Abstract 3191: The Akt-mTOR axis determines cell fate through the regulation of eIF2α phosphorylation pathway.
Bibliographic record
Abstract
Abstract Metazoans respond to environmental stress by inducing the phosphorylation of the α subunit of translation initiation factor eIF2 at S51 (eIF2αP), a modification that leads to protein synthesis inhibition. We demonstrate that eIF2αP is induced by pharmacological inhibition or genetic ablation of the PI3K-Akt-mTOR pathway (1). Increased eIF2αP is an evolutionary conserved process that involves the endoplasmic reticulum (ER)-resident protein kinase PERK, which is negatively regulated by Akt dependent phosphorylation at T799. The PERK-eIF2αP arm is downregulated by Akt in cells exposed to ER stress or oxidative stress leading to the induction of cell survival or death respectively. In unstressed cells, the PERK-eIF2αP pathway guards survival and facilitates adaptation to the deleterious effects of PI3K, Akt or mTOR inactivation. Inactivation of the PERK-eIF2αP arm sensitizes tumor death from pharmacological inhibition of the PI3K-Akt-mTOR pathway. The PERK-eIF2αP pathway links Akt and mTOR signaling to translational control with implications in tumor treatment with pharmacological inhibitors of PI3K-Akt-mTOR pathway. Citation Format: Clara Tenkerian, Zineb Mounir, Jothi Krishnamoorthy, Antonis E. Koromilas. The Akt-mTOR axis determines cell fate through the regulation of eIF2α phosphorylation pathway. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3191. doi:10.1158/1538-7445.AM2013-3191 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".