Systems-biology analysis of rheumatoid arthritis fibroblast-like synoviocytes implicates cell line-specific transcription factor function

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作者
Richard I. Ainsworth
Deepa Hammaker
Gyrid Nygaard
Cecilia Ansalone
Camilla Machado
Kai Zhang
Lina Zheng
Lucy Carrillo
Andre Wildberg
Amanda Kuhs
Mattias N. D. Svensson
David L. Boyle
Gary S. Firestein
Wei Wang
机构
[1] University of California,Department of Chemistry and Biochemistry
[2] San Diego,Department of Cellular and Molecular Medicine
[3] University of California,Department of Medicine
[4] San Diego,Bioinformatics and Systems Biology Program
[5] University of California,Division of Psychiatry
[6] San Diego,Department of Rheumatology and Inflammation Research, Sahlgrenska Academy, Institute of Medicine
[7] University of California,undefined
[8] San Diego,undefined
[9] Haukeland University Hospital,undefined
[10] University of Gothenburg,undefined
来源
Nature Communications | / 13卷
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摘要
Rheumatoid arthritis (RA) is an immune-mediated disease affecting diarthrodial joints that remains an unmet medical need despite improved therapy. This limitation likely reflects the diversity of pathogenic pathways in RA, with individual patients demonstrating variable responses to targeted therapies. Better understanding of RA pathogenesis would be aided by a more complete characterization of the disease. To tackle this challenge, we develop and apply a systems biology approach to identify important transcription factors (TFs) in individual RA fibroblast-like synoviocyte (FLS) cell lines by integrating transcriptomic and epigenomic information. Based on the relative importance of the identified TFs, we stratify the RA FLS cell lines into two subtypes with distinct phenotypes and predicted active pathways. We biologically validate these predictions for the top subtype-specific TF RARα and demonstrate differential regulation of TGFβ signaling in the two subtypes. This study characterizes clusters of RA cell lines with distinctive TF biology by integrating transcriptomic and epigenomic data, which could pave the way towards a greater understanding of disease heterogeneity.
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