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p53 Mediates Autophagy and Cell Death by a Mechanism Contingent On Bnip3

2013· article· en· W2100153355 on OpenAlexaff
Erika Yan Wang, Hongying Gang, Yaron Aviv, Rimpy Dhingra, Victoria Margulets, Lorrie A. Kirshenbaum

Bibliographic record

VenueHypertension · 2013
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsAutophagyProgrammed cell deathCell biologyBiologyMitochondrionCellApoptosisGenetics

Abstract

fetched live from OpenAlex

Myocardial ischemia and angiotensin II activate the tumor suppressor p53 protein, which promotes cell death. Previously, we showed that the Bcl-2 death gene Bnip3 is highly induced during ischemia, where it triggers mitochondrial perturbations resulting in autophagy and cell death. However, whether p53 regulates Bnip3 and autophagy is unknown. Herein, we provide new compelling evidence for a novel signaling axis that commonly links p53 and Bnip3 for autophagy and cell death. p53 overexpression increased endogenous Bnip3 mRNA and protein levels resulting in mitochondrial defects leading to loss of mitochondrial ΔΨ(m). This was accompanied by an increase in autophagic flux and cell death. Notably, genetic loss of function studies, such as Atg7 knock-down or pharmacological inhibition of autophagy with 3-methyl adenine, suppressed cell death induced by p53--indicating that p53 induces maladaptive autophagy. Our previous work demonstrated that Bnip3 induces mitochondrial defects and autophagic cell death. Conversely, loss of function of Bnip3 in cardiac myocytes or Bnip3(-/-) mouse embryonic fibroblasts prevented mitochondrial targeting of p53, autophagy, and cell death. To our knowledge, these data provide the first evidence for the dual regulation of autophagy and cell death of cardiac myocytes by p53 that is mutually dependent on and obligatorily linked to Bnip3 gene activation. Hence, our findings may explain more fundamentally, how, autophagy and cell death are dually regulated during cardiac stress conditions where p53 is activated.

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 score0.025
Threshold uncertainty score0.474

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 head0.221
Teacher spread0.207 · 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

Citations81
Published2013
Admission routes1
Has abstractyes

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