Akt Determines Cell Fate Through Inhibition of the PERK-eIF2α Phosphorylation Pathway
Bibliographic record
Abstract
Metazoans respond to various forms of environmental stress by inducing the phosphorylation of the α subunit of eukaryotic translation initiation factor 2 (eIF2α) at serine-51, a modification that leads to global inhibition of mRNA translation. We demonstrate induction of the phosphorylation of eIF2α in mammalian cells after either pharmacological inhibition of the phosphoinositide 3-kinase (PI3K)-Akt pathway or genetic or small interfering RNA-mediated ablation of Akt. This increase in the extent of eIF2α phosphorylation also occurred in Drosophila cells and depended on the endoplasmic reticulum (ER)-resident protein kinase PERK, which was inhibited by Akt-dependent phosphorylation at threonine-799. The activity of PERK and the abundance of phosphorylated eIF2α (eIF2αP) were reduced in mouse mammary gland tumors that contained activated Akt, as well as in cells exposed to ER stress or oxidative stress. In unstressed cells, the PERK-eIF2αP pathway mediated survival and facilitated adaptation to the deleterious effects of the inactivation of PI3K or Akt. Inactivation of the PERK-eIF2αP pathway increased the susceptibility of tumor cells to death by pharmacological inhibitors of PI3K or Akt. Thus, we suggest that the PERK-eIF2αP pathway provides a link between Akt signaling and translational control, which has implications for tumor formation and treatment.
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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".