Pharmacological and Genetic Evaluation of Proposed Roles of Mitogen-activated Protein Kinase/Extracellular Signal-regulated Kinase Kinase (MEK), Extracellular Signal-regulated Kinase (ERK), and p90RSK in the Control of mTORC1 Protein Signaling by Phorbol Esters
Bibliographic record
Abstract
The mammalian target of rapamycin complex 1 (mTORC1) links the control of mRNA translation, cell growth, and metabolism to diverse stimuli. Inappropriate activation of mTORC1 can lead to cancer. Phorbol esters are naturally occurring products that act as potent tumor promoters. They activate isoforms of protein kinase C (PKCs) and stimulate the oncogenic MEK/ERK signaling cascade. They also activate mTORC1 signaling. Previous work indicated that mTORC1 activation by the phorbol ester PMA (phorbol 12-myristate 13-acetate) depends upon PKCs and may involve MEK. However, the precise mechanism(s) through which they activate mTORC1 remains unclear. Recent studies have implicated both the ERKs and the ERK-activated 90-kDa ribosomal S6 kinases (p90(RSK)) in activating mTORC1 signaling via phosphorylation of TSC2 (a regulator of mTORC1) and/or the mTORC1 component raptor. However, the relative importance of each of these kinases and phosphorylation events for the activation of mTORC1 signaling is unknown. The recent availability of MEK (PD184352) and p90(RSK) (BI-D1870) inhibitors of improved specificity allowed us to address the roles of these protein kinases in controlling mTORC1 in a variety of human and rodent cell types. In parallel, we used specific shRNAs against p90(RSK1) and p90(RSK2) to further test their roles in regulating mTORC1 signaling. Our data indicate that p90(RSKs) are dispensable for the activation of mTORC1 signaling by phorbol esters in all cell types tested. Our data also reveal striking diversity in the requirements for MEK/ERK in the control of mTORC1 between different cell types, pointing to additional signaling connections between phorbol esters and mTORC1, which do not involve MEK/ERK. This study provides important information for the design of efficient strategies to combat the hyperactivation of mTORC1 signaling by oncogenic pathways.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".