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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".