Loss of hypoxia-inducible factor 1α affects hypoxia tolerance in larval and adult zebrafish (<i>Danio rerio</i>)
Bibliographic record
Abstract
The coordination of the hypoxic response is attributed, in part, to hypoxia-inducible factor 1α (Hif-1α), a regulator of hypoxia-induced transcription. After the teleost-specific genome duplication, most teleost fishes lost the duplicate copy of Hif-1α, except species in the cyprinid lineage that retained both paralogues of Hif-1α (Hif1aa and Hif1ab). Little is known about the contribution of Hif-1α, and specifically of each paralogue, to hypoxia tolerance. Here, we examined hypoxia tolerance in wild-type (Hif1aa+/+ab+/+) and Hif-1α knockout lines (Hif1aa−/−; Hif1ab−/−; Hif1aa−/−ab−/−) of zebrafish (Danio rerio). Critical O2tension (Pcrit; the partial pressure of oxygen (PO2) at which O2consumption can no longer be maintained) and time to loss of equilibrium (LOE), two indices of hypoxia tolerance, were assessed in larvae and adults. Knockout of both paralogues significantly increasedPcrit(decreased hypoxia tolerance) in larval fish. Prior exposure of larvae to hypoxia decreasedPcritin wild-type fish, an effect mediated by the Hif1aa paralogue. In adults, individuals with a knockout of either paralogue exhibited significantly decreased time to LOE but no difference inPcrit. Together, these results demonstrate that in zebrafish, tolerance to hypoxia and improved hypoxia tolerance after pre-exposure to hypoxia (pre-conditioning) are mediated, at least in part, by Hif-1α.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".