Role of PTEN in Hypoxic Pulmonary Vasoconstriction
Bibliographic record
Abstract
Rationale Hypoxic pulmonary vasoconstricition (HPV) optimizes ventilation/perfusion matching in the lung, and critically depends on transient receptor potential canonical (TRPC) 6 activation and trafficking to caveolae. In endothelial cells, phosphatase and tensin homolog (PTEN) has been shown to serve as a scaffold for TRPC6, enabling cell surface expression of the channel and subsequent Ca 2+ entry. Moreover, Rho kinase (ROCK) is known to phosphorylate PTEN and play a role in its localization in chemotaxing neutrophils. Hence, we hypothesized that the interaction between PTEN and TRPC6 in PASMC may be required for TRPC6 translocation to caveolae and an intact HPV response in a ROCK‐dependent manner. Methods Interaction between PTEN and TRPC6 was tested by co‐immunoprecipitation in whole lung lysates, and by proximity ligation assay in pulmonary artery smooth muscle cells (PASMC). Caveolar recruitment of proteins was assessed in PASMC by sucrose density gradient ultracentrifugation. HPV was analyzed in isolated perfused lungs as hypoxia‐induced increase in perfusion pressure. Calcium entry to PASMC was measured by Fura2 imaging. Results SMC specific PTEN KO mice had a lower HPV response compared to WT mice. Similarly, PTEN knock down in PASMC blunted Ca 2+ influx in response to hypoxia. Hypoxia induced protein‐protein interaction between PTEN and TRPC6 in both whole lungs and PASMC, and translocated PTEN to caveolae (caveolin 1‐rich membrane fraction). Inhibition of ROCK (Y27632; 10 μM) prevented both effects and attenuated the HPV response in intact lungs. Conclusion Hypoxia induces PTEN interaction with TRPC6 in PASMC, and PTEN translocation to caveolae in a ROCK‐dependent manner. Our findings identify an important role for PTEN in TRPC6 recruitment and activation downstream of ROCK and thus, in triggering HPV in a manner that is independent of its phosphatase activity. Support or Funding Information Funded by a CIHR operational grant to WMK
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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) | 0.001 | 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".