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Record W1998939489 · doi:10.1074/jbc.m304049200

Ataxia Telangiectasia-mutated Protein Can Regulate p53 and Neuronal Death Independent of Chk2 in Response to DNA Damage

2003· article· en· W1998939489 on OpenAlexafffund
Elizabeth Keramaris, Atsushi Hirao, Ruth S. Slack, Tak W. Mak, David S. Park

Bibliographic record

VenueJournal of Biological Chemistry · 2003
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity of TorontoOntario Institute for Cancer ResearchUniversity of Ottawa
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsDNA damageCyclin-dependent kinasePhosphorylationCell biologyBiologyKinaseAtaxia-telangiectasiaProgrammed cell deathCheckpoint Kinase 2DNACancer researchApoptosisProtein kinase AProtein-Serine-Threonine KinasesBiochemistryCell cycle

Abstract

fetched live from OpenAlex

DNA damage is a key initiator of neuronal death. We have previously shown that the tumor suppressor p53, in conjunction with cyclin-dependent kinases (CDKs), regulates the mitochondrial pathway of death in neurons exposed to genotoxic agents. However, the mechanisms by which p53 is regulated is unclear. Presently, we show that p53 is phosphorylated on Ser-15 following DNA damage and this occurs independently of the CDK pathway. Instead, we show that p53 phosphorylation, stability, as well as neuronal death is regulated, in part, by the ataxia telangiectasia-mutated (ATM) protein. Previous reports have suggested that ATM regulation of p53 occurs through Chk2. However, in our present paradigms, we show that ATM functions separately from Chk2 to regulate p53 stability and neuronal death. Chk2 deficiency does not affect p53 stability or neuronal death induced by Topoisomerase I or II inhibition. Taken together, our results provide a model by which DNA damage can activate an ATM-dependent, Chk2-independent pathway of p53-mediated neuronal death.

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)0.001
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 score0.108
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.255
Teacher spread0.236 · 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

Citations44
Published2003
Admission routes2
Has abstractyes

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