Rheumatoid arthritis susceptibility genes show pathotype-specific expression in synovial tissue of early treatment-naïve RA patients

medRxiv

Abstract

Abstract Objectives Rheumatoid arthritis (RA) exhibits clinical and biological heterogeneity, with synovial tissue stratified into histological pathotypes: lympho-myeloid, diffuse-myeloid, and pauci-immune fibroid. Although GWAS have uncovered RA risk loci, how genetic risk relates to synovial immunopathology remains unclear. To better understand how genetic predisposition may shape divergent early disease mechanisms, we characterised the expression patterns of GWAS-identified RA susceptibility genes and related rheumatic diseases across the synovial pathotypes. Methods RNA-sequencing data from synovium of 87 treatment-naïve, early RA patients from the Pathobiology of Early Arthritis Cohort. Differential gene expression between pathotypes and pathway enrichment analyses were performed using susceptibility genes for RA, osteoarthritis (OA), ankylosing spondylitis, psoriatic arthritis and systemic lupus erythematosus. Results RA susceptibility gene expression in synovial tissue separated patients by pathotype and correlated with markers of disease activity. RA susceptibility genes were significantly enriched among genes upregulated in lympho-myeloid synovium and linked to lymphocyte activation and differentiation pathways. In contrast, OA susceptibility genes were upregulated in diffuse-myeloid and fibroid synovium. Both patterns were most pronounced in ACPA-positive and directionally consistent in ACPA-negative patients. Conclusion RA genetic susceptibility is not evenly distributed across synovial pathotypes but is strongly biased toward the lympho-myeloid pathotype, indicating that current GWAS signals preferentially capture immune-driven disease mechanisms. Enrichment of OA susceptibility genes in diffuse-myeloid and fibroid pathotypes, even among ACPA-positive patients, suggests shared biological features between auto-immune and non-inflammatory degenerative joint diseases in certain RA subtypes. Synovial pathotype stratification is therefore essential for interpreting genetic risk and understanding disease heterogeneity. Key messages What is already known on this topic - Rheumatoid arthritis (RA) is clinically and biologically heterogeneous, and its affected synovial tissue can be stratified into distinct immunohistological pathotypes. - GWAS have identified numerous genetic risk loci for RA and related rheumatic and inflammatory diseases. - It remains poorly understood how RA genetic risk relates to synovial tissue heterogeneity. What this study adds - GWAS-identified RA susceptibility genes show strong, pathotype-specific expression in synovial tissue, with marked enrichment in the lympho-myeloid pathotype. - OA susceptibility genes are primarily upregulated in diffuse-myeloid and pauci-immune fibroid RA synovium, indicating shared fibroblast- and remodelling-related pathways. - These gene expression patterns are most pronounced in ACPA-positive RA but remain directionally consistent in ACPA-negative RA. How this study might affect research, practice or policy - Synovial pathotype stratification should be incorporated into genetic studies of RA. - Pathotype-aware genetic studies may improve patient stratification and guide development of more targeted therapeutic strategies.

Authors 7

1. Inez C Den Hond Aachen Department of Rheumatology, Leiden

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Pattern Recognition & Bioinformatics, Delft, Netherlands,

   Leiden University Medical Center, Department of Rheumatology, Leiden, Netherlands,

2. Marcel J. T. Reinders Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Pattern Recognition & Bioinformatics, Delft, Netherlands,

   Leiden University Medical Center, Department of Biomedical Data Sciences, Leiden, Netherlands,

3. Myles Lewis

   Queen Mary University of London · National Institute for Health and Care Research · William Harvey Research Institute · Barts Health NHS Trust

   Affiliation as printed

   Barts Health NHS Trust and National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre (BRC), London, UK

   Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

4. Felice Rivellese

   Queen Mary University of London · National Institute for Health and Care Research · William Harvey Research Institute · Barts Health NHS Trust

   Affiliation as printed

   Barts Health NHS Trust and National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre (BRC), London, UK

   Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

5. Costantino Pitzalis

   Queen Mary University of London · National Institute for Health and Care Research · William Harvey Research Institute · Barts Health NHS Trust

   Affiliation as printed

   Barts Health NHS Trust and National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre (BRC), London, UK

   Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

6. Rachel Knevel Aachen Department of Rheumatology, Leiden

   Newcastle University · Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Pattern Recognition & Bioinformatics, Delft, Netherlands,

   Leiden University Medical Center, Department of Rheumatology, Leiden, Netherlands,

   Newcastle University, School of Clinical Medical Sciences, Newcastle Upon Tyne, UK

7. Erik B. van den Akker corresponding Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Pattern Recognition & Bioinformatics, Delft, Netherlands,

   Leiden University Medical Center, Department of Biomedical Data Sciences, Leiden, Netherlands,