Role of protein kinase C β<sub>2</sub>activation in TNF-α-induced human vascular endothelial cell apoptosis
Bibliographic record
Abstract
The circulatory inflammatory cytokine tumor necrosis factor alpha (TNF-alpha) is increased in pathologic conditions that initiate or exacerbate vascular endothelial injury, such as diabetes. Protein kinase C (PKC) has been shown to play a critical role in TNF-alpha-induced human endothelial cell apoptosis. However, the relative roles played by specific isoforms of PKC in TNF-alpha-induced human endothelial cell apoptosis have not been addressed. We investigated the effects of a selective PKCbeta(2) inhibitor (CGP53353) on TNF-alpha-induced apoptosis in human vascular endothelial cells (cell line ECV304) and on the production of reactive oxygen species and nitric oxide, and compared its effects with rottlerin, a reagent that has been shown to reduce PKCdelta protein levels. Cultured human vascular endothelial cells (ECV304) were treated for 24 h with one of 4 regimes: 40 ng/mL TNF-alpha alone (TNF-alpha), TNF-alpha with 10 micromol/L rottlerin (T+rottlerin), TNF-alpha with 1 micromol/L CGP53353 (T+CGP), or untreated (control). Cell viability was measured by MTT assay, and cell apoptosis was assessed by flow cytometry. TNF-alpha-induced endothelial cell apoptosis was associated with dramatic increases in production of intracellular hydrogen peroxide (approximately 20 times greater than control) and superoxide (approximately 16 times greater than control), as measured by dichlorofluorescein and dihydroethidium fluorescent staining, respectively. This increase was accompanied by reduced activity of superoxide dismutase and glutathione peroxidase and, subsequently, an increase in the lipid peroxidation product malondialdehyde. CGP53353, but not rottlerin, abolished or attenuated all these changes. We conclude that PKCbeta(2) plays a major role in TNF-alpha-induced human vascular endothelial cell apoptosis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".