ERK Activation Mediates Cell Cycle Arrest and Apoptosis after DNA Damage Independently of p53
Bibliographic record
Abstract
In response to DNA damage, ataxia-telangiectasia mutant and ataxia-telangiectasia and Rad-3 activate p53, resulting in either cell cycle arrest or apoptosis. We report here that DNA damage stimuli, including etoposide (ETOP), adriamycin (ADR), ionizing irradiation (IR), and ultraviolet irradiation (UV) activate ERK1/2 (ERK) mitogen-activated protein kinase in primary (MEF and IMR90), immortalized (NIH3T3) and transformed (MCF-7) cells. ERK activation in response to ETOP was abolished in ATM-/- fibroblasts (GM05823) and was independent of p53. The MEK1 inhibitor PD98059 prevented ERK activation but not p53 stabilization. Maximal ERK activation in response to DNA damage was not attenuated in MEF(p53-/-). However, ERK activation contributes to either cell cycle arrest or apoptosis in response to low or high intensity DNA insults, respectively. Inhibition of ERK activation by PD98059 or U0126 attenuated p21(CIP1) induction, resulting in partial release of the G(2)/M cell cycle arrest induced by ETOP. Furthermore, PD98059 or U0126 also strongly attenuated apoptosis induced by high dose ETOP, ADR, or UV. Conversely, enforced activation of ERK by overexpression of MEK-1/Q56P sensitized cells to DNA damage-induced apoptosis. Taken together, these results indicate that DNA damage activates parallel ERK and p53 pathways in an ATM-dependent manner. These pathways might function cooperatively in cell cycle arrest and apoptosis.
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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".