Downregulation of PERK activity and eIF2α serine 51 phosphorylation by mTOR complex 1 elicits pro-oxidant and pro-death effects in tuberous sclerosis-deficient cells
Bibliographic record
Abstract
Oxidative stress determines cell fate through several mechanisms, among which regulation of mRNA translation by the phosphorylation of the alpha (α) subunit of the translation initiation factor eIF2α at serine 51 (eIF2αP) plays a prominent role. Increased eIF2αP can contribute to tumor progression as well as tumor suppression. While eIF2αP is increased in most cells to promote survival and adaptation to different forms of stress, we demonstrate that eIF2αP is reduced in tuberous sclerosis complex 2 (TSC2)-deficient cells subjected to oxidative insults. Decreased eIF2αP in TSC2-deficient cells depends on reactive oxygen species (ROS) production and is associated with a reduced activity of the endoplasmic reticulum (ER)-resident kinase PERK owing to the hyper-activation of the mammalian target of rapamycin complex 1 (mTORC1). Downregulation of PERK activity and eIF2αP is accompanied by increased ROS production and enhanced susceptibility of TSC2-deficient cells to extrinsic pro-oxidant stress. The decreased levels of eIF2αP delay tumor formation of TSC2-deficient cells in immune deficient mice, an effect that is significantly alleviated in mice subjected to an anti-oxidant diet. Our findings reveal a previously unidentified connection between mTORC1 and eIF2αP in TSC2-deficient cells with potential implications in tumor suppression in response to oxidative insults.
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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".