Etoposide induces cell death via mitochondrial-dependent actions of p53
Bibliographic record
Abstract
BACKGROUND: Etoposide has been used clinically in cancer treatment, as well as in numerous research studies, for many years. However, there is incomplete information about its exact mechanism of action in induction of cell death. METHODS: Etoposide was compared at various concentrations to characterize the mechanisms by which it induces cell death. We investigated its effects on mouse embryonic fibroblasts (MEFs) and focused on both transcriptional and non-transcriptional responses of p53. RESULTS: Here we demonstrate that treatment of MEFs with higher concentrations of etoposide induce apoptosis and activate the transcription-dependent functions of p53. Interestingly, lower concentrations of etoposide also induced apoptosis, but without any evidence of p53-dependent transcription up-regulation. Treatment of MEFs with an inhibitor of p53, Pifithrin-α, blocked p53-dependent transcription but failed to rescue the cells from etoposide-induced apoptosis. Treatment with PES, which inhibits the mitochondrial arm of the p53 pathway inhibited etoposide-induced cell death at all concentrations tested. CONCLUSIONS: We have demonstrated that transcriptional functions of p53 are dispensable for etoposide-induced cell death. The more recently characterized effects of p53 at the mitochondria, likely involving its interactions with BCL-2 family members, are thus more important for etoposide's actions.
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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) | 0.002 | 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".