Nicotinic Acetylcholine Receptors Modulate Bone Marrow-Derived Pro-Inflammatory Monocyte Production and Survival
Bibliographic record
Abstract
It is increasingly clear that nicotinic acetylcholine receptors (nAChRs) are involved in immune regulation, and that their activation can protect against inflammatory diseases. Previous data have shown that nicotine diminishes the numbers of peripheral monocytes and macrophages, especially those of the pro-inflammatory phenotype. The goal of the present study was to determine if nicotine modulates the production of bone marrow -derived monocytes/macrophages. In this study, we first found that murine bone marrow cells express multiple nAChR subunits, and that the α7 and α9 nAChRs most predominant subtypes found in immune cells and their precursors. Using primary cultures of murine bone marrow cells, we then determined the effect of nicotine on monocyte colony-stimulating factor and interferon gamma (IFNγ)-induced monocyte production. We found that nicotine lowered the overall number of monocytes, and more specifically, inhibited the IFNγ-induced increase in pro-inflammatory monocytes by reducing cell proliferation and viability. These data suggested that nicotine diminishes the ratio of pro-inflammatory versus anti-inflammatory monocyte produced in the bone marrow. We thus confirmed this hypothesis by measuring cytokine expression, where we found that nicotine inhibited the production of the pro-inflammatory cytokines TNFα, IL-1β and IL-12, while stimulating the secretion of IL-10, an anti-inflammatory cytokine. Finally, nicotine also reduced the number of pro-inflammatory monocytes in the bone marrow of LPS-challenged mice. Overall, our data demonstrate that both α7 and α9 nAChRs are involved in the regulation of pro-inflammatory M1 monocyte numbers.
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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) | 0.001 | 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".