17 -Estradiol induces protein S-nitrosylation in the endothelium
Bibliographic record
Abstract
AIMS: Estrogen induces nitric oxide (NO) in the endothelium and appears to protect against inflammation and atherosclerosis. NO can induce post-translational protein modifications such as cysteine S-nitrosylation in the cellular proteins which may exert anti-inflammatory effects. However, whether estrogen can induce protein S-nitrosylation in the endothelium is not known. Given this background, we investigated the role of 17beta-estradiol (E2beta), the major form of estrogen in the body, on endothelial protein S-nitrosylation. METHODS AND RESULTS: Experiments were performed in human umbilical vein endothelial cells (HUVECs). S-nitrosylation was detected by immunostaining for nitrosocysteine and further confirmed by biotin switch method. Ovariectomized 12-month-old Sprague-Dawley rats with/without estradiol supplementation were used for in vivo validation of findings. We found that physiologically relevant doses of E2beta increased protein S-nitrosylation in HUVECs through estrogen receptor-alpha (ERalpha) and endothelial nitric oxide synthase (eNOS). Interestingly, specific agonists for both ERalpha and ERbeta increased eNOS protein expression, while only the former could activate eNOS through phosphorylation. S-nitrosylation by E2beta prevented angiotensin II-induced upregulation of intercellular cell adhesion molecule-1, suggesting a potential anti-inflammatory mechanism. Finally, we showed that exogenous E2beta could increase endothelial S-nitrosylation in vivo in a rat model. CONCLUSION: Our results demonstrate for the first time that E2beta increases protein S-nitrosylation in the vascular endothelium, which might be a novel pathway to mediate the protective effects on the vasculature.
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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) | 0.002 | 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".