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

Abstract

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.

Fibroblast-like synoviocytes (FLS) are used as a model of rheumatoid arthritis synoviocytes, although cell lines derived from individual patients can have heterogeneous biology. Here the authors use a Taiji computational approach to analyze gene expression, chromatin accessibility and functional differences between individual patient-derived RA FLS lines.