) for 90 min and then pelleted using a magnetic plate. To obtain the biotinylated protein fraction, the resulting preparations were washed three times with 500 µL of ice-cold 1 M KCl for 15 min, three times again with 500 µL of ice-cold 0.1 M Na2CO3, pH 11.5 and lastly once with ice-cold hypotonic buffer for 10 min. The trypsin digestion was performed directly on the beads.

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 399

Two FASTA sequence protein datasets were extracted from UniProt using the sequence retrieval system at the European Bioinformatics Institute [Zdobnov et al., 2002]. The first FASTA sequence dataset corresponds to the list (with 18,187 entries) of all mammalian proteins having at least one transmembrane domain (the SRS-coding criteria are as follows: [uniprot-Taxonomy:mammalia\*] & [uniprot-FtKey:transmem\*]). The second FASTA sequence dataset corresponds to the list (424,819 entries) of all mammalian proteins lacking transmembrane domains (the SRS-coding criteria are as follows: [uniprot-Taxonomy:mammalia\*] ! [uniprot-FtKey:transmem\*]). The FASTA sequence datasets were subjected to *in silico* trypsin proteolysis using Proteogest [Cagney et al., 2003] and the

The protein lists were compared using nwCompare software [Pont & Fournie, 2010] and classified according to the Protein Analysis Through Evolutionary Relationships (PANTHER) system [Mi et al., 2007; Thomas et al., 2003] (www.pantherdb.org). PANTHER is a resource in which genes have been functionally classified by expert biologists on the basis of published scientific experimental evidence and evolutionary relationships. Proteins are classified into families and subfamilies of shared function, which are then categorized by

For fluorescence microscopy observations, the BCECs were biotinylated according to the above-described method, except that a non-cleavable biotinylation reagent (sulfosuccinimidyl-6-[biotinamido]-6-hexanamido hexanoate; EZ-link sulfo-NHS-LC-biotin (Thermo Scientific, Cergy Pontoise, France)) was used. Filters with BCECs were fixed for 10 min in 2% w/v paraformaldehyde at RT and washed in PBS. Biotinylated proteins were revealed by incubation with a Streptavidin-Cy3 conjugate (1:50 v/v) for 30 min. After washing with PBS, cells were incubated for 2 min with the nuclear stain Hoechst 33258 (1 μg/mL) and the filter sections were mounted in Mowiol (Merck, France). Fluorescence was visualized with a Leica

Briefly, 15 µg of proteins from pellets and supernatants were dot-blotted on a nitrocellulose membrane. The membrane was incubated in blocking buffer [5% bovine serum albumin (BSA) in 20 mM Tris-HCl, 150 mM NaCl; pH 7.5, and 0.05% Tween-20 (TBS-T)] for one hour at RT and then immersed for 30 min at RT in a solution of alkaline phosphatase-conjugated avidin (1:1000 v/v in BSA/TBS-T). After three 15-min washes with TBS-T and one 10 minute wash with TBS (20 mM Tris-HCl, 150 mM NaCl; pH 7.5), the membrane was incubated with 5-bromo-4-chloro-3-indoyl phosphate p-toluidine salt/p-nitro blue tetrazolium chloride substrate solution. The reaction was stopped by rinsing with deionised water during gentle shaking. The membrane image was acquired at 300 dpi with a Umax Scanner (Amersham Biosciences, Orsay, France) and stored in a Tagged Image File format.

Once primary capillary ECs are isolated *in vitro*, they rapidly lose some of their BBB functions. The cells' barrier properties were restored by a 12-day co-culture in which bovine

following command line: >perl proteogest.pl –i *filename* –c trypsin –d –a –g1.

**2.6 Bioinformatics resources and sorting protein lists** 

molecular function and biological process ontology terms.

DMR fluorescence microscope (Leica Microsystems, Wetzlar, Germany).

**2.8 Dot blots for estimating the biotinylation efficiency** 

**2.7 Fluorescence microscopy** 

**3. Results and discussion** 

**3.1 Confirmation of BBB-like properties** 

#### **2.4 On-bead proteolysis and isolation of tryptic peptides**

The on-bead proteolysis of biotinylated protein fractions was carried out overnight at 37°C in 400 µL of a proteolysis buffer containing 40 mM NH4CO3 (pH 8.0), 0.5 mM CaCl2 and 12.5 ng/µL trypsin (Promega, Charbonnières-les-Bains, France). The enzyme reaction was stopped by heat denaturation at 100°C for 5 min. The magnetic beads were pelleted using a magnetic plate and the tryptic digest peptides were transferred into a clean microtube.

The peptides attached to the streptavidin-coupled beads were eluted from beads by means of a reduction reaction for 15 min at 60°C with 100 µL of 40 mM NH4CO3 (pH 8.0) containing 200 mM dithiothreitol (to disrupt the disulphide bond in the sulfo-NHS-SSbiotin). The eluate was pooled and tryptic peptides were concentrated under vacuum and immediately resolubilized in 30 µL of 0.1% TFA/10% acetonitrile/water prior to nano-LC separation.

#### **2.5 Nano-LC-MALDI-TOF-MS/MS experiments**

Separations were performed on an U3000 nano-LC system (Dionex-LC-Packings, Sunnyvale, CA, USA). After a pre-concentration step (C18 cartridge, 300 μm, 1 mm), the peptide samples were separated on a Pepmap C18 column (75 μm, 15 cm) using an acetonitrile gradient from 5% to 15% over 10 min, from 15% to 65% over 38 min and from 65% to 100% over 15 min and, lastly, 15 min in 100% acetonitrile. The flow was set to 300 nl/min and 115 fractions were automatically collected (one per 30 s) on an AnchorChip™ MALDI target using a Proteineer™ fraction collector (Bruker Daltonics, Bremen, Germany). Next, 2 μl of MALDI matrix (0.3 mg/ml -cyano-4-hydroxycinnamic acid in acetone:ethanol:0.1% TFA-acidified water, 3:6:1 v/v/v) were added during the collection process. The MS and MS/MS measurements were performed off-line using an Ultraflex™ II TOF/TOF mass spectrometer (Bruker Daltonics) in automatic mode (using FlexControl™ 2.4 software), reflectron mode (for MALDI-TOF PMF) or LIFT mode (for MALDI-TOF/TOF peptide fragmentation fingerprint (PFF)). External calibration over the 1000-3500 mass range was performed with the [M+H]+ mono-isotopic ions of bradykinins 1-7, angiotensin I, angiotensin II, substance P, bombesin and adrenocorticotropic hormone (clips 1-17 and clips 18-39) from a peptide calibration standard kit (Bruker Daltonics). Briefly, a 25 kV accelerating voltage, a 26.3 kV reflector voltage and a 160 ns pulsed ion extraction were used to obtain the MS spectrum. Each spectrum was produced by accumulating data from 500 laser shots. Peptide fragmentation was driven by Warp-LC software 1.0 (Bruker Daltonics) with the following parameters: signal-to-noise ratio > 15, more than 3 MS/MS by fraction if the MS signal was available, 0.15 Da of MS tolerance for peak merge and the elimination of peaks which appears in more than 35% of fractions. Precursor ions were accelerated to 8 kV and selected in a timed ion gate. Metastable ions generated by laserinduced decomposition were further accelerated by 19 kV in the LIFT cell and their masses were measured in reflectron mode. Peak lists were generated from MS and MS/MS spectra using Flexanalysis™ 2.4 software (Bruker Daltonics). Database searches with Mascot 2.2 (Matrix Science Ltd) using combined PMF and PFF datasets were performed in the UniProt 56.0 and 56.6 databases via ProteinScape 1.3 (Bruker Daltonics). A mass tolerance of 75 ppm and 1 missing cleavage site were allowed for PMF, with an MS/MS tolerance of 0.5 Da and 1 missing cleavage site allowed for MS/MS searching. The relevance of protein identities was judged according to the probability-based Mowse score [Perkins et al., 1999], calculated with p < 0.05.

#### **2.6 Bioinformatics resources and sorting protein lists**

Two FASTA sequence protein datasets were extracted from UniProt using the sequence retrieval system at the European Bioinformatics Institute [Zdobnov et al., 2002]. The first FASTA sequence dataset corresponds to the list (with 18,187 entries) of all mammalian proteins having at least one transmembrane domain (the SRS-coding criteria are as follows: [uniprot-Taxonomy:mammalia\*] & [uniprot-FtKey:transmem\*]). The second FASTA sequence dataset corresponds to the list (424,819 entries) of all mammalian proteins lacking transmembrane domains (the SRS-coding criteria are as follows: [uniprot-Taxonomy:mammalia\*] ! [uniprot-FtKey:transmem\*]). The FASTA sequence datasets were subjected to *in silico* trypsin proteolysis using Proteogest [Cagney et al., 2003] and the following command line: >perl proteogest.pl –i *filename* –c trypsin –d –a –g1.

The protein lists were compared using nwCompare software [Pont & Fournie, 2010] and classified according to the Protein Analysis Through Evolutionary Relationships (PANTHER) system [Mi et al., 2007; Thomas et al., 2003] (www.pantherdb.org). PANTHER is a resource in which genes have been functionally classified by expert biologists on the basis of published scientific experimental evidence and evolutionary relationships. Proteins are classified into families and subfamilies of shared function, which are then categorized by molecular function and biological process ontology terms.

#### **2.7 Fluorescence microscopy**

398 Proteomics – Human Diseases and Protein Functions

The on-bead proteolysis of biotinylated protein fractions was carried out overnight at 37°C in 400 µL of a proteolysis buffer containing 40 mM NH4CO3 (pH 8.0), 0.5 mM CaCl2 and 12.5 ng/µL trypsin (Promega, Charbonnières-les-Bains, France). The enzyme reaction was stopped by heat denaturation at 100°C for 5 min. The magnetic beads were pelleted using a magnetic plate and the tryptic digest peptides were transferred into a clean

The peptides attached to the streptavidin-coupled beads were eluted from beads by means of a reduction reaction for 15 min at 60°C with 100 µL of 40 mM NH4CO3 (pH 8.0) containing 200 mM dithiothreitol (to disrupt the disulphide bond in the sulfo-NHS-SSbiotin). The eluate was pooled and tryptic peptides were concentrated under vacuum and immediately resolubilized in 30 µL of 0.1% TFA/10% acetonitrile/water prior to nano-LC

Separations were performed on an U3000 nano-LC system (Dionex-LC-Packings, Sunnyvale, CA, USA). After a pre-concentration step (C18 cartridge, 300 μm, 1 mm), the peptide samples were separated on a Pepmap C18 column (75 μm, 15 cm) using an acetonitrile gradient from 5% to 15% over 10 min, from 15% to 65% over 38 min and from 65% to 100% over 15 min and, lastly, 15 min in 100% acetonitrile. The flow was set to 300 nl/min and 115 fractions were automatically collected (one per 30 s) on an AnchorChip™ MALDI target using a Proteineer™ fraction collector (Bruker Daltonics, Bremen, Germany). Next, 2 μl of MALDI matrix (0.3 mg/ml -cyano-4-hydroxycinnamic acid in acetone:ethanol:0.1% TFA-acidified water, 3:6:1 v/v/v) were added during the collection process. The MS and MS/MS measurements were performed off-line using an Ultraflex™ II TOF/TOF mass spectrometer (Bruker Daltonics) in automatic mode (using FlexControl™ 2.4 software), reflectron mode (for MALDI-TOF PMF) or LIFT mode (for MALDI-TOF/TOF peptide fragmentation fingerprint (PFF)). External calibration over the 1000-3500 mass range was performed with the [M+H]+ mono-isotopic ions of bradykinins 1-7, angiotensin I, angiotensin II, substance P, bombesin and adrenocorticotropic hormone (clips 1-17 and clips 18-39) from a peptide calibration standard kit (Bruker Daltonics). Briefly, a 25 kV accelerating voltage, a 26.3 kV reflector voltage and a 160 ns pulsed ion extraction were used to obtain the MS spectrum. Each spectrum was produced by accumulating data from 500 laser shots. Peptide fragmentation was driven by Warp-LC software 1.0 (Bruker Daltonics) with the following parameters: signal-to-noise ratio > 15, more than 3 MS/MS by fraction if the MS signal was available, 0.15 Da of MS tolerance for peak merge and the elimination of peaks which appears in more than 35% of fractions. Precursor ions were accelerated to 8 kV and selected in a timed ion gate. Metastable ions generated by laserinduced decomposition were further accelerated by 19 kV in the LIFT cell and their masses were measured in reflectron mode. Peak lists were generated from MS and MS/MS spectra using Flexanalysis™ 2.4 software (Bruker Daltonics). Database searches with Mascot 2.2 (Matrix Science Ltd) using combined PMF and PFF datasets were performed in the UniProt 56.0 and 56.6 databases via ProteinScape 1.3 (Bruker Daltonics). A mass tolerance of 75 ppm and 1 missing cleavage site were allowed for PMF, with an MS/MS tolerance of 0.5 Da and 1 missing cleavage site allowed for MS/MS searching. The relevance of protein identities was judged according to the probability-based Mowse score [Perkins et al., 1999],

**2.4 On-bead proteolysis and isolation of tryptic peptides** 

**2.5 Nano-LC-MALDI-TOF-MS/MS experiments** 

microtube.

separation.

calculated with p < 0.05.

For fluorescence microscopy observations, the BCECs were biotinylated according to the above-described method, except that a non-cleavable biotinylation reagent (sulfosuccinimidyl-6-[biotinamido]-6-hexanamido hexanoate; EZ-link sulfo-NHS-LC-biotin (Thermo Scientific, Cergy Pontoise, France)) was used. Filters with BCECs were fixed for 10 min in 2% w/v paraformaldehyde at RT and washed in PBS. Biotinylated proteins were revealed by incubation with a Streptavidin-Cy3 conjugate (1:50 v/v) for 30 min. After washing with PBS, cells were incubated for 2 min with the nuclear stain Hoechst 33258 (1 μg/mL) and the filter sections were mounted in Mowiol (Merck, France). Fluorescence was visualized with a Leica DMR fluorescence microscope (Leica Microsystems, Wetzlar, Germany).

#### **2.8 Dot blots for estimating the biotinylation efficiency**

Briefly, 15 µg of proteins from pellets and supernatants were dot-blotted on a nitrocellulose membrane. The membrane was incubated in blocking buffer [5% bovine serum albumin (BSA) in 20 mM Tris-HCl, 150 mM NaCl; pH 7.5, and 0.05% Tween-20 (TBS-T)] for one hour at RT and then immersed for 30 min at RT in a solution of alkaline phosphatase-conjugated avidin (1:1000 v/v in BSA/TBS-T). After three 15-min washes with TBS-T and one 10 minute wash with TBS (20 mM Tris-HCl, 150 mM NaCl; pH 7.5), the membrane was incubated with 5-bromo-4-chloro-3-indoyl phosphate p-toluidine salt/p-nitro blue tetrazolium chloride substrate solution. The reaction was stopped by rinsing with deionised water during gentle shaking. The membrane image was acquired at 300 dpi with a Umax Scanner (Amersham Biosciences, Orsay, France) and stored in a Tagged Image File format.

#### **3. Results and discussion**

#### **3.1 Confirmation of BBB-like properties**

Once primary capillary ECs are isolated *in vitro*, they rapidly lose some of their BBB functions. The cells' barrier properties were restored by a 12-day co-culture in which bovine

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 401

Taxonomy: mammalia Ftkey: **transmem**

Taxonomy: mammalia Ftkey: **no transmem**

>=5000

45 - 50

**Isotopic mass (Da)**

>=50

**Length (AA)**

4500 - 5000

40 - 45

0

500 - 1000

2 - 5

> 1000 - 1500

5 - 10

1500 - 2000

10 - 15

2000 - 2500

15 - 20

2500 - 3000

20 - 25

3000 - 3500

25 - 30

3500 - 4000

30 - 35

4000 - 4500

35 - 40

0 - 500

**B**

**%**

> 500 - 1000

2 - 5

> 1000 - 1500

5 - 10

1500 - 2000

10 - 15

significantly differ from that of non-transmembrane proteins.

2000 - 2500

15 - 20

2500 - 3000

FASTA sequence datasets for (A) all mammalian proteins displaying at least one transmembrane domain (18,187 entries) and (B) all mammalian proteins lacking

20 - 25

3000 - 3500

Fig. 3. Histograms of peptide counts according to the number of amino acid residues (AA) or the isotopic mass after *in silico* trypsin digestion with Proteogest [Cagney et al., 2003] of

transmembrane domains (424,819 entries). The command line was >perl proteogest.pl –i *filename* –c trypsin –d –a –g1. The histograms show that the overall distribution of tryptic peptides (in terms of length or isotopic mass) is essentially the same in the two datasets and suggest that the trypsin susceptibility of mammalian transmembrane proteins does not

25 - 30

3500 - 4000

30 - 35

4000 - 4500

35 - 40

4500 - 5000

40 - 45

>=5000

>=50

**Length (AA)**

**Isotopic mass (Da)**

45 - 50

0 - 500

5

10

15

**%**

20

25

**A**

BCECs were seeded on the upper side of a filter placed in a Petri box and glial cells were seeded on the underside (see the Materials and Methods for details). Re-induction of BBB properties was confirmed by the fact that PeLY for bovine BCECs with re-induced BBB functions (0.6 x10-3 cm/min) was just over half that for cells with limited BBB functions (1.0 x10-3 cm/min). Immunostaining also confirmed the presence and localization of the main tight junction proteins occludin and claudin-5 and the associated protein ZO-1, as described elsewhere by our group [Gosselet et al., 2009; Pottiez et al., 2009b].

#### **3.2 Assessment of the susceptibility of BCEC membrane proteins to trypsin cleavage**

Prior to MS identification, membrane proteins are usually cleaved by proteolytic enzymes. Whatever the protein studied, trypsin is often considered as the enzyme of choice for proteomics, because it (i) has a specific cleavage site (on the C-terminal side of Arg- Xaa and Lys-Xaa, except when Xaa is a Pro), (ii) generates peptides of the right length for MS (in terms of sensitivity and accuracy) because the relatively high abundance of Arg and Lys (around 6%, compared with 10% for Leu, the most life abundant amino acid) and (iii) yields peptides with positive trapped charges. Due to the hydrophobic nature of PM proteins, several improvements of trypsin-based digestion methods have been especially developed to improve trypsin accessibility to proteins of interest. Most use buffers containing organic solvents (methanol, acetone, acetonitrile, etc.) or detergents (SDS, CYMAL-5, noctylglucoside, etc.) ([Lu X. & Zhu, 2005]; see [Josic & Clifton, 2007] for a review).

