Modulatory effects of M3 muscarinic acetylcholine receptor on inflammatory profiles of human memory T helper cells
Bibliographic record
Abstract
Memory T helper (Th) cells, generated in response to immunogenic challenges, are crucial in orchestrating adaptive immune responses. Acetylcholine (ACh), a key neurotransmitter of the parasympathetic nervous system, modulates immune function via muscarinic ACh receptors (mAChRs). This study investigates the role of mAChRs, particularly the M3 muscarinic ACh receptor (M3R), in regulating the cytokine and chemokine profile and NF-κB p65 activity in primary human memory Th cells. Memory Th cells were isolated from healthy donors and stimulated with anti-CD3/CD28/CD2 in the presence of oxotremorine-M (M1R-M5R agonist), atropine (M1R-M5R antagonist), or J104129 (M3R-selective antagonist). CHRM1-CHRM5 expression was quantified using RT-qPCR. M3R and phosphorylated NF-κB p65 were analyzed by Western blot. IFN-γ, IL-17A, and IL-4 were assessed by ELISA, while intracellular cytokine and chemokine receptor expression were measured by flow cytometry. CHRM3 knockout was performed using CRISPR-Cas9. Memory Th cells expressed all 5 mAChR subtypes. Oxotremorine-M increased IFN-γ and IL-17A while reducing IL-4 in an atropine-sensitive manner. Blocking or knocking out M3R prevented oxotremorine-M-induced increases in IFN-γ and IL-17A, but the suppression of IL-4 remained unchanged. Stimulation of mAChRs, particularly M3R, enhanced NF-κB p65 activity but did not affect chemokine receptor expression, cell proliferation, viability, or M3R levels. These findings indicate that mAChRs, including M3R, drive a pro-inflammatory memory Th-cell response through NF-κB p65 activation, while IL-4 suppression occurs independently of M3R. Targeting M3R specifically may provide a strategy for modulating adaptive immunity and treating inflammatory diseases.
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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 | 0.000 | 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".