Neutrophil-neuronal crosstalk drives arthritis-induced pain
Bibliographic record
Abstract
Abstract Pain in rheumatoid arthritis (RA) often persists despite effective control of inflammation, suggesting distinct mechanisms driving nociception. In both patients and animal models, pain severity does not strongly correlate with the degree of inflammation 1,2 . Sensory neurons, with cell bodies located in the dorsal root ganglia (DRG), innervate peripheral tissues, including joints, and transmit pain signals to the central nervous system. Crosstalk between sensory neurons and immune cells occurs at all of these sites. While sensory neurons can be directly activated by immune mediators, it remains unclear whether pain-like behaviour in antibody-induced arthritis models arises independently of immune cell activity, or which immune cell populations and mediators are required to activate pronociceptive mechanisms. Through temporal profiling of the CAIA joint–DRG transcriptomic axis, we identified SEMA4D and OSM signalling as candidate molecular mediators of neutrophil–neuron communication and neuronal sprouting. The joint–DRG atlas also revealed persistent changes in the fibroblast-immune cellular composition of the joint, along with molecular changes in DRG neurons. We showed that mechanical and cold hypersensitivity, as well as sprouting of CGRP+ nociceptive fibers in synovial tissue of mice with collagen antibody-induced arthritis (CAIA), require neutrophils but not macrophages. Analysis of publicly available datasets showed that neutrophils from the synovium of RA patients express high levels of SEMA4D and OSM, and corresponding expression of their receptors, PLEXINB1 and OSMR, in human DRG neurons, underscoring the translational relevance of this axis. Both murine and human-derived DRG neurons sprout in response to OSM. Our findings demonstrate that neutrophils produce molecules that act as cues for nociceptor sensitization and structural remodelling. Targeting these molecules could improve the efficacy of RA treatments by reducing pain while simultaneously preventing disease progression.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".