Phosphoproteomic analysis identifies the tumor suppressor PDCD4 as a RSK substrate negatively regulated by 14-3-3
Bibliographic record
Abstract
The Ras/MAPK signaling cascade regulates various biological functions, including cell growth and proliferation. As such, this pathway is frequently deregulated in several types of cancer, including most cases of melanoma. RSK (p90 ribosomal S6 kinase) is a MAPK-activated protein kinase required for melanoma growth and proliferation, but relatively little is known about its exact function and the nature of its substrates. Herein, we used a quantitative phosphoproteomics approach to define the signaling networks regulated by RSK in melanoma. To more accurately predict direct phosphorylation substrates, we defined the RSK consensus phosphorylation motif and found significant overlap with the binding consensus of 14-3-3 proteins. We thus characterized the phospho-dependent 14-3-3 interactome in melanoma cells and found that a large proportion of 14-3-3 binding proteins are also potential RSK substrates. Our results show that RSK phosphorylates the tumor suppressor PDCD4 (programmed cell death protein 4) on two serine residues (Ser76 and Ser457) that regulate its subcellular localization and interaction with 14-3-3 proteins. We found that 14-3-3 binding promotes PDCD4 degradation, suggesting an important role for RSK in the inactivation of PDCD4 in melanoma. In addition to this tumor suppressor, our results suggest the involvement of RSK in a vast array of unexplored biological functions with relevance in oncogenesis.
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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) | 0.001 | 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".