Phosphoproteome dynamics reveal novel ERK1/2 MAP kinase substrates with broad spectrum of functions
Bibliographic record
Abstract
The ERK1/2 MAP kinase pathway is an evolutionarily conserved signaling module that controls many fundamental physiological processes. Deregulated activity of ERK1/2 MAP kinases is associated with developmental syndromes and several human diseases. Despite the importance of this pathway, a comprehensive picture of the natural substrate repertoire and biochemical mechanisms regulated by ERK1/2 is still lacking. In this study, we used large-scale quantitative phosphoproteomics and bioinformatics analyses to identify novel candidate ERK1/2 substrates based on their phosphorylation signature and kinetic profiles in epithelial cells. We identified a total of 7936 phosphorylation sites within 1861 proteins, of which 155 classify as candidate ERK1/2 substrates, including 128 new targets. Candidate ERK1/2 substrates are involved in diverse cellular processes including transcriptional regulation, chromatin remodeling, RNA splicing, cytoskeleton dynamics, cellular junctions and cell signaling. Detailed characterization of one newly identified substrate, the transcriptional regulator JunB, revealed that ERK1/2 phosphorylate JunB on a serine adjacent to the DNA-binding domain, resulting in increased DNA-binding affinity and transcriptional activity. Our study expands the spectrum of cellular functions controlled by ERK1/2 kinases.
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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 | 0.001 | 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 | 0.000 |
| 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".