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Record W2116918328 · doi:10.1128/mcb.00670-13

AMP-Activated Protein Kinase Induces p53 by Phosphorylating MDMX and Inhibiting Its Activity

2013· article· en· W2116918328 on OpenAlexaff
Guifen He, Yi-Wei Zhang, Jun‐Ho Lee, Shelya X. Zeng, Yunyuan V. Wang, Zhijun Luo, Xiaochen Dong, Benoı̂t Viollet, Geoffrey M. Wahl, Hua Lu

Bibliographic record

VenueMolecular and Cellular Biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsCanadian Nautical Research Society
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsBiologyPhosphorylationCell biologyProtein kinase AAMP-activated protein kinaseKinaseCancer researchBiochemistryAMPK

Abstract

fetched live from OpenAlex

AMP-activated protein kinase (AMPK) has been shown to activate p53 in response to metabolic stress. However, the underlying mechanisms remain unclear. Here we show that metabolic stresses induce AMPK-mediated phosphorylation of human MDMX on Ser342 in vitro and in cells, leading to enhanced association between MDMX and 14-3-3. This markedly inhibits p53 ubiquitylation and significantly stabilizes and activates p53. By striking contrast, no phosphorylation of MDM2 by AMPK was noted. AMPK-mediated MDMX phosphorylation, MDMX-14-3-3 binding, and p53 activation were drastically reduced in mouse embryo fibroblasts harboring endogenous MDMX with S341A (mouse homologue of human serine 342), S367A, and S402A (mouse homologue of human serine 403) mutations. Moreover, deficiency of AMPK prevented MDMX-14-3-3 interaction and p53 activation. The activation of p53 through AMPK-mediated MDMX phosphorylation and inactivation was further confirmed by using cell and animal model systems with two AMPK activators, metformin and salicylate (the active form of aspirin). Together, the results unveil a mechanism by which metabolic stresses activate AMPK, which, in turn, phosphorylates and inactivates MDMX, resulting in p53 stabilization and activation.

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 categoriesMeta-epidemiology (narrow)
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 score0.115
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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.

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

Citations108
Published2013
Admission routes1
Has abstractyes

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