The binding of PRAS40 to 14-3-3 proteins is not required for activation of mTORC1 signalling by phorbol esters/ERK
Bibliographic record
Abstract
PRAS40 binds to the mTORC1 (mammalian target of rapamycin complex 1) and is released in response to insulin. It has been suggested that this effect is due to 14-3-3 binding and leads to activation of mTORC1 signalling. In a similar manner to insulin, phorbol esters also activate mTORC1 signalling, in this case via PKC (protein kinase C) and ERK (extracellular-signal-regulated kinase). However, phorbol esters do not induce phosphorylation of PRAS40 at Thr(246), binding of 14-3-3 proteins to PRAS40 or its release from mTORC1. Mutation of Thr(246) to a serine residue permits phorbol esters to induce phosphorylation and binding to 14-3-3 proteins. Such phosphorylation is apparently mediated by RSKs (ribosomal S6 kinases), which lie downstream of ERK. However, although the PRAS40(T246S) mutant binds to 14-3-3 better than wild-type PRAS40, each inhibits mTORC1 signalling to a similar extent. Our results show that activation of mTORC1 signalling by phorbol esters does not require PRAS40 to be phosphorylated at Thr(246), bind to 14-3-3 or be released from mTORC1. It is conceivable that phorbol esters activate mTORC1 by a distinct mechanism not involving PRAS40. Indeed, our results suggest that PRAS40 may not actually be involved in controlling mTORC1, but rather be a downstream target of mTORC1 that is regulated in response only to specific stimuli, such as insulin.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".