Peroxynitrite increases arginase expression in endothelial cells: An effect mimicked by preeclamptic plasma
Bibliographic record
Abstract
Preeclampsia is characterized by hypertension and proteinuria occurring after the 20th week of gestation. Decreased nitric oxide (NO) bioavailability could be one of the causative factors in the pathogenesis of preeclampsia. In addition to scavenging of NO by superoxide to form peroxynitrite, another mechanism for reduced NO bioavailability may be through increased arginase activity. Arginase catalyzes the conversion of L‐arginine to L‐ornithine and urea. As arginase and NO synthase compete for L‐arginine, up regulation of arginase could decrease L‐arginine bioavailability and decrease NO formation. We hypothesized that circulating factors in preeclamptic plasma will increase peroxynitrite in endothelial cells and result in a feed forward mechanism to up regulate arginase. HUVECs were incubated with SIN‐1 (0.25 mM), a peroxynitrite donor for 12 hours or 2% plasma from either normotensive pregnant (Preg, n=3) or preeclamptic (PE, n=3) women for 24 hours. Nitrotyrosine staining, a marker of peroxynitrite formation and arginase II protein expression were assessed. PE plasma increased nitrotyrosine staining in endothelial cells 6 fold when compared to Preg plasma. SIN‐1 increased arginase II protein expression by ~ 180% (P<0.01) when compared to untreated cells as assessed by western blot. PE plasma increased arginase II protein expression when compared to Preg plasma (P<0.05). Circulating factors in preeclamptic plasma increase arginase expression likely via generation of peroxynitrite which further provides a feed forward mechanism to up regulate arginase. Funded by CIHR.
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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 | 0.001 |
| 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".