Cytochrome<i>P</i>-450 epoxygenases protect endothelial cells from apoptosis induced by tumor necrosis factor-α via MAPK and PI3K/Akt signaling pathways
Bibliographic record
Abstract
Endothelial cells play a vital role in the maintenance of cardiovascular homeostasis. Epoxyeicosatrienoic acids (EETs), cytochrome P-450 (CYP) epoxygenase metabolites of arachidonic acid in endothelial cells, possess potent and diverse biological effects within the vasculature. We evaluated the effects of overexpression of CYP epoxygenases on tumor necrosis factor-alpha (TNF-alpha)-induced apoptosis in bovine aortic endothelial cells. CYP epoxygenase overexpression significantly increased endothelial cell viability and inhibited TNF-alpha induction of endothelial cell apoptosis as evaluated by morphological analysis of nuclear condensation, DNA laddering, and fluorescent-activated cell sorting (FACS) analysis. CYP epoxygenase overexpression also significantly inhibited caspase-3 activity and downregulation of Bcl-2 expression induced by TNF-alpha. The antiapoptotic effects of CYP epoxygenase overexpression were significantly attenuated by inhibition of the phosphatidylinositol 3-kinase (PI3K)/Akt and MAPK signaling pathways; however, inhibition of endothelial nitric oxide synthase activity had no effect. Furthermore, CYP epoxygenase overexpression significantly attenuated the extent of TNF-alpha-induced ERK1/2 dephosphorylation in a time-dependent manner and significantly increased PI3K expression and Akt phosphorylation in both the presence and absence of TNF-alpha. Collectively, these results suggest that CYP epoxygenase overexpression, which is known to increase EET biosynthesis, significantly protects endothelial cells from apoptosis induced by TNF-alpha. This effect is mediated, at least in part, through inhibition of ERK dephosphorylation and activation of PI3K/Akt signaling.
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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) | 0.001 | 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".