Regulation of the PI3K pathway by the eIF2α kinase PKR
Bibliographic record
Abstract
Objective Study the role of PKR in PI3K signaling pathway. Two important steps of translation initiation include the recognition of the mRNA cap structure by eIF4E and the recycling of eIF2. Each step is thought to be regulated independently through the interaction of eIF4E with 4E binding proteins (4E‐BPs) and the phosphorylation of the α subunit of eIF2 at serine 51. Results we demonstrate that the eIF2α kinase PKR provides a link between the two steps. PKR induces phosphoinositide‐3 kinase (PI3K) activity leading to activation of Akt and the mammalian target of Rapamycin (mTOR) and phosphorylation of 4E‐BP1. Despite 4E‐BP1 phosphorylation, its interaction with eIF4E is enhanced in cells with activated PKR and occurs in distinct cytoplasmic granules containing phosphorylated eIF2α. Induction of the PI3K pathway antagonizes the apoptotic effects of PKR caused by eIF2α phosphorylation. PKR is also involved in the activation of PI3K and 4E‐BP1 phosphorylation by serum or interferon (IFN)‐ 1 _. Conclusion Our data demonstrate a novel signaling property of PKR through the regulation of the PI3K pathway. Source of Funding This work has been supported by a grant from the Canadian Institutes of Health Research (CIHR) to Dr. A. Koromilas. S. Kazemi and D. Baltzis are both Research Students of the Terry Fox Foundation through awards from the National Cancer Institute of Canada (NCIC).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".