It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We identified a module of 815 genes associated with pain, using a novel machine learning approach, Graph-based Gene expression Module Identification (GbGMI), in samples from patients with longstanding RA, but limited synovial inflammation at arthroplasty, and validated this finding in an independent cohort of synovial biopsy samples from early, untreated RA patients. Single-cell RNA-seq analyses indicated these genes were most robustly expressed by lining layer fibroblasts and receptor-ligand interaction analysis predicted robust lining layer fibroblast crosstalk with pain sensitive CGRP+ dorsal root ganglion sensory neurons. Netrin-4, which is abundantly expressed by lining fibroblasts and associated with pain, significantly increased the branching of pain-sensitive CGRP+ neurons in vitro. We conclude GbGMI is a useful method for identifying a module of genes that associate with a clinical feature of interest. Using this approach, we find that Netrin-4 is produced by synovial fibroblasts in the absence of inflammation and can enhance the outgrowth of CGRP+ pain sensitive nerve fibers.

The authors have declared no competing interest.

National Science Foundation 1750326 National Institute of Arthritis and Musculoskeletal and Skin Diseases NIH R01 AR078268 National Institute of Arthritis and Musculoskeletal and Skin Diseases UC2AR081025 National Institute of Arthritis and Musculoskeletal and Skin Diseases R01AR077019

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This study was approved by the HSS Institutional Review Board (approval no. 2014-233), the Rockefeller University Institutional Review Board (approval no. DOR0822), and the Biomedical Research Alliance of New York (approval no. 15-08-114-385).

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