RSK1 and RSK2 modulate the translatome of glioblastoma cells in an isoform-specific and mTORC1 independent manner
Bibliographic record
Abstract
Background: The p90 ribosomal S6 kinase (RSK) family, downstream target of Ras/ERK signaling, encompasses four human isoforms (RSK1-4). Glioblastomas (GBMs) predominantly express RSK1 and RSK2, whereby RSK1 is markedly upregulated in a subset of GBMs associated with dismal prognosis and immune infiltration, while RSK2 expression is constant. RSKs were proposed as regulators of mRNA translation through the activation of mTORC1 and other factors, such as eIF4B, but nothing is known about their effect on the translatome of GBM cells. Methods: Through the generation of RSK1 and RSK2 knockout as well as double knockout (DKO) GBM cells, we investigated RSK isoform-specific functions in cell signaling, followed by the identification of their distinct transcriptome and translatome. Results: We find that mTORC1 is not activated by RSK isoforms and that eIF4B phosphorylation at S422 is more potently targeted by RSK1 than mTORC1/S6K in GBM cells. Intriguingly, RSK isoforms display differential effects on translation, with RSK1 specifically sustaining translation of a subset of mRNAs upon mTORC1 inhibition. We demonstrate that RSK1 modulates expression in the translatome of mRNAs encoding proteins affecting cell cycle, DNA replication, and repair, while RSK2 impacts mitochondria-related functions. Notably, DKO cells exhibit compounded phenotypes, underscoring the existence of isoform-specific gene regulation. Conclusions: Our findings offer mechanistic insights into the role of RSK in GBMs and provide evidence for a mTORC1-independent and RSK1-dependent translation regulatory program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".