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Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

ABSTRACT: BACKGROUND:Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis. METHODS:Biopsies were obtained from affected and unaffected skin areas of SSc patients. Samples were cryo-pulverised and proteins were extracted and analysed using mass spectrometry (MS) discovery analysis. Differentially expressed proteins were revealed after analysis with the Progenesis QIp software. Pathway analysis was performed using the Enrichr Web server. Using specific criteria, fifteen proteins were selected for further validation with targeted-MS analysis. RESULTS:Proteomic analysis led to the identification and quantification of approximately 2000 non-redundant proteins. Statistical analysis showed that 169 of these proteins were significantly differentially expressed in affected versus unaffected tissues. Pathway analyses showed that these proteins are involved in multiple pathways that are associated with autoimmune diseases (AIDs) and fibrosis. Fifteen of these proteins were further investigated using targeted-MS approaches, and five of them were confirmed to be significantly differentially expressed in SSc affected versus unaffected skin biopsies. CONCLUSION:Using MS-based proteomics analysis of human skin biopsies from patients with SSc, we identified a number of proteins and pathways that might be involved in SSc progression and pathogenesis. Fifteen of these proteins were further validated, and results suggest that five of them may serve as potential biomarkers for SSc.

SUBMITTER: Chairta P

PROVIDER: S-EPMC7206756 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

Chairta Paraskevi P Nicolaou Paschalis P Sokratous Kleitos K Galant Christine C Houssiau Frédéric F Oulas Anastasis A Spyrou George M GM Alarcon-Riquelme Marta E ME Lauwerys Bernard R BR Christodoulou Kyproula K

Arthritis research & therapy 20200507 1

<h4>Background</h4>Pathogenesis and aetiology of systemic sclerosis (SSc) are currently unclear, thus rendering disease prognosis, diagnosis and treatment challenging. The aim of this study was to use paired skin biopsy samples from affected and unaffected areas of the same patient, in order to compare the proteomes and identify biomarkers and pathways which are associated with SSc pathogenesis.<h4>Methods</h4>Biopsies were obtained from affected and unaffected skin areas of SSc patients. Sample ...[more]

PMID: 32381114

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Project description:BackgroundSkin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin.MethodsWe analyzed biopsies from two independent (primary and secondary) cohorts. One investigator performed mRSS assessments and forearm biopsies, and trichrome-stained biopsy sections were photomicrographed. We used the AlexNet DNN to generate a numerical signature of 4096 quantitative image features (QIFs) for 100 randomly selected dermal image patches/biopsy. In the primary cohort, we used principal components analysis (PCA) to summarize the QIFs into a Biopsy Score for comparison with mRSS. In the secondary cohort, using QIF signatures as the input, we fit a logistic regression model to discriminate between SSc vs. control biopsy, and a linear regression model to estimate mRSS, yielding Diagnostic Scores and Fibrosis Scores, respectively. We determined the correlation between Fibrosis Scores and the published Scleroderma Skin Severity Score (4S) and between Fibrosis Scores and longitudinal changes in mRSS on a per patient basis.ResultsIn the primary cohort (n =?6, 26 SSc biopsies), Biopsy Scores significantly correlated with mRSS (R?=?0.55, p =?0.01). In the secondary cohort (n =?60 SSc and 16 controls, 164 biopsies; divided into 70% training and 30% test sets), the Diagnostic Score was significantly associated with SSc-status (misclassification rate?=?1.9% [training], 6.6% [test]), and the Fibrosis Score significantly correlated with mRSS (R?=?0.70 [training], 0.55 [test]). The DNN-derived Fibrosis Score significantly correlated with 4S (R?=?0.69, p =?3?×?10-?17).ConclusionsDNN analysis of SSc biopsies is an unbiased, quantitative, and reproducible outcome that is associated with validated SSc outcomes.

Project description:Systemic sclerosis is a connective tissue disease affecting skin and internal organs, characterized by a triad of inflammation, vasculopathy and progressive fibrosis, due to deposition of mainly type I collagen. Out of the intricate mechanisms involved in the pathogenesis of the disease, evidence indicates that TGFbeta signaling plays a central role in mediating the effects of several pro-fibrotic effectors. In addition, TGFbeta is induced by hypoxia in cultured fibroblasts, an observation suggesting a role for this cytokine in linking vasculopathy and fibrosis in the disease. Not surprisingly, TGFbeta and Wnt signaling are among the most prevalent pathways found in global gene expression studies performed on systemic sclerosis skin biopsies. In this perspective, modulation of TGFbeta activity remains a top therapeutic target in systemic sclerosis drug development. We recently performed whole-body magnetic resonance imaging (MRI) studies in systemic sclerosis patients, and evidenced deep connective tissue infiltrates surrounding tendons in patients with active disease, and tendon friction rubs. Tenosynovitis and arthritis were also found by MRI in one third of the patients. We performed tenosynovial biopsies in patients with clinically active tenosynovitis, in order to evaluate whether such samples would provide additional information on disease mechanisms. Here, we report that these samples are characterized by the over-expression of genes involved in fibrosis, TGFbeta/Wnt signaling, chemokines and cytokines, but also by the concurrent over-expression of several ubiquitin-specific peptidases (USPs). Among the USPs overexpressed in systemic sclerosis tenosynovial biopsies, USP15 is known to specifically deubiquitinate SMAD3, and the TGFbeta Receptor 1. These results triggered us to perform additional experiments in order to test whether USP15 overexpression plays a role in the pathogenesis of systemic sclerosis via decreased ubiquitin-mediated degradation of proteins involved in TGFbeta signaling.

Dataset Information

Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

Publications

Comparative analysis of affected and unaffected areas of systemic sclerosis skin biopsies by high-throughput proteomic approaches.

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