B cell–derived acetylcholine mitigates skin inflammation in mice through α9 nicotinic acetylcholine receptor–mediated signaling
Bibliographic record
Abstract
Chronic inflammatory skin disorders are characterized by keratinocyte hyperproliferation and hyperactivation as well as immune cell infiltration. We investigated whether immune cell–derived acetylcholine (ACh) is a modulator of skin inflammation in mice. Here, we identify skin epithelial B cells as a key source of ACh that damps down inflammation. We used imiquimod (IMQ) to induce inflammatory skin disease (ISD) in mice lacking ACh production specifically in B cells (ChAT fl/fl;Mb1-Cre mice). Increased keratinocyte proliferation, epidermal thickening, and elevated levels of proinflammatory cytokines resulted. ACh binding to α9 nicotinic ACh receptor (encoded by Chrna9 ) expressed on wild-type mouse keratinocytes reduced their proliferation. Chrna9 -deficient mice exhibited the same exacerbated ISD phenotype as ChAT fl/fl;Mb1-Cre mice following IMQ induction. Our data suggest that B cell–derived ACh maintains skin homeostasis by modulating keratinocyte turnover and controlling immune-related inflammation. Therapeutic manipulation of this cholinergic pathway might mitigate both keratinocyte dysfunction and immune dysregulation in human patients, potentially pointing to treatments for ISDs such as psoriasis and related disorders.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".