A Novel Function of eIF2α Kinases as Inducers of the Phosphoinositide-3 Kinase Signaling Pathway
Bibliographic record
Abstract
Phosphoinositide-3 kinase (PI3K) plays an important role in signal transduction in response to a wide range of cellular stimuli involved in cellular processes that promote cell proliferation and survival. Phosphorylation of the alpha subunit of the eukaryotic translation initiation factor eIF2 at Ser51 takes place in response to various types of environmental stress and is essential for regulation of translation initiation. Herein, we show that a conditionally active form of the eIF2alpha kinase PKR acts upstream of PI3K and turns on the Akt/PKB-FRAP/mTOR pathway leading to S6 and 4E-BP1 phosphorylation. Also, induction of PI3K signaling antagonizes the apoptotic and protein synthesis inhibitory effects of the conditionally active PKR. Furthermore, induction of the PI3K pathway is impaired in PKR(-/-) or PERK(-/-) mouse embryonic fibroblasts (MEFs) in response to various stimuli that activate each eIF2alpha kinase. Mechanistically, PI3K signaling activation is indirect and requires the inhibition of protein synthesis by eIF2alpha phosphorylation as demonstrated by the inactivation of endogenous eIF2alpha by small interfering RNA or utilization of MEFs bearing the eIF2alpha Ser51Ala mutation. Our data reveal a novel property of eIF2alpha kinases as activators of PI3K signaling and cell survival.
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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) | 0.001 | 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".