Molecular basis for clinical diversity between autoantibody subsets in diffuse cutaneous systemic sclerosis

Objectives Clinical heterogeneity is a cardinal feature of systemic sclerosis (SSc). Hallmark SSc autoantibodies are central to diagnosis and associate with distinct patterns of skin-based and organ-based complications. Understanding molecular differences between patients will benefit clinical practice and research and give insight into pathogenesis of the disease. We aimed to improve understanding of the molecular differences between key diffuse cutaneous SSc subgroups as defined by their SSc-specific autoantibodies Methods We have used high-dimensional transcriptional and proteomic analysis of blood and the skin in a well-characterised cohort of SSc (n=52) and healthy controls (n=16) to understand the molecular basis of clinical diversity in SSc and explore differences between the hallmark antinuclear autoantibody (ANA) reactivities. Results Our data define a molecular spectrum of SSc based on skin gene expression and serum protein analysis, reflecting recognised clinical subgroups. Moreover, we show that antitopoisomerase-1 antibodies and anti-RNA polymerase III antibodies specificities associate with remarkably different longitudinal change in serum protein markers of fibrosis and divergent gene expression profiles. Overlapping and distinct disease processes are defined using individual patient pathway analysis. Conclusions Our findings provide insight into clinical diversity and imply pathogenetic differences between ANA-based subgroups. This supports stratification of SSc cases by ANA antibody subtype in clinical trials and may explain different outcomes across ANA subgroups in trials targeting specific pathogenic mechanisms.

[1]  M. Karsdal,et al.  Circulating collagen neo-epitopes and their role in the prediction of fibrosis in patients with systemic sclerosis: a multicentre cohort study. , 2021, The Lancet. Rheumatology.

[2]  J. Inamo Association of differentially expressed genes and autoantibody type in patients with systemic sclerosis. , 2020, Rheumatology.

[3]  J. Goldin,et al.  Tocilizumab in systemic sclerosis: a randomised, double-blind, placebo-controlled, phase 3 trial. , 2020, The Lancet. Respiratory medicine.

[4]  Ryanne A. Brown,et al.  CD47 prevents the elimination of diseased fibroblasts in scleroderma , 2020, bioRxiv.

[5]  S. Werner,et al.  Activin-mediated alterations of the fibroblast transcriptome and matrisome control the biomechanical properties of skin wounds , 2020, Nature Communications.

[6]  W. Ye,et al.  Relationship of Baseline Measures to the Change in the Forced Vital Capacity in a Phase 3 Randomized Controlled Trial of Tocilizumab for the Treatment of Early Systemic Sclerosis , 2020 .

[7]  J. Inamo Association between gene expression profiling of skin lesion and autoantibody in patients with systemic sclerosis , 2020, medRxiv.

[8]  Sindhu R. Johnson,et al.  Riociguat in patients with early diffuse cutaneous systemic sclerosis (RISE-SSc): randomised, double-blind, placebo-controlled multicentre trial , 2020, Annals of the Rheumatic Diseases.

[9]  F. Ingegnoli,et al.  Scleroderma-specific autoantibodies embedded in immune complexes mediate endothelial damage: an early event in the pathogenesis of systemic sclerosis , 2020, Arthritis Research & Therapy.

[10]  C. Denton,et al.  Pathogenesis of systemic sclerosis associated interstitial lung disease , 2020, Journal of scleroderma and related disorders.

[11]  C. Denton,et al.  Using Autoantibodies and Cutaneous Subset to Develop Outcome‐Based Disease Classification in Systemic Sclerosis , 2020, Arthritis & rheumatology.

[12]  Ami A. Shah,et al.  Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile , 2019, Annals of the rheumatic diseases.

[13]  Viktor Martyanov,et al.  A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets , 2019, Arthritis & rheumatology.

[14]  D. Wuttge,et al.  FRI0326 SEROLOGICAL ASSESSMENT OF THE FIBROTIC INDEX IN SCLEROSIS: A CROSS SECTIONAL STUDY , 2019, Scleroderma, myositis and related syndromes.

[15]  M. Mogensen,et al.  Association of metabolites reflecting type III and VI collagen formation with modified Rodnan skin score in systemic sclerosis – a cross-sectional study , 2019, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.

[16]  P. Emery,et al.  European multicentre study validates enhanced liver fibrosis test as biomarker of fibrosis in systemic sclerosis , 2018, Rheumatology.

[17]  L. Rice,et al.  Single Cell RNA Sequencing Identifies HSPG2 and APLNR as Markers of Endothelial Cell Injury in Systemic Sclerosis Skin , 2018, Front. Immunol..

[18]  F. Ingegnoli,et al.  Immune complexes containing scleroderma-specific autoantibodies induce a profibrotic and proinflammatory phenotype in skin fibroblasts , 2018, Arthritis Research & Therapy.

[19]  Sun Ku Lee,et al.  Novel lung imaging biomarkers and skin gene expression subsetting in dasatinib treatment of systemic sclerosis-associated interstitial lung disease , 2017, PloS one.

[20]  Y. Tada,et al.  A possible implication of reduced levels of LIF, LIFR, and gp130 in vasculopathy related to systemic sclerosis , 2017, Archives of Dermatological Research.

[21]  Y. Asano,et al.  Critical contribution of the interleukin‐6/signal transducer and activator of transcription 3 axis to vasculopathy associated with systemic sclerosis , 2017, The Journal of dermatology.

[22]  Julio C. Mantero,et al.  Blockade of PDGF Receptors by Crenolanib Has Therapeutic Effect in Patient Fibroblasts and in Preclinical Models of Systemic Sclerosis. , 2017, The Journal of investigative dermatology.

[23]  M. Baron,et al.  Safety and efficacy of subcutaneous tocilizumab in adults with systemic sclerosis (faSScinate): a phase 2, randomised, controlled trial , 2016, The Lancet.

[24]  K. Chakravarty,et al.  BSR and BHPR guideline for the treatment of systemic sclerosis. , 2016, Rheumatology.

[25]  Jeffrey T. Chang,et al.  Dissecting the Heterogeneity of Skin Gene Expression Patterns in Systemic Sclerosis , 2015, Arthritis & rheumatology.

[26]  Tammara A. Wood,et al.  Gene expression changes reflect clinical response in a placebo-controlled randomized trial of abatacept in patients with diffuse cutaneous systemic sclerosis , 2015, Arthritis Research & Therapy.

[27]  A. Wells,et al.  Prediction of Pulmonary Complications and Long‐Term Survival in Systemic Sclerosis , 2014, Arthritis & rheumatology.

[28]  Oliver Distler,et al.  2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League against Rheumatism collaborative initiative. , 2013, Arthritis and rheumatism.

[29]  Tammara A. Wood,et al.  Molecular Signatures in Skin Associated with Clinical Improvement During Mycophenolate Treatment in Systemic Sclerosis , 2013, The Journal of investigative dermatology.

[30]  C. Denton,et al.  Autoantibodies as predictive tools in systemic sclerosis , 2010, Nature Reviews Rheumatology.

[31]  Sarah A. Pendergrass,et al.  Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin , 2008, PloS one.

[32]  C. Denton,et al.  Scleroderma renal crisis: patient characteristics and long-term outcomes. , 2007, QJM : monthly journal of the Association of Physicians.

[33]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[35]  Y. Shoenfeld,et al.  The complement system and systemic sclerosis , 1993, Immunologic research.

[36]  T. Medsger,et al.  Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. , 1988, The Journal of rheumatology.