Multiple omic investigations of freeze tolerance adaptation in the aquatic ectothermic vertebrate, the Amur sleeper
Bibliographic record
Abstract
Abstract Freeze tolerance is an amazing overwintering strategy that enables ectotherms to occupy new niches and survive in cold climates. However, the genetic basis underpinning this ecologically relevant adaptation is largely unknown. Amur sleeper is the only known freeze-tolerant fish species that can overwinter with its entire body frozen in ice. Here, we sequenced the chromosome-level genome of the Amur sleeper and performed comparative genomic, transcriptomic, and metabolomic analyses to investigate this remarkable adaptation. Phylogenetic analyses showed that the Amur sleeper diverged from its close relative with no cold hardiness about 15.07 million years ago and revealed two unusual population expansions during the glacial epochs. Integrative omics data identified a synchronous regulation of genes and metabolites involved in hypometabolism and cellular stress response, and several related genes showed strong evidence of accelerated evolution and positive selection. Potential evolutionary innovations that might aid in freezing survival were found to be associated with the dynamic rearrangement of the cytoskeleton to maintain cell viability, redistribution of water and cryoprotectants to limit cell volume reduction, and inhibition in nerve activity to facilitate dormancy, demonstrating a coordinated evolution for this complex adaptation. Overall, our work provides valuable resources and opportunities to unