Modulatory Effects of M3 Muscarinic Acetylcholine Receptor on Inflammatory Profiles of Human Memory T Helper Cells
Bibliographic record
Abstract
Abstract Memory T helper (Th) cells, generated after immunogenic challenge, are crucial in directing the adaptive immune response. Muscarinic ACh receptor (mAChR) subtypes expressed by immune cells can be stimulated with acetylcholine or muscarinic-selective drug oxotremorine-M. Cholinergic signaling can influence immune cells, but it is not known how cholinergic stimuli regulate memory Th cells. This study focused on the role of mAChRs, specifically the M3 muscarinic ACh receptor (M3R), in the cytokine profile and NF-κB p65 activity of primary human memory Th cells. Memory Th cells (CD3 + CD4 + CD45RA - CD45RO + ) were isolated from healthy participants’ peripheral blood. Cell culture was performed with anti-CD3/anti-CD28/anti-CD2 reagent, oxotremorine-M (M1R-M5R agonist), atropine (M1R-M5R antagonist), and J104129 (M3R-selective antagonist). MR1-MR5 genes CHRM1 - CHRM5 were measured with RT-qPCR. Protein expression of M3R and phosphorylated NF-κB p65 were quantified by Western blot. The secretion of IFN-γ, IL-17A, and IL-4 was assessed by ELISA and intracellular cytokine staining flow cytometry. CHRM3 , encoding M3R, was knocked out using CRISPR-Cas9 gene targeting. Memory Th cells expressed all five mAChR subtypes. Oxotremorine-M increased IFN-γ and IL-17A while reducing IL-4 in an atropine-sensitive manner. Stimulation of mAChRs in cells with CHRM3 -knockout or M3R blockade prevented increases in IFN-γ and IL-17A but continued to inhibit IL-4. mAChR stimulation enhanced NF-κB p65 activity without affecting cell proliferation, viability, or M3R expression. This investigation demonstrates that muscarinic signaling increases the pro-inflammatory profile of memory Th cells, including NF-κB p65, IFN-γ, and IL-17A, with a reduction in IL-4. Focusing on M3R blockers could modulate adaptive immune responses and alleviate immune-related conditions.
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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) | 0.002 | 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".