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Record W3042711090 · doi:10.1098/rsbl.2020.0292

Hypoxia inducible factor-1 <i>α</i> knockout does not impair acute thermal tolerance or heat hardening in zebrafish

2020· article· en· W3042711090 on OpenAlexafffund
William Joyce, Steve F. Perry

Bibliographic record

VenueBiology Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZebrafishBiologyHypoxia (environmental)Cell biologyHardening (computing)AcclimatizationDanioHypoxia-inducible factorsGeneticsGeneOxygenChemistry

Abstract

fetched live from OpenAlex

Abstract The rapid increase in critical thermal maximum (CTmax) in fish (or other animals) previously exposed to critically high temperature is termed ‘heat hardening’, which likely represents a key strategy to cope with increasingly extreme environments. The physiological mechanisms that determine acute thermal tolerance, and the underlying pathways facilitating heat hardening, remain debated. It has been posited, however, that exposure to high temperature is associated with tissue hypoxia and may be associated with the increased expression of hypoxia-inducible factor-1 (Hif-1). We studied acute thermal tolerance in zebrafish (Danio rerio) lacking functional Hif-1α paralogs (Hif-1aa and Hif-1ab double knockout; Hif-1α−/−), which are known to exhibit markedly reduced hypoxia tolerance. We hypothesized that Hif-1α−/− zebrafish would suffer reduced acute thermal tolerance relative to wild type and that the heat hardening ability would be lost. However, on the contrary, we observed that Hif-1α−/− and wild-type fish did not differ in CTmax, and both genotypes exhibited heat hardening of a similar degree when CTmax was re-tested 48 h later. Despite exhibiting impaired hypoxia tolerance, Hif-1α−/− zebrafish display unaltered thermal tolerance, suggesting that these traits are not necessarily functionally associated. Hif-1α is accordingly not required for short-term acclimation in the form of heat hardening.

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 categoriesInsufficient payload (model declined to judge)
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 score0.373
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)0.0010.000

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 head0.027
GPT teacher head0.235
Teacher spread0.208 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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