RAS/ERK Signaling Promotes Site-specific Ribosomal Protein S6 Phosphorylation via RSK and Stimulates Cap-dependent Translation
Bibliographic record
Abstract
Converging signals from the mammalian target of rapamycin (mTOR) and phosphoinositide 3-kinase (PI3K) pathways are well established to modulate translation initiation. Less is known regarding the molecular basis of protein synthesis regulated by other inputs, such as agonists of the Ras/extracellular signal-regulated kinase (ERK) signaling cascade. Ribosomal protein (rp) S6 is a component of the 40S ribosomal subunit that becomes phosphorylated at several serine residues upon mitogen stimulation, but the exact molecular mechanisms regulating its phosphorylation and the function of phosphorylated rpS6 is poorly understood. Here, we provide evidence that activation of the p90 ribosomal S6 kinases (RSKs) by serum, growth factors, tumor promoting phorbol esters, and oncogenic Ras is required for rpS6 phosphorylation downstream of the Ras/ERK signaling cascade. We demonstrate that while ribosomal S6 kinase 1 (S6K1) phosphorylates rpS6 at all sites, RSK exclusively phosphorylates rpS6 at Ser(235/236) in vitro and in vivo using an mTOR-independent mechanism. Mutation of rpS6 at Ser(235/236) reveals that phosphorylation of these sites promotes its recruitment to the 7-methylguanosine cap complex, suggesting that Ras/ERK signaling regulates assembly of the translation preinitiation complex. These data demonstrate that RSK provides an mTOR-independent pathway linking the Ras/ERK signaling cascade to the translational machinery.
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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) | 0.002 | 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".