The coordination of S-sulfhydration, S-nitrosylation, and phosphorylation of endothelial nitric oxide synthase by hydrogen sulfide
Bibliographic record
Abstract
The gasotransmitter hydrogen sulfide (H(2)S), which is generated by cystathionine γ-lyase (CSE), signals by modifying proteins through S-sulfhydration and potentially other mechanisms. A target protein for H(2)S is endothelial nitric oxide synthase (eNOS), an enzyme that generates nitric oxide (NO), which causes vasodilation. We investigated whether H(2)S-induced S-sulfhydration affected the S-nitrosylation and phosphorylation of eNOS and the functional effects of changes in these posttranslational modifications on eNOS activity. In vitro, different NO donors induced the S-nitrosylation of eNOS without affecting its S-sulfhydration, whereas the H(2)S donor sodium hydrosulfide (NaHS) decreased the S-nitrosylation of eNOS. Cys(443) was the primary S-sulfhydration site in eNOS and was one site that could be S-nitrosylated. Phosphorylation increases eNOS activity. Although exposure of eNOS-expressing HEK-293 cells to NaHS or vascular endothelial growth factor (VEGF) triggered the phosphorylation of wild-type and C443G-eNOS, VEGF did not affect the S-sulfhydration of eNOS and a mutant of eNOS that could not be phosphorylated was still S-sulfhydrated. eNOS can be present in cells in monomeric or dimeric form, but only eNOS dimers produce NO. In wild-type mice, eNOS proteins were predominantly dimerized, whereas eNOS from CSE-knockout (KO) mice, S-nitrosylated eNOS, and heterologously expressed C443G-eNOS was mostly monomeric. Accordingly, basal production of NO was lower in CSE-KO endothelial cells than in wild-type endothelial cells. Our data suggest that H(2)S increases eNOS activity by inducing the S-sulfhydration of eNOS, promoting its phosphorylation, inhibiting its S-nitrosylation, and increasing eNOS dimerization, whereas NO decreases eNOS activity by promoting the formation of eNOS monomers.
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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) | 0.000 | 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 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".