Vagal α7 Nicotinic Acetylcholine Receptor Signaling Amplifies Allergic Airway Inflammation Through Megakaryocyte-Derived IL-33 Production
Bibliographic record
Abstract
The vagus nerve-α7 nicotinic acetylcholine receptor (α7nAChR) signaling axis has recently emerged as a key neuroimmune modulator in pulmonary pathophysiology. Although lung-resident megakaryocytes have shown immunoregulatory potential in allergic airway inflammation, the precise role of vagal α7nAChR signaling within this cellular population remains unexplored. In this study, we demonstrated that lung megakaryocytes play a functional role in papain-induced airway inflammation, with conditional depletion of megakaryocytes significantly reducing inflammatory responses. Allergic inflammation led to an upregulation of α7nAChR expression on megakaryocytes. Megakaryocyte-specific knockout of Chrna7 (encoding α7nAChR) reduced inflammation in both papain and IL-33 challenge models. Pharmacological activation with GTS-21 (an α7nAChR agonist) exacerbated inflammation through megakaryocyte-dependent mechanisms. Mechanistically, α7nAChR activation enhanced IL-33 synthesis and secretion in megakaryocytes. Western blot analysis revealed that p38MAPK phosphorylation is a critical downstream signaling pathway. This study establishes a novel neuroimmune regulatory circuit, wherein vagal α7nAChR signaling amplifies allergic airway inflammation through megakaryocyte-derived IL-33 production, mediated by p38 MAPK activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".