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Record W2899326228 · doi:10.1038/s41467-018-07127-8

TAp73-induced phosphofructokinase-1 transcription promotes the Warburg effect and enhances cell proliferation

2018· article· en· W2899326228 on OpenAlexaff
Le Li, Lijia Li, Wěi Li, Taiqi Chen, Bin Zou, Lina Zhao, Huili Wang, Xueying Wang, Lina Xu, Xiaohui Liu, Dong Wang, Bo Li, Tak W. Mak, Wenjing Du, Xiaolu Yang, Peng Jiang

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer InstituteNational Institutes of HealthTsinghua UniversityNational Natural Science Foundation of ChinaU.S. Department of Defense
KeywordsWarburg effectGlycolysisPhosphofructokinaseCell biologyCancer researchRegulatorCell growthTranscription factorSuppressorBiologyAnaerobic glycolysisChemistryBiochemistryMetabolismGene

Abstract

fetched live from OpenAlex

The Warburg effect is a prominent metabolic feature associated with neoplastic diseases; however, the underlying mechanism remains incompletely understood. TAp73, a structural homolog of the tumor suppressor p53, is frequently overexpressed in human tumors, indicating a proliferative advantage that it can confer to tumor cells. Here we show that TAp73 stimulates the expression of phosphofructokinase-1, liver type (PFKL), which catalyzes the committed step in glycolysis. Through this regulation, TAp73 enhances glucose consumption and lactate excretion, promoting the Warburg effect. By activating PFKL, TAp73 also increases ATP production and bolsters anti-oxidant defense. TAp73 deficiency results in a pronounced reduction in tumorigenic potential, which can be rescued by forced PFKL expression. These findings establish TAp73 as a critical regulator of glycolysis and reveal a mechanism by which tumor cells achieve the Warburg effect to enable oncogenic growth.

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 score0.252
Threshold uncertainty score0.374

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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

Citations94
Published2018
Admission routes1
Has abstractyes

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