5. Mass spectrometric detection and bioinformatics analysis

Upon separation, peptides are being detected using mass spectrometry and analyzed by comparing experimental data and databases of in-silico digested proteins. Several MS approaches have been applied for detection of salivary peptides: Electrospray Time-of-Flight (ESI-ToF), MALDI-Time-of-Flight (MALDI-ToF), ESI-Orbitrap analysis, ESI-Quadrupole ToF, etc.

Depending on MS type and selected instrumental method, posttranslational modifications of proteins can also be identified and thoroughly analyzed thus enabled a deeper insight into the proteome. The majority of top-down analysis, i.e. analysis of undigested proteins is performed using MALDI mass spectrometers, and the majority of analysis for digested proteins (peptides) is performed using electrospray (ESI) ionization and ToF and Orbitrap mass analyzer.

The analysis of obtained raw data is performed by searching protein databases such as SwissProt, Uniprot, NR (by NCBI), and user-generated databases. A number of commercially available software packages such as Mascot (Matrix Science, London), ProteinScape (Bruker, Germany), Proteome Discoverer (Thermo Scientific, Bremen, Germany), and of free available software such as The Global Proteome Machine (www.thegpm.org), MaxQuant (http://www. coxdocs.org/doku.php?id=maxquant:start), PeptideShaker (http://www.uib.no/en/rg/probe/ 65218/peptideshaker), Skyline (https://skyline.ms/project/home/begin.view?), OpenMS (https:// www.openms.de/), and other packages. The choice of the software to be used strongly depends not only on personal preferences but also on data to be analyzed and the information needed to be extracted.

Figure 6 shows a screenshot of two software packages preferably used at the Proteomics Core Facility at the Medical University of Vienna, Peptide Shaker and ProteinScape.

In addition to database search and protein identification, the analysis of the pathways where proteins are being over- or underexpressed and the analysis of interactions with other proteins have been performed using free software such as DAVID® (https://david.ncifcrf.gov/), STRING (https://string-db.org/), Reactome (http://reactome.org/) or commercially available MetaCore® (http://lsresearch.thomsonreuters.com/) or similar.

Figure 6. Screenshots showing analysis of a salivary sample by applying two distinct software packages. Note that

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identified proteins have been listed based on their scores, which can be calculated using different algorithms.

improved for identified proteins. Improved sequence coverage for identified proteins is one of the major challenges for proteomics analysis with high importance since it enhances chances for detection of posttranslational modifications (PTM) such as phosphorylation, glycosylation,

Different types of separation approaches can be used for the two-dimensional approach: strong cation exchange columns, weak anion exchange columns, reversed phase columns at high pH, and other combinations are possible [61–63]. A schematic of possible combinations of

Generally, the use of multidimensional separation will result in increased number of identifications, and Figure 5 shows the comparison of the number of detected proteins upon applying the two-dimensional chromatographic separation with strong cation exchange column used

Upon separation, peptides are being detected using mass spectrometry and analyzed by comparing experimental data and databases of in-silico digested proteins. Several MS approaches have been applied for detection of salivary peptides: Electrospray Time-of-Flight (ESI-ToF),

Depending on MS type and selected instrumental method, posttranslational modifications of proteins can also be identified and thoroughly analyzed thus enabled a deeper insight into the proteome. The majority of top-down analysis, i.e. analysis of undigested proteins is performed using MALDI mass spectrometers, and the majority of analysis for digested proteins (peptides) is performed using electrospray (ESI) ionization and ToF and Orbitrap

The analysis of obtained raw data is performed by searching protein databases such as SwissProt, Uniprot, NR (by NCBI), and user-generated databases. A number of commercially available software packages such as Mascot (Matrix Science, London), ProteinScape (Bruker, Germany), Proteome Discoverer (Thermo Scientific, Bremen, Germany), and of free available software such as The Global Proteome Machine (www.thegpm.org), MaxQuant (http://www. coxdocs.org/doku.php?id=maxquant:start), PeptideShaker (http://www.uib.no/en/rg/probe/ 65218/peptideshaker), Skyline (https://skyline.ms/project/home/begin.view?), OpenMS (https:// www.openms.de/), and other packages. The choice of the software to be used strongly depends not only on personal preferences but also on data to be analyzed and the information needed to

Figure 6 shows a screenshot of two software packages preferably used at the Proteomics Core

In addition to database search and protein identification, the analysis of the pathways where proteins are being over- or underexpressed and the analysis of interactions with other proteins have been performed using free software such as DAVID® (https://david.ncifcrf.gov/), STRING (https://string-db.org/), Reactome (http://reactome.org/) or commercially available MetaCore®

Facility at the Medical University of Vienna, Peptide Shaker and ProteinScape.

