P112 Cytokine driven disease and epigenetic heterogeneity in rheumatoid arthritis
Bibliographic record
Abstract
Abstract Background/Aims Therapies that target cytokine signals have revolutionised the treatment of immune-mediated inflammatory diseases. However, many patients show an inadequate response to particular drug classes, reflecting the complex and often heterogeneous nature of pathology. In rheumatoid arthritis, synovial biopsies display a broad range of cellular and molecular hallmarks of disease that classify the occurrence of myeloid-rich, fibroblast-rich, and lymphoid-rich synovitis. It is unclear whether these differences in immune pathology reflect individual disease processes or different stages in disease progression. Studies of synovial histopathology in antigen-induced arthritis (AIA) showed that Il6ra-/- mice develop a low-inflammatory pathology (lacking an immune cell infiltrate) resembling fibroblast-rich synovitis. This pathology contrasted with AIA in WT mice, which presented with a myeloid-rich inflammatory infiltrate and Il27ra-/- mice, where synovial ectopic lymphoid-like structures (ELS) resembled lymphoid-rich synovitis. Methods To identify the mechanisms driving synovitis in these animals, we conducted RNA seq, the assay for transposase-accessible chromatin (ATAC)-seq and Chromatin immunoprecipitation (ChIP)-seq on synovial tissue obtained at baseline and Day-3 and Day-10 of AIA to identify gene signature of disease induction. Results Analysis showed that the transcriptional profiles of synovitis in WT, Il6ra-/- and Il27ra-/- mice resembled those identified by RNA-seq of synovial biopsies from patients with rheumatoid arthritis. These include cytokines (IL-17A, IL-21), transcription factors (Bcl6, BATF, JunB), checkpoint regulators (CD274) and chemokine receptors (CXCR4, CXCR5) associated with lymphoid-rich synovitis (for Il27ra-/- mice), and signatures of ‘fibrosis’, ‘macrophage and fibroblast responses’ and ‘osteoclast and osteoblast activation’ common to fibroblast-rich synovitis (for Il6ra-/- mice). These mouse models provide new opportunities to identify the importance of Jak-STAT cytokine signaling in determining the heterogeneity of synovitis. For example, inhibition of synovial STAT3 activity (with CpG Stat3siRNA) significantly reduced the size and frequency of synovial ELS in Il27ra-/-mice with AIA. Conclusion These findings provide novel insights into the epigenetic regulation of synovitis heterogeneity and identify potential genomic signatures linked to differential clinical responses to therapies in RA. This work suggests a potential future role for ultrasound-guided small needle biopsies and NGS methods to stratify patients with RA and select an appropriate treatment based upon the “epigenetic fingerprint” of their disease. Disclosure S.T.O. Hughes: None. D. Costa: None. A. Derrac Soria: None. D. Hill: None. A. Cardus Figueras: None. R. Scott: None. S. Dimonte: None. F. Monaco: None. R. Jenkins: None. J. Twohig: None. C. Guy: None. B. Cossins: None. R. Andrews: None. B. Szomolay: None. E. Choy: None. N. Vinh: None. M. Lewis: None. B. Jenkins: None. S. Turner: None. T. Tiganis: None. N. Williams: None. H. Yu: None. C. Pitzalis: None. G. Jones: None. S.A. Jones: None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".