A silent agonist of α7 nicotinic acetylcholine receptors modulates inflammation ex vivo and attenuates EAE
Bibliographic record
Abstract
Nicotinic acetylcholine receptors (nAChRs) are best known to function as ligand-gated ion channels in the nervous system. However, recent evidence suggests that nicotine modulates inflammation by desensitizing non-neuronal nAChRs, rather than by inducing channel opening. Silent agonists are molecules that selectively induce the desensitized state of nAChRs while producing little or no channel opening. A silent agonist of α7 nAChRs has recently been shown to reduce inflammation in an animal model of inflammatory pain. The objective of this study was to determine whether a silent agonist of α7 nAChRs can also effectively modulate inflammation and disease manifestation in an animal model of multiple sclerosis. We first evaluated the effects of various nAChR ligands and of an α7 nAChR-selective silent agonist, 1-ethyl-4-(3-(bromo)phenyl)piperazine (m-bromo PEP), on the modulation of mouse bone marrow-derived monocyte/macrophage (BMDM) numbers, phenotype and cytokine production. The non-competitive antagonist mecamylamine and the silent agonist m-bromo PEP reduced pro-inflammatory BMDM numbers by affecting their viability and proliferation. Both molecules also significantly reduced cytokine production by mouse BMDMs and significantly ameliorated disease in experimental autoimmune encephalomyelitis. Finally, m-bromo PEP also reduced chronic inflammatory pain in mice. Taken together, our results further support the hypothesis that nAChRs may modulate inflammation via receptor desensitization rather than channel opening. α7 nAChR-selective silent agonists may thus be a novel source of anti-inflammatory compounds that could be used for the treatment of inflammatory 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) | 0.001 | 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 | 0.001 | 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".