Fish models to explore epigenetic determinants of hypoxia-tolerance
Bibliographic record
Abstract
The occurrence of environmental hypoxia in freshwater and marine aquatic systems has increased over the last century and is predicted to further increase with climate change. As members of the largest extant vertebrate group, freshwater fishes, and to a much lesser extent marine fishes, are vulnerable to increased occurrence of hypoxia. This is important as fishes render important ecosystem services and have important cultural and economic roles. Evolutionarily successful, fishes have adapted to diverse aquatic freshwater and marine habitats with different oxygen conditions. While some fishes exhibit genetic adaptions to tolerate hypoxia and even anoxia, others are limited to oxygen-rich habitats. Recent advances in molecular epigenetics have shown that some epigenetic machinery, especially histone- and DNA demethylases, is directly dependent on oxygen and modulates important transcription-regulating epigenetic marks in the process. At the post-transcriptional level, hypoxia has been shown to affect non-coding microRNA abundance. Together, this evidence adds a new molecular epigenetic basis to study hypoxia tolerance in fishes. Here, we review the documented and predicted changes in environmental hypoxia in aquatic systems and discuss the diversity and comparative physiology of hypoxia tolerance in fishes, including molecular and physiological adaptations. We then discuss how recent mechanistic advances in environmental epigenetics can inform future work probing the role of oxygen-dependent epigenetic marks in shaping organismal hypoxia-tolerance in fishes with a focus on within- and between-species variation, acclimation, inter- and multigenerational plasticity, and multiple climate-change stressors. We conclude by describing the translational potential of this approach for conservation physiology, ecotoxicology, and aquaculture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".