Eukaryotic initiation factor 5B (eIF5B) provides a critical cell survival switch to glioblastoma cells via regulation of apoptosis
Bibliographic record
Abstract
Abstract Physiological stress conditions attenuate global mRNA translation via modifications of key eukaryotic initiation factors. However, non-canonical translation initiation mechanisms allow cap-independent translation of certain mRNAs. We have previously demonstrated that eIF5B promotes cap-independent translation of the mRNA encoding the antiapoptotic factor, XIAP, during cellular stress. Here, we show that depletion of eIF5B sensitizes glioblastoma multiforme cells to TRAIL-induced apoptosis by a pathway involving caspases-8, −9, and −7, with no significant effect on cell cycle progression. eIF5B promotes evasion of apoptosis by promoting the translation of several IRES-containing mRNAs, encoding the antiapoptotic proteins XIAP, Bcl-xL, cIAP1, and c-FLIPS. We also show that eIF5B promotes translation of nuclear factor erythroid 2-related factor 2 and suggest that reactive oxygen species contribute to increased apoptosis under conditions of eIF5B depletion. Finally, eIF5B depletion leads to decreased activation of the canonical NF-κB pathway. Taken together, our data suggest that eIF5B represents a regulatory node, allowing cancer cells to evade apoptosis by promoting the translation of pro-survival proteins from IRES-containing mRNAs.
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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".