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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".