Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues

Front Immunol. 2024 Aug 5:15:1428773. doi: 10.3389/fimmu.2024.1428773. eCollection 2024.

Abstract

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease characterized by inflammation and hyperplasia of the synovial tissues. RA pathogenesis involves multiple cell types, genes, transcription factors (TFs) and networks. Yet, little is known about the TFs, and key drivers and networks regulating cell function and disease at the synovial tissue level, which is the site of disease. In the present study, we used available RNA-seq databases generated from synovial tissues and developed a novel approach to elucidate cell type-specific regulatory networks on synovial tissue genes in RA. We leverage established computational methodologies to infer sample-specific gene regulatory networks and applied statistical methods to compare network properties across phenotypic groups (RA versus osteoarthritis). We developed computational approaches to rank TFs based on their contribution to the observed phenotypic differences between RA and controls across different cell types. We identified 18 (fibroblast-like synoviocyte), 16 (T cells), 19 (B cells) and 11 (monocyte) key regulators in RA synovial tissues. Interestingly, fibroblast-like synoviocyte (FLS) and B cells were driven by multiple independent co-regulatory TF clusters that included MITF, HLX, BACH1 (FLS) and KLF13, FOSB, FOSL1 (B cells). However, monocytes were collectively governed by a single cluster of TF drivers, responsible for the main phenotypic differences between RA and controls, which included RFX5, IRF9, CREB5. Among several cell subset and pathway changes, we also detected reduced presence of Natural killer T (NKT) cells and eosinophils in RA synovial tissues. Overall, our novel approach identified new and previously unsuspected Key driver genes (KDG), TF and networks and should help better understanding individual cell regulation and co-regulatory networks in RA pathogenesis, as well as potentially generate new targets for treatment.

Keywords: FLS; T cell; co-regulation; gene regulatory network (GRN); key driver; monocyte; rheumatoid arthritis; transcriptomic factor.

MeSH terms

  • Arthritis, Rheumatoid* / genetics
  • Arthritis, Rheumatoid* / immunology
  • B-Lymphocytes / immunology
  • B-Lymphocytes / metabolism
  • Computational Biology / methods
  • Gene Expression Profiling
  • Gene Expression Regulation
  • Gene Regulatory Networks*
  • Humans
  • Osteoarthritis / genetics
  • Osteoarthritis / metabolism
  • Synovial Membrane* / immunology
  • Synovial Membrane* / metabolism
  • Synovial Membrane* / pathology
  • Synoviocytes / metabolism
  • Transcription Factors / genetics
  • Transcription Factors / metabolism
  • Transcriptome

Substances

  • Transcription Factors

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work presented in this research received funding from the European Union’s Horizon 2020 research and innovation program under two grant agreements: the COSMIC European Training Network (grant No 765158) and the EU project iPC (grant No 826121). PG and TL were funded by the NIH R01AR07316, by the Icahn School of Medicine at Mount Sinai, and by the Eunice Bernhard fund.