Tumor Suppression by PTEN Requires the Activation of the PKR-eIF2α Phosphorylation Pathway
Bibliographic record
Abstract
Inhibition of protein synthesis by phosphorylation of the alpha subunit of eukaryotic translation initiation factor 2 (eIF2) at Ser(51) occurs as a result of the activation of a family of kinases in response to various forms of stress. Although some consequences of eIF2alpha phosphorylation are cytoprotective, phosphorylation of eIF2alpha by RNA-dependent protein kinase (PKR) is largely proapoptotic and tumor suppressing. Phosphatase and tensin homolog deleted from chromosome 10 (PTEN) is a tumor suppressor protein that is mutated or deleted in various human cancers, with functions that are mediated through phosphatase-dependent and -independent pathways. Here, we demonstrate that the eIF2alpha phosphorylation pathway is downstream of PTEN. Inactivation of PTEN in human melanoma cells reduced eIF2alpha phosphorylation, whereas reconstitution of PTEN-null human glioblastoma or prostate cancer cells with either wild-type PTEN or phosphatase-defective mutants of PTEN induced PKR activity and eIF2alpha phosphorylation. The antiproliferative and proapoptotic effects of PTEN were compromised in mouse embryonic fibroblasts that lacked PKR or contained a phosphorylation-defective variant of eIF2alpha. Induction of the pathway leading to phosphorylation of eIF2alpha required an intact PDZ-binding motif in PTEN. These findings establish a link between tumor suppression by PTEN and inhibition of protein synthesis that is independent of PTEN's effects on phosphoinositide 3'-kinase signaling.
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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".