Identification of RSK substrates using an analog-sensitive kinase approach
Bibliographic record
Abstract
The p90 ribosomal S6 kinases (RSK) family of serine/threonine kinases comprises four isoforms (RSK1-4) that lie downstream of the ERK1/2 mitogen-activated protein kinase pathway. RSKs are implicated in fine tuning of cellular processes such as translation, transcription, proliferation, and motility. Previous work showed that pathogens such as Cardioviruses could hijack any of the four RSK isoforms to inhibit PKR activation or to disrupt cellular nucleocytoplasmic trafficking. In contrast, some reports suggest nonredundant functions for distinct RSK isoforms, whereas Coffin-Lowry syndrome has only been associated with mutations in the gene encoding RSK2. In this work, we used the analog-sensitive kinase strategy to ask whether the cellular substrates of distinct RSK isoforms differ. We compared the substrates of two of the most distant RSK isoforms: RSK1 and RSK4. We identified a series of potential substrates for both RSKs in cells and validated RanBP3, PDCD4, IRS2, and ZC3H11A as substrates of both RSK1 and RSK4, and SORBS2 as an RSK1 substrate. In addition, using mutagenesis and inhibitors, we confirmed analog-sensitive kinase data showing that endogenous RSKs phosphorylate TRIM33 at S1119. Our data thus identify a series of potential RSK substrates and suggest that the substrates of RSK1 and RSK4 largely overlap and that the specificity of the various RSK isoforms likely depends on their cell- or tissue-specific expression pattern.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".