Inactivation of the mTORC1-Eukaryotic Translation Initiation Factor 4E Pathway Alters Stress Granule Formation
Bibliographic record
Abstract
Stress granules (SG) are cytoplasmic multimeric RNA bodies that form under stress conditions known to inhibit cap-dependent translation. SG contain translation initiation factors, RNA binding proteins, and signaling molecules. SG are known to inhibit apoptotic pathways, thus contributing to chemo- and radioresistance in tumor cells. However, whether stress granule formation involves oncogenic signaling pathways is currently unknown. Here, we report a novel role of the mTORC1-eukaryotic translation initiation factor 4E (eIF4E) pathway, a key regulator of cap-dependent translation initiation of oncogenic factors, in SG formation. mTORC1 specifically drives the eIF4E-mediated formation of SG through the phosphorylation of 4E-BP1, a key factor known to inhibit formation of the mTORC1-dependent eIF4E-eIF4GI interactions. Disrupting formation of SG by inactivation of mTOR with its specific inhibitor pp242 or by depletion of eIF4E or eIF4GI blocks the SG-associated antiapoptotic p21 pathway. Finally, pp242 sensitizes cancer cells to death in vitro and inhibits the growth of chemoresistant tumors in vivo. This work therefore highlights a novel role of the oncogenic mTORC1-eIF4E pathway, namely, the promotion of formation of antiapoptotic SG.
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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 | 0.001 |
| 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".