Other enzymes can also be used in this essential step in proteomics [Wu et al., 2003]. Other methods involve enzyme-free, hydrolytic cleavage using various combinations of acidic conditions, cyanogen bromide and microwave irradiation [Josic & Clifton, 2007; Zhong et al., 2005]. These enzyme-free methods cleave either specifically at methionine (with an average abundance of around 2.5%) or non-specifically at any peptide bond [Zhong et al., 2005]. Clearly, it is important to choose the right cleavage method when seeking to reduce bias and erroneous conclusions in the proteomic identification of membrane proteins.

The susceptibility of mammalian PM proteins to trypsin cleavage was assessed *in silico*. The two Uniprot FASTA sequence datasets (corresponding to all known mammalian transmembrane proteins and non-transmembrane proteins, respectively) were analysed with Proteogest software. This Perl-written software performs the *in silico* trypsin digestion of all listed proteins and lists the generated peptides according to length or isotopic mass. Expression of the results as histograms (Fig. 3) shows that the overall distribution of tryptic peptides (in terms of length or isotopic mass) is essentially the same for both datasets and suggests that the susceptibility of mammalian transmembrane proteins does not differ from that of non-transmembrane proteins.

As expected, the length-based distribution of peptides matches the isotopic mass distribution. Additionally, more than 75% of the potential trypsin-generated peptides in each dataset have fewer than 30 amino acids or an isotopic mass below 3000 atomic mass units, meaning that mass measurement or mass fragmentation will give unambiguous results. Even though between 10 and 17% of the *in silico* peptides have an isotopic mass below 500 atomic mass units, more than 50% of the potentially generated peptides are located in the optimal mass range for standard mass spectrometers.

#### **3.3 Assessment of** *in vitro* **biotinylation**

The efficiency of *in vitro* biotinylation with the non-cleavable reagent (EZ-link sulfo-NHS-LC-biotin) was assessed by florescence microscopy. The fluorescence pattern and intensity

BCECs were seeded on the upper side of a filter placed in a Petri box and glial cells were seeded on the underside (see the Materials and Methods for details). Re-induction of BBB properties was confirmed by the fact that PeLY for bovine BCECs with re-induced BBB functions (0.6 x10-3 cm/min) was just over half that for cells with limited BBB functions (1.0 x10-3 cm/min). Immunostaining also confirmed the presence and localization of the main tight junction proteins occludin and claudin-5 and the associated protein ZO-1, as described

**3.2 Assessment of the susceptibility of BCEC membrane proteins to trypsin cleavage**  Prior to MS identification, membrane proteins are usually cleaved by proteolytic enzymes. Whatever the protein studied, trypsin is often considered as the enzyme of choice for proteomics, because it (i) has a specific cleavage site (on the C-terminal side of Arg- Xaa and Lys-Xaa, except when Xaa is a Pro), (ii) generates peptides of the right length for MS (in terms of sensitivity and accuracy) because the relatively high abundance of Arg and Lys (around 6%, compared with 10% for Leu, the most life abundant amino acid) and (iii) yields peptides with positive trapped charges. Due to the hydrophobic nature of PM proteins, several improvements of trypsin-based digestion methods have been especially developed to improve trypsin accessibility to proteins of interest. Most use buffers containing organic solvents (methanol, acetone, acetonitrile, etc.) or detergents (SDS, CYMAL-5, n-

octylglucoside, etc.) ([Lu X. & Zhu, 2005]; see [Josic & Clifton, 2007] for a review).

that of non-transmembrane proteins.

**3.3 Assessment of** *in vitro* **biotinylation** 

Other enzymes can also be used in this essential step in proteomics [Wu et al., 2003]. Other methods involve enzyme-free, hydrolytic cleavage using various combinations of acidic conditions, cyanogen bromide and microwave irradiation [Josic & Clifton, 2007; Zhong et al., 2005]. These enzyme-free methods cleave either specifically at methionine (with an average abundance of around 2.5%) or non-specifically at any peptide bond [Zhong et al., 2005]. Clearly, it is important to choose the right cleavage method when seeking to reduce bias and erroneous conclusions in the proteomic identification of membrane proteins. The susceptibility of mammalian PM proteins to trypsin cleavage was assessed *in silico*. The two Uniprot FASTA sequence datasets (corresponding to all known mammalian transmembrane proteins and non-transmembrane proteins, respectively) were analysed with Proteogest software. This Perl-written software performs the *in silico* trypsin digestion of all listed proteins and lists the generated peptides according to length or isotopic mass. Expression of the results as histograms (Fig. 3) shows that the overall distribution of tryptic peptides (in terms of length or isotopic mass) is essentially the same for both datasets and suggests that the susceptibility of mammalian transmembrane proteins does not differ from

As expected, the length-based distribution of peptides matches the isotopic mass distribution. Additionally, more than 75% of the potential trypsin-generated peptides in each dataset have fewer than 30 amino acids or an isotopic mass below 3000 atomic mass units, meaning that mass measurement or mass fragmentation will give unambiguous results. Even though between 10 and 17% of the *in silico* peptides have an isotopic mass below 500 atomic mass units, more than 50% of the potentially generated peptides are

The efficiency of *in vitro* biotinylation with the non-cleavable reagent (EZ-link sulfo-NHS-LC-biotin) was assessed by florescence microscopy. The fluorescence pattern and intensity

located in the optimal mass range for standard mass spectrometers.

elsewhere by our group [Gosselet et al., 2009; Pottiez et al., 2009b].

Fig. 3. Histograms of peptide counts according to the number of amino acid residues (AA) or the isotopic mass after *in silico* trypsin digestion with Proteogest [Cagney et al., 2003] of FASTA sequence datasets for (A) all mammalian proteins displaying at least one transmembrane domain (18,187 entries) and (B) all mammalian proteins lacking transmembrane domains (424,819 entries). The command line was >perl proteogest.pl –i *filename* –c trypsin –d –a –g1. The histograms show that the overall distribution of tryptic peptides (in terms of length or isotopic mass) is essentially the same in the two datasets and suggest that the trypsin susceptibility of mammalian transmembrane proteins does not significantly differ from that of non-transmembrane proteins.

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 403

Fig. 5. Nano-LC-MALDI-TOF/TOF mass spectrometry analysis. The figure shows typical chromatograms of tryptic digests of *in vitro* biotinylated bovine BCECs monolayers with limited BBB functions (panel A) and re-induced BBB functions (panel B). The Y axis

**B**

**A**

corresponds to the chromatographic retention time (expressed as a spectrum number, Spect. #), whereas the X axis displays the mass/charge (m/z) ratio of the detected peptide ions. Each peptide is characterized by its retention time (Spect. #) and molecular mass (more exactly, its isotopic distribution). Peptide abundance is grey-scale coded; the darker the

data from the *in silico* digestion of all database-referenced proteins (or subsets of the latter). The concordance between experimental and theoretical data is then expressed as a Mascot score (-10 x log10 (p), where p is the likelihood (with 95% confidence) than the match is not due to chance). In other words, if the Mascot score for a given peptide is above the predefine threshold, the matching is not probably due to chance (and vice versa). Ultimately, the

signal, the more abundant the peptide.

scores for each peptide matching the same protein are summed.

did not differ significantly from one condition to another (Fig. 4) and the signal was principally located at the cell boundaries (red colour). Likewise, the EC permeabilities (deduced from the PeLY values) evolved similarly in treated and untreated cells.. Taken as a whole, these findings demonstrated that biotinylation did not affect the integrity of the BBB and did not introduce experimental bias.

Fig. 4. Fluorescence microscopy of a bovine BCEC monolayer with limited BBB functions ("Lim. BBB", panel A) or re-induced BBB functions (Re-ind. BBB, panel B). The monolayers were biotinylated with a non-cleavable reagent (EZ-link sulfo-NHS-LC-biotin). Biotinylated proteins were revealed by incubation with a Streptavidin-Cy3 conjugate (in red), whereas nuclei were stained with Hoechst 33258 (in blue).

#### **3.4 Nano-LC-MALDI-TOF-MS/MS maps**

After *in vitro* biotinylation, adherent bovine BCECs with limited or re-induced BBB functions were detached from the extracellular matrix by collagenase treatment, in order to avoid proteolytic damage to the PM proteins. The collected cells were lysed in ice-cold hypotonic buffer and pelleted at 1,000 x *g* for 10 min at 4 °C. Biotinylated proteins in the supernatant were trapped using streptavidin-coupled magnetic beads. Elution of nonbound proteins was monitored with dot blots. Biotinylated proteins immobilised on the streptavidin-coated magnetic beads were then on-bead digested with trypsin. The resulting peptides were collected, released by reduction and concentrated prior to nano-LC-MALDI-TOF/TOF mass spectrometry analysis.

Typical chromatograms for each of the two conditions are shown in Fig. 5. As with twodimensional gel electrophoresis, these peptide maps provide an overall, graphic representation of a sample's peptide diversity and abundance. The fact that the chromatograms for limited or re-induced BBB sample differ significantly underlines the quality of the sample preparation. Indeed, chromatograms that are too similar and/or too dense reflect inefficient labelling and purification, leading to the identification of a large set of cytosolic proteins.

#### **3.5 Protein identification**

Proteins were identified according to published guidelines [Wilkins et al., 2006] on the basis of PFF data. Briefly, the MS/MS data of all fragmented peptides were processed with the Mascot search algorithm, which compares the experimental MS/MS data to the theoretical

did not differ significantly from one condition to another (Fig. 4) and the signal was principally located at the cell boundaries (red colour). Likewise, the EC permeabilities (deduced from the PeLY values) evolved similarly in treated and untreated cells.. Taken as a whole, these findings demonstrated that biotinylation did not affect the integrity of the BBB

Fig. 4. Fluorescence microscopy of a bovine BCEC monolayer with limited BBB functions ("Lim. BBB", panel A) or re-induced BBB functions (Re-ind. BBB, panel B). The monolayers were biotinylated with a non-cleavable reagent (EZ-link sulfo-NHS-LC-biotin). Biotinylated proteins were revealed by incubation with a Streptavidin-Cy3 conjugate (in red), whereas

After *in vitro* biotinylation, adherent bovine BCECs with limited or re-induced BBB functions were detached from the extracellular matrix by collagenase treatment, in order to avoid proteolytic damage to the PM proteins. The collected cells were lysed in ice-cold hypotonic buffer and pelleted at 1,000 x *g* for 10 min at 4 °C. Biotinylated proteins in the supernatant were trapped using streptavidin-coupled magnetic beads. Elution of nonbound proteins was monitored with dot blots. Biotinylated proteins immobilised on the streptavidin-coated magnetic beads were then on-bead digested with trypsin. The resulting peptides were collected, released by reduction and concentrated prior to nano-LC-MALDI-

Typical chromatograms for each of the two conditions are shown in Fig. 5. As with twodimensional gel electrophoresis, these peptide maps provide an overall, graphic representation of a sample's peptide diversity and abundance. The fact that the chromatograms for limited or re-induced BBB sample differ significantly underlines the quality of the sample preparation. Indeed, chromatograms that are too similar and/or too dense reflect inefficient labelling and purification, leading to the identification of a large set

Proteins were identified according to published guidelines [Wilkins et al., 2006] on the basis of PFF data. Briefly, the MS/MS data of all fragmented peptides were processed with the Mascot search algorithm, which compares the experimental MS/MS data to the theoretical

**A B**

and did not introduce experimental bias.

nuclei were stained with Hoechst 33258 (in blue).

**3.4 Nano-LC-MALDI-TOF-MS/MS maps** 

TOF/TOF mass spectrometry analysis.

of cytosolic proteins.

**3.5 Protein identification** 

Fig. 5. Nano-LC-MALDI-TOF/TOF mass spectrometry analysis. The figure shows typical chromatograms of tryptic digests of *in vitro* biotinylated bovine BCECs monolayers with limited BBB functions (panel A) and re-induced BBB functions (panel B). The Y axis corresponds to the chromatographic retention time (expressed as a spectrum number, Spect. #), whereas the X axis displays the mass/charge (m/z) ratio of the detected peptide ions. Each peptide is characterized by its retention time (Spect. #) and molecular mass (more exactly, its isotopic distribution). Peptide abundance is grey-scale coded; the darker the signal, the more abundant the peptide.

data from the *in silico* digestion of all database-referenced proteins (or subsets of the latter). The concordance between experimental and theoretical data is then expressed as a Mascot score (-10 x log10 (p), where p is the likelihood (with 95% confidence) than the match is not due to chance). In other words, if the Mascot score for a given peptide is above the predefine threshold, the matching is not probably due to chance (and vice versa). Ultimately, the scores for each peptide matching the same protein are summed.

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 405

**Sodium/potassium-transporting ATPase subunit alpha-1 precursor (EC 3.6.3.9)**

Fig. 6. Identification of AT1A1\_BOVIN (sodium/potassium-transporting ATPase subunit alpha-1 precursor). The summary report describes some of the structural characteristics of the three matching peptides. The high Mascot scores correspond to an unambiguous identification. The sequences of the matching peptides are highlighted in bold red type within the AT1A1 amino acid sequence. For the sake of clarity, the amino acid sequence displayed here is truncated (ranging from amino acids #451 to #850). Lastly, the MS/MS spectrum of ionized peptides of 2834.3838 atomic mass units illustrates the amino acid

K.EQPLDEELKDAFQNAYLELGGLGER.V

**Peptide 2834.3838**

K.QAADMILLDDNFASIVTGVEEGR.L

5.4 No. of unique Peptides: 3

R.LNIPVSQVNPR.D

m/z meas. m/z [ppm] Score Range Sequence

ESALLKCIEV CCGSVKEMRE RYTKIVEIPF NSTNKYQLSI HKNANAGEPR HLLVMKGAPE RILDRCSSIL IHGK**EQPLDE ELKDAFQNAY LELGGLGER**V LGFCHLLLPD EQFPEGFQFD TDDVNFPVDN LCFVGLISMI DPPRAAVPDA VGKCRSAGIK VIMVTGDHPI TAKAIAKGVG IISEGNETVE DIAAR**LNIPV 651 SQVNPR**DARA CVVHGSDLKD MTPEQLDDIL KYHTEIVFAR TSPQQKLIIV EGCQRQGAIV AVTGDGVNDS PALKKADIGV AMGIAGSDVS K**QAADMILLD 751 DNFASIVTGV EEGR**LIFDNL KKSIAYTLTS NIPEITPFLI FIIANIPLPL GTVTILCIDL GTDMVPAISL AYEQAESDIM KRQPRNPQTD KLVNERLISM

Sequence Coverage [%]:

pI: 5. 36

2834.3838 -0.17 76.9 525-549 1236.6960 -8.00 34.7 646-656 2464.1980 -1.80 57.0 742-764

Matched peptides shown in **Bold Red**

**AT1A1\_BOVIN**

MW [kDa]: 112

sequence deduction.

Score: 168

An illustration of the rigorousness of protein identification is shown in Fig. 6 for the sodium/potassium-transporting ATPase subunit alpha-1 precursor (AT1A1\_BOVIN), a protein with 10 transmembrane domains. This catalytic subunit is located at the PM and enables creation of the electrochemical gradient required for the active transport of various nutrients. The mature form of bovine AT1A1 is composed of 1016 amino acids and has an average molecular weight of around 112 kDa. The individual identification scores of the three peptides belonging to this protein are presented in the summary box in Fig. 6. All the scores are over the chance-related threshold, demonstrating that the identification is relevant. Hence, the cumulative Mascot score of 168.6 for AT1A1 denotes unambiguous identification.

The location of the matching peptides (in bold red type) within the protein amino acid sequence shows that they are clustered in the large cytosolic region (described as "potential" in Uniprot database (aa #337 to #770)) of the Na+/K+-transporting ATPase subunit. Accordingly, no transmembrane domain-containing peptides served as the basis for protein identification, suggesting that the hydrophilic (i.e. cytoplasmic) regions of a given, nondenatured protein are more accessible to trypsin than their hydrophobic counterparts. Lastly, a typical MS/MS spectrum is shown in Fig. 6. The precursor ions displaying an m/z ratio of 2834.3838 atomic mass units are in-source fragmented and all the generated daughter ions had an m/z ratio below that of the parent ions. The mass differences between daughter ions allow deducing the amino acid sequence.

A typical nano-LC MS/MS analysis reported 761 fragmented peptides from samples of BCECs with limited BBB functions, whereas 957 fragmented peptides were reported for samples of BCECs with re-induced BBB functions. The efficient MS fragmentation led to the identification of 145 and 124 proteins in BCEC samples with limited and re-induced BBB functions, respectively. Sixty-three proteins were common to both conditions (Figure 7A). Following duplicate experiments on fraction-specific proteins, only 51 and 32 proteins were identified twice in BCECs with limited BBB functions and re-induced BBB functions, respectively. In all, this approach identified 211 distinct genes, of which 58 are referenced in Uniprot as coding for membrane-related proteins. Five of the 63 common proteins were PM or membrane-associated proteins, whereas 2 and 3 membrane-related proteins were identified in fraction-specific protein sets from BCECs with limited and re-induced BBB functions, respectively. Of the 15 membrane-related proteins (Figure 7B), 5 had more than one transmembrane domain (range: 2 to 17), 4 had a single transmembrane domain and 6 were lipid-anchored. Hence, the majority of membrane-related proteins were anchored to the membrane by a single domain or a lipid moiety.

#### **3.6 Sorting protein lists**

After conversion of identified proteins into their corresponding gene names via webavailable bioinformatics resources, protein lists were sorted with PANTHER. The cellular locations of identified proteins (Fig. 8) were similar in the two kinds of BCEC. The protein sorting results showed that about two thirds of the identified proteins came from the cytoplasm or the PM, whereas a quarter were related to the endoplasmic reticulum, the mitochondrion, the nucleus and secreted proteins. Very few of the identified proteins belonged to the cell junction, endosome or Golgi apparatus. This ranking shows that very few proteins belonging to cytoplasm or secreted proteins (or proteins added to the cell culture medium) were recovered, despite their cellular abundance. Our findings demonstrate the efficiency of the enrichment approach used in the present study, even though only about 30 proteins came from the BCEC PM.

