Cholinergic modulation is independent of T lymphocytes in a mouse model of neuropathic pain
Bibliographic record
Abstract
T lymphocytes are increasingly implicated in pain signaling. A subset of T lymphocytes, termed TChAT, express the rate-limiting enzyme for acetylcholine (ACh) production, choline acetyltransferase (ChAT), and mediate numerous physiological functions. Given that cholinergic signaling has long been known to modulate pain processing and is the basis for several analgesics used clinically, we asked whether TChAT could be the intersection between T lymphocyte and cholinergic mediation of pain signaling. In this study, we used a mouse gene knockout strategy to ablate ChAT specifically from T lymphocytes and examined the development and expression of mechanical and thermal hypersensitivity in a spared nerve injury (SNI) mouse model of neuropathic pain. We found that mice with ChAT knockout in T cells (floxed Chat plus CD4-Cre recombinase) did not differ from control mice with intact ChAT (floxed Chat, but no Cre recombinase) in their expression of mechanical sensitivity before or after injury. Similarly, thermal sensitivity was unaffected after injury, with control mice expressing similar patterns of thermal preference to mice whose T cells do not express ChAT. Our experiments demonstrate that cholinergic signaling initiated by T lymphocytes neither dampens nor exacerbates the expression of mechanical or thermal sensitivity in neuropathic mice. Thus, while both cholinergic signaling and T lymphocytes have established roles in modulating pain phenotypes, it is not cholinergic signaling initiated by T lymphocytes that drive this. Our findings will help to narrow in on which aspects of T-cell modulation may prove useful as therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".