Molecular comparison of WT and F92A Caveolin‐1: Direction towards increasing Nitric Oxide bioavailability
Bibliographic record
Abstract
INTRODUCTION Endothelial dysfunction is characterized by the loss of endothelial cell (EC)‐derived Nitric Oxide (NO) release and increased superoxide anion (O 2 − ). Endothelial NO synthase (eNOS) is the enzyme that synthesizes NO and O 2 − in physiological and pathological settings, respectively. Caveolin‐1 (Cav‐1) is the major coat protein of caveolae that binds to and inhibits eNOS NO release, and we have shown that Cav‐1 F92 is the amino acid that inhibits NO release (Bernatchez, PNAS 2005). Interestingly, our unpublished observations show that F92A Cav‐1 mutant can INCREASE NO release. OBJECTIVE To determine how F92A Cav‐1 increases NO release METHODS We performed a biochemical comparison of WT vs F92A Cav‐1 to determine if the F92A mutation affects its activity. We also looked at Cav‐1 F92A substitution on eNOS O 2 − release. RESULTS WT Cav‐1 (25kDa) targets to caveolae and oligomerizes into high MW homo‐oligomers (350–600 kDa). Interestingly, F92A Cav‐1 enrichment in caveolae and homo‐oligomers are identical to WT Cav‐1, and by GST pulldown assays we show that WT and F92A Cav‐1 bind identically to eNOS. However, we have observed that F92A Cav‐1 can decrease O 2 − release as compared to WT Cav‐1. CONCLUSION These data show that F92A Cav‐1 binds eNOS, prevents WT Cav‐1 inhibition of eNOS and decreases O 2 − release, and suggest that eNOS‐Cav‐1 interaction is a therapeutic target for improving NO bioavailability.
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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 | 0.001 | 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".