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Record W2026072962 · doi:10.1074/jbc.m309917200

DNA-dependent Protein Kinase and Checkpoint Kinase 2 Synergistically Activate a Latent Population of p53 upon DNA Damage

2004· article· en· W2026072962 on OpenAlexaff
Melissa T. Jack, Richard A. Woo, Noboru Motoyama, Patrick W.K. Lee

Bibliographic record

VenueJournal of Biological Chemistry · 2004
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsHealth Sciences CentreUniversity of Calgary
Fundersnot available
KeywordsDNA damageCheckpoint Kinase 2DNA-PKcsBiologyG2-M DNA damage checkpointDNA repairCHEK1Cell biologyMolecular biologyDNAKinaseProtein kinase ACell cycle checkpointApoptosisBiochemistryCell cycleProtein-Serine-Threonine Kinases

Abstract

fetched live from OpenAlex

The role of the checkpoint kinase 2 (Chk2) as an upstream activator of p53 following DNA damage has been controversial. We have recently shown that Chk2 and the DNA-dependent protein kinase (DNA-PK) are both involved in DNA damage-induced apoptosis but not G(1) arrest in mouse embryo fibroblasts. Here we demonstrate that Chk2 is required to activate p53 in vitro as measured by its ability to bind its consensus DNA target sequence following DNA damage and is in fact the previously unidentified factor working synergistically with DNA-PK to activate p53. The gene mutated in ataxia telangiectasia is not involved in this p53 activation. Using wortmannin, serine 15 mutants of p53, DNA-PK null cells and Chk2 null cells, we demonstrate that DNA-PK and Chk2 act independently and sequentially on p53. Furthermore, the p53 target of these two kinases represents a latent (preexisting) population of p53. Taken together, the results from these studies are consistent with a model in which DNA damage causes an immediate and sequential modification of latent p53 by DNA-PK and Chk2, which under appropriate conditions can lead to apoptosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.253
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations40
Published2004
Admission routes1
Has abstractyes

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