#### **AT1A1\_BOVIN**

404 Proteomics – Human Diseases and Protein Functions

An illustration of the rigorousness of protein identification is shown in Fig. 6 for the sodium/potassium-transporting ATPase subunit alpha-1 precursor (AT1A1\_BOVIN), a protein with 10 transmembrane domains. This catalytic subunit is located at the PM and enables creation of the electrochemical gradient required for the active transport of various nutrients. The mature form of bovine AT1A1 is composed of 1016 amino acids and has an average molecular weight of around 112 kDa. The individual identification scores of the three peptides belonging to this protein are presented in the summary box in Fig. 6. All the scores are over the chance-related threshold, demonstrating that the identification is relevant. Hence,

The location of the matching peptides (in bold red type) within the protein amino acid sequence shows that they are clustered in the large cytosolic region (described as "potential" in Uniprot database (aa #337 to #770)) of the Na+/K+-transporting ATPase subunit. Accordingly, no transmembrane domain-containing peptides served as the basis for protein identification, suggesting that the hydrophilic (i.e. cytoplasmic) regions of a given, nondenatured protein are more accessible to trypsin than their hydrophobic counterparts. Lastly, a typical MS/MS spectrum is shown in Fig. 6. The precursor ions displaying an m/z ratio of 2834.3838 atomic mass units are in-source fragmented and all the generated daughter ions had an m/z ratio below that of the parent ions. The mass differences between

A typical nano-LC MS/MS analysis reported 761 fragmented peptides from samples of BCECs with limited BBB functions, whereas 957 fragmented peptides were reported for samples of BCECs with re-induced BBB functions. The efficient MS fragmentation led to the identification of 145 and 124 proteins in BCEC samples with limited and re-induced BBB functions, respectively. Sixty-three proteins were common to both conditions (Figure 7A). Following duplicate experiments on fraction-specific proteins, only 51 and 32 proteins were identified twice in BCECs with limited BBB functions and re-induced BBB functions, respectively. In all, this approach identified 211 distinct genes, of which 58 are referenced in Uniprot as coding for membrane-related proteins. Five of the 63 common proteins were PM or membrane-associated proteins, whereas 2 and 3 membrane-related proteins were identified in fraction-specific protein sets from BCECs with limited and re-induced BBB functions, respectively. Of the 15 membrane-related proteins (Figure 7B), 5 had more than one transmembrane domain (range: 2 to 17), 4 had a single transmembrane domain and 6 were lipid-anchored. Hence, the majority of membrane-related proteins were anchored to

After conversion of identified proteins into their corresponding gene names via webavailable bioinformatics resources, protein lists were sorted with PANTHER. The cellular locations of identified proteins (Fig. 8) were similar in the two kinds of BCEC. The protein sorting results showed that about two thirds of the identified proteins came from the cytoplasm or the PM, whereas a quarter were related to the endoplasmic reticulum, the mitochondrion, the nucleus and secreted proteins. Very few of the identified proteins belonged to the cell junction, endosome or Golgi apparatus. This ranking shows that very few proteins belonging to cytoplasm or secreted proteins (or proteins added to the cell culture medium) were recovered, despite their cellular abundance. Our findings demonstrate the efficiency of the enrichment approach used in the present study, even

the cumulative Mascot score of 168.6 for AT1A1 denotes unambiguous identification.

daughter ions allow deducing the amino acid sequence.

the membrane by a single domain or a lipid moiety.

though only about 30 proteins came from the BCEC PM.

**3.6 Sorting protein lists** 

#### **Sodium/potassium-transporting ATPase subunit alpha-1 precursor (EC 3.6.3.9)**

Score: 168 pI: 5. 36 5.4 No. of unique Peptides: 3 MW [kDa]: 112 Sequence Coverage [%]:


Matched peptides shown in **Bold Red**


**Peptide 2834.3838**

Fig. 6. Identification of AT1A1\_BOVIN (sodium/potassium-transporting ATPase subunit alpha-1 precursor). The summary report describes some of the structural characteristics of the three matching peptides. The high Mascot scores correspond to an unambiguous identification. The sequences of the matching peptides are highlighted in bold red type within the AT1A1 amino acid sequence. For the sake of clarity, the amino acid sequence displayed here is truncated (ranging from amino acids #451 to #850). Lastly, the MS/MS spectrum of ionized peptides of 2834.3838 atomic mass units illustrates the amino acid sequence deduction.

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 407

The sorting of protein lists by molecular function is presented in Fig. 9. Proteins identified from the bovine BCECs with limited (Fig. 9A) or re-induced (Fig. 9B) BBB functions could be divided into 8 and 9 activity classes, respectively, of which 7 were common (binding, catalytic activity, enzyme regulation activity, ion channel activity, receptor activity, transporter activity and structural molecule activity). Briefly, there were twice as many proteins with catalytic or receptor activity for BCECs with re-induced BBB functions than for BCECs with limited BBB functions. In contrast, the proteins involved in binding, enzyme regulation activity and structural molecule activity were less represented (at least two fold) in BCECs with re-induced BBB functions. Interestingly, proteins displaying transcription regulation activity were only identified in BCECs with limited BBB functions; whereas lists from BCECs with re-induced BBB functions also included proteins with motor activity and

As expected for our experimental model, 59% of the 211 identified proteins were identified as bovine proteins. Indeed, certain proteins not yet reported in bovine samples were identified on the basis of inter-species sequence homologies. Given their location, this subset of proteins complements the results of our previous work, in which we used a large-scale electrophoresis- and chromatography-based approach to identify more than 430 cytoplasmic proteins [Pottiez et al., 2010]. Proteins found in both BCECs with limited and re-induced

Fig. 9. Distribution of the proteins specifically identified in each condition according to their molecular function. The lists were generated under the PANTHER classification system. Proteins only identified in samples of BCECs with limited BBB function were distributed across 8 categories (Panel A), whereas those only identified in samples from BCECs with re-

15%

7% 4%

7%

15%

4%

4%

29%

binding

catalytic activity

enzyme regulator activity ion channel activity receptor activity

structural molecule activity transcription regulator activity

transporter activity antioxidant activity motor activity

The literature-based sorting of proteins identified in one condition only generated a lot of valuable information. A selected subset of these proteins is presented in Table 1. All of the listed proteins are found in the inner mitochondria membrane, the endoplasmic reticulum, the Golgi membrane and the vesicle membrane as well as the PM; this shows that the biotinylation reaction takes also place inside the cell, despite the experimental precautions taken. However, for the first time, we report a few PM proteins that had not previously been

induced BBB functions were distributed over 9 categories (panel B).

antioxidant activity.

BBB functions will not be discussed further here.

**A B**

28%

18% 15%

12%

6% 12%

6%

12%

6%

Fig. 7. Overall distribution of proteins identified using this approach. Panel A: A Venn diagram of proteins identified in BCECs with limited BBB functions and re-induced BBB functions, respectively, showing the distribution of proteins identified in both conditions and in only one condition. Panel B: The distribution of membrane-related proteins according to the number of transmembrane domains and the presence of a lipid anchor.

Fig. 8. Cellular location-based sorting of identified proteins. The white histogram shows the sorting results for proteins in samples of BCECs with limited BBB functions (Lim. BBB) and the grey histogram depicts the result for BCECs with re-induced BBB (Re-ind. BBB) functions. Clearly, the grey and white histograms are very similar. More than 50% of the identified proteins were related to the cytoplasm and membrane. The remaining proteins were related to the mitochondrion, the nucleus and secretory pathways.

**A B**

Lim. BBB Re-ind. BBB

51 63 32

Fig. 7. Overall distribution of proteins identified using this approach. Panel A: A Venn diagram of proteins identified in BCECs with limited BBB functions and re-induced BBB functions, respectively, showing the distribution of proteins identified in both conditions and in only one condition. Panel B: The distribution of membrane-related proteins according

6 TM 11%

> 9 TM 2%

10 TM 7%

1 TM 31%

2 TM 5%

to the number of transmembrane domains and the presence of a lipid anchor.

Endosome

Golgi apparatus

Fig. 8. Cellular location-based sorting of identified proteins. The white histogram shows the sorting results for proteins in samples of BCECs with limited BBB functions (Lim. BBB) and

the grey histogram depicts the result for BCECs with re-induced BBB (Re-ind. BBB) functions. Clearly, the grey and white histograms are very similar. More than 50% of the identified proteins were related to the cytoplasm and membrane. The remaining proteins

were related to the mitochondrion, the nucleus and secretory pathways.

Membrane

Mitochondrion

Nucleus

Lim. BBB Re-ind. BBB

17 TM 2%

Secreted

3 anchors 2%

2 anchors 5%

1 anchor 14%

0 anchor 21%

0 TM 42%

Cytoplasm

Endoplasmic reticulum

Cell junction

Number of identifed proteins

The sorting of protein lists by molecular function is presented in Fig. 9. Proteins identified from the bovine BCECs with limited (Fig. 9A) or re-induced (Fig. 9B) BBB functions could be divided into 8 and 9 activity classes, respectively, of which 7 were common (binding, catalytic activity, enzyme regulation activity, ion channel activity, receptor activity, transporter activity and structural molecule activity). Briefly, there were twice as many proteins with catalytic or receptor activity for BCECs with re-induced BBB functions than for BCECs with limited BBB functions. In contrast, the proteins involved in binding, enzyme regulation activity and structural molecule activity were less represented (at least two fold) in BCECs with re-induced BBB functions. Interestingly, proteins displaying transcription regulation activity were only identified in BCECs with limited BBB functions; whereas lists from BCECs with re-induced BBB functions also included proteins with motor activity and antioxidant activity.

As expected for our experimental model, 59% of the 211 identified proteins were identified as bovine proteins. Indeed, certain proteins not yet reported in bovine samples were identified on the basis of inter-species sequence homologies. Given their location, this subset of proteins complements the results of our previous work, in which we used a large-scale electrophoresis- and chromatography-based approach to identify more than 430 cytoplasmic proteins [Pottiez et al., 2010]. Proteins found in both BCECs with limited and re-induced BBB functions will not be discussed further here.

Fig. 9. Distribution of the proteins specifically identified in each condition according to their molecular function. The lists were generated under the PANTHER classification system. Proteins only identified in samples of BCECs with limited BBB function were distributed across 8 categories (Panel A), whereas those only identified in samples from BCECs with reinduced BBB functions were distributed over 9 categories (panel B).

The literature-based sorting of proteins identified in one condition only generated a lot of valuable information. A selected subset of these proteins is presented in Table 1. All of the listed proteins are found in the inner mitochondria membrane, the endoplasmic reticulum, the Golgi membrane and the vesicle membrane as well as the PM; this shows that the biotinylation reaction takes also place inside the cell, despite the experimental precautions taken. However, for the first time, we report a few PM proteins that had not previously been


Table 1.

Proteomic Analysis of Plasma Membrane Proteins in an *In Vitro* Blood-Brain Barrier Model 409

identified as such in BCECs (notably integrin alpha-D, nitric oxide synthase 3, dysferlin, myoferlin, transmembrane 9 superfamily member 4) and confirmed the presence of previously reported PM proteins (notably ATP-binding cassette sub-family C member 8, platelet endothelial cell adhesion molecule and Na+/K+ ATPase) [Uchida et al., 2011]. Although moesin is not a PM protein, it appears to be associated with the PM in BCECs with

*In vivo* BCECs displaying a BBB phenotype display specific endocytic trafficking that regulates (at least in part) the molecular exchanges between the blood and the brain [Abbott et al., 2010]. Interestingly, our samples from BCECs with re-induced BBB functions contained several proteins involved in cellular endocytosis, endocytic recycling, membrane trafficking and receptor internalization, such as EH domain-containing protein 2, myoferlin, dysferlin and certain cellular partners. Ferlin proteins are calcium-sensing proteins involved in vesicle trafficking and PM repair [Glover & Brown, 2007] and regulate the fusion of lipid vesicles at the PM. Myoferlin is reportedly strongly expressed in ECs and vascular tissues and was identified in a proteomics study of caveolae/lipid raft microdomains [Bernatchez et al., 2007]. Repression of myoferlin expression reduces not only lipid vesicle fusion in ECs but also protein expression levels of the vascular endothelial growth factor receptor-2 (VEGFR-2). In contrast to dysferlin, myoferlin regulates the membrane stability and function of VEGFR-2, [Sharma et al., 2010]. Dysferlin has been reported as a new marker for leaky brain blood vessels [Hochmeister

Furthermore, *in vitro* myoferlin gene silencing not only decreases both clathrin- and caveolin-/raft-dependent endocytosis [Bernatchez et al., 2009] but also attenuates the expression of the angiogenic second tyrosine kinase receptor (Tie-2) [Yu et al., 2011]. In general, myoferlin appears to be critical for endocytosis events in ECs and could be a potential candidate for drug-mediated enhancement of transcytosis pathway and/or angiogenic targets. Accordingly, it has been shown that caveolin's main effect is to retain dysferlin at the cell surface [Hernandez-Deviez et al., 2008]; this inhibits the endocytosis of dysferlin through clathrin-independent pathway and therefore reinforces its PM-resealing activity. Recently, Doherty et al. have described a third interaction partner, EH domaincontaining protein 2 (EHD2) [Doherty et al., 2008]. Although its role was demonstrated in myoblasts, EHD2 is an endocytic recycling protein that interacts with myoferlin to regulate lipid vesicle fusion. EHD2 binds to lipid membranes and deforms them into tubules. The protein regulates trafficking from the PM by controlling Rac1 activity [Benjamin et al., 2011] and is important for internalization of the glucose-transporter 4 (GLUT-4) [Park et al., 2004] Lastly, EHD2 is required for the translocation of a newly identified ferlin-like protein

The aim of our study was to determine the distribution and the nature of PM proteins in BCECs displaying the BBB phenotype. Based in our BBB *in vitro* model, we developed a strategy for labelling these proteins (with biotin), isolating them (with streptavidin affinity chromatography) and identify them (with nano-LC MS/MS). The most frequently used methods for the enrichment of PMs are based on affinity chromatography, cationic colloidal silica particles, cell biotinylation or a tissue-specific polyclonal antiserum. We decided to use a biotinylation approach because it avoids many of the drawbacks of the other methods. For

re-induced BBB functions (as previously reported [Pottiez et al., 2009b]).

et al., 2006].

(Fer1L5) to the PM [Posey et al., 2011].

**4. Conclusions** 

identified as such in BCECs (notably integrin alpha-D, nitric oxide synthase 3, dysferlin, myoferlin, transmembrane 9 superfamily member 4) and confirmed the presence of previously reported PM proteins (notably ATP-binding cassette sub-family C member 8, platelet endothelial cell adhesion molecule and Na+/K+ ATPase) [Uchida et al., 2011]. Although moesin is not a PM protein, it appears to be associated with the PM in BCECs with re-induced BBB functions (as previously reported [Pottiez et al., 2009b]).

*In vivo* BCECs displaying a BBB phenotype display specific endocytic trafficking that regulates (at least in part) the molecular exchanges between the blood and the brain [Abbott et al., 2010]. Interestingly, our samples from BCECs with re-induced BBB functions contained several proteins involved in cellular endocytosis, endocytic recycling, membrane trafficking and receptor internalization, such as EH domain-containing protein 2, myoferlin, dysferlin and certain cellular partners. Ferlin proteins are calcium-sensing proteins involved in vesicle trafficking and PM repair [Glover & Brown, 2007] and regulate the fusion of lipid vesicles at the PM. Myoferlin is reportedly strongly expressed in ECs and vascular tissues and was identified in a proteomics study of caveolae/lipid raft microdomains [Bernatchez et al., 2007]. Repression of myoferlin expression reduces not only lipid vesicle fusion in ECs but also protein expression levels of the vascular endothelial growth factor receptor-2 (VEGFR-2). In contrast to dysferlin, myoferlin regulates the membrane stability and function of VEGFR-2, [Sharma et al., 2010]. Dysferlin has been reported as a new marker for leaky brain blood vessels [Hochmeister et al., 2006].

Furthermore, *in vitro* myoferlin gene silencing not only decreases both clathrin- and caveolin-/raft-dependent endocytosis [Bernatchez et al., 2009] but also attenuates the expression of the angiogenic second tyrosine kinase receptor (Tie-2) [Yu et al., 2011]. In general, myoferlin appears to be critical for endocytosis events in ECs and could be a potential candidate for drug-mediated enhancement of transcytosis pathway and/or angiogenic targets. Accordingly, it has been shown that caveolin's main effect is to retain dysferlin at the cell surface [Hernandez-Deviez et al., 2008]; this inhibits the endocytosis of dysferlin through clathrin-independent pathway and therefore reinforces its PM-resealing activity. Recently, Doherty et al. have described a third interaction partner, EH domaincontaining protein 2 (EHD2) [Doherty et al., 2008]. Although its role was demonstrated in myoblasts, EHD2 is an endocytic recycling protein that interacts with myoferlin to regulate lipid vesicle fusion. EHD2 binds to lipid membranes and deforms them into tubules. The protein regulates trafficking from the PM by controlling Rac1 activity [Benjamin et al., 2011] and is important for internalization of the glucose-transporter 4 (GLUT-4) [Park et al., 2004] Lastly, EHD2 is required for the translocation of a newly identified ferlin-like protein (Fer1L5) to the PM [Posey et al., 2011].

#### **4. Conclusions**

408 Proteomics – Human Diseases and Protein Functions

Protein name

Table 1.

Class I histocompatibility antigen

ATP-binding cassette sub-family C member 8

Very long-chain specific acyl-CoA dehydrogenase

Cell division control protein 42 homolog

Cytoskeleton-associated protein 4

Integrin alpha-D

Myosin-Ic Nitric oxide synthase, endothelial

Platelet endothelial cell adhesion molecule

Spectrin beta chain, brain

Protein name Neutral cholesterol ester hydrolase 1

Dysferlin EH domain-containing protein

Guanine nucleotide-binding protein subunit beta-2-like

Moesin Myoferlin Membrane-associated progesterone receptor component 1

1-acyl-sn-glycerol-3-phosphate acyltransferase alpha

Ras-related protein Rab-35

Transforming protein RhoA

Signal peptidase complex subunit 2

Translocon-associated protein subunit delta

Transmembrane 9 superfamily member 4

Zinc transporter 1

Gene name a

*Nceh1* *Dysf* *Ehd2* *Gnb2l1*

*Msn* *Myof*

*Pgrmc1* *Agpat1*

*Rab35* *Rhoa* *Spcs2*

*Ssr4* *Tm9sf4* *Slc30a1* Table 1. List of identified plasma membrane proteins that were present only in solo-cultured (Lim. BBB)or in co-culured (re-ind. BBB) BCECs.

a Gene name and accession number according to Uniprot. b Isoelectric point (pI) and molecular weight (MW). c Sequence coverage.

