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Record W2142210400 · doi:10.1074/jbc.m206141200

Activated pp60c-Src Leads to Elevated Hypoxia-inducible Factor (HIF)-1α Expression under Normoxia

2002· article· en· W2142210400 on OpenAlexaboutno aff
Rotem Karni, Yuval Dor, Eli Keshet, Oded Meyuhas, Alexander Levitzki

Bibliographic record

VenueJournal of Biological Chemistry · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
FundersJames S. McDonnell Foundation
KeywordsHypoxia (environmental)Hypoxia-Inducible Factor 1Hypoxia-inducible factorsCell biologyProto-oncogene tyrosine-protein kinase SrcHIF1AChemistryInternal medicineBiologyEndocrinologyCancer researchDownregulation and upregulationPhosphorylationMedicineOxygenBiochemistryGene

Abstract

fetched live from OpenAlex

Hypoxia-inducible factor (HIF)-1 is a master transcription factor, which up-regulates glycolysis, erythropoiesis, and angiogenesis under hypoxia. HIF-1alpha accumulates in normoxic tumor cells, leading to glycolysis under aerobic conditions. This phenomenon, known as the "Warburg effect," is caused by a yet unknown mechanism. Here we show that transformed cells that express constitutively active pp60(c-Src) (Src) express HIF-1alpha protein under normoxia, which results in the expression of multiple HIF-1alpha target genes. We show that this occurrence is due to an enhanced rate of HIF-1alpha protein synthesis and not due to reduced HIF-1alpha degradation. Furthermore, we show that the Src-induced increase in protein synthesis is due to the global increase in the rate of cap-dependent translation and does not involve inhibition of HIF-1alpha degradation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations117
Published2002
Admission routes1
Has abstractyes

Explore more

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