Role of CFTR and sphingolipids in hypoxic pulmonary vasoconstriction
Bibliographic record
Abstract
Background Hypoxic pulmonary vasoconstriction (HPV) optimizes ventilation‐perfusion matching in the lung. As cystic fibrosis patients suffer from ventilation‐perfusion mismatches, we probed for a potential role of cystic fibrosis transmembrane conductance regulator (CFTR) in HPV. Methods HPV was quantified in isolated lungs as increase in perfusion pressure in response to hypoxia. In cultured human pulmonary artery smooth muscle cells (PASMC), Ca 2+ concentration ([Ca 2+ ] i ) was imaged, and caveolae were isolated and probed for translocated Ca 2+ entry channels. Results HPV was attenuated by CFTR inhibitors or in lungs of CFTR −/− mice. In PASMC, CFTR inhibition blocked the hypoxia‐induced [Ca 2+ ] i increase and the caveolar translocation of TRPC6 channels. CFTR's role in HPV was not attributable to Cl − transport, as modulation of extracellular Cl − did not alter HPV. Instead, the role of CFTR was related to sphingolipid signaling in HPV. In isolated lungs, inhibition of either neutral sphingomyelinase (SMase) or sphingosine kinase attenuated HPV. Exogenous SMase mimicked hypoxia, in that it caused pulmonary vasoconstriction, increased PASMC [Ca 2+ ] i , and translocated TRPC6 to caveolae, yet responses to SMase were blocked by CFTR inhibition. Conclusion Our findings identify a critical role for CFTR in HPV that relates to sphingolipid signaling by a mechanism currently under investigation.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".