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Record W3092349385 · doi:10.1242/dev.185116

Zebrafish <i>hif-3α</i> modulates erythropoiesis via regulation of <i>gata-1</i> to facilitate hypoxia tolerance

2020· article· en· W3092349385 on OpenAlexaff
Xiaolian Cai, Ziwen Zhou, Junji Zhu, Qian Liao, Dawei Zhang, Xing Liu, Jing Wang, Gang Ouyang, Wuhan Xiao

Bibliographic record

VenueDevelopment · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsMinistry of Agriculture
FundersNational Natural Science Foundation of China
KeywordsBiologyErythropoiesisZebrafishTranscription factorHypoxia-inducible factorsCell biologyHypoxia (environmental)HaematopoiesisGene isoformHypoxia-Inducible Factor 1Regulation of gene expressionGeneMolecular biologyGeneticsStem cellInternal medicineAnemia

Abstract

fetched live from OpenAlex

The hypoxia-inducible factors 1α and 2α (HIF-1α and HIF-2α) are master regulators of the cellular response to O2. In addition to HIF-1α and HIF-2α, HIF-3α is another identified member of the HIF-α gene family. Even though whether some HIF-3α isoforms have transcriptional activity or repressive activity is still under debate, it is evident that the full length of HIF-3α acts as a transcription factor. However, its function in hypoxia signaling is largely unknown. Here, we showed that loss of hif-3α in zebrafish reduced hypoxia tolerance. Further assays indicated that erythrocyte number was decreased because red blood cell maturation was impeded by hif-3α disruption. We found that gata-1 expression was downregulated in hif-3α-null zebrafish, as were several hematopoietic marker genes, including alas2, band3, hbae1, hbae3 and hbbe1. hif-3α recognized the hypoxia response element (HRE) located in the promoter of gata-1 and directly bound to the promoter to transactivate gata-1 expression. Our results suggested that hif-3α facilities hypoxia tolerance by modulating erythropoiesis via gata-1 regulation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.865

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 head0.021
GPT teacher head0.217
Teacher spread0.196 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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