Hypoxia inducible factor-1 <i>α</i> knockout does not impair acute thermal tolerance or heat hardening in zebrafish
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".