A noninhibitory mutant of the caveolin-1 scaffolding domain enhances eNOS-derived NO synthesis and vasodilation in mice
Bibliographic record
Abstract
Aberrant regulation of eNOS and associated NO release are directly linked with various vascular diseases. Caveolin-1 (Cav-1), the main coat protein of caveolae, is highly expressed in endothelial cells. Its scaffolding domain serves as an endogenous negative regulator of eNOS function. Structure-function analysis of Cav-1 has shown that phenylalanine 92 (F92) is critical for the inhibitory actions of Cav-1 toward eNOS. Herein, we show that F92A-Cav-1 and a mutant cell-permeable scaffolding domain peptide called Cavnoxin can increase basal NO release in eNOS-expressing cells. Cavnoxin reduced vascular tone ex vivo and lowered blood pressure in normal mice. In contrast, similar experiments performed with eNOS- or Cav-1-deficient mice showed that the vasodilatory effect of Cavnoxin is abolished in the absence of these gene products, which indicates a high level of eNOS/Cav-1 specificity. Mechanistically, biochemical assays indicated that noninhibitory F92A-Cav-1 and Cavnoxin specifically disrupted the inhibitory actions of endogenous Cav-1 toward eNOS and thereby enhanced basal NO release. Collectively, these data raise the possibility of studying the inhibitory influence of Cav-1 on eNOS without interfering with the other actions of endogenous Cav-1. They also suggest a therapeutic application for regulating the eNOS/Cav-1 interaction in diseases characterized by decreased NO release.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".