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      Elucidating the endogenous synovial fluid proteome and peptidome of inflammatory arthritis using label-free mass spectrometry

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          Abstract

          Background

          Inflammatory arthritis (IA) is an immunological disorder in which loss of immune tolerance to endogenous self-antigens perpetuates synovitis and eventual destruction of the underlying cartilage and bone. Pathological changes in the joint are expected to be represented by synovial fluid (SF) proteins and peptides. In the present study, a mass spectrometry-based approach was utilized for the identification of key protein and peptide mediators of IA.

          Methods

          Age-matched SF samples from 10 rheumatoid arthritis patients, 10 psoriatic arthritis patients and 10 cadaveric controls were subjected to an integrated proteomic and peptidomic protocol using liquid chromatography tandem mass spectrometry. Significant differentially abundant proteins and peptides were identified between cohorts according to the results of a Mann–Whitney U test coupled to the Benjamini–Hochberg correction for multiple hypothesis testing. Fold change ratios were computed for each protein and peptide according to their log-transformed extracted ion current. Pathway analysis and antimicrobial peptide (AMP) prediction were conducted to clarify the pathophysiological relevance of identified proteins and peptides to IA.

          Results

          We determined that 144 proteins showed significant differential abundance between the IA and control SF proteomes, of which 11 protein candidates were selected for future follow-up studies. Similar analyses applied to our peptidomic data identified 15 peptide sequences, originating from 4 protein precursors, to have significant differential abundance in IA compared to the control SF peptidome. Pathway enrichment analysis of the IA SF peptidome along with AMP prediction suggests a possible mechanistic role of microbes in eliciting an immune response which drives the development of IA.

          Conclusions

          The discovery-phase data generated herein has provided a basis for the identification of candidates with the greatest potential to serve as novel serum biomarkers specific to inflammatory arthritides. Moreover, these findings facilitate the understanding of possible disease mechanisms specific to each subtype.

          Electronic supplementary material

          The online version of this article (10.1186/s12014-019-9243-3) contains supplementary material, which is available to authorized users.

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          Most cited references42

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          Decreased bacterial diversity characterizes the altered gut microbiota in patients with psoriatic arthritis, resembling dysbiosis in inflammatory bowel disease.

          To characterize the diversity and taxonomic relative abundance of the gut microbiota in patients with never-treated, recent-onset psoriatic arthritis (PsA).
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            The microbiome and rheumatoid arthritis.

            Humans are not (and have never been) alone. From the moment we are born, millions of micro-organisms populate our bodies and coexist with us rather peacefully for the rest of our lives. This microbiome represents the totality of micro-organisms (and their genomes) that we necessarily acquire from the environment. Micro-organisms living in or on us have evolved to extract the energy they require to survive, and in exchange they support the physiological, metabolic and immune capacities that have contributed to our evolutionary success. Although currently categorized as an autoimmune disorder and regarded as a complex genetic disease, the ultimate cause of rheumatoid arthritis (RA) remains elusive. It seems that interplay between predisposing genetic factors and environmental triggers is required for disease manifestation. New insights from DNA sequence-based analyses of gut microbial communities and a renewed interest in mucosal immunology suggest that the microbiome represents an important environmental factor that can influence autoimmune disease manifestation. This Review summarizes the historical clues that suggest a possible role for the microbiota in the pathogenesis of RA, and will focus on new technologies that might provide scientific evidence to support this hypothesis.
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              ProteomicsDB

              Abstract ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.
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                Author and article information

                Contributors
                m.mahendran@mail.utoronto.ca
                ed.keystone@sinaihealthsystem.ca
                rkrawetz@ucalgary.ca
                kun.liang@uwaterloo.ca
                eleftherios.diamadis@sinaihealthsystem.ca
                vinod.chandran@uhnresearch.ca
                Journal
                Clin Proteomics
                Clin Proteomics
                Clinical Proteomics
                BioMed Central (London )
                1542-6416
                1559-0275
                30 May 2019
                30 May 2019
                2019
                : 16
                : 23
                Affiliations
                [1 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Laboratory Medicine and Pathobiology, , University of Toronto, ; Toronto, ON Canada
                [2 ]ISNI 0000 0004 0473 9881, GRID grid.416166.2, Department of Pathology and Laboratory Medicine, , Mount Sinai Hospital, ; Toronto, ON Canada
                [3 ]ISNI 0000 0004 0473 9881, GRID grid.416166.2, Department of Rheumatology, , Mount Sinai Hospital, ; Toronto, ON Canada
                [4 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, McCaig Institute for Bone and Joint Health, , University of Calgary, ; Calgary, AB Canada
                [5 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Department of Surgery, , University of Calgary, ; Calgary, AB Canada
                [6 ]ISNI 0000 0004 1936 7697, GRID grid.22072.35, Department of Anatomy and Cell Biology, , University of Calgary, ; Calgary, AB Canada
                [7 ]ISNI 0000 0000 8644 1405, GRID grid.46078.3d, Department of Statistics and Actuarial Science, , University of Waterloo, ; Waterloo, ON Canada
                [8 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Department of Clinical Biochemistry, , University Health Network, ; Toronto, ON Canada
                [9 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Centre for Prognosis Studies in Rheumatic Diseases, Krembil Research Institute, Toronto Western Hospital, , University Health Network, ; Toronto, ON Canada
                [10 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Division of Rheumatology, Department of Medicine, , University of Toronto, ; Toronto, ON Canada
                [11 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Institute of Medical Science, , University of Toronto, ; Toronto, ON Canada
                Author information
                http://orcid.org/0000-0002-8297-0275
                Article
                9243
                10.1186/s12014-019-9243-3
                6542032
                20cf73ea-9ad6-4237-80d9-3e010ab92056
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 11 February 2019
                : 24 May 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004089, Krembil Foundation;
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Molecular medicine
                proteomics,peptidomics,biomarkers,synovial fluid,mass spectrometry,inflammatory arthritis,rheumatoid arthritis,psoriatic arthritis

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