**3. Biomarkers**

228 Autoimmune Disorders – Current Concepts and Advances from Bedside to Mechanistic Insights

We analyzed the effect of two anticytokine agents, anti-IL-6R monoclonal antibody (mAb) and TNFR-Fc, on collagen-induced arthritis (CIA), a murine model of human RA (Fujimoto et al., 2008). In accordance with the pivotal proinflammatory role of IL-6 and TNF in this arthritis model, both agents could inhibit the development of arthritis. However, while anti-IL-6R mAb potently inhibited the differentiation of Th17 cells, a highly inflammatory subset of T helper cells, TNFR-Fc exhibited no effect on Th17 cells. This observation suggests that these two agents have different action points: IL-6 blockade acts on initial phase of adoptive immune response and regulates T helper cell differentiation, whereas TNF inhibitors act much later, presumably at inflamed sites. Our study also suggests that IL-6 inhibitors may be applicable to other Th17-related autoimmune diseases. Indeed, anti-IL-6R mAb suppressed disease in a murine model of multiple sclerosis via the inhibition of Th17 cell differentiation (Serada et al., 2008). The different modes of action in anti-IL-6R mAb and TNF inhibitors may explain the difference in their efficiency in a murine model of uveoretinitis. Anti-IL-6R mAb treatment had a significant protective effect in experimental autoimmune uveoretinitis (EAU) mice, but either TNFR-Fc or anti-TNF mAb treatment did not (Hohki et al., 2010). Interestingly, in the EAU model, anti-IL-6R mAb not only suppressed Th17 cell differentiation but also suppressed autoantigen-specific Th1 cells via the generation of induced regulatory T cells, supporting the notion that IL-6 inhibitors act on

Confusingly, biological agents may act differently on different autoimmune diseases. Indeed, anti-IL-6R mAb and anti-TNF mAb, but not TNFR-Fc exerted similar effect on a murine inflammatory bowel disease (IBD) model (Terabe et al., 2011). This model is a T cell dependent colitis and is induced by the transfer of purified naïve CD4 T cells into lymphopenic mice. Both anti-IL-6R mAb and anti-TNF mAb successfully inhibited colitis, whereas TNFR-Fc did not show any protective effect on colitis. In addition, anti-IL-6R mAb and anti-TNF mAb could comparably inhibit the expansion of colitogenic T cells in this model, although like in other models, anti-IL-6R mAb additionally could modulate the profile of T helper cell differentiation (Terabe et al., 2011). Thus, anti-IL-6R mAb and anti-TNF mAb may share a similar mode of action in the inhibition of IBD. It is also notable that TNFR-Fc failed to inhibit inflammation in this colitis model (Terabe et al., 2011). Similar discrepancy in the effect of anti-TNF mAb and TNFR-Fc has been observed in human IBD. Many mechanisms have been proposed so far to explain the difference of action between these two agents. For example, anti-TNF mAb binds not only to soluble TNF-α, but also to membrane-bound TNF-α, leading to the induction of antibody-dependent and complement dependent cytotoxicity (Maini, 2004). The anti-TNF mAb may also have more capacity than TNFR-Fc to induce apoptosis via reverse signaling with cross-linking by binding firmly to transmembrane TNF(Terabe et al., 2011). Nevertheless, these hypotheses are still controversial and it remains to be explained why anti-TNF mAb and TNFR-Fc have differential effectiveness in some autoimmune diseases such as Crohn's disease. We believe

that further study on this murine IBD model is useful for elucidation of this issue.

**2.2 Biological therapeutic agents tested in animal models of autoimmune disorders**  The analyses on murine disease models have contributed greatly to gain insight into pathogenesis and therapeutic strategy of autoimmune disorders. These models are also useful to clarify the detailed mechanisms of action of biological agents. We have recently investigated several disease models and reveled that anticytokine biological agents have different mechanism of action and show different effects on clinical manifestations of

disease models (Fujimoto et al., 2008; Terabe et al., 2011).

initial phase of adoptive immune response (Haruta et al., 2011).

### **3.1 A need for new biomarkers in the era of biological agents**

Given the difference in mechanism and therapeutic effect of each biologic agent, it is desirable to select an effective biological agent on each patient before initiating therapy or after failure of the initial therapy. However, no reliable guidance is available at present for the selection of biological therapies. There is a growing need for the development of biomarkers that predict individual treatment response before therapy.

