Nitric oxide modulates natriuretic peptide receptor C expression in aortic vascular smooth muscle cells
Bibliographic record
Abstract
We have recently shown that cyclic GMP (cGMP) decreased the expression of natriuretic peptide receptor C (NPR‐C) in vascular smooth muscle cells (VSMC). Since cGMP is the second messenger of nitric oxide (NO), the present studies were undertaken to investigate if NO can mimic the effect of cGMP in attenuating the expression and associated Gia‐adenylyl cyclase signaling of NPR‐C in aortic VSMC.Treatment of VSMC with SNAP (100 μM) decreased the expression of NPR‐C, Gia‐2 and Gia‐3 proteins in a time‐dependent manner, as determined by Western Blotting. The maximal inhibition of Gia‐2, Gia‐3 and NPR‐C proteins was between 30–35%. The NO‐induced decreased levels of NPR‐C and Giá proteins at 24 h was also reflected in decreased NPR‐C‐mediated adenylyl cyclase inhibition, as determined by C‐ANP4‐23‐mediated inhibition of adenylyl cyclase. Furthermore, ODQ, an inhibitor of soluble guanylyl cyclase, KT5823, an inhibitor of protein kinase G, and MnTBAP, a scavenger of peroxynitrite, were unable to restore the SNAP‐induced decreased expression of NPR‐C protein to control levels, suggesting that SNAP‐mediated decreased expression of NPR‐C is not through peroxynitrite or cGMP‐dependent mechanism. These results suggest that NO decreases NPR‐C expression and that the NO‐induced decreased expression of NPR‐C is mediated through a cGMP‐independent pathway. (Supported by Canadian Institutes of Health Research)
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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 | 0.001 |
| 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".