Epigenetic and post-transcriptional repression support metabolic suppression in chronically hypoxic goldfish
Bibliographic record
Abstract
Goldfish enter a hypometabolic state to survive chronic hypoxia. We recently described tissue-specific contributions of membrane lipid composition remodeling and mitochondrial function to metabolic suppression across different goldfish tissues. However, the molecular and especially epigenetic foundations of hypoxia tolerance in goldfish under metabolic suppression are not well understood. Here we show that components of the molecular oxygen-sensing machinery are robustly activated across tissues irrespective of hypoxia duration. Induction of gene expression of enzymes involved in DNA methylation turnover and microRNA biogenesis suggest a role for epigenetic transcriptional and post-transcriptional suppression of gene expression in the hypoxia-acclimated brain. Conversely, mechanistic target of rapamycin-dependent translational machinery activity is not reduced in liver and white muscle, suggesting this pathway does not contribute to lowering cellular energy expenditure. Finally, molecular evidence supports previously reported chronic hypoxia-dependent changes in membrane cholesterol, lipid metabolism and mitochondrial function via changes in transcripts involved in cholesterol biosynthesis, β-oxidation, and mitochondrial fusion in multiple tissues. Overall, this study shows that chronic hypoxia robustly induces expression of oxygen-sensing machinery across tissues, induces repressive transcriptional and post-transcriptional epigenetic marks especially in the chronic hypoxia-acclimated brain and supports a role for membrane remodeling and mitochondrial function and dynamics in promoting metabolic suppression.
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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".