Q9Y6M5

Q28250

Q2TBX5

A5D7E2

Q95JH2

Q15286

P61585

 10.3

 9.4

 5.8

 9.5

 5.4

 6.2

 6.0

 55.3

 3.9

 74.3

 3.0

 18.8

 11.0

 24.9

 8.4

 21.8

 8.8

 23.0

 9.0

 32.0

 7.3

Q17QC0

Q69ZN7

Q2HJ49

A6QQP7

Q9NZN4

P63243

Q1JQE6

 6.2

 5.3

 6.0

 8.9

 5.8

 5.8

 4.4

 21.6

 4.6

 233.2

 0.7

 67.9

 3.6

 35.1

 5.0

 61.1

 5.0

 237.1

 0.9

 46.0

 3.4

1

1

2

1

1

1

1

1

1

1 1

1

1

1

 Ion transport

Cytoplasmic vesicule

Signal peptide processing

Intracellular protein transport

Protein transport

Small GTPase mediated signal transduction

Receptor activity

Phospholipid biosynthesis

Plasma membrane repair

Membrane to membrane docking

 Apoptosis

Endocytic recycling

Vesicle fusion

Lipid catabolic process

Accession number a

pI b

MW (kDa) b

Seq Cov (%) c

Matched peptides

Biological Process

Gene name a

*1b04* *Abcc8* *Acadvl* *Cdc42* *Ckap4*

*Itgad* *Myo1c*

*Nos3* *Pecam1* *Sptbn1*

Q01082

P51866

Q27966

P29473

Q13349

Q07065

Q2KJ93

P30382

Q09427

P48818

 5.4

 9.1

 9.5

 6.2

 5.6

 5.4

 9.9

 6.5

 7.0

 5.3 **Re-ind. BBB**

 274.4

 1.0

 82.5

 4.7

 133.2

 2.4

 121.9

 4.6

 126.7

 2.4

 66.0

 2.5

 21.2

 8.9

 70.6

 2.6

 177.0

 1.2

 40.4

 7.2

2

2

1

1 1

2

3

1

2

2

Actin filament capping

Cell adhesion

Protein transport

Blood vessel remodeling

Cell adhesion

Membrane fraction

Lipid metabolism

Small GTPase mediated signal transduction

 Transport

Histocompatibility antigen

Accession number a

**Lim. BBB** pI b

MW (kDa) b

Seq. Cov. (%) c

Matched peptides

Biological Process

The aim of our study was to determine the distribution and the nature of PM proteins in BCECs displaying the BBB phenotype. Based in our BBB *in vitro* model, we developed a strategy for labelling these proteins (with biotin), isolating them (with streptavidin affinity chromatography) and identify them (with nano-LC MS/MS). The most frequently used methods for the enrichment of PMs are based on affinity chromatography, cationic colloidal silica particles, cell biotinylation or a tissue-specific polyclonal antiserum. We decided to use a biotinylation approach because it avoids many of the drawbacks of the other methods. For

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K., Katzav, S., Ehrlich, M. & Horowitz, M. (2011). EHD2 mediates trafficking from the plasma membrane by modulating Rac1 activity. *Biochem J*, Vol. No. (Jul 15

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cells from embryonic chick, rat and human brain in flask cultures. *Neurobiology*,

quantitative proteomics. *Journal of proteomics*, Vol. 72, No. 5, (Jul 21 2009), pp. 740-9,

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reproducible, and mass-production method to study the blood-brain barrier in

example, proteolytic shaving offers many advantages in theory (since surface-exposed peptides are more water-soluble than their intrabilayer counterparts) but is handicapped by its tendency to trigger cell lysis and thus significantly contaminate surface-exposed membrane peptides with cytosol-derived peptides.

By using the biotinylation approach, we showed that very few cytoplasmic proteins, secreted proteins or proteins added to the cell culture medium were recovered - despite their relatively high cellular abundance. We reported on the novel identification of transmembrane and membrane-associated proteins in bovine BCECs displaying the BBB phenotype. Our findings demonstrated the efficiency of the enrichment approach used, even though only about 30 proteins came from the BCEC PM. The proteins are variously involved in cellular endocytosis, membrane trafficking and receptor internalization and may thus have significant roles in BBB function. The fact that transmembrane and membraneassociated proteins accounted for less than half the identified proteins shows how difficult it still is to isolate, solubilise and digest hydrophobic proteins of low cellular abundance. Our results suggest that the specific properties of PM proteins must be taken into account when seeking to improve biotinylation, purification and identification methods. Moreover, the glycocalyx can also impede biotinylation [Ueno, 2009]. The biotinylation targeting could probably be improved by the use of new biotin derivatives that are less likely to cross the PM.

Furthermore, the present study reports the identification of several proteins involved in cellular endocytosis, membrane trafficking and receptor internalization (such as EHD2 and myoferlin), together with their cellular partners. These proteins and the pathways of which they are a part may become new targets for increasing drug transport across the BBB.

#### **5. Acknowledgments**

This research was funded by the Ministère de la Recherche et de l'Enseignement Supérieur and Oseo-Anvar. The mass spectrometry facilities used for this study were funded by the European Regional Development Fund, the Fonds d'Industrialisation des Bassins Miniers (FIBM), the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche and the Université d'Artois. We thank Dr F. Pont (INSERM, Toulouse) for his help with use of nwCompare software.

#### **6. References**


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By using the biotinylation approach, we showed that very few cytoplasmic proteins, secreted proteins or proteins added to the cell culture medium were recovered - despite their relatively high cellular abundance. We reported on the novel identification of transmembrane and membrane-associated proteins in bovine BCECs displaying the BBB phenotype. Our findings demonstrated the efficiency of the enrichment approach used, even though only about 30 proteins came from the BCEC PM. The proteins are variously involved in cellular endocytosis, membrane trafficking and receptor internalization and may thus have significant roles in BBB function. The fact that transmembrane and membraneassociated proteins accounted for less than half the identified proteins shows how difficult it still is to isolate, solubilise and digest hydrophobic proteins of low cellular abundance. Our results suggest that the specific properties of PM proteins must be taken into account when seeking to improve biotinylation, purification and identification methods. Moreover, the glycocalyx can also impede biotinylation [Ueno, 2009]. The biotinylation targeting could probably be improved by the use of new biotin derivatives that are less likely to cross the

Furthermore, the present study reports the identification of several proteins involved in cellular endocytosis, membrane trafficking and receptor internalization (such as EHD2 and myoferlin), together with their cellular partners. These proteins and the pathways of which they are a part may become new targets for increasing drug transport across the

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**19** 

*Denmark* 

**Quantitative Proteomics for Investigation of** 

Jeanette Henningsen, Blagoy Blagoev and Irina Kratchmarova

*Department of Biochemistry and Molecular Biology,* 

*University of Southern Denmark, Odense M* 

**Secreted Factors: Focus on Muscle Secretome** 

The response of cells to even slight changes in the cellular microenvironment determines the reaction of the whole organism and its ability to adapt to macroenvironmental alterations. Generally, it is well recognized that the communication between cells, tissues, and organs is critical for the maintenance of the entire body homeostasis. The different cell types that build the various organs and tissues have an enormous potential to produce proteins that once secreted in the extracellular space exert their action in an auto-, para- and/or endocrine manner. It is estimated that out of the total 20.500 protein-coding genes in human, approximately 10% encode secreted proteins (Clamp et al., 2007; Skalnikova et al., 2011). The separate and combinatorial action of these ~2200 secreted proteins can influence the biology not only at adjacent sites but also have a clear effect on the whole organism (Lin et al., 2008). The secreted factors, which can range from large proteins to short peptides, are divided into different groups or classes according to their structural properties and function. The prototypical secreted proteins are represented by the group of proteins found in the blood stream and other body fluids, the components of the extracellular matrix (ECM) and enzymes released in the intestine and stomach. An intriguing group of secreted factors comprise cell surface receptor ligands, such as hormones, growth factors, and cytokines. These proteins can exert their actions either on a limited number of responsive tissues or can act on virtually all cell types dependent on the expression of their specific receptors. It is essential to decipher in depth the signaling events that are triggered by the various hormones and growth factors to understand the general mechanisms of the biological processes that occur in a strictly controlled fashion in both space and time. The processes that secreted factors influence and directly regulate range from cellular differentiation, growth and survival to apoptosis, autophagy, and ageing. In addition, a growth factor can often exert a divergent and even opposite effect depending on the cell type and cellular state. Taken in consideration the role of secreted factors in directing biological processes, malfunction of the signaling cascades orchestrated by secreted factors can have severe consequences and lead to development of a series of complicated diseases and disorders (Flier, 2001; Pedersen, 2009; Walsh, 2009). Therefore, a comprehensive characterization of secreted molecules by different cellular subtypes, tissues, and organs can contribute to the elucidation of the physiological state of a given organism and to the determination of the

**1. Introduction** 

Zhong, H., Marcus, S. L. & Li, L. (2005). Microwave-assisted acid hydrolysis of proteins combined with liquid chromatography MALDI MS/MS for protein identification. *J Am Soc Mass Spectrom*, Vol. 16, No. 4, (Apr 2005), pp. 471-81.

### **Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome**

Jeanette Henningsen, Blagoy Blagoev and Irina Kratchmarova *Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M* 

*Denmark* 

#### **1. Introduction**

416 Proteomics – Human Diseases and Protein Functions

Zhong, H., Marcus, S. L. & Li, L. (2005). Microwave-assisted acid hydrolysis of proteins

*Am Soc Mass Spectrom*, Vol. 16, No. 4, (Apr 2005), pp. 471-81.

combined with liquid chromatography MALDI MS/MS for protein identification. *J* 

The response of cells to even slight changes in the cellular microenvironment determines the reaction of the whole organism and its ability to adapt to macroenvironmental alterations. Generally, it is well recognized that the communication between cells, tissues, and organs is critical for the maintenance of the entire body homeostasis. The different cell types that build the various organs and tissues have an enormous potential to produce proteins that once secreted in the extracellular space exert their action in an auto-, para- and/or endocrine manner. It is estimated that out of the total 20.500 protein-coding genes in human, approximately 10% encode secreted proteins (Clamp et al., 2007; Skalnikova et al., 2011). The separate and combinatorial action of these ~2200 secreted proteins can influence the biology not only at adjacent sites but also have a clear effect on the whole organism (Lin et al., 2008). The secreted factors, which can range from large proteins to short peptides, are divided into different groups or classes according to their structural properties and function. The prototypical secreted proteins are represented by the group of proteins found in the blood stream and other body fluids, the components of the extracellular matrix (ECM) and enzymes released in the intestine and stomach. An intriguing group of secreted factors comprise cell surface receptor ligands, such as hormones, growth factors, and cytokines. These proteins can exert their actions either on a limited number of responsive tissues or can act on virtually all cell types dependent on the expression of their specific receptors. It is essential to decipher in depth the signaling events that are triggered by the various hormones and growth factors to understand the general mechanisms of the biological processes that occur in a strictly controlled fashion in both space and time. The processes that secreted factors influence and directly regulate range from cellular differentiation, growth and survival to apoptosis, autophagy, and ageing. In addition, a growth factor can often exert a divergent and even opposite effect depending on the cell type and cellular state. Taken in consideration the role of secreted factors in directing biological processes, malfunction of the signaling cascades orchestrated by secreted factors can have severe consequences and lead to development of a series of complicated diseases and disorders (Flier, 2001; Pedersen, 2009; Walsh, 2009). Therefore, a comprehensive characterization of secreted molecules by different cellular subtypes, tissues, and organs can contribute to the elucidation of the physiological state of a given organism and to the determination of the

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 419

quantitation of changes in protein abundance (Ong and Mann, 2005; Schulze and Usadel,

Quantitation without stable isotopes generally encompasses a gel electrophoresis approach or a chromatography-based approach. In the gel-based approach one-dimensional or twodimensional gel electrophoresis is used as a mean of resolving the proteins from complex mixtures. This is followed by visualization of the protein bands or spots using different types of stains or fluorescent dyes. Typically, the protein samples originating from different cellular stages are separated on a gel and then the bands or spots that show distinct changes are excised, digested with proteases and identified by mass spectrometry. A major disadvantage of two dimensional gel electrophoresis is the relatively low dynamic range and inefficient access into the gel of high or very low molecular weight proteins. This results in the identification of mainly high abundant molecules, such as cytoskeletal proteins and highly expressed metabolic enzymes (Gygi et al., 2000). Reducing the complexity of the sample can at least partially overcome such limitation. The introduction of the difference in gel electrophoresis (DIGE) approach, which allows proteins from two different samples to be separated on the same gel, led to improved quantitatively accuracy of this gel-based

The chromatography approach can be divided into two groups, namely peptide-based methods and protein-based methods. The peptide-based strategy relies on comparing the signal intensity of a peptide originating from one sample to the signal intensity of the same peptide originating from a different sample. The extracted ion chromatogram (XIC) for every peptide can be derived from the liquid chromatography profile of the two individual samples during the analysis by the mass spectrometer and the samples can thereby be compared quantitatively. Furthermore, a method called protein correlation profiling was established, where the total ion chromatograms of different samples are aligned and quantitative comparison of samples is then based on both retention time and accurate mass of the peptides. The relative protein quantitation is based on the fact that the peak areas obtained from liquid chromatography mass spectrometry correlate to the relative concentration of the protein in the sample (Andersen et al., 2003; Ong and Mann, 2005). It has been used to obtain semi quantitative data in complex mixtures such as human sera (Chelius and Bondarenko, 2002). A disadvantage is that it is only partially quantitative and

Another label-free mass spectrometry-based approach used to retrieve quantitative measurements is based on spectral count. The "spectral counting" method uses the numbers of peptide identification spectra obtained for each protein as representation of the protein abundance in a mixture (Liu et al., 2004). One disadvantage of the spectral count method is that it is biased toward high abundant proteins since they can mask or suppress the lowabundance ones in the sample, which is a key issue when analyzing e. g. plasma samples. The two label-free methods for quantitation, using either peptide ion intensities or spectral counts, are becoming increasingly popular, since they are simpler than the isotope-based strategy, despite being less accurate. In addition, both methods require very good reproducibility between the different liquid chromatography tandem mass spectrometry (LC-MS/MS) runs, high accuracy measurements and higher number of replica analyses. In general, the label-free approaches are widely applicable but the methods using stable

requires highly reliable and reproducible analysis of the samples.

2010; Walther and Mann, 2010).

approach (Unlu et al., 1997).

**2.1.1 Quantitation without stable isotopes** 

malfunction in diseased stages. Analyzing on a large scale and in an unbiased manner the secretome of any given cell type or tissue, which comprise a unique combination of growth factors, hormones, cytokines, inhibitory factors, and components of the extracellular environment, has become a whole distinct research field. Although still challenging, this endeavor may ultimately prove beneficial for improving human health as it can accelerate the bridging of basic research and applied medicine.

#### **2. Proteomics**

Proteomics has many sides and it is often difficult to combine the different aspects that can define or characterize this broad topic. The term proteomics was introduced in 1995, describing the entire set of proteins expressed by a given cell, tissue, or organism (Wasinger et al., 1995). At present, proteomics is defined as large scale studies of the proteomes that encompass protein expression, folding, and localization. It also includes functional analyses of large complexes within a cell, tissue, or organism as well as comparison of different proteomes. Some of the different aspects of proteomics include analysis of body fluids, defining proteomes of pathogens, investigation of tissue proteomes, characterization of signaling pathways and the effects of inhibitors and drugs. The term systems biology was also introduced to describe the incorporation of genomics, metabolomics, and proteomics data for creation of dynamic networks of interacting molecules at a system level. Typically, such studies involve following the changes in protein profiles in response to changes in the environment and determination of combined action of diverse signaling networks that lead to a differential outcome for the living organism. Obtaining and combining information for such networks is of particular importance when investigating the role of secreted factors in the regulation of major signaling events in any given cell or tissue. Functional quantitative mass spectrometry-based proteomics (QMSP) is a powerful approach for creation of maps that describe the differential expression and dynamic changes of secretomes. Correlation of these results with clinics can help resolve some of the still missing links in the development of different syndromes. In this review chapter, we focus on the latest advances in QMSP for the investigation of secreted factors and we discuss some of the issues and challenges that remain to be unveiled.

#### **2.1 Quantitative mass spectrometry-based proteomics**

The fast development of QMSP techniques added yet another dimension to the proteomic research, namely the ability to follow differences and changes of the proteomes in space and time (Aebersold and Mann, 2003; Cox and Mann, 2007; Dengjel et al., 2009). QMSP permits observation and investigation of a combination of events and interplay of pathways involving hundreds of molecules that lead to a defined outcome for the cell. It facilitates determination of even slight changes in protein expression or post-translational modifications as a result of a drug treatment, changes in the cellular environment or alterations in the total body homeostasis. Up to date, QMSP is the only available approach that can, with high confidence and in a high throughput manner, generate and combine data for the spatial and temporal order of events that take place in a cell directly at protein level in order to decipher dynamic complex processes (Dengjel et al., 2009; Rigbolt and Blagoev, 2010; Walther and Mann, 2010). There are two main QMSP strategies for relative quantitation based either on the use of stable isotopes or the label-free approach for quantitation of changes in protein abundance (Ong and Mann, 2005; Schulze and Usadel, 2010; Walther and Mann, 2010).

#### **2.1.1 Quantitation without stable isotopes**

418 Proteomics – Human Diseases and Protein Functions

malfunction in diseased stages. Analyzing on a large scale and in an unbiased manner the secretome of any given cell type or tissue, which comprise a unique combination of growth factors, hormones, cytokines, inhibitory factors, and components of the extracellular environment, has become a whole distinct research field. Although still challenging, this endeavor may ultimately prove beneficial for improving human health as it can accelerate

Proteomics has many sides and it is often difficult to combine the different aspects that can define or characterize this broad topic. The term proteomics was introduced in 1995, describing the entire set of proteins expressed by a given cell, tissue, or organism (Wasinger et al., 1995). At present, proteomics is defined as large scale studies of the proteomes that encompass protein expression, folding, and localization. It also includes functional analyses of large complexes within a cell, tissue, or organism as well as comparison of different proteomes. Some of the different aspects of proteomics include analysis of body fluids, defining proteomes of pathogens, investigation of tissue proteomes, characterization of signaling pathways and the effects of inhibitors and drugs. The term systems biology was also introduced to describe the incorporation of genomics, metabolomics, and proteomics data for creation of dynamic networks of interacting molecules at a system level. Typically, such studies involve following the changes in protein profiles in response to changes in the environment and determination of combined action of diverse signaling networks that lead to a differential outcome for the living organism. Obtaining and combining information for such networks is of particular importance when investigating the role of secreted factors in the regulation of major signaling events in any given cell or tissue. Functional quantitative mass spectrometry-based proteomics (QMSP) is a powerful approach for creation of maps that describe the differential expression and dynamic changes of secretomes. Correlation of these results with clinics can help resolve some of the still missing links in the development of different syndromes. In this review chapter, we focus on the latest advances in QMSP for the investigation of secreted factors and we discuss some of the issues and

The fast development of QMSP techniques added yet another dimension to the proteomic research, namely the ability to follow differences and changes of the proteomes in space and time (Aebersold and Mann, 2003; Cox and Mann, 2007; Dengjel et al., 2009). QMSP permits observation and investigation of a combination of events and interplay of pathways involving hundreds of molecules that lead to a defined outcome for the cell. It facilitates determination of even slight changes in protein expression or post-translational modifications as a result of a drug treatment, changes in the cellular environment or alterations in the total body homeostasis. Up to date, QMSP is the only available approach that can, with high confidence and in a high throughput manner, generate and combine data for the spatial and temporal order of events that take place in a cell directly at protein level in order to decipher dynamic complex processes (Dengjel et al., 2009; Rigbolt and Blagoev, 2010; Walther and Mann, 2010). There are two main QMSP strategies for relative quantitation based either on the use of stable isotopes or the label-free approach for

the bridging of basic research and applied medicine.

challenges that remain to be unveiled.

