Insights into FcγR involvement in pain-like behavior induced by an RA-derived anti-modified protein autoantibody
Bibliographic record
Abstract
Joint pain is one of the most debilitating symptoms of rheumatoid arthritis (RA) and patients frequently rate improvements in pain management as their priority. RA is hallmarked by the presence of anti-modified protein autoantibodies (AMPA) against post-translationally modified citrullinated, carbamylated and acetylated proteins. It has been suggested that autoantibody-mediated processes represent distinct mechanisms contributing to pain in RA. In this study, we investigated the pronociceptive properties of monoclonal AMPA 1325:01B09 (B09 mAb) derived from the plasma cell of an RA patient. We found that B09 mAb induces pain-like behavior in mice that is not associated with any visual, histological or transcriptional signs of inflammation in the joints, and not alleviated by non-steroidal anti-inflammatory drugs (NSAIDs). Instead, we found that B09 mAb is retained in dorsal root ganglia (DRG) and alters the expression of several satellite glia cell (SGC), neuron and macrophage-related factors in DRGs. Using mice that lack activating FcγRs, we uncovered that FcγRs are critical for the development of B09-induced pain-like behavior, and partially drive the transcriptional changes in the DRGs. Finally, we observed that B09 mAb binds SGC in vitro and in combination with external stimuli like ATP enhances transcriptional changes and protein release of pronociceptive factors from SGCs. We propose that certain RA antibodies bind epitopes in the DRG, here on SGCs, form immune complexes and activate resident macrophages via FcγR cross-linking. Our work supports the growing notion that autoantibodies can alter nociceptor signaling via mechanisms that are at large independent of local inflammatory processes in the joint.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".