Evolution of a novel regulatory mechanism of hypoxia inducible factor in hypoxia-tolerant electric fishes
Bibliographic record
Abstract
Hypoxia is a significant source of metabolic stress that activates many cellular pathways involved in cellular differentiation, proliferation, and cell death. Hypoxia is also a major component in many human diseases and a known driver of many cancers. Despite the challenges posed by hypoxia, there are animals that display impressive capacity to withstand lethal levels of hypoxia for prolonged periods of time and thus offer a gateway to a more comprehensive understanding of the hypoxic response in vertebrates. The weakly electric fish genus Brachyhypopomus inhabits some of the most challenging aquatic ecosystems in the world, with some species experiencing seasonal anoxia, thus providing a unique system to study the cellular and molecular mechanisms of hypoxia tolerance. In this study, we use closely related species of Brachyhypopomus that display a range of hypoxia tolerances to probe for the underlying molecular mechanisms via hypoxia inducible factors (HIFs)-transcription factors known to coordinate the cellular response to hypoxia in vertebrates. We find that HIF1⍺ from hypoxia tolerant Brachyhypopomus species displays higher transactivation in response to hypoxia than that of intolerant species, when overexpressed in live cells. Moreover, we identified two SUMO-interacting motifs near the oxygen-dependent degradation and transactivation domains of the HIF1⍺ protein that appear to boost transactivation of HIF1, regardless of the genetic background. Together with computational analyses of selection, this shows that evolution of HIF1⍺ are likely to underlie adaptations to hypoxia tolerance in Brachyhypopomus electric fishes, with changes in two SUMO-interacting motifs facilitating the mechanism of this tolerance.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".