Cell fate decisions and anti-tumor effects of the mRNA translation initiation factor eIF2
Bibliographic record
Abstract
T lecture will address the function of the translation initiation factor eIF2 in stressed-induced tumorigenesis as well as in anti-tumor treatments with chemotherapeutic drugs. eIF2 is a master regulator of stress through its ability to control protein synthesis in response to various forms of stress including DNA damage, oxidative stress, oncogenic stress as well as stress in the tumor microenvironment. Cells respond to stress by inducing the phosphorylation of the alpha (α) subunit of eIF2 at serine 51 (S51) (herein referred to as eIF2αP), a modification that leads to the inhibition of global protein synthesis. eIF2αP is mediated by four kinases, namely HRI, PKR, PERK/PEK and GCN2 each of which becomes activated to distinct form of stress. Despite the general inhibition of protein synthesis, specific mRNAs can bypass the blockade, and in fact, be efficiently translated under stress. Such mRNAs encode for proteins that facilitate cell adaptation to stress as shown for transcription factors ATF4 and ATF5 in mammalian cells or GCN4 in yeast. Our work focuses on eIF2αP function as a cell fate decision maker through its ability to induce either cell survival or death in stressed tumor cells. We investigate how the dual but opposing function of eIF2αP relates to the activation of the MAPK and Akt/PKB-mTORC1 pathways in stressed cells. Our work suggests that inhibition of eIF2αP is a powerful approach to disarm cell survival and induce death in tumor cells treated with pro-oxidant drugs or drugs targeting the PI3K-Akt/PKB-mTORC1 pathway.
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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) | 0.000 | 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) | 0.000 | 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".