**2.1 Quantitative mass spectrometry-based proteomics** 

**2. Proteomics** 

Quantitation without stable isotopes generally encompasses a gel electrophoresis approach or a chromatography-based approach. In the gel-based approach one-dimensional or twodimensional gel electrophoresis is used as a mean of resolving the proteins from complex mixtures. This is followed by visualization of the protein bands or spots using different types of stains or fluorescent dyes. Typically, the protein samples originating from different cellular stages are separated on a gel and then the bands or spots that show distinct changes are excised, digested with proteases and identified by mass spectrometry. A major disadvantage of two dimensional gel electrophoresis is the relatively low dynamic range and inefficient access into the gel of high or very low molecular weight proteins. This results in the identification of mainly high abundant molecules, such as cytoskeletal proteins and highly expressed metabolic enzymes (Gygi et al., 2000). Reducing the complexity of the sample can at least partially overcome such limitation. The introduction of the difference in gel electrophoresis (DIGE) approach, which allows proteins from two different samples to be separated on the same gel, led to improved quantitatively accuracy of this gel-based approach (Unlu et al., 1997).

The chromatography approach can be divided into two groups, namely peptide-based methods and protein-based methods. The peptide-based strategy relies on comparing the signal intensity of a peptide originating from one sample to the signal intensity of the same peptide originating from a different sample. The extracted ion chromatogram (XIC) for every peptide can be derived from the liquid chromatography profile of the two individual samples during the analysis by the mass spectrometer and the samples can thereby be compared quantitatively. Furthermore, a method called protein correlation profiling was established, where the total ion chromatograms of different samples are aligned and quantitative comparison of samples is then based on both retention time and accurate mass of the peptides. The relative protein quantitation is based on the fact that the peak areas obtained from liquid chromatography mass spectrometry correlate to the relative concentration of the protein in the sample (Andersen et al., 2003; Ong and Mann, 2005). It has been used to obtain semi quantitative data in complex mixtures such as human sera (Chelius and Bondarenko, 2002). A disadvantage is that it is only partially quantitative and requires highly reliable and reproducible analysis of the samples.

Another label-free mass spectrometry-based approach used to retrieve quantitative measurements is based on spectral count. The "spectral counting" method uses the numbers of peptide identification spectra obtained for each protein as representation of the protein abundance in a mixture (Liu et al., 2004). One disadvantage of the spectral count method is that it is biased toward high abundant proteins since they can mask or suppress the lowabundance ones in the sample, which is a key issue when analyzing e. g. plasma samples. The two label-free methods for quantitation, using either peptide ion intensities or spectral counts, are becoming increasingly popular, since they are simpler than the isotope-based strategy, despite being less accurate. In addition, both methods require very good reproducibility between the different liquid chromatography tandem mass spectrometry (LC-MS/MS) runs, high accuracy measurements and higher number of replica analyses. In general, the label-free approaches are widely applicable but the methods using stable

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 421

based assays, the stable isotope is fully incorporated thereby encoding the whole proteome. There are two means of introducing the stable isotope using either media containing 15N labeled ammonium sulfate or media with the addition of a stable isotope labeled amino acid. The 15N labeling strategy has been used for quantitative analysis of protein phosphorylation in bacteria and a mouse melanoma cell line (Conrads et al., 2001; Oda et al., 1999). Additionally, entire organisms have been metabolically labeled using the 15N strategy, including bacteria (E. coli and Deinococcus), C. elegans, D. melanogaster, and rat

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) is an accurate and resourceful quantitative proteomics platform, that in combination with high speed and accuracy mass spectrometry allows detailed characterization of complex biological systems (Ong et al., 2002; Ong et al., 2003). It involves usage of heavy non- radioactive stable isotope-labeled amino acids, which are incorporated directly into the newly synthesized proteins of the cell. After SILAC labeling, the entire proteome of a given cell population becomes encoded either with a light or heavier version of the same amino acid, thereby enabling direct comparison and quantitation using mass spectrometry. With SILAC, the "light" and "heavy" samples can be mixed in equal ratios at the initial stages of the workflow, which can include subsequent protein purification, interaction assay or other manipulation of the mixed sample. Combining samples prior to any further sample preparation represents a tremendous advantage, since it results in reduced quantitation errors introduced by differences in individual sample handling. Major strength of the SILAC method is the ability to discriminate true interaction partners from background, when investigating functional protein-protein interactions (Blagoev et al., 2003; Dengjel et al., 2010). Therefore, it facilitates investigation of cellular signaling cascades and creation of reliable protein interaction networks, which represents one of the biggest challenges in the field of system biology (Blagoev et al., 2004; Dengjel et al., 2009; Kratchmarova et al., 2005; Olsen et al., 2006; Osinalde et al., 2011). In addition, SILAC is invaluable for the investigation of secreted factors since it allows the distinction of specific proteins released by the cells to the extracellular environment from contaminating proteins like keratins and serum derived factors that originate from cell culture media supplements (Henningsen et al., 2010). One potential disadvantage with the SILAC protocol arises from cultures of primary cells, which usually require specific growth media with a defined formulation. Furthermore, such cells have limited division capacity in culture, whereas at least 5 population doublings are required for complete SILAC encoding of the entire proteome. Nevertheless, SILAC-based analyses have been successfully extended to include microorganisms, entire mice, and quantitation of proteins in tumor biopsies (Geiger et al., 2010; Kruger et al., 2008; Soufi et al., 2010). It was also utilized for the quantitative analyses

of proteins released by omental adipose tissue explants (Alvarez-Llamas et al., 2007).

Analysis of secreted proteins using QMSP allows in depth characterization of different cellular systems that secrete auto-, para-, and endocrine factors, which can influence the entire body homeostasis. Investigation of cellular models such as adult and embryonic stem cells, cells originating from a diseased state, immortalized cells representing various models for functional abnormalities, extends the knowledge of how changes in secretomes contribute to various types of human disorders. It also enables determination and discovery

**3. Application of QMSP for investigation of secreted proteins** 

(Conrads et al., 2001; Krijgsveld et al., 2003; Wu et al., 2004).

isotope labels result in better accuracy of quantitation (Lundgren et al., 2010; Schulze and Usadel, 2010).

#### **2.1.2 Quantitation using stable isotopes**

The quantitative mass spectrometry-based methods utilizing stable isotopes can be achieved either by *in vivo* metabolic labeling or *in vitro* biochemical methods. The principle of the two labeling strategies is the generation of peptides labeled with stable isotopes that differ in mass from the unlabeled peptides making it possible to distinguish them within the same spectrum.

#### **2.1.2.1 Chemical labeling strategies**

The prototype of the chemical modification-based methodology for quantitation of protein is the isotope coded affinity tag (ICAT) that binds to cysteine residues (Gygi et al., 1999). It employs usage of two isotopically labeled tags - one light and one heavy, which contains eight deuterium atoms, to distinctly label the peptides originating from two separate samples. The peptides originating from one sample can thereby be distinguished from the second sample, since the heavier tag will result in a mass shift readily observable in the mass spectrum. One of the advantages of ICAT is the presence of a biotin group in the light and heavy tags allowing selective enrichment of the labeled peptides using avidin affinity chromatography, thus reducing greatly the complexity of the mixture.

ICAT has been applied to a variety of cell culture and tissue samples and has been demonstrated as a reliable and relatively easy applicable method for performing QMSP analysis. Among other applications, ICAT has been used to investigate differential expression profiles of microsomal proteins from *naive* and *in vitro*- differentiated human myeloid leukemia cells, secreted proteins during osteoclast differentiation, the dynamic changes of transcription factors during erythroid differentiation as well as comparison of livers of mice treated with different peroxisome proliferator-activated receptor agonists (Brand et al., 2004; Han et al., 2001; Kubota et al., 2003; Tian et al., 2004). Disadvantages of the ICAT strategy are that it targets only the cysteine containing peptides and the retention times of the light and heavy form during chromatographic separation are altered due to the presence of the deuterium atoms. To overcome some of those problems, a cleavable 12Cand 13C-based reagent (cICAT) has been developed, which has an improved peptide coelution profile during the liquid chromatography separation and increased recovery after enrichment of the labeled peptides (Yi et al., 2005).

Several other chemical labeling strategies have been developed over the recent years. Probably the most popular of those being the isobaric tags for relative and absolute quantitation (iTRAQ) where the isobaric chemical groups are attached to the primary amine groups of the peptides. With iTRAQ, up to eight different conditions can be compared simultaneously since eight distinct isobaric tags for labeling are currently available. The quantitation is based on the intensities of the isotopically distinct fragments derived from the corresponding isobaric tags obtained in the peptide fragmentation spectrum. This is the main advantage of the method but it can also be a disadvantage since often a single fragment spectrum per peptide is available, thereby compromising the accuracy of quantitation (Ross et al., 2004).

#### **2.1.2.2 Metabolic labeling**

The metabolic labeling strategies rely on the incorporation of a stable isotope in proteins, while they are being *de novo* synthesized in the cell. In contrast to the standard radioactivity-

isotope labels result in better accuracy of quantitation (Lundgren et al., 2010; Schulze and

The quantitative mass spectrometry-based methods utilizing stable isotopes can be achieved either by *in vivo* metabolic labeling or *in vitro* biochemical methods. The principle of the two labeling strategies is the generation of peptides labeled with stable isotopes that differ in mass from the unlabeled peptides making it possible to distinguish them within the same spectrum.

The prototype of the chemical modification-based methodology for quantitation of protein is the isotope coded affinity tag (ICAT) that binds to cysteine residues (Gygi et al., 1999). It employs usage of two isotopically labeled tags - one light and one heavy, which contains eight deuterium atoms, to distinctly label the peptides originating from two separate samples. The peptides originating from one sample can thereby be distinguished from the second sample, since the heavier tag will result in a mass shift readily observable in the mass spectrum. One of the advantages of ICAT is the presence of a biotin group in the light and heavy tags allowing selective enrichment of the labeled peptides using avidin affinity

ICAT has been applied to a variety of cell culture and tissue samples and has been demonstrated as a reliable and relatively easy applicable method for performing QMSP analysis. Among other applications, ICAT has been used to investigate differential expression profiles of microsomal proteins from *naive* and *in vitro*- differentiated human myeloid leukemia cells, secreted proteins during osteoclast differentiation, the dynamic changes of transcription factors during erythroid differentiation as well as comparison of livers of mice treated with different peroxisome proliferator-activated receptor agonists (Brand et al., 2004; Han et al., 2001; Kubota et al., 2003; Tian et al., 2004). Disadvantages of the ICAT strategy are that it targets only the cysteine containing peptides and the retention times of the light and heavy form during chromatographic separation are altered due to the presence of the deuterium atoms. To overcome some of those problems, a cleavable 12Cand 13C-based reagent (cICAT) has been developed, which has an improved peptide coelution profile during the liquid chromatography separation and increased recovery after

Several other chemical labeling strategies have been developed over the recent years. Probably the most popular of those being the isobaric tags for relative and absolute quantitation (iTRAQ) where the isobaric chemical groups are attached to the primary amine groups of the peptides. With iTRAQ, up to eight different conditions can be compared simultaneously since eight distinct isobaric tags for labeling are currently available. The quantitation is based on the intensities of the isotopically distinct fragments derived from the corresponding isobaric tags obtained in the peptide fragmentation spectrum. This is the main advantage of the method but it can also be a disadvantage since often a single fragment spectrum per peptide is available, thereby compromising the accuracy of

The metabolic labeling strategies rely on the incorporation of a stable isotope in proteins, while they are being *de novo* synthesized in the cell. In contrast to the standard radioactivity-

chromatography, thus reducing greatly the complexity of the mixture.

enrichment of the labeled peptides (Yi et al., 2005).

quantitation (Ross et al., 2004). **2.1.2.2 Metabolic labeling** 

Usadel, 2010).

**2.1.2 Quantitation using stable isotopes** 

**2.1.2.1 Chemical labeling strategies** 

based assays, the stable isotope is fully incorporated thereby encoding the whole proteome. There are two means of introducing the stable isotope using either media containing 15N labeled ammonium sulfate or media with the addition of a stable isotope labeled amino acid. The 15N labeling strategy has been used for quantitative analysis of protein phosphorylation in bacteria and a mouse melanoma cell line (Conrads et al., 2001; Oda et al., 1999). Additionally, entire organisms have been metabolically labeled using the 15N strategy, including bacteria (E. coli and Deinococcus), C. elegans, D. melanogaster, and rat (Conrads et al., 2001; Krijgsveld et al., 2003; Wu et al., 2004).

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) is an accurate and resourceful quantitative proteomics platform, that in combination with high speed and accuracy mass spectrometry allows detailed characterization of complex biological systems (Ong et al., 2002; Ong et al., 2003). It involves usage of heavy non- radioactive stable isotope-labeled amino acids, which are incorporated directly into the newly synthesized proteins of the cell. After SILAC labeling, the entire proteome of a given cell population becomes encoded either with a light or heavier version of the same amino acid, thereby enabling direct comparison and quantitation using mass spectrometry. With SILAC, the "light" and "heavy" samples can be mixed in equal ratios at the initial stages of the workflow, which can include subsequent protein purification, interaction assay or other manipulation of the mixed sample. Combining samples prior to any further sample preparation represents a tremendous advantage, since it results in reduced quantitation errors introduced by differences in individual sample handling. Major strength of the SILAC method is the ability to discriminate true interaction partners from background, when investigating functional protein-protein interactions (Blagoev et al., 2003; Dengjel et al., 2010). Therefore, it facilitates investigation of cellular signaling cascades and creation of reliable protein interaction networks, which represents one of the biggest challenges in the field of system biology (Blagoev et al., 2004; Dengjel et al., 2009; Kratchmarova et al., 2005; Olsen et al., 2006; Osinalde et al., 2011). In addition, SILAC is invaluable for the investigation of secreted factors since it allows the distinction of specific proteins released by the cells to the extracellular environment from contaminating proteins like keratins and serum derived factors that originate from cell culture media supplements (Henningsen et al., 2010). One potential disadvantage with the SILAC protocol arises from cultures of primary cells, which usually require specific growth media with a defined formulation. Furthermore, such cells have limited division capacity in culture, whereas at least 5 population doublings are required for complete SILAC encoding of the entire proteome. Nevertheless, SILAC-based analyses have been successfully extended to include microorganisms, entire mice, and quantitation of proteins in tumor biopsies (Geiger et al., 2010; Kruger et al., 2008; Soufi et al., 2010). It was also utilized for the quantitative analyses of proteins released by omental adipose tissue explants (Alvarez-Llamas et al., 2007).

#### **3. Application of QMSP for investigation of secreted proteins**

Analysis of secreted proteins using QMSP allows in depth characterization of different cellular systems that secrete auto-, para-, and endocrine factors, which can influence the entire body homeostasis. Investigation of cellular models such as adult and embryonic stem cells, cells originating from a diseased state, immortalized cells representing various models for functional abnormalities, extends the knowledge of how changes in secretomes contribute to various types of human disorders. It also enables determination and discovery

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 423

Fig. 1. General outline of QMSP for the identification and quantitation of secreted proteins. bovine serum (FBS), but the SILAC protocol requires the use of dialyzed sera to prevent the presence of unlabeled sera-derived amino acids, which would ultimately result in inaccurate quantitation. Commercially available dFBS is dialyzed utilizing 10 kDa molecular weight cut off (MWCO) filters to remove any amino acids. Unfortunately, this also leads to the reduction of low-molecular weight proteins (<10 kDa) including certain growth factors, hormones, and cytokines, that may be needed for the growth and maintenance of certain cells. Therefore, dFBS is not compatible with all cell types and slower growth rate is observed in some cases. Ultimately, dialysis with MWCO 1,000 Da could be sufficient to

The myoblasts were cultured in SILAC media for at least 5 passages to ensure complete incorporation of labeled amino acids into the proteome. Before the collection of conditioned medium (CM), cells were washed and starved for 12 hours in sera-free

remove amino acids, but it is more costly.

of new roads of tissue cross talk and interaction. QMSP has been applied to study secretomes of a variety of cell types and tissues including adipose cells and tissues, mouse embryonic fibroblasts, astrocytes, mesenchymal stem cells, neuronal progenitor cells, kidney, and endothelial cells (Skalnikova et al., 2011). Although, there have been several proteomics reports describing the secretory function of cells from mesenchymal origin, the role of the muscle secretome has remained elusive. A limited number of studies so far have employed mass spectrometry to elucidate the secretory function of the skeletal muscle. In a study presented by Chan and coworkers condition media (CM) was collected from differentiated C2C12 myotubes at day 5 of differentiation and analyzed by 1D-gel electrophoresis combined with matrix-assisted laser desorption/ ionization tandem mass spectrometry (MALDI-MS/MS) (Chan et al., 2007). This work led to the identification of 80 proteins released from skeletal muscle of which 27 were classified as secreted proteins based on literature searches. In another study isolated primary human skeletal muscle cells were SILAC-labeled (13C6-Lys) to make a quantitative evaluation of muscle secreted proteins between extreme obese and lean women (Hittel et al., 2009). Assessment of the identified proteins based on published literature and the Swiss-Prot database revealed 28 secreted proteins from 42 identified skeletal muscle proteins. Interestingly, the secretion of myostatin, a negative regulator of skeletal muscle growth and development but also implicated in metabolic homeostasis, was found to be markedly upregulated in extreme obesity cases. Subsequently, Yoon and colleagues presented a study investigating the effects of insulin on the secretory profile of differentiated myotubes (Yoon et al., 2009). The authors combined off-line reverse-phased HPLC fractionation with LC-MS/MS and identified 153 secreted proteins from rat L6 myotubes. Based on spectral count quantitation, 33 of these proteins were classified as differentially regulated in response to insulin. The list of secreted proteins was extracted from a total list of 254 identified proteins using three different prediction tools, Gene Ontology, SignalP, and SecretomeP. In two more recent studies, a total of 108 secreted proteins by skeletal muscle cells were identified (Chan et al., 2011; Norheim et al., 2011).

We have developed a general quantitative proteomics approach for investigation of secreted factors released by skeletal muscle cells during the course of muscle differentiation. The method utilizes a combination of SILAC labeling and advanced mass spectrometry (Fig. 1) (Henningsen et al., 2010).

