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p53 Activation Domain 1 Is Essential for PUMA Upregulation and p53-Mediated Neuronal Cell Death

2004· article· en· W2077442598 on OpenAlexafffund
Sean P. Cregan, Nicole Arbour, Jason G. MacLaurin, Steven Callaghan, Andre Fortin, Eric C. Cheung, Daniel S. Guberman, David S. Park, Ruth S. Slack

Bibliographic record

VenueJournal of Neuroscience · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsRobarts Clinical TrialsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchCanadian Stroke NetworkHeart and Stroke Foundation of Canada
KeywordsTransactivationPumaProgrammed cell deathDownregulation and upregulationBiologyCell biologyApoptosisGeneTranscription factorGenetics

Abstract

fetched live from OpenAlex

The p53 tumor suppressor gene has been implicated in the regulation of apoptosis in a number of different neuronal death paradigms. Because of the importance of p53 in neuronal injury, we questioned the mechanism underlying p53-mediated apoptosis in neurons. Using adenoviral-mediated gene delivery, reconstitution experiments, and mice carrying a knock-in mutation in the endogenous p53 gene, we show that the transactivation function of p53 is essential to induce neuronal cell death. Although p53 possesses two transactivation domains that can activate p53 targets independently, we demonstrate that the first activation domain (ADI) is required to drive apoptosis after neuronal injury. Furthermore, the BH3-only proteins Noxa and PUMA exhibit differential regulation by the two transactivation domains. Here, we show that Noxa can be induced by either activation domain, whereas PUMA induction requires both activation domains to be intact. Unlike Noxa, the upregulation of PUMA alone is sufficient to induce neuronal cell death. We demonstrate, therefore, that the first transactivation domain of p53 is indispensable for the induction of neuronal cell 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)0.000
metaresearch head score (Gemma)0.000
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 score0.040
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations92
Published2004
Admission routes2
Has abstractyes

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