Ebselen inhibits NO‐induced apoptosis of differentiated PC12 cells via inhibition of ASK1‐p38 MAPK‐p53 and JNK signaling and activation of p44/42 MAPK and Bcl‐2
Bibliographic record
Abstract
Ebselen, a selenium-containing heterocyclic compound, prevents ischemia-induced cell death. However, the molecular mechanism through which ebselen exerts its cytoprotective effect remains to be elucidated. Using sodium nitroprusside (SNP) as a nitric oxide (NO) donor, we show here that ebselen potently inhibits NO-induced apoptosis of differentiated PC12 cells. This was associated with inhibition of NO-induced phosphatidyl Serine exposure, cytochrome c release, and caspase-3 activation by ebselen. Analysis of key apoptotic regulators during NO-induced apoptosis of differentiated PC12 cells showed that ebselen blocks the activation of the apoptosis signaling-regulating kinase 1 (ASK1), and inhibits phosphorylation of p38 mitogen-activated protein kinase (MAPK) and c-jun N-terminal protein kinase (JNK). Moreover, ebselen inhibits NO-induced p53 phosphorylation at Ser15 and c-Jun phosphorylation at Ser63 and Ser73. It appears that inhibition of p38 MAPK and p53 phosphorylation by ebselen occurs via a thiol-redox-dependent mechanism. Interestingly, ebselen also activates p44/42 MAPK, and inhibits the downregulation of the antiapoptotic protein Bcl-2 in SNP-treated PC12 cells. Together, these findings suggest that ebselen protects neuronal cells from NO cytotoxicity by reciprocally regulating the apoptotic and antiapoptotic signaling cascades.
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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) | 0.001 | 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) | 0.001 | 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".