Triple encoding SILAC, (Blagoev et al., 2004) was applied to investigate protein secretion at three different time points during the course of C2C12 differentiation. Initial evaluation of the differentiation protocol with SILAC-labeled cells demonstrated the formation of a high number of multinucleated myotubes and increased expression of different muscle-specific proteins. The use of three different versions of each labeled amino acid enabled the comparison of the secretome at three different time points (day 0, day 2, and day 5) during skeletal muscle differentiation. Furthermore, cells were cultured using both labeled arginine and lysine, since trypsin, which cleaves solely C-terminal to arginine and lysine, was used for in-gel digestion (Olsen et al., 2004). This "double-triple" labeling with isotopic forms of both arginine and lysine ensures that every tryptic peptide, except the C-terminal peptides of the proteins, contains at least one labeled residue and can therefore be used for quantitation (Blagoev and Mann, 2006). This increases the probability of positive protein identification and accuracy of quantitation due to the increased number of labeled peptides. It is noteworthy that under normal culture conditions cells are grown in the presence of fetal

of new roads of tissue cross talk and interaction. QMSP has been applied to study secretomes of a variety of cell types and tissues including adipose cells and tissues, mouse embryonic fibroblasts, astrocytes, mesenchymal stem cells, neuronal progenitor cells, kidney, and endothelial cells (Skalnikova et al., 2011). Although, there have been several proteomics reports describing the secretory function of cells from mesenchymal origin, the role of the muscle secretome has remained elusive. A limited number of studies so far have employed mass spectrometry to elucidate the secretory function of the skeletal muscle. In a study presented by Chan and coworkers condition media (CM) was collected from differentiated C2C12 myotubes at day 5 of differentiation and analyzed by 1D-gel electrophoresis combined with matrix-assisted laser desorption/ ionization tandem mass spectrometry (MALDI-MS/MS) (Chan et al., 2007). This work led to the identification of 80 proteins released from skeletal muscle of which 27 were classified as secreted proteins based on literature searches. In another study isolated primary human skeletal muscle cells were SILAC-labeled (13C6-Lys) to make a quantitative evaluation of muscle secreted proteins between extreme obese and lean women (Hittel et al., 2009). Assessment of the identified proteins based on published literature and the Swiss-Prot database revealed 28 secreted proteins from 42 identified skeletal muscle proteins. Interestingly, the secretion of myostatin, a negative regulator of skeletal muscle growth and development but also implicated in metabolic homeostasis, was found to be markedly upregulated in extreme obesity cases. Subsequently, Yoon and colleagues presented a study investigating the effects of insulin on the secretory profile of differentiated myotubes (Yoon et al., 2009). The authors combined off-line reverse-phased HPLC fractionation with LC-MS/MS and identified 153 secreted proteins from rat L6 myotubes. Based on spectral count quantitation, 33 of these proteins were classified as differentially regulated in response to insulin. The list of secreted proteins was extracted from a total list of 254 identified proteins using three different prediction tools, Gene Ontology, SignalP, and SecretomeP. In two more recent studies, a total of 108 secreted proteins by skeletal muscle cells were identified (Chan et al., 2011;

We have developed a general quantitative proteomics approach for investigation of secreted factors released by skeletal muscle cells during the course of muscle differentiation. The method utilizes a combination of SILAC labeling and advanced mass spectrometry (Fig. 1)

Triple encoding SILAC, (Blagoev et al., 2004) was applied to investigate protein secretion at three different time points during the course of C2C12 differentiation. Initial evaluation of the differentiation protocol with SILAC-labeled cells demonstrated the formation of a high number of multinucleated myotubes and increased expression of different muscle-specific proteins. The use of three different versions of each labeled amino acid enabled the comparison of the secretome at three different time points (day 0, day 2, and day 5) during skeletal muscle differentiation. Furthermore, cells were cultured using both labeled arginine and lysine, since trypsin, which cleaves solely C-terminal to arginine and lysine, was used for in-gel digestion (Olsen et al., 2004). This "double-triple" labeling with isotopic forms of both arginine and lysine ensures that every tryptic peptide, except the C-terminal peptides of the proteins, contains at least one labeled residue and can therefore be used for quantitation (Blagoev and Mann, 2006). This increases the probability of positive protein identification and accuracy of quantitation due to the increased number of labeled peptides. It is noteworthy that under normal culture conditions cells are grown in the presence of fetal

Norheim et al., 2011).

(Henningsen et al., 2010).

Fig. 1. General outline of QMSP for the identification and quantitation of secreted proteins.

bovine serum (FBS), but the SILAC protocol requires the use of dialyzed sera to prevent the presence of unlabeled sera-derived amino acids, which would ultimately result in inaccurate quantitation. Commercially available dFBS is dialyzed utilizing 10 kDa molecular weight cut off (MWCO) filters to remove any amino acids. Unfortunately, this also leads to the reduction of low-molecular weight proteins (<10 kDa) including certain growth factors, hormones, and cytokines, that may be needed for the growth and maintenance of certain cells. Therefore, dFBS is not compatible with all cell types and slower growth rate is observed in some cases. Ultimately, dialysis with MWCO 1,000 Da could be sufficient to remove amino acids, but it is more costly.

The myoblasts were cultured in SILAC media for at least 5 passages to ensure complete incorporation of labeled amino acids into the proteome. Before the collection of conditioned medium (CM), cells were washed and starved for 12 hours in sera-free

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 425

Combining these results indicate that muscle is a prominent secretory organ participating actively in the general regulation of body homeostasis. The muscle specific secreted factors exert their effects in local and/or systemic manner. In Henningsen et. al., 2010 we have identified and characterized the semaphorins as a new family of muscle secreted proteins. Semaphorins constitute a large family of secreted, GPI-anchored, and transmembrane proteins defined by a conserved semaphorin (sema) domain in their amino terminus (Gherardi et al., 2004; Neufeld and Kessler, 2008; Roth et al., 2009; Serini et al., 2009; Suzuki et al., 2008; Zhou et al., 2008). Initially, semaphorins were described as important regulators of axon guidance during neuronal development. However, an increasing number of studies have recognized the semaphorins as pleiotropic signaling molecules influencing a wide array of biological processes, such as angiogenesis, immune responses, and organ morphogenesis. In addition, semaphorins have also been linked to various pathologies including cancer and different diseases of the nervous system (Neufeld and Kessler, 2008; Roth et al., 2009). Currently, the mammalian semaphorin gene family consists of 20 members and although expression of individual semaphorins has been best described in the nervous system, semaphorins appear to be expressed by most if not all tissues (Yazdani and Terman, 2006). We have identified several members of the semaphorin family belonging to different subfamilies to be secreted from skeletal myoblasts including the soluble Sema3A, Sema3B, Sema3D, Sema3E, the transmembrane Sema4B, Sema4C, and Sema6A, and finally the GPI-linked Sema7A. Examination of the dynamic secretion profiles of the identified semaphorins demonstrated differential secretion of Sema3A, Sema3D, Sema3E, Sema6A, and Sema7A during the course of C2C12 myoblast differentiation. Interestingly, secretion of Sema3A, Sema3E, Sema3D, and Sema6A was markedly enhanced at the early stage of the differentiation, indicating that they may serve a role during the initial phase of the conversion process. In contrast, a gradually increased secretion of Sema7A was observed during differentiation, suggesting that Sema7A plays a role both during early and terminal differentiation. Identification of both the transmembrane and GPI-anchor semaphorins in the media would suggest that they are released from the plasma membrane in a soluble form either by proteolytic shedding, in the case of Sema4 and Sema6, or proteolytic cleavage catalyzed by a phospholipase, in the case of Sema7A. Earlier studies have shown that the enzymatic activity of metalloproteases can generate and modulate the activity of a soluble form of Sema4D (Basile et al., 2007; Elhabazi et al., 2001). Indeed, we did observe an increased secretion of various proteases including MMP-2. Western blot analysis of sema6A in conditioned media collected from C2C12 myoblasts during differentiation supported the idea of Sema6A shedding, as the secreted protein migrated at an apparent molecular weight corresponding to the size of the extracellular domain (approx. 71 kDa) and not to the size of the full-length Sema6A (approx. 113 kDa). Different members of the semaphorin family have been shown to orchestrate the development of different organs including bone, lung, kidney, and the cardiovascular system (Roth et al., 2009; Tamagnone and Giordano, 2006). The number of studies investigating the function of semaphorins in skeletal muscle development and regeneration are more limited. So far, studies have demonstrated an enhanced expression of Sema4C but no alterations of Sema4B expression during C2C12 myogenesis were detected (Ko et al., 2005; Wu et al., 2007). In addition, enhanced expression of Sema4C was also observed *in vivo* in injuryinduced skeletal muscle regeneration. Targeted knockdown of Sema4C expression by

medium to minimize the presence of sera proteins, that would interfere with the subsequent mass spectrometry (MS)-analysis. CM was collected from myoblasts on day 0 and during conversion of myoblasts into myotubes at day 2 and day 5, followed by filtration using 0.2 μm filters to remove any floating cells or cell debris, thereby reducing the risk of contaminating samples with intracellular proteins. The CM, collected from the three time points of differentiation was combined in a 1:1:1 ratio according to measured protein concentration. Subsequently, the pool of CM was concentrated by ultrafiltration using Vivaspin columns, MWCO 3,000 Da to ensure that proteins were retained in the concentrate. To reduce sample complexity, thereby effectively increasing the dynamic range of the MS-analysis, concentrated muscle-derived proteins were separated by size using 1D- gel electrophoresis. The excised gel bands were subjected to in-gel digestion and analyzed via LC-MS/MS using an linear ion trap (LTQ)-Orbitrap mass spectrometer followed by processing of the obtained data with the MaxQuant software (Box 2) (Cox and Mann, 2008; Cox et al., 2009). The described strategy resulted in the identification of 635 putatively secreted proteins by skeletal myoblasts based on the GO term "extracellular" and signal peptide prediction inbuilt in the MaxQuant and ProteinCenter. The commercially available database, ProteinCenter, (www. Proxeon.com) utilizes annotation from all major protein sequence databases including Swiss-Prot, NCBI, and Ensembl. It allows analysis of large scale proteomic studies to isolate putatively secreted factors from the total list of identified proteins. The obtained identification list of IPI numbers is filtered and extracted according to the category "extracellular" within GO term cellular component. Then, the remaining proteins are filtered using a signal peptide predictor incorporated into the ProteinCenter platform, the PrediSi algorithm. Using the SILAC strategy, 624 secreted proteins were quantitatively evaluated during the course of skeletal muscle differentiation. Proteins already known to be secreted by skeletal muscle were identified, in addition to many novel proteins not previously shown to be secreted by skeletal myoblasts. Characterization of identified secreted proteins according to GOannotations demonstrated proteins involved in many different cellular processes including proliferation, differentiation, ECM reorganization, metabolic processes, and angiogenesis. According to the statistical analyses provided by MaxQuant, 188 secreted proteins were found to be dynamically regulated during skeletal myogenesis suggesting their regulatory involvement in skeletal muscle development, which could occur both in autocrine and paracrine manner. In a follow up study, focused on comprehensive characterization of the low abundant low molecular weight fraction of proteins secreted by muscle cells, application of triple encoding SILAC resulted in the generation of quantitative profiles of 59 growth factors and cytokines, including nine classical chemokines (Henningsen et al., 2011).

The depicted triple encoding SILAC strategy led to the characterization of the muscle secretome and creation of dynamic secretion profiles during the process of muscle differentiation. Among the identified secreted factors, we have found components of the extracellular matrix, such as collagen, fibronectin and SPARC (secreted protein acidic and rich in cysteine), growth factors, including members of the transforming growth factor and insulin-like growth factor families, members of the serpin and matrix metalloproteases classes, chemokines, and modulators. In addition, proteins such as angiopoietin-1, VEGF (Vascular endothelial growth factor), PDGF (Platelet-derived growth factor), and FGF21 (Fibroblast growth factor 21) were identified and quantitated.

medium to minimize the presence of sera proteins, that would interfere with the subsequent mass spectrometry (MS)-analysis. CM was collected from myoblasts on day 0 and during conversion of myoblasts into myotubes at day 2 and day 5, followed by filtration using 0.2 μm filters to remove any floating cells or cell debris, thereby reducing the risk of contaminating samples with intracellular proteins. The CM, collected from the three time points of differentiation was combined in a 1:1:1 ratio according to measured protein concentration. Subsequently, the pool of CM was concentrated by ultrafiltration using Vivaspin columns, MWCO 3,000 Da to ensure that proteins were retained in the concentrate. To reduce sample complexity, thereby effectively increasing the dynamic range of the MS-analysis, concentrated muscle-derived proteins were separated by size using 1D- gel electrophoresis. The excised gel bands were subjected to in-gel digestion and analyzed via LC-MS/MS using an linear ion trap (LTQ)-Orbitrap mass spectrometer followed by processing of the obtained data with the MaxQuant software (Box 2) (Cox and Mann, 2008; Cox et al., 2009). The described strategy resulted in the identification of 635 putatively secreted proteins by skeletal myoblasts based on the GO term "extracellular" and signal peptide prediction inbuilt in the MaxQuant and ProteinCenter. The commercially available database, ProteinCenter, (www. Proxeon.com) utilizes annotation from all major protein sequence databases including Swiss-Prot, NCBI, and Ensembl. It allows analysis of large scale proteomic studies to isolate putatively secreted factors from the total list of identified proteins. The obtained identification list of IPI numbers is filtered and extracted according to the category "extracellular" within GO term cellular component. Then, the remaining proteins are filtered using a signal peptide predictor incorporated into the ProteinCenter platform, the PrediSi algorithm. Using the SILAC strategy, 624 secreted proteins were quantitatively evaluated during the course of skeletal muscle differentiation. Proteins already known to be secreted by skeletal muscle were identified, in addition to many novel proteins not previously shown to be secreted by skeletal myoblasts. Characterization of identified secreted proteins according to GOannotations demonstrated proteins involved in many different cellular processes including proliferation, differentiation, ECM reorganization, metabolic processes, and angiogenesis. According to the statistical analyses provided by MaxQuant, 188 secreted proteins were found to be dynamically regulated during skeletal myogenesis suggesting their regulatory involvement in skeletal muscle development, which could occur both in autocrine and paracrine manner. In a follow up study, focused on comprehensive characterization of the low abundant low molecular weight fraction of proteins secreted by muscle cells, application of triple encoding SILAC resulted in the generation of quantitative profiles of 59 growth factors and cytokines, including nine classical

The depicted triple encoding SILAC strategy led to the characterization of the muscle secretome and creation of dynamic secretion profiles during the process of muscle differentiation. Among the identified secreted factors, we have found components of the extracellular matrix, such as collagen, fibronectin and SPARC (secreted protein acidic and rich in cysteine), growth factors, including members of the transforming growth factor and insulin-like growth factor families, members of the serpin and matrix metalloproteases classes, chemokines, and modulators. In addition, proteins such as angiopoietin-1, VEGF (Vascular endothelial growth factor), PDGF (Platelet-derived growth factor), and FGF21 (Fibroblast growth factor 21) were identified and quantitated.

chemokines (Henningsen et al., 2011).

Combining these results indicate that muscle is a prominent secretory organ participating actively in the general regulation of body homeostasis. The muscle specific secreted factors exert their effects in local and/or systemic manner. In Henningsen et. al., 2010 we have identified and characterized the semaphorins as a new family of muscle secreted proteins. Semaphorins constitute a large family of secreted, GPI-anchored, and transmembrane proteins defined by a conserved semaphorin (sema) domain in their amino terminus (Gherardi et al., 2004; Neufeld and Kessler, 2008; Roth et al., 2009; Serini et al., 2009; Suzuki et al., 2008; Zhou et al., 2008). Initially, semaphorins were described as important regulators of axon guidance during neuronal development. However, an increasing number of studies have recognized the semaphorins as pleiotropic signaling molecules influencing a wide array of biological processes, such as angiogenesis, immune responses, and organ morphogenesis. In addition, semaphorins have also been linked to various pathologies including cancer and different diseases of the nervous system (Neufeld and Kessler, 2008; Roth et al., 2009). Currently, the mammalian semaphorin gene family consists of 20 members and although expression of individual semaphorins has been best described in the nervous system, semaphorins appear to be expressed by most if not all tissues (Yazdani and Terman, 2006). We have identified several members of the semaphorin family belonging to different subfamilies to be secreted from skeletal myoblasts including the soluble Sema3A, Sema3B, Sema3D, Sema3E, the transmembrane Sema4B, Sema4C, and Sema6A, and finally the GPI-linked Sema7A. Examination of the dynamic secretion profiles of the identified semaphorins demonstrated differential secretion of Sema3A, Sema3D, Sema3E, Sema6A, and Sema7A during the course of C2C12 myoblast differentiation. Interestingly, secretion of Sema3A, Sema3E, Sema3D, and Sema6A was markedly enhanced at the early stage of the differentiation, indicating that they may serve a role during the initial phase of the conversion process. In contrast, a gradually increased secretion of Sema7A was observed during differentiation, suggesting that Sema7A plays a role both during early and terminal differentiation. Identification of both the transmembrane and GPI-anchor semaphorins in the media would suggest that they are released from the plasma membrane in a soluble form either by proteolytic shedding, in the case of Sema4 and Sema6, or proteolytic cleavage catalyzed by a phospholipase, in the case of Sema7A. Earlier studies have shown that the enzymatic activity of metalloproteases can generate and modulate the activity of a soluble form of Sema4D (Basile et al., 2007; Elhabazi et al., 2001). Indeed, we did observe an increased secretion of various proteases including MMP-2. Western blot analysis of sema6A in conditioned media collected from C2C12 myoblasts during differentiation supported the idea of Sema6A shedding, as the secreted protein migrated at an apparent molecular weight corresponding to the size of the extracellular domain (approx. 71 kDa) and not to the size of the full-length Sema6A (approx. 113 kDa). Different members of the semaphorin family have been shown to orchestrate the development of different organs including bone, lung, kidney, and the cardiovascular system (Roth et al., 2009; Tamagnone and Giordano, 2006). The number of studies investigating the function of semaphorins in skeletal muscle development and regeneration are more limited. So far, studies have demonstrated an enhanced expression of Sema4C but no alterations of Sema4B expression during C2C12 myogenesis were detected (Ko et al., 2005; Wu et al., 2007). In addition, enhanced expression of Sema4C was also observed *in vivo* in injuryinduced skeletal muscle regeneration. Targeted knockdown of Sema4C expression by

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 427

of high concentrations of calcium ions (Marie et al., 2008; Mayor and Riezman, 2004; Sallese et al., 2009; Strating and Martens, 2009). In addition, post-translational modifications of soluble and membrane proteins occur in the ER lumen including oxidation of proline, Nlinked glycosylation, proteolytic processing, formation of disulfide bonds, oligomerization, and attachment of a GPI-anchor. Whereas the main functions of the Golgi apparatus include carbohydrate synthesis, O-linked glycosylation, processing, post-translational modification, and sorting both proteins and lipids (De Matteis and Luini, 2008; Marie et al., 2008; Marsh and Howell, 2002). Regardless of their subsequent fate, most proteins containing a Nterminal or internal signal sequence peptide can be targeted to the ER membrane. These include transmembrane proteins destined to reside in the ER, plasma membrane or other organellar membranes as well as soluble proteins destined to the lumen of an organelle or for secretion. With the exception of mitochondria, nuclei, and peroxisomes, all other organelles receive their proteins via the ER. Signal peptides show extreme variations in their length and amino acid composition, but do contain three distinct domains: a positively charged N-terminal region, a hydrophobic core region, typically consisting of at least 6 hydrophobic residues, and C-terminal region of polar uncharged residues (Hiller et al., 2004). Soluble proteins are transported from the Golgi to the cell exterior via the constitutive secretory pathway transporting proteins directly to the cell surface or the regulated secretory pathway in which soluble proteins and other substances are initially stored in secretory vesicles, which release proteins to the extracellular space upon extracellular signals (Brunner et al., 2009; De Matteis and Luini, 2008; Strating and Martens, 2009). The latter pathway only exists in specialized secretory cells including pancreatic β-cell releasing insulin from secretory vesicles, nerve cells, and endocrine cells. The secretory vesicles of the

Box 1. Classification of secretory pathways

siRNA caused inhibition of C2C12 myotube formation, demonstrating that semaphorins could exert an active autocrine/paracrine function in myogenesis. Interestingly, animal models have suggested that semaphorins could be important paracrine factors regulating neurogenesis during skeletal muscle growth, development, and regeneration. A delayed transient increase of Sema3A expression was observed in response to muscle-induced injury (Tatsumi et al., 2009). In addition, a similar delay of Sema3A expression and secretion was seen in isolated skeletal muscle cells in response to HGF, which is an essential factor in muscle growth and regeneration. In our study, we have identified both Sema3A and Sema4C to be released by C2C12 myoblasts during differentiation.

