Arginase contributes to endothelial cell oxidative stress in response to plasma from women with preeclampsia
Bibliographic record
Abstract
AIMS: Preeclampsia is a hypertensive disorder characterized by vascular oxidative stress. Decreased availability of the vasodilator nitric oxide (NO) has been postulated to be involved in the pathophysiology of this disorder. Arginase, an enzyme that competes with nitric oxide synthase (NOS) for l-arginine, not only reduces NO formation but also increases superoxide production by NOS. In placenta of preeclamptic women, arginase upregulation has been shown to be increased and contributes to superoxide formation via uncoupling of NOS. However, the role of arginase in the maternal vasculature is not clear. We hypothesized that arginase would be upregulated in the maternal vasculature of women with preeclampsia and contribute to oxidative stress within the endothelium. METHODS AND RESULTS: We observed increased arginase expression in the maternal vasculature of women with preeclampsia compared with normotensive pregnant women. Furthermore, human umbilical vein endothelial cells treated with 2% plasma from preeclamptic women show increased arginase II expression and activity that was reduced by a peroxynitrite scavenger. Also, both 3-morpholino sydnonimine and exogenous peroxynitrite increased arginase expression and activity. Preeclamptic plasma treatment increased superoxide and peroxynitrite levels. Superoxide levels were significantly reduced after arginase and NOS inhibition with [(S)-(2-boronoethyl)-l-cysteine] and N(omega)-nitro-l-arginine methyl ester, respectively, but peroxynitrite levels were in fact increased after arginase inhibition. Moreover, in the presence of preeclamptic plasma, l-arginine supplementation increased peroxynitrite formation during arginase inhibition. CONCLUSION: Increased arginase expression in preeclampsia can induce uncoupling of NOS as a source of superoxide in the maternal vasculature in preeclampsia. However, l-arginine supplementation in the face of oxidative stress could lead to a further increase in peroxynitrite.
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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 | 0.001 |
| 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".