Tumor suppressor activity of the ERK/MAPK pathway by promoting selective protein degradation
Bibliographic record
Abstract
Constitutive activation of growth factor signaling pathways paradoxically triggers a cell cycle arrest known as cellular senescence. In primary cells expressing oncogenic ras, this mechanism effectively prevents cell transformation. Surprisingly, attenuation of ERK/MAP kinase signaling by genetic inactivation of Erk2, RNAi-mediated knockdown of ERK1 or ERK2, or MEK inhibitors prevented the activation of the senescence mechanism, allowing oncogenic ras to transform primary cells. Mechanistically, ERK-mediated senescence involved the proteasome-dependent degradation of proteins required for cell cycle progression, mitochondrial functions, cell migration, RNA metabolism, and cell signaling. This senescence-associated protein degradation (SAPD) was observed not only in cells expressing ectopic ras, but also in cells that senesced due to short telomeres. Individual RNAi-mediated inactivation of SAPD targets was sufficient to restore senescence in cells transformed by oncogenic ras or trigger senescence in normal cells. Conversely, the anti-senescence viral oncoproteins E1A, E6, and E7 prevented SAPD. In human prostate neoplasms, high levels of phosphorylated ERK were found in benign lesions, correlating with other senescence markers and low levels of STAT3, one of the SAPD targets. We thus identified a mechanism that links aberrant activation of growth signaling pathways and short telomeres to protein degradation and cellular senescence.
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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".