We have analyzed the mRNA and protein expression of selected regulated members of the semaphorin family (Sema3A, Sema3E, Sema6A, and Sema7A) to investigate if their dynamic secretion pattern was regulated by post-transcriptional and post-translational mechanisms. Only minor changes were observed in the mRNA expression of Sema3A, Sema3E, Sema6A, and Sema7A. The mRNA expression of Sema3A and Sema7A remained constant during differentiation, whereas there was a slight decrease and increase in the level of Sema3E and Sema6A, respectively. We found that the high levels of secreted Sema3A and Sema3E at early stage of myotube formation did not reflect the intracellular protein levels of these semaphorins. Expression of Sema3A protein remained constant, whereas a slight decrease was observed of Sema3E protein expression in accordance with the corresponding RNA profile of Sema3A and Sema3E. Moreover, although the intracellular level of Sema7A protein was increased at day 5 of differentiation, it did not correlate with the gradually enhanced level of secreted protein. These finding shows that the level of secreted semaphorin proteins can be regulated both by post-transcriptional and post-translational mechanisms. This is in agreement with previous findings in which imperfect correlation between RNA and protein expression was observed (Bonaldi et al., 2008; de Godoy et al., 2008; Kratchmarova et al., 2002). It also emphasizes the necessity to quantitatively investigate protein abundance to understand the functional role exhibited by individual genes and their corresponding proteins. These results clearly illustrate, that when studying the complex nature of the secreted factors it is important to observe both the intracellular level of proteins and their secretion profiles since they might differ due to post-translational modification or modulation of their release via the secretory pathway, turnover rate, and/or processing.

#### **4. Pitfalls of the studies on secreted proteins**

One of the major challenges in secretome studies is the identification and classification of secreted proteins from the total number of identified proteins from the proteomics experiment. Secreted proteins are released in the extracellular space via two routes: the classical and non-classical secretory pathways (Box 1).

#### **4.1 Secretory pathway, classical**

Majority of eukaryotic proteins are secreted by the classical endoplasmic reticulum (ER)- Golgi secretory pathway consisting of a number of distinct membrane-bound compartments interconnected by vesicular traffic (Baines and Zhang, 2007; De Matteis and Luini; Nickel and Rabouille, 2009; Nickel and Wieland, 1998; Park and Loh, 2008; Pelham, 1996; Strating and Martens, 2009). Many basic cellular functions take place in the ER including folding of newly synthesized transmembrane and secretory proteins, lipid synthesis, and the storage

Box 1. Classification of secretory pathways

siRNA caused inhibition of C2C12 myotube formation, demonstrating that semaphorins could exert an active autocrine/paracrine function in myogenesis. Interestingly, animal models have suggested that semaphorins could be important paracrine factors regulating neurogenesis during skeletal muscle growth, development, and regeneration. A delayed transient increase of Sema3A expression was observed in response to muscle-induced injury (Tatsumi et al., 2009). In addition, a similar delay of Sema3A expression and secretion was seen in isolated skeletal muscle cells in response to HGF, which is an essential factor in muscle growth and regeneration. In our study, we have identified both

We have analyzed the mRNA and protein expression of selected regulated members of the semaphorin family (Sema3A, Sema3E, Sema6A, and Sema7A) to investigate if their dynamic secretion pattern was regulated by post-transcriptional and post-translational mechanisms. Only minor changes were observed in the mRNA expression of Sema3A, Sema3E, Sema6A, and Sema7A. The mRNA expression of Sema3A and Sema7A remained constant during differentiation, whereas there was a slight decrease and increase in the level of Sema3E and Sema6A, respectively. We found that the high levels of secreted Sema3A and Sema3E at early stage of myotube formation did not reflect the intracellular protein levels of these semaphorins. Expression of Sema3A protein remained constant, whereas a slight decrease was observed of Sema3E protein expression in accordance with the corresponding RNA profile of Sema3A and Sema3E. Moreover, although the intracellular level of Sema7A protein was increased at day 5 of differentiation, it did not correlate with the gradually enhanced level of secreted protein. These finding shows that the level of secreted semaphorin proteins can be regulated both by post-transcriptional and post-translational mechanisms. This is in agreement with previous findings in which imperfect correlation between RNA and protein expression was observed (Bonaldi et al., 2008; de Godoy et al., 2008; Kratchmarova et al., 2002). It also emphasizes the necessity to quantitatively investigate protein abundance to understand the functional role exhibited by individual genes and their corresponding proteins. These results clearly illustrate, that when studying the complex nature of the secreted factors it is important to observe both the intracellular level of proteins and their secretion profiles since they might differ due to post-translational modification or modulation of their release via the secretory pathway, turnover rate, and/or

One of the major challenges in secretome studies is the identification and classification of secreted proteins from the total number of identified proteins from the proteomics experiment. Secreted proteins are released in the extracellular space via two routes: the

Majority of eukaryotic proteins are secreted by the classical endoplasmic reticulum (ER)- Golgi secretory pathway consisting of a number of distinct membrane-bound compartments interconnected by vesicular traffic (Baines and Zhang, 2007; De Matteis and Luini; Nickel and Rabouille, 2009; Nickel and Wieland, 1998; Park and Loh, 2008; Pelham, 1996; Strating and Martens, 2009). Many basic cellular functions take place in the ER including folding of newly synthesized transmembrane and secretory proteins, lipid synthesis, and the storage

Sema3A and Sema4C to be released by C2C12 myoblasts during differentiation.

processing.

**4. Pitfalls of the studies on secreted proteins** 

classical and non-classical secretory pathways (Box 1).

**4.1 Secretory pathway, classical** 

of high concentrations of calcium ions (Marie et al., 2008; Mayor and Riezman, 2004; Sallese et al., 2009; Strating and Martens, 2009). In addition, post-translational modifications of soluble and membrane proteins occur in the ER lumen including oxidation of proline, Nlinked glycosylation, proteolytic processing, formation of disulfide bonds, oligomerization, and attachment of a GPI-anchor. Whereas the main functions of the Golgi apparatus include carbohydrate synthesis, O-linked glycosylation, processing, post-translational modification, and sorting both proteins and lipids (De Matteis and Luini, 2008; Marie et al., 2008; Marsh and Howell, 2002). Regardless of their subsequent fate, most proteins containing a Nterminal or internal signal sequence peptide can be targeted to the ER membrane. These include transmembrane proteins destined to reside in the ER, plasma membrane or other organellar membranes as well as soluble proteins destined to the lumen of an organelle or for secretion. With the exception of mitochondria, nuclei, and peroxisomes, all other organelles receive their proteins via the ER. Signal peptides show extreme variations in their length and amino acid composition, but do contain three distinct domains: a positively charged N-terminal region, a hydrophobic core region, typically consisting of at least 6 hydrophobic residues, and C-terminal region of polar uncharged residues (Hiller et al., 2004). Soluble proteins are transported from the Golgi to the cell exterior via the constitutive secretory pathway transporting proteins directly to the cell surface or the regulated secretory pathway in which soluble proteins and other substances are initially stored in secretory vesicles, which release proteins to the extracellular space upon extracellular signals (Brunner et al., 2009; De Matteis and Luini, 2008; Strating and Martens, 2009). The latter pathway only exists in specialized secretory cells including pancreatic β-cell releasing insulin from secretory vesicles, nerve cells, and endocrine cells. The secretory vesicles of the

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 429

studies, most commonly being assessment based on literature searches, GO-annotations and/or algorithms predicting secretion by classical (SignalP) or non-classical (SecretomeP) mechanisms (Box 2). Extraction of secreted proteins based on previous reported studies is extremely time-consuming considering the large number of identified proteins by today's advanced MS. In addition, this will only result in the identification of proteins already shown by experimental data to be secreted. Isolation of secreted proteins from a large list of identified proteins can be done combining GO classification as extracellular and/or prediction of a signal peptide. However, all these tools do come with certain restrictions that could lead to either false positive or false negative identifications of secretion status. The presence of a signal peptide is not restricted to extracellular proteins. Proteins destined for other intracellular compartments, such as the ER or Golgi, also contains a signal peptide. In addition, GO terms are assigned according to different parameters, including computational analyses of sequences in addition to experimental data. Again, predictions based on sequence information could result in false positive identification of secreted proteins as well. Prediction tools always have their own limitations and therefore bona fide secreted proteins could also be lost by these tools. Most affected in this regard are the proteins released from cells by nonconventional mechanisms whose number is still low but steadily increasing (Nickel and

MaxQuant (http://maxquant.org): Advanced software program used as a tool for both

SignalP (http://www.cbs.dtu.dk/services/SignalP): Predicting the presence of a signal peptide, suggesting proteins could be secreted through the classical pathway (Bendtsen et

SecretomeP (http://www.cbs.dtu.dk/services/SecretomeP): Prediction of proteins to be

(http://www.ebi.ac.uk/GOA): Bioinformatics resource integrating various databases to assign subcellular localization and functional annotation according to GO terms (Barrell

ProteinCenter (http://www.proxeon.com): Software tool combining several data bases to

Box 2. Software and databases commonly used in quantitative mass spectrometry-based

GProX (http://gprox.sourceforge.net): Bioinformatics platform, which support the analysis and visualization of large-scale proteomics data (Rigbolt et al., 2011).

protein identification and quantitation (Cox and Mann, 2008; Cox et al., 2009).

secreted by non-classical mechanisms (Bendtsen et al., 2004a).

QuickGO provided by the Gene Ontology Annotation (GOA) group

analyze the biological context of complex proteomics experiments.

Rabouille, 2009; Prudovsky et al., 2008).

**Open source software and databases** 

al., 2004b; Emanuelsson et al., 2007).

et al., 2009; Binns et al., 2009).

**Commercially available database** 

proteomics research of secreted proteins

constitutive or regulated pathways fuse with the plasma membrane and release their contents by exocytosis.

#### **4.2 Secretory pathway, non-classical**

Although most identified extracellular proteins are secreted through the classical secretory pathway, emerging evidence has shown that several soluble proteins are released to the cell exterior via non-classical mechanisms (Nickel and Rabouille, 2009; Nickel and Seedorf, 2008; Prudovsky et al., 2008). For example FGF2 and IL-1β, well known extracellular proteins but lacking a signal peptide, are being secreted by non-classical routes either directly across the membrane or via vesicle intermediates. More specifically, studies investigating IL-1β secretion have demonstrated three alternative routes of extracellular translocation involving activation of caspase 1 and proteolytic processing of IL-1β. IL-1β can be released through (i) microvesicle shedding from the cell surface, (ii) translocation to secretory lysosomes, which upon fusion with the PM releases IL-1β to the cell exterior, and (iii) the caspase 1-Il-1β complex can be captured by endosomal vesicles creating multivesicular bodies that release internal vesicles as exosomes. At present, more than 20 proteins, belonging to different functional groups, have been described to be released to the cell exterior by non-classical pathways, including proteins that mainly function in the extracellular space as well as proteins that serve a role both intracellular and extracellular (Nickel and Seedorf, 2008; Prudovsky et al., 2008). Some of these proteins are constitutively secreted, whereas others are first released upon specific stimulation. Future studies are warranted to understand the biological function and regulation of the many different secretory pathways as well as the number and function of proteins devoid of a signal peptide but released to the extracellular space. Secretion of proteins by alternative pathways, which require interaction with other proteins and/or proteolytic activation, could impose additional levels of regulation to protein secretion (Nickel and Rabouille, 2009; Prudovsky et al., 2008). In addition, alternative secretion of signal peptide containing proteins that bypass the Golgi apparatus, could cause alterations in the structures of post-transcriptional modifications, such as glycosylation, or prevent proper proteolytic processing. This could be a way to modulate the biological activity of secreted proteins under certain physiological conditions.

In addition to the two general pathways of secretion, proteins are also being released to the cell exterior due to apoptosis or cell leakage, thereby contaminating the pool of true secreted proteins. In this regard, the increased performance of MS-instrumentation not only improves the dynamic range for the identification of secreted proteins but also increases the number of identified proteins originating from the intracellular space. One example was presented in the study by Henningsen et al., 2011 focusing on the low molecular weight proteins. The quantitative mass spectrometry analysis resulted in the identification of more than 2000 proteins however, less than 25% of these proteins were predicted to be secreted according to conventional database analyses based on the GO term extracellular and signal peptide prediction. Among the predicted secreted proteins, there were also tubulins, a number of ribosomal proteins, and membrane proteins that are not encountered as being truly secreted. Nevertheless, some of the cytoskeletal and ribosomal proteins have been demonstrated to be part of the exosomes and as such are being released in the extracellular environment. Major part of the exosomes consists of tubulins and Tsg101, which is a well-known exosome marker, was also identified as a secreted protein (Henningsen et al., 2011; Thery et al., 2002). Different tools are being used to classify the extracellular compartment in various secretome studies, most commonly being assessment based on literature searches, GO-annotations and/or algorithms predicting secretion by classical (SignalP) or non-classical (SecretomeP) mechanisms (Box 2). Extraction of secreted proteins based on previous reported studies is extremely time-consuming considering the large number of identified proteins by today's advanced MS. In addition, this will only result in the identification of proteins already shown by experimental data to be secreted. Isolation of secreted proteins from a large list of identified proteins can be done combining GO classification as extracellular and/or prediction of a signal peptide. However, all these tools do come with certain restrictions that could lead to either false positive or false negative identifications of secretion status. The presence of a signal peptide is not restricted to extracellular proteins. Proteins destined for other intracellular compartments, such as the ER or Golgi, also contains a signal peptide. In addition, GO terms are assigned according to different parameters, including computational analyses of sequences in addition to experimental data. Again, predictions based on sequence information could result in false positive identification of secreted proteins as well. Prediction tools always have their own limitations and therefore bona fide secreted proteins could also be lost by these tools. Most affected in this regard are the proteins released from cells by nonconventional mechanisms whose number is still low but steadily increasing (Nickel and Rabouille, 2009; Prudovsky et al., 2008).

#### **Open source software and databases**

428 Proteomics – Human Diseases and Protein Functions

constitutive or regulated pathways fuse with the plasma membrane and release their

Although most identified extracellular proteins are secreted through the classical secretory pathway, emerging evidence has shown that several soluble proteins are released to the cell exterior via non-classical mechanisms (Nickel and Rabouille, 2009; Nickel and Seedorf, 2008; Prudovsky et al., 2008). For example FGF2 and IL-1β, well known extracellular proteins but lacking a signal peptide, are being secreted by non-classical routes either directly across the membrane or via vesicle intermediates. More specifically, studies investigating IL-1β secretion have demonstrated three alternative routes of extracellular translocation involving activation of caspase 1 and proteolytic processing of IL-1β. IL-1β can be released through (i) microvesicle shedding from the cell surface, (ii) translocation to secretory lysosomes, which upon fusion with the PM releases IL-1β to the cell exterior, and (iii) the caspase 1-Il-1β complex can be captured by endosomal vesicles creating multivesicular bodies that release internal vesicles as exosomes. At present, more than 20 proteins, belonging to different functional groups, have been described to be released to the cell exterior by non-classical pathways, including proteins that mainly function in the extracellular space as well as proteins that serve a role both intracellular and extracellular (Nickel and Seedorf, 2008; Prudovsky et al., 2008). Some of these proteins are constitutively secreted, whereas others are first released upon specific stimulation. Future studies are warranted to understand the biological function and regulation of the many different secretory pathways as well as the number and function of proteins devoid of a signal peptide but released to the extracellular space. Secretion of proteins by alternative pathways, which require interaction with other proteins and/or proteolytic activation, could impose additional levels of regulation to protein secretion (Nickel and Rabouille, 2009; Prudovsky et al., 2008). In addition, alternative secretion of signal peptide containing proteins that bypass the Golgi apparatus, could cause alterations in the structures of post-transcriptional modifications, such as glycosylation, or prevent proper proteolytic processing. This could be a way to modulate the

biological activity of secreted proteins under certain physiological conditions.

In addition to the two general pathways of secretion, proteins are also being released to the cell exterior due to apoptosis or cell leakage, thereby contaminating the pool of true secreted proteins. In this regard, the increased performance of MS-instrumentation not only improves the dynamic range for the identification of secreted proteins but also increases the number of identified proteins originating from the intracellular space. One example was presented in the study by Henningsen et al., 2011 focusing on the low molecular weight proteins. The quantitative mass spectrometry analysis resulted in the identification of more than 2000 proteins however, less than 25% of these proteins were predicted to be secreted according to conventional database analyses based on the GO term extracellular and signal peptide prediction. Among the predicted secreted proteins, there were also tubulins, a number of ribosomal proteins, and membrane proteins that are not encountered as being truly secreted. Nevertheless, some of the cytoskeletal and ribosomal proteins have been demonstrated to be part of the exosomes and as such are being released in the extracellular environment. Major part of the exosomes consists of tubulins and Tsg101, which is a well-known exosome marker, was also identified as a secreted protein (Henningsen et al., 2011; Thery et al., 2002). Different tools are being used to classify the extracellular compartment in various secretome

contents by exocytosis.

