mTORC2 Balances AKT Activation and eIF2α Serine 51 Phosphorylation to Promote Survival under Stress
Bibliographic record
Abstract
UNLABELLED: The mTOR nucleates two complexes, namely mTOR complex 1 and 2 (mTORC1 and mTORC2), which are implicated in cell growth, survival, metabolism, and cancer. Phosphorylation of the α-subunit of translation initiation factor eIF2 at serine 51 (eIF2αS51P) is a key event of mRNA translation initiation and a master regulator of cell fate during cellular stress. Recent studies have implicated mTOR signaling in the stress response, but its connection to eIF2αS51P has remained unclear. Herein, we report that genetic as well as catalytic inhibition of mTORC2 induces eIF2αS51P. On the other hand, the allosteric inhibitor rapamycin induces eIF2αS51P through pathways that are independent of mTORC1 inactivation. Increased eIF2αS51P by impaired mTORC2 depends on the inactivation of AKT, which primes the activation of the endoplasmic reticulum (ER)-resident kinase PERK/PEK. The biologic function of eIF2αS51P was characterized in tuberous sclerosis complex (TSC)-mutant cells, which are defective in mTORC2 and AKT activity. TSC-mutant cells exhibit increased PERK activity, which is downregulated by the reconstitution of the cells with an activated form of AKT1. Also, TSC-mutant cells are increasingly susceptible to ER stress, which is reversed by AKT1 reconstitution. The susceptibility of TSC-mutant cells to ER stress is further enhanced by the pharmacologic inhibition of PERK or genetic inactivation of eIF2αS51P. Thus, the PERK/eIF2αS51P arm is an important compensatory prosurvival mechanism, which substitutes for the loss of AKT under ER stress. IMPLICATIONS: A novel mechanistic link between mTOR function and protein synthesis is identified in TSC-null tumor cells under stress and reveals potential for the development of antitumor treatments with stress-inducing chemotherapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".