The mTORC1/S6K/PDCD4/eIF4A axis determines outcome of mitosis
Bibliographic record
Abstract
Abstract mTOR is a serine/threonine kinase which acts a master regulator of cell growth and proliferation. Raptor, a scaffolding protein that recruits substrates to mTOR complex 1 (mTORC1), is known to be phosphorylated during mitosis, but the significance of this phosphorylation remains largely unknown. Here we show that raptor expression and mTORC1 activity are dramatically reduced in mitotic arrested cells across a variety of cancer and normal cell lines. Prevention of raptor phosphorylation during mitosis resulted in reactivation of mTORC1 in a rapamycin-sensitive manner. Importantly, expression of a non-phosphorylatable raptor mutant caused a dramatic reduction in cytotoxicity of the spindle poison Taxol. This effect was mediated via degradation of Programmed Cell Death Protein 4 (PDCD4), a tumor suppressor protein that inhibits eIF4A activity and is negatively regulated by the mTORC1/S6K pathway. Moreover, pharmacological inhibition of eIF4A was able to enhance the effects of taxol and restore sensitivity in Taxol resistant cancer cells. These findings indicate that the mTORC1/S6K/PDCD4/eIF4A axis has a pivotal role in death vs. slippage decision during prolonged mitotic arrest and may be exploited to gain a clinical benefit in treating cancers resistant to anti-mitotic drugs.
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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) | 0.003 | 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".