In addition, in patients treated with biological agents in whom immune response is substantially suppressed, conventional laboratory biomarkers such as CRP and ESR do not always reflect disease activity. In particular, since serum CRP is primarily dependent on liver by circulating IL-6, CRP is unable to reflect disease activity in patients treated with IL-6 inhibitors. Moreover, conventional markers may also be inadequate for the detection of inflammation unrelated to original diseases. In RA patients after joint surgery, anti-IL-6R mAb tocilizumab completely suppressed the increase in CRP and partially suppressed the rise in body temperature (Hirao et al., 2009). More importantly, biological agents may mask typical symptoms of bacterial infection and inhibit the elevation of serum biomarkers. Indeed, RA patients treated with tocilizumab did not present characteristic clinical symptoms and typical elevation of serum CRP after bacterial pneumonia and septic shock (Fujiwara et al., 2009). Even without biologic treatment, current inflammatory biomarkers are not useful to distinguish infection from flares of autoimmune diseases. This is an important issue in clinical settings, because therapeutic strategies for infection and disease flares are completely opposite. Infection must be treated primarily with antibiotics and discontinuation of biological agents should be considered. In contrast, disease flares should be treated intensively with the same or alternative biological agents. Thus, new biomarkers are needed for the detection and discrimination of inflammation by either infection or disease flares.

Even after the successful repression of disease with biologic therapies, it remains unknown yet whether biological agents can be terminated safely without disease recurrence. Therefore, a biomarker that indicates clinical remission or cure of autoimmune diseases is helpful to determine the timing to stop biological agents.

Collectively, the development of a number of novel biomarkers, such as those that can help to select biological agents before therapy, can precisely evaluate disease activity and therapeutic effect during the therapy or can instruct the timing of therapy completion after achievement of remission, are warranted for the appropriate clinical management of patients receiving biological therapies.

#### **3.2 Serum proteome analysis using the new technology iTRAQ**

The pathogenesis of autoimmune diseases involves alterations in the expression of genes that control pathways regulating self tolerance. However, gene transcripts may not faithfully reflect their protein levels. In addition, post-translational modifications are not amenable to the study of transcriptional profiling (Hueber and Robinson, 2006). Recently, there has been the remarkable improvement of the proteomic approaches as represented by the development of sophisticated methods of protein sample preparation and the improvement of the sensitivity, accuracy and resolution in mass spectrometer. Therefore, direct proteomic measurement may provide greater utility for the discovery of new biomarkers monitoring autoimmune diseases in the post genomic era. Current efforts to

Application of Novel Quantitative Proteomic Technologies to

al., 2009; Hardt et al., 2005; Zhang et al., 2005).

therapeutic interventions (Dwivedi et al., 2009).

**3.3 Leucin-rich-α-2 glycoprotein as a novel biomarker** 

Identify New Serological Biomarkers in Autoimmune Diseases 231

external reagent tags. These methods address some of the limitations faced in traditional gel-based proteomic approaches. However, these approaches still suffer from some limitations such as inability to multiplex and to quantify zero protein expression level. In contrast, a novel quantitative proteomic technology, isobaric tagging of peptides enable simultaneous identification and quantification of peptides by tandem MS and permit

The isobaric tags for relative and absolute quantitation (iTRAQ), which is one such method commercialized, uses four amine specific isobaric reagents to label the primary amines of peptides from four to eight different biological samples. The labeled peptides from each sample are mixed, separated using two-dimensional liquid chromatography and analyzed using MS and tandem mass spectrometry (Figure 3). The isobaric tagging strategy provides multiple independent measures of the relative abundance of a protein. The capability of iTRAQ for protein quantitation has been verified by analyzing standard mixtures of proteins of known proportions (Aggarwal et al., 2006). The iTRAQ approach has now been successfully used to identify and quantify the proteins in variety of prokaryotic and eukaryotic samples (Aggarwal et al., 2006; Cong et al., 2006; DeSouza et al., 2005; Dwivedi et

We reported for the first time that the iTRAQ technology is applicable to identify novel biomarkers in sera from patients with autoimmune diseases (Serada et al., 2010). Before we publish our results, a study was published and reported the serum proteome of RA patients treated with anti-TNF mAb therapy. They provided evidence that iTRAQ strategy can be used to obtain quantitative data that reflect changes in the serum proteome after targeted

