The Involvement of MAPK Signaling Pathways in Determining the Cellular Response to p53 Activation
Bibliographic record
Abstract
The effect of ERK, p38, and JNK signaling on p53-dependent apoptosis and cell cycle arrest was investigated using a Friend murine erythroleukemia virus (FVP)-transformed cell line that expresses a temperature-sensitive p53 allele, DP16.1/p53ts. In response to p53 activation at 32 degrees C, DP16.1/p53ts cells undergo p53-dependent G(1) cell cycle arrest and apoptosis. As a result of viral transformation, these cells express the spleen focus forming env-related glycoprotein gp55, which can bind to the erythropoietin receptor (EPO-R) and mimics many aspects of EPO-induced EPO-R signaling. We demonstrate that ERK, p38 and JNK mitogen-activated protein kinases (MAPKs) are constitutively active in DP16.1/p53ts cells. Constitutive MEK activity contributes to p53-dependent apoptosis and phosphorylation of p53 on serine residue 15. The pro-apoptotic effect of this MAPK kinase signal likely reflects an aberrant Ras proliferative signal arising from FVP-induced viral transformation. Inhibition of MEK alters the p53-dependent cellular response of DP16.1/p53ts from apoptosis to G(1) cell cycle arrest, with a concomitant increase in p21(WAF1), suggesting that the Ras/MEK pathway may influence the cellular response to p53 activation. p38 and JNK activity in DP16.1/p53ts cells is anti-apoptotic and capable of limiting p53-dependent apoptosis at 32 degrees C. Moreover, JNK facilitates p53 protein turnover, which could account for the enhanced apoptotic effects of inhibiting this MAPK pathway in DP16.1/p53ts cells. Overall, these data show that intrinsic MAPK signaling pathways, active in transformed cells, can both positively and negatively influence p53-dependent apoptosis, and illustrate their potential to affect cancer therapies aimed at reconstituting or activating p53 function.
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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 | 0.000 |
| 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".