**4.2 Secretory pathway, non-classical** 

MaxQuant (http://maxquant.org): Advanced software program used as a tool for both protein identification and quantitation (Cox and Mann, 2008; Cox et al., 2009).

SignalP (http://www.cbs.dtu.dk/services/SignalP): Predicting the presence of a signal peptide, suggesting proteins could be secreted through the classical pathway (Bendtsen et al., 2004b; Emanuelsson et al., 2007).

SecretomeP (http://www.cbs.dtu.dk/services/SecretomeP): Prediction of proteins to be secreted by non-classical mechanisms (Bendtsen et al., 2004a).

QuickGO provided by the Gene Ontology Annotation (GOA) group (http://www.ebi.ac.uk/GOA): Bioinformatics resource integrating various databases to assign subcellular localization and functional annotation according to GO terms (Barrell et al., 2009; Binns et al., 2009).

GProX (http://gprox.sourceforge.net): Bioinformatics platform, which support the analysis and visualization of large-scale proteomics data (Rigbolt et al., 2011).

#### **Commercially available database**

ProteinCenter (http://www.proxeon.com): Software tool combining several data bases to analyze the biological context of complex proteomics experiments.

Box 2. Software and databases commonly used in quantitative mass spectrometry-based proteomics research of secreted proteins

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 431

Mammalian cell culture models are broadly used in proteomics experiments and often contamination with bovine serum proteins, originating from the serum supplement used for the culturing of cells, is observed in the results from the mass spectrometric analyses. Naturally, when studying the proteins released by specific types of cells, one of the biggest challenges remains the presence of serum proteins that could interfere with the identification of proteins secreted by the cells. Presence of serum proteins in the sample can disrupt the concentration of the CM as well as interfere with the MS analysis, masking the presence of other proteins. It has been estimated that a 10% FBS serum complement, which is commonly added to the culture media, adds 5-6 mg/ml protein to the media and even extensive washing of the cells might not be sufficient to remove the bovine proteins to levels below the detection limit of the mass spectrometer (Bunkenborg et al., 2010). The seraderived proteins could be falsely identified as proteins being secreted by the cells due to sequence homology between species. One suggested solution to exclude bovine contaminants was based on expanding the database to include both the human and the bovine proteome. Another alternative is to extend the database to include known bovine contaminants as a common contaminant list, which is already incorporated in the data analysis by programs such as MaxQuant (Bunkenborg et al., 2010; Henningsen et al., 2011; Henningsen et al., 2010). In this way, it is possible to exclude the proteins recognized as contaminants from the initial list of identifications and thereby to minimize the number of identifications originating from sera proteins. Nevertheless, the SILAC strategy is so far the best known applicable method to investigate secreted factors by a given type of cells since the metabolic labeling makes it possible to distinguish cell-derived secreted proteins, as these are SILAC labeled, from residual sera proteins. Only proteins that are synthesized in the cells in the presence of the heavy SILAC amino acid will be labeled, thereby these are easily distinguished from sera contaminants, which remain unlabeled. In addition, the accuracy of protein quantitation could also be compromised by the presence of sera proteins in the samples. It is therefore advisable to perform a replica experiment with reverse SILAC labeling strategy (Schulze and Mann, 2004), which is an easy solution to overcome this possible drawback as well as to ensure high quality quantitation of bona fide secreted

**4.3 Serum contaminants** 

proteins.

**4.4 Post-translational modifications of secreted proteins** 

Glycosylation of secreted proteins is one of the most abundant post-translational modifications (PTM), which affects the proteins folding, stability, and activity. The oligosaccharides are linked to the proteins via asparagine (N-linked) or serine/threonine (Olinked) residues. Enrichment of secreted proteins through their glycan structures is an alternative experimental strategy for the identification of secreted proteins. Various types of enrichment methods have been utilized to capture glycosylated proteins, one of the common approaches being lectin affinity chromatography. In an elegant study by Zielinska et al., the combinatorial use of optimized lectin-based enrichment step, subcellular fractionation, deglycosylation assays, SILAC labeling, advanced mass spectrometry followed by integrative bioinformatic analyses resulted in the identification of 6367 Nglycosylation sites on 2352 proteins in four mouse tissues and blood plasma. Nglycosylation was found to occur exclusively on secreted proteins, on the extracellular face of membrane proteins, and on the lumenal side of ER, Golgi apparatus, and lysosomes

The high number of identified proteins in the QMSP experiments, which are not classified as secreted could be present in the extracellular space (conditioned medium) due to cell leakage or release of intracellular proteins from necrotic or apoptotic cells. The apoptotic process is a normal process that all cells grown in culture undergo at a given time point. However, the number of dead cells is limited since the protocol for collection of media is optimized such as to reduce the number of dying cells. In addition, the collected CM is typically filtrated using a 0.2 μm filter to ensure removal of any dead cells and thereby to reduce contamination from intracellular proteins. The presence of intracellular proteins might be explained by other structures present in the extracellular space such as exosomes and their cargo. Another point is that prediction of a signal peptide by itself does not exclude the possibility that these proteins are in fact located in other intracellular compartments of the cells, such as the ER and Golgi. On the other hand, an increasing number of proteins are being recognized as extracellular despite lacking a signal peptide and thought to be released through non-classical pathways (Nickel and Rabouille, 2009; Prudovsky et al., 2008). In the literature, more than 20 proteins devoid of any signal peptide have been shown to reside in the extracellular space and being released by nonconventional mechanisms. SecretomeP (Box 2) has been designed to predict non-classical secreted proteins (Bendtsen et al., 2004a). For that purpose 13 known human non-classical secreted proteins were analyzed, but no specific sequence motif was identified to characterize non-classical secretion. Instead, the non-classical software for prediction was developed using the multiple sequence features of the 13 non-classical secreted proteins combined with sequence information obtained from more than 3,000 classical secreted proteins. Due to the limited number of identified non-classical proteins, the value of this prediction approach is difficult to assess. Submitting either the murine or human sequence of galectin-1 to SecretomeP resulted in probability score of < 0.5, thereby exemplifying a false negative identification. Galectin-1 is a well-known extracellular protein lacking a signal peptide but released by non-conventional ways (Hughes, 1999; Sango et al., 2004). On the other hand HMGB1, also not containing a signal peptide and mainly known for its role as a chromatin modifying protein, is also serving an extracellular function suggesting that proteins with typically intracellular functions could also be released to the cell exterior (Bonaldi et al., 2003 2002; Gardella et al., 2002). Future studies will help to elucidate how many proteins deficient in a signal peptide are being released to the extracellular space. Classifying proteins as extracellular based on GO annotation can also lead to false negative or false positive classifications. GO annotation are based both on experimental data but also on computational analysis of sequence information. With the increasing number of biomarker directed studies analyzing biofluids by mass spectrometry, the number of classified secreted proteins is steadily increasing. The increased number of identified secreted proteins could be due to improvements of mass spectrometry technology, which increased the overall sensitivity of protein identification, but could also be artifacts derived from dead cells floating around in the circulation.

In summary, combination of different tools and manually curated data might be beneficial when validating the large lists obtained from QMSP experiments focusing on secreted factors. Nevertheless, release of cellular components can occur via microvesicles and/or exosomes adding to the complexity of secretome studies, thus some of the factors commonly counted as contaminants might be truly secreted ones.

#### **4.3 Serum contaminants**

430 Proteomics – Human Diseases and Protein Functions

The high number of identified proteins in the QMSP experiments, which are not classified as secreted could be present in the extracellular space (conditioned medium) due to cell leakage or release of intracellular proteins from necrotic or apoptotic cells. The apoptotic process is a normal process that all cells grown in culture undergo at a given time point. However, the number of dead cells is limited since the protocol for collection of media is optimized such as to reduce the number of dying cells. In addition, the collected CM is typically filtrated using a 0.2 μm filter to ensure removal of any dead cells and thereby to reduce contamination from intracellular proteins. The presence of intracellular proteins might be explained by other structures present in the extracellular space such as exosomes and their cargo. Another point is that prediction of a signal peptide by itself does not exclude the possibility that these proteins are in fact located in other intracellular compartments of the cells, such as the ER and Golgi. On the other hand, an increasing number of proteins are being recognized as extracellular despite lacking a signal peptide and thought to be released through non-classical pathways (Nickel and Rabouille, 2009; Prudovsky et al., 2008). In the literature, more than 20 proteins devoid of any signal peptide have been shown to reside in the extracellular space and being released by nonconventional mechanisms. SecretomeP (Box 2) has been designed to predict non-classical secreted proteins (Bendtsen et al., 2004a). For that purpose 13 known human non-classical secreted proteins were analyzed, but no specific sequence motif was identified to characterize non-classical secretion. Instead, the non-classical software for prediction was developed using the multiple sequence features of the 13 non-classical secreted proteins combined with sequence information obtained from more than 3,000 classical secreted proteins. Due to the limited number of identified non-classical proteins, the value of this prediction approach is difficult to assess. Submitting either the murine or human sequence of galectin-1 to SecretomeP resulted in probability score of < 0.5, thereby exemplifying a false negative identification. Galectin-1 is a well-known extracellular protein lacking a signal peptide but released by non-conventional ways (Hughes, 1999; Sango et al., 2004). On the other hand HMGB1, also not containing a signal peptide and mainly known for its role as a chromatin modifying protein, is also serving an extracellular function suggesting that proteins with typically intracellular functions could also be released to the cell exterior (Bonaldi et al., 2003 2002; Gardella et al., 2002). Future studies will help to elucidate how many proteins deficient in a signal peptide are being released to the extracellular space. Classifying proteins as extracellular based on GO annotation can also lead to false negative or false positive classifications. GO annotation are based both on experimental data but also on computational analysis of sequence information. With the increasing number of biomarker directed studies analyzing biofluids by mass spectrometry, the number of classified secreted proteins is steadily increasing. The increased number of identified secreted proteins could be due to improvements of mass spectrometry technology, which increased the overall sensitivity of protein identification, but could also be artifacts derived from dead cells floating around

In summary, combination of different tools and manually curated data might be beneficial when validating the large lists obtained from QMSP experiments focusing on secreted factors. Nevertheless, release of cellular components can occur via microvesicles and/or exosomes adding to the complexity of secretome studies, thus some of the factors commonly

in the circulation.

counted as contaminants might be truly secreted ones.

Mammalian cell culture models are broadly used in proteomics experiments and often contamination with bovine serum proteins, originating from the serum supplement used for the culturing of cells, is observed in the results from the mass spectrometric analyses. Naturally, when studying the proteins released by specific types of cells, one of the biggest challenges remains the presence of serum proteins that could interfere with the identification of proteins secreted by the cells. Presence of serum proteins in the sample can disrupt the concentration of the CM as well as interfere with the MS analysis, masking the presence of other proteins. It has been estimated that a 10% FBS serum complement, which is commonly added to the culture media, adds 5-6 mg/ml protein to the media and even extensive washing of the cells might not be sufficient to remove the bovine proteins to levels below the detection limit of the mass spectrometer (Bunkenborg et al., 2010). The seraderived proteins could be falsely identified as proteins being secreted by the cells due to sequence homology between species. One suggested solution to exclude bovine contaminants was based on expanding the database to include both the human and the bovine proteome. Another alternative is to extend the database to include known bovine contaminants as a common contaminant list, which is already incorporated in the data analysis by programs such as MaxQuant (Bunkenborg et al., 2010; Henningsen et al., 2011; Henningsen et al., 2010). In this way, it is possible to exclude the proteins recognized as contaminants from the initial list of identifications and thereby to minimize the number of identifications originating from sera proteins. Nevertheless, the SILAC strategy is so far the best known applicable method to investigate secreted factors by a given type of cells since the metabolic labeling makes it possible to distinguish cell-derived secreted proteins, as these are SILAC labeled, from residual sera proteins. Only proteins that are synthesized in the cells in the presence of the heavy SILAC amino acid will be labeled, thereby these are easily distinguished from sera contaminants, which remain unlabeled. In addition, the accuracy of protein quantitation could also be compromised by the presence of sera proteins in the samples. It is therefore advisable to perform a replica experiment with reverse SILAC labeling strategy (Schulze and Mann, 2004), which is an easy solution to overcome this possible drawback as well as to ensure high quality quantitation of bona fide secreted proteins.

#### **4.4 Post-translational modifications of secreted proteins**

Glycosylation of secreted proteins is one of the most abundant post-translational modifications (PTM), which affects the proteins folding, stability, and activity. The oligosaccharides are linked to the proteins via asparagine (N-linked) or serine/threonine (Olinked) residues. Enrichment of secreted proteins through their glycan structures is an alternative experimental strategy for the identification of secreted proteins. Various types of enrichment methods have been utilized to capture glycosylated proteins, one of the common approaches being lectin affinity chromatography. In an elegant study by Zielinska et al., the combinatorial use of optimized lectin-based enrichment step, subcellular fractionation, deglycosylation assays, SILAC labeling, advanced mass spectrometry followed by integrative bioinformatic analyses resulted in the identification of 6367 Nglycosylation sites on 2352 proteins in four mouse tissues and blood plasma. Nglycosylation was found to occur exclusively on secreted proteins, on the extracellular face of membrane proteins, and on the lumenal side of ER, Golgi apparatus, and lysosomes

Quantitative Proteomics for Investigation of Secreted Factors: Focus on Muscle Secretome 433

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Hydroxyproline is another type of PTM that is identified predominantly on components of the extracellular matrix and especially on different types of collagens. We have recently reported the identification of 299 unique high-confidence hydroxyproline sites from 48 distinct secreted proteins in muscle cells, representing the largest data set so far on this proline modification. 231 of the modified prolines were located on various collagen types with a large variation of the number of modified sites per individual protein. The number ranged from 1 site on collagen alpha-3 type VI to more than 70 on collagen alpha-1 type I, highlighting the importance of the proline modification in maintaining the structure of the collagens. Motif sequence analysis revealed the canonical motif previously reported for collagen proteins as well as a novel hydroxyproline motif. Modified peptides containing hydroxyproline sites extend over more than 40 proteins including fatty acid binding protein (FABP), several components of the ECM such as SPARC, fibronectin, Lama2, perlecan, and different inhibitors of proteolytic enzymes such as serine protease inhibitors (serpinf1 and serphinh1) and the metalloproteinase inhibitor 2 (Timp2). These results indicate that hydroxyproline could serve as an important secondary modification to confer protein stability and interaction with other secreted proteins.

#### **5. Conclusion**

The rapid development of MS-instrumentation combined with the advances of quantitative proteomics strategies, such as SILAC, has had a tremendous impact in the analysis of complex biological systems. QMSP is increasingly becoming an essential approach, especially for the characterization of entire secretomes and generation of dynamic quantitative profiles of secreted factors during the course of cellular differentiation or in response to drugs, inhibitors, and modulators. Although there have been a marked improvement of the proteomics strategies to characterize secretomes, many challenges still remain: minimizing the suppressive effect of growth supplements present in the sample during MS analyses, identification of whole tissue secretomes, collection of samples under specific conditions to avoid induction of cell death, identification of low abundant low molecular weight proteins. However, the most critical point presented by secretome studies today is to isolate the bona fide secreted proteins and to validate the obtained results. Integrative approaches that combine highly advanced proteomics methodology followed by biological functional analyses can lead to the creation of secretome maps that underline tissue crosstalk and communication.

#### **6. Acknowledgment**

This work was supported by a grant from the Novo Nordisk Foundation, the Lundbeck Foundation and the Augustinus Foundation. IK is supported by grants from the Danish Natural Science Research Council and the Danish Medical Research Council. BB is supported by grant from the Lundbeck Foundation and the Danish Natural Science Research Council.

#### **7. References**

432 Proteomics – Human Diseases and Protein Functions

(Zielinska et al., 2010). In a complementary study using formalin-fixed paraffin-embedded tissue samples, 1500 N-glycosylation sites were found underlying the increased sensitivity and accuracy of the mass spectrometry-based proteomics for identification of posttranslationally modified proteins, even in fixed samples. The comparison of fresh tissue using SILAC-labeled mouse (Kruger et al., 2008) with the paraffin embedded tissue showed no significant qualitative or quantitative differences between these samples, either at protein or peptide level, thereby permitting the use of this methodology in clinical studies

Hydroxyproline is another type of PTM that is identified predominantly on components of the extracellular matrix and especially on different types of collagens. We have recently reported the identification of 299 unique high-confidence hydroxyproline sites from 48 distinct secreted proteins in muscle cells, representing the largest data set so far on this proline modification. 231 of the modified prolines were located on various collagen types with a large variation of the number of modified sites per individual protein. The number ranged from 1 site on collagen alpha-3 type VI to more than 70 on collagen alpha-1 type I, highlighting the importance of the proline modification in maintaining the structure of the collagens. Motif sequence analysis revealed the canonical motif previously reported for collagen proteins as well as a novel hydroxyproline motif. Modified peptides containing hydroxyproline sites extend over more than 40 proteins including fatty acid binding protein (FABP), several components of the ECM such as SPARC, fibronectin, Lama2, perlecan, and different inhibitors of proteolytic enzymes such as serine protease inhibitors (serpinf1 and serphinh1) and the metalloproteinase inhibitor 2 (Timp2). These results indicate that hydroxyproline could serve as an important secondary modification to confer protein

The rapid development of MS-instrumentation combined with the advances of quantitative proteomics strategies, such as SILAC, has had a tremendous impact in the analysis of complex biological systems. QMSP is increasingly becoming an essential approach, especially for the characterization of entire secretomes and generation of dynamic quantitative profiles of secreted factors during the course of cellular differentiation or in response to drugs, inhibitors, and modulators. Although there have been a marked improvement of the proteomics strategies to characterize secretomes, many challenges still remain: minimizing the suppressive effect of growth supplements present in the sample during MS analyses, identification of whole tissue secretomes, collection of samples under specific conditions to avoid induction of cell death, identification of low abundant low molecular weight proteins. However, the most critical point presented by secretome studies today is to isolate the bona fide secreted proteins and to validate the obtained results. Integrative approaches that combine highly advanced proteomics methodology followed by biological functional analyses can lead to the creation of secretome maps that underline

This work was supported by a grant from the Novo Nordisk Foundation, the Lundbeck Foundation and the Augustinus Foundation. IK is supported by grants from the Danish

(Ostasiewicz et al., 2010).

**5. Conclusion** 

stability and interaction with other secreted proteins.

tissue crosstalk and communication.

**6. Acknowledgment** 


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### *Edited by Tsz-Kwong Man and Ricardo J. Flores*

Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and highdensity antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research.

Proteomics - Human Diseases and Protein Functions

Proteomics

Human Diseases and Protein Functions

*Edited by Tsz-Kwong Man and Ricardo J. Flores*

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