TRPV4 and activation of airway sensory nerves: The role of ATP
Bibliographic record
Abstract
Activation of TRPV4 causes vagal nerve depolarisation, single airway fibre firing and reflex cough in the guinea pig (GP) through activating nodose derived Aδ fibres(Belvisi M.et al.AJRCCM:2013.A5265). Ca2+ signal and fibre firing demonstrated a short delay prior to activation, suggesting an indirect mechanism. Activation of TRPV4 possibly on the urothelium has been linked to ATP release and activation of P2X3 receptors on bladder afferents (Aizawa N.et al. Neuro Urod,2010:148-55), and so the aim of this study was to investigate the role of ATP and P2X3 in TRPV4 induced activation of airway afferents. Ca2+ imaging of GP airway neurons indicated that αβ-MeATP, an agonist of P2X1 and P2X3 containing receptors, induced Ca2+ flux in the nodose ganglia and caused depolarisation of GP and human vagal tissue in an in vitro vagal nerve preparation. Depolarisation of human and GP nerves by the TRPV4 agonists GSK1016790a (GSK101) and hypoosmolarity was inhibited following the use of 2 selective P2X3 antagonists; AF-353 and TNP-ATP. A mechanism for ATP release was indicated as GSK101 induced depolarisation was abolished in Pannexin 1 KO mice, indicating that TRPV4 initiates ATP release through the Pannexin 1 pore. In vivo, GSK101 induced firing of airway Aδ fibres was significantly inhibited following AF-353 administration, and cough induced by GSK101 was inhibited by AF-353 to a similar extent as by the TRPV4 antagonist GSK2193874. The data suggests that TRPV4 agonists activate airway sensory nerves and cause cough in the conscious GP through release of ATP and activation of P2X3 on nodose derived Aδ fibres. TRPV4 is therefore a novel modulator of airway sensory nerves and as such is a good candidate for anti-tussive therapies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".