Human Pulmonary Neuroendocrine Cells Respond to House Dust Mite Extract With <scp>PAR</scp> ‐1 Dependent Release of <scp>CGRP</scp>
Bibliographic record
Abstract
BACKGROUND: Pulmonary neuroendocrine cells (PNEC) are rare airway epithelial cells that have recently gained attention as potential amplifiers of allergic asthma. However, studying PNEC function in humans has been challenging due to a lack of cell isolation methods, and little is known about human PNEC function in response to asthma-relevant stimuli. Here we developed and characterized an in vitro human PNEC model and investigated the neuroendocrine response to extracts of the common aeroallergen house dust mite (HDM). METHODS: PNEC-enriched cultures were generated from human induced pluripotent stem cells (iPNEC) and primary bronchial epithelial cells (ePNEC). Characterized PNEC cultures were exposed to HDM extract, a volatile chemical odorant (Bergamot oil), or the bacterial membrane component, lipopolysaccharide (LPS), and neuroendocrine gene expression and neuropeptide release determined. RESULTS: Both iPNEC and ePNEC models demonstrated similar baseline neuroendocrine characteristics and a stimuli-specific modulation of gene expression. Most notably, exposure to HDM but not Bergamot oil or LPS, leads to dose-dependent induction of the CGRP encoding gene, CALCB, and corresponding release of the neuropeptide. HDM-induced CALCB expression and CGRP release could be inhibited by a protease-activated receptor 1 (PAR1) antagonist or protease inhibitors and was mimicked by a PAR1 agonist. CONCLUSIONS: We have characterized a novel model of PNEC-enriched human airway epithelium and utilized this model to demonstrate a previously unrecognized role for human PNEC in mediating a direct neuroendocrine response to aeroallergen exposure.
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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 | 0.000 |
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".