We used iTRAQ technology to obtain profiles of serum proteome in RA patients before and after TNF inhibitor treatment. We then listed serum proteins that declined remarkably after treatment. Our strategy was verified by the detection of familiar biomarkers including CRP and serum amyloid A (SAA) as reduced serum proteins after treatment. Among the candidate proteins that declined after therapy, we focused on an uncharacterized protein called leucine-rich-α-2 glycoprotein (LRG) and examined further on this protein using other methods such as Western blot and ELISA. Indeed, taking advantage of ELISA analysis of many serum samples from RA patients, we found that serum levels of LRG significantly declined after therapy with TNF inhibitors and correlate well with disease activity of RA patients. In addition, LRG levels were significantly high in patients with other autoimmune diseases such as Crohn's disease and Behcet disease. As expected, the LRG correlated well with a conventional biomarker CRP in patients with these autoimmune inflammatory diseases. Interestingly, however, while CRP correlated with serum IL-6 levels, LRG did not. In accordance with this, in some Crohn's disease patients with active disease, CRP levels remained low but serum LRG concentrations were significantly elevated. Thus, LRG exhibits similarity with CRP but also has a unique property. Moreover, because serum LRG concentrations of Crohn's disease patients before starting therapy were higher in the nonresponders to anti-TNF therapy than in the responders, LRG may predict therapeutic

responses to TNF inhibitors in Crohn's disease patients (Serada et al., 2010).

Until now, LRG has been reported to be expressed by liver cells and neutrophils, and regulated by multiple factors and produced at local inflammatory sites. According to the

parallel proteome analysis of more than two samples (Aggarwal et al., 2006).

identify autoimmune disease biomarkers have focused on three groups of proteins reflective of the autoimmune disease process. These groups include 1) degradation products arising from destruction of the affected tissues, 2) enzymes that play a role in tissue degradation, and 3) cytokines and other proteins associated with immune system activation and the inflammatory response (Prince, 2005). Recent proteomics technologies have enabled us to screen these markers from proteins extracted from tissues and sera from patients (Hueber and Robinson, 2006). Accordingly, there are many proteomic studies that analyzed protein profiles and searched new biomarkers in autoimmune diseases (Dwivedi et al., 2009; Ferraccioli et al., 2010; Ling et al., 2010; Serada et al., 2010; Takeuchi et al., 2007).

The quantitative proteome analysis by mass spectrometry (MS) usually involves differential isotope labeling of proteins and peptides metabolically, enzymatically or chemically using

In a single experiment of iTRAQ analysis, 4 to 8 samples differentially labeled with iTRAQ reagents can be quantitatively analyzed by mass spectrometry (this figure shows a four-plex reagent experiment). First, proteins extracted from cells, tissue and/or body fluid such as blood are reduced, alkylated and digested with trypsin. Second, obtained peptides in each sample are labeled with each iTRAQTM reagent at N-terminal amino group or epsilon amino group from lysine. The iTRAQ tags are isobaric and the labeling with iTRAQ reagents results in the uniform increase in molecular weight of peptides in every sample. After labeling, samples are mixed into one tube and analyzed by liquid chromatographytandem mass spectrometry (LC-MS/MS). Mass spectrometry is performed by full scan MS, followed by MS/MS spectra of peptides. In MS/MS spectra, iTRAQ tag-specific reporter ions (114.1, 115.1, 116.1, 117.1 in the figure) are detected in low m/z region, and these reporter ion intensities represent the abundance of the peptide from each sample. Peptide sequence information is obtained from high m/z region of MS/MS spectra and the protein is identified by database search after comparing obtained MS/MS spectra with theoretical MS/MS spectra in the database. Usually, candidate biomarker proteins obtained by iTRAQ analysis are further verified by other methods such as ELISA analysis.

Fig. 3. Flowchart of iTRAQ analysis

identify autoimmune disease biomarkers have focused on three groups of proteins reflective of the autoimmune disease process. These groups include 1) degradation products arising from destruction of the affected tissues, 2) enzymes that play a role in tissue degradation, and 3) cytokines and other proteins associated with immune system activation and the inflammatory response (Prince, 2005). Recent proteomics technologies have enabled us to screen these markers from proteins extracted from tissues and sera from patients (Hueber and Robinson, 2006). Accordingly, there are many proteomic studies that analyzed protein profiles and searched new biomarkers in autoimmune diseases (Dwivedi et al., 2009;

The quantitative proteome analysis by mass spectrometry (MS) usually involves differential isotope labeling of proteins and peptides metabolically, enzymatically or chemically using

In a single experiment of iTRAQ analysis, 4 to 8 samples differentially labeled with iTRAQ reagents can be quantitatively analyzed by mass spectrometry (this figure shows a four-plex reagent experiment). First, proteins extracted from cells, tissue and/or body fluid such as blood are reduced, alkylated and digested with trypsin. Second, obtained peptides in each sample are labeled with each iTRAQTM reagent at N-terminal amino group or epsilon amino group from lysine. The iTRAQ tags are isobaric and the labeling with iTRAQ reagents results in the uniform increase in molecular weight of peptides in every sample. After labeling, samples are mixed into one tube and analyzed by liquid chromatographytandem mass spectrometry (LC-MS/MS). Mass spectrometry is performed by full scan MS, followed by MS/MS spectra of peptides. In MS/MS spectra, iTRAQ tag-specific reporter ions (114.1, 115.1, 116.1, 117.1 in the figure) are detected in low m/z region, and these reporter ion intensities represent the abundance of the peptide from each sample. Peptide sequence information is obtained from high m/z region of MS/MS spectra and the protein is identified by database search after comparing obtained MS/MS spectra with theoretical MS/MS spectra in the database. Usually, candidate biomarker proteins

obtained by iTRAQ analysis are further verified by other methods such as ELISA analysis.

Fig. 3. Flowchart of iTRAQ analysis

Ferraccioli et al., 2010; Ling et al., 2010; Serada et al., 2010; Takeuchi et al., 2007).

external reagent tags. These methods address some of the limitations faced in traditional gel-based proteomic approaches. However, these approaches still suffer from some limitations such as inability to multiplex and to quantify zero protein expression level. In contrast, a novel quantitative proteomic technology, isobaric tagging of peptides enable simultaneous identification and quantification of peptides by tandem MS and permit parallel proteome analysis of more than two samples (Aggarwal et al., 2006).

The isobaric tags for relative and absolute quantitation (iTRAQ), which is one such method commercialized, uses four amine specific isobaric reagents to label the primary amines of peptides from four to eight different biological samples. The labeled peptides from each sample are mixed, separated using two-dimensional liquid chromatography and analyzed using MS and tandem mass spectrometry (Figure 3). The isobaric tagging strategy provides multiple independent measures of the relative abundance of a protein. The capability of iTRAQ for protein quantitation has been verified by analyzing standard mixtures of proteins of known proportions (Aggarwal et al., 2006). The iTRAQ approach has now been successfully used to identify and quantify the proteins in variety of prokaryotic and eukaryotic samples (Aggarwal et al., 2006; Cong et al., 2006; DeSouza et al., 2005; Dwivedi et al., 2009; Hardt et al., 2005; Zhang et al., 2005).

#### **3.3 Leucin-rich-α-2 glycoprotein as a novel biomarker**

We reported for the first time that the iTRAQ technology is applicable to identify novel biomarkers in sera from patients with autoimmune diseases (Serada et al., 2010). Before we publish our results, a study was published and reported the serum proteome of RA patients treated with anti-TNF mAb therapy. They provided evidence that iTRAQ strategy can be used to obtain quantitative data that reflect changes in the serum proteome after targeted therapeutic interventions (Dwivedi et al., 2009).

We used iTRAQ technology to obtain profiles of serum proteome in RA patients before and after TNF inhibitor treatment. We then listed serum proteins that declined remarkably after treatment. Our strategy was verified by the detection of familiar biomarkers including CRP and serum amyloid A (SAA) as reduced serum proteins after treatment. Among the candidate proteins that declined after therapy, we focused on an uncharacterized protein called leucine-rich-α-2 glycoprotein (LRG) and examined further on this protein using other methods such as Western blot and ELISA. Indeed, taking advantage of ELISA analysis of many serum samples from RA patients, we found that serum levels of LRG significantly declined after therapy with TNF inhibitors and correlate well with disease activity of RA patients. In addition, LRG levels were significantly high in patients with other autoimmune diseases such as Crohn's disease and Behcet disease. As expected, the LRG correlated well with a conventional biomarker CRP in patients with these autoimmune inflammatory diseases. Interestingly, however, while CRP correlated with serum IL-6 levels, LRG did not. In accordance with this, in some Crohn's disease patients with active disease, CRP levels remained low but serum LRG concentrations were significantly elevated. Thus, LRG exhibits similarity with CRP but also has a unique property. Moreover, because serum LRG concentrations of Crohn's disease patients before starting therapy were higher in the nonresponders to anti-TNF therapy than in the responders, LRG may predict therapeutic responses to TNF inhibitors in Crohn's disease patients (Serada et al., 2010).

Until now, LRG has been reported to be expressed by liver cells and neutrophils, and regulated by multiple factors and produced at local inflammatory sites. According to the

Application of Novel Quantitative Proteomic Technologies to

Identify New Serological Biomarkers in Autoimmune Diseases 233

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previous reports, it seems that LRG is not a unique biomarker in autoimmune disease but rather is a generalized inflammatory biomarker, because serum LRG levels are reported to be increased in patients with bacterial infection and several types of cancers. Nevertheless, serum LRG satisfies the condition of an inflammatory biomarker in the point that its concentration is high at diagnosis, correlated well with disease activity and is a possible predictor of the responsiveness to biological agents. For these reasons, serum LRG is a novel inflammatory biomarker potentially surrogate for CRP. Further studies are in progress in our laboratory to determine the pathophysiological function of LRG and the clinical benefit of LRG measurement.
