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Parsing multiomics landscape of activated synovial fibroblasts highlights drug targets linked to genetic risk of rheumatoid arthritis
  1. Haruka Tsuchiya1,
  2. Mineto Ota1,2,
  3. Shuji Sumitomo1,
  4. Kazuyoshi Ishigaki3,
  5. Akari Suzuki4,
  6. Toyonori Sakata5,
  7. Yumi Tsuchida1,
  8. Hiroshi Inui6,
  9. Jun Hirose6,
  10. Yuta Kochi4,7,
  11. Yuho Kadono8,
  12. Katsuhiko Shirahige5,
  13. Sakae Tanaka6,
  14. Kazuhiko Yamamoto4,
  15. Keishi Fujio1
  1. 1 Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  2. 2 Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  3. 3 Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
  4. 4 Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
  5. 5 Laboratory of Genome Structure and Function, Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan
  6. 6 Department of Orthopaedic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  7. 7 Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
  8. 8 Department of Orthopaedic Surgery, Saitama Medical University, Saitama, Japan
  1. Correspondence to Dr Keishi Fujio, Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; kfujio-tky{at}


Objectives Synovial fibroblasts (SFs) are one of the major components of the inflamed synovium in rheumatoid arthritis (RA). We aimed to gain insight into the pathogenic mechanisms of SFs through elucidating the genetic contribution to molecular regulatory networks under inflammatory condition.

Methods SFs from RA and osteoarthritis (OA) patients (n=30 each) were stimulated with eight different cytokines (interferon (IFN)-α, IFN-γ, tumour necrosis factor-α, interleukin (IL)-1β, IL-6/sIL-6R, IL-17, transforming growth factor-β1, IL-18) or a combination of all 8 (8-mix). Peripheral blood mononuclear cells were fractioned into five immune cell subsets (CD4+ T cells, CD8+ T cells, B cells, natural killer (NK) cells, monocytes). Integrative analyses including mRNA expression, histone modifications (H3K27ac, H3K4me1, H3K4me3), three-dimensional (3D) genome architecture and genetic variations of single nucleotide polymorphisms (SNPs) were performed.

Results Unstimulated RASFs differed markedly from OASFs in the transcriptome and epigenome. Meanwhile, most of the responses to stimulations were shared between the diseases. Activated SFs expressed pathogenic genes, including CD40 whose induction by IFN-γ was significantly affected by an RA risk SNP (rs6074022). On chromatin remodelling in activated SFs, RA risk loci were enriched in clusters of enhancers (super-enhancers; SEs) induced by synergistic proinflammatory cytokines. An RA risk SNP (rs28411362), located in an SE under synergistically acting cytokines, formed 3D contact with the promoter of metal-regulatory transcription factor-1 (MTF1) gene, whose binding motif showed significant enrichment in stimulation specific-SEs. Consistently, inhibition of MTF1 suppressed cytokine and chemokine production from SFs and ameliorated mice model of arthritis.

Conclusions Our findings established the dynamic landscape of activated SFs and yielded potential therapeutic targets associated with genetic risk of RA.

  • synovitis
  • fibroblasts
  • arthritis
  • rheumatoid
  • cytokines
  • autoimmune diseases

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  • Handling editor Josef S Smolen

  • HT and MO contributed equally.

  • Contributors HT, SS, KI, YKo, KY and KF designed the research project. MO conducted bioinformatics analysis on the advice of KI, AS performed RNA sequencing. HT performed ChIP sequencing and other in vitro experiments. TS and KS performed Hi-C. HT, YT, HI, JH, YKa and ST contributed human samples. SS performed figure editing. HT and MO wrote the manuscript with critical inputs from YKo, KY and KF.

  • Funding This research was supported by funding from Takeda Pharmaceutical (YKo, KY and KF), the Ministry of Health, Labour and Welfare, Ministry of Education, Culture, Sports, Science and Technology KAKENHI Grant-in-Aid for Scientific Research (B) (18H02846) and Grant-in-Aid for Scientific Research (C) (17K09972) from the Japan Society for the Promotion of Science.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available in a public, open access repository. The datasets generated during this study are available at the National Bioscience Database Center (NBDC) with the study accession code hum0207.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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