Pain-like behavior in the collagen antibody-induced arthritis model is regulated by lysophosphatidic acid and activation of satellite glia cells
Bibliographic record
Abstract
Inflammatory and neuropathic-like components underlie rheumatoid arthritis (RA)-associated pain, and lysophosphatidic acid (LPA) is linked to both joint inflammation in RA patients and to neuropathic pain. Thus, we investigated a role for LPA signalling using the collagen antibody-induced arthritis (CAIA) model. Pain-like behavior during the inflammatory phase and the late, neuropathic-like phase of CAIA was reversed by a neutralizing antibody generated against LPA and by an LPA1/3 receptor inhibitor, but joint inflammation was not affected. Autotaxin, an LPA synthesizing enzyme was upregulated in dorsal root ganglia (DRG) neurons during both CAIA phases, but not in joints or spinal cord. Late-phase pronociceptive neurochemical changes in the DRG were blocked in Lpar1 receptor deficient mice and reversed by LPA neutralization. In vitro and in vivo studies indicated that LPA regulates pain-like behavior via the LPA1 receptor on satellite glia cells (SGCs), which is expressed by both human and mouse SGCs in the DRG. Furthermore, CAIA-induced SGC activity is reversed by phospholipid neutralization and blocked in Lpar1 deficient mice. Our findings suggest that the regulation of CAIA-induced pain-like behavior by LPA signalling is a peripheral event, associated with the DRGs and involving increased pronociceptive activity of SGCs, which in turn act on sensory neurons.
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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) | 0.001 | 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) | 0.001 | 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".