Regulation of Protein Synthesis by Hypoxia via Activation of the Endoplasmic Reticulum Kinase PERK and Phosphorylation of the Translation Initiation Factor eIF2α
Bibliographic record
Abstract
Hypoxia profoundly influences tumor development and response to therapy. While progress has been made in identifying individual gene products whose synthesis is altered under hypoxia, little is known about the mechanism by which hypoxia induces a global downregulation of protein synthesis. A critical step in the regulation of protein synthesis in response to stress is the phosphorylation of translation initiation factor eIF2alpha on Ser51, which leads to inhibition of new protein synthesis. Here we report that exposure of human diploid fibroblasts and transformed cells to hypoxia led to phosphorylation of eIF2alpha, a modification that was readily reversed upon reoxygenation. Expression of a transdominant, nonphosphorylatable mutant allele of eIF2alpha attenuated the repression of protein synthesis under hypoxia. The endoplasmic reticulum (ER)-resident eIF2alpha kinase PERK was hyperphosphorylated upon hypoxic stress, and overexpression of wild-type PERK increased the levels of hypoxia-induced phosphorylation of eIF2alpha. Cells stably expressing a dominant-negative PERK allele and mouse embryonic fibroblasts with a homozygous deletion of PERK exhibited attenuated phosphorylation of eIF2alpha and reduced inhibition of protein synthesis in response to hypoxia. PERK(-/-) mouse embryo fibroblasts failed to phosphorylate eIF2alpha and exhibited lower survival after prolonged exposure to hypoxia than did wild-type fibroblasts. These results indicate that adaptation of cells to hypoxic stress requires activation of PERK and phosphorylation of eIF2alpha and suggest that the mechanism of hypoxia-induced translational attenuation may be linked to ER stress and the unfolded-protein response.
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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".