Nicotinic acetylcholine receptor signaling regulates cytokine production in Jurkat T cells
Bibliographic record
Abstract
Abstract Nicotinic acetylcholine receptors (nAChRs) regulate immune cell functions, yet their expression patterns and roles in human T cells remain incompletely defined. The immunomodulatory effects of α7 nAChR signaling in human immune cells is complicated by the presence of the human-specific duplicated α7 (dupα7) subunit. Here, we investigated the expression and function of nAChRs in human Jurkat T cells, focusing on α7, dupα7, α9, and α10. Quantitative PCR revealed transcripts for all four subunits. Mitogenic stimulation with PMA, ionomycin, and ConA significantly downregulated α7, α9, and α10 expression while upregulating dupα7, suggesting dynamic remodeling of receptor composition during T cell activation. Functional assays showed that α7 antagonism with ArIB[V11L,V16D] strongly suppressed mitogen-induced IL-2 and TNF-α secretion, while nicotine pretreatment produced more modest reductions. Flow cytometry confirmed a decreased frequency of IL-2+ cells following treatment with nicotine or nAChR antagonists. These findings establish Jurkat cells as a tractable model for studying nAChR signaling in human T cells. Our results demonstrate that α7-containing nAChRs positively regulate cytokine production, while dupα7 expression increases during activation and may act as a negative regulator of α7 function. Together, these data highlight nAChRs as key modulators of T cell activity and identify α7 and dupα7 as potential therapeutic targets for regulating adaptive immunity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".