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Snail is a Target Gene for HIF

2007· article· en· W76007183 on OpenAlexaff
Daochun Luo, Jinxia Wang, Jeff Li, Martin Post

Bibliographic record

VenueThe FASEB Journal · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsSnailBiologyTranscription factorGeneHypoxia (environmental)Cell biologyTranscription (linguistics)Chromatin immunoprecipitationRegulation of gene expressionMolecular biologyGene expressionPromoterGeneticsChemistry

Abstract

fetched live from OpenAlex

Snail family proteins play a key role in epithelial-mesenchymal transition. Up-regulated expression of Snail has been detected during embryogenesis and tumor progression. Although hypoxia has been implicated in up-regulating Snail expression, no hypoxia-responsive elements in the Snail promoter have been identified. To investigate the underlying mechanism responsible for hypoxia-induced Snail expression, we searched in silico the human and mouse Snail promoter for potential HRE (Hypoxia-Inducible Elements) sites. Two potential HREs were identified of which one was located in the proximal promoter of both species. In subsequent experiments with endothelial cells, we isolated and characterized the proximal Snail hypoxia-responsive element using gel shift and reporter gene assays. Furthermore, we demonstrated that hypoxia-inducible transcription factors, HIF-1α and HIF-2α, bound to this HRE, thereby activating Snail transcription. Chromatin immuno-precipitation assay confirmed the interaction between HIF proteins and the Snail HRE in endothelial cells. Our findings identify Snail as a new HIF target gene and provide new insights into the regulation of Snail.

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 score0.004
Threshold uncertainty score0.014

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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