(http://lsresearch.thomsonreuters.com/) or similar.

MALDI-Time-of-Flight (MALDI-ToF), ESI-Orbitrap analysis, ESI-Quadrupole ToF, etc.

5. Mass spectrometric detection and bioinformatics analysis

methylation, etc., which are important as drug targets.

74 Salivary Glands - New Approaches in Diagnostics and Treatment

chromatographic approaches is shown in Figure 4.

for the first separation dimension.

mass analyzer.

be extracted.


Figure 6. Screenshots showing analysis of a salivary sample by applying two distinct software packages. Note that identified proteins have been listed based on their scores, which can be calculated using different algorithms.


An exemplary result of the pathway analysis of a salivary sample using DAVID® is shown in

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Finally, the analysis of all generated data and extracted information shall enable detection of

As already mentioned, analysis of the salivary proteome can be applied to study a large area of conditions and diseases. The most intensively studied area of saliva as a diagnostic tool was its appliance for dental [21, 57, 64, 65], oral cancer [31, 66], diabetes [67, 68] or gastric cancer [58]. Furthermore, salivary proteomics was also applied for studying neurological and psychiatric

The use of proteomics for diagnostics and treatment of oral diseases has been described in a number of publications. Jancsik et al. [70] describe the use of salivary proteomics to identify squamous oral cancer in diabetes patients. Authors have performed an additional sample homogenization, which is rarely described in other approaches to analyze saliva. Following analysis was performed by applying 2D gel separation of proteins and MS analysis using MALDI-TOF without the previous chromatographic separation and fractionation of proteins. It is known that inflammatory processes have a well-documented carcinogenetic role. Patients suffering from type-2 diabetes have also a higher risk of inflammatory diseases in the gastrointestinal tract such as ulcerative colitis or Crohn's disease. These patients have also a higher risk of developing gastrointestinal cancer. It was shown that the incidence of developing benign tumors, leukoplakia, and malignancies was significantly increased in the group of patients with diabetes than in the healthy control group. The authors have shown a discovery of several putative biomarkers such as, e.g. Annexin A8-like, Annexin A8-like 1, Tyrosine kinase, AX969656, Protein kinase, Peroxiredoxin-2, and Annexin A2. Annexins are known to be overexpressed in colorectal cancer but also to have altered in tumorigenesis in several types of tumor. Furthermore, loss of Annexin A1 has been found to be an early event in esophageal squamous cell carcinoma. Obviously, these results show that diabetic patients have a higher risk of developing esophageal squamous cell carcinoma than the control healthy group and

Delaleu et al. [71] have performed a particularly interesting and thorough investigation of the salivary proteome from patients suffering from Sjörgen's syndrome. Salivary proteome was analyzed using a 187-plex capture antibody-based assay, and the salivary proteomic biomarker profiles were generated from patients with primary Sjörgen's syndrome, patients with rheumatoid arthritis, and from asymptomatic controls. Authors were able to characterize putative biomarkers by detecting significant changes in 61 and 55 proteins, respectively, in samples of patients compared to that of donors without the diagnosis of Sjörgen's syndrome. Authors were able to detect, based on 4-plex and 6-plex biomarker signatures, markers

6. Application of salivary proteomics analysis for clinical research

6.1. Application of salivary proteomics for diagnostics of oral diseases

putative biomarkers for diseases and therapy monitoring.

close monitoring shall be applied for early detection.

Figure 7.

disorders [69].

Figure 7. Pathway analysis of salivary proteins using DAVID® resulted in expected output and identification of salivary secretion as the pathway with the highest number of expressed proteins. Data courtesy of Zofia Świątczak (Master Thesis).

An exemplary result of the pathway analysis of a salivary sample using DAVID® is shown in Figure 7.

Finally, the analysis of all generated data and extracted information shall enable detection of putative biomarkers for diseases and therapy monitoring.
