S‐Nitrosoglutathione Reductase Deficiency Causes Aberrant Placental S‐Nitrosylation and Preeclampsia
Bibliographic record
Abstract
Background Preeclampsia, a leading cause of maternal and fetal mortality and morbidity, is characterized by an increase in S‐nitrosylated proteins and reactive oxygen species, suggesting a pathophysiologic role for dysregulation in nitrosylation and nitrosative stress. Methods and Results Here, we show that mice lacking S‐nitrosoglutathione reductase ( GSNOR −⁄− ), a denitrosylase regulating protein S‐nitrosylation, exhibit a preeclampsia phenotype, including hypertension, proteinuria, renal pathology, cardiac concentric hypertrophy, decreased placental vascularization, and fetal growth retardation. Reactive oxygen species, NO, and peroxynitrite levels are elevated. Importantly, mass spectrometry reveals elevated placental S‐nitrosylated amino acid residues in GSNOR −⁄− mice. Ascorbate reverses the phenotype except for fetal weight, reduces the difference in the S‐nitrosoproteome, and identifies a unique set of S‐nitrosylated proteins in GSNOR −⁄− mice. Importantly, human preeclamptic placentas exhibit decreased GSNOR activity and increased nitrosative stress. Conclusions Therefore, deficiency of GSNOR creates dysregulation of placental S‐nitrosylation and preeclampsia in mice, which can be rescued by ascorbate. Coupled with similar findings in human placentas, these findings offer valuable insights and therapeutic implications for preeclampsia.
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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 | 0.001 | 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 | 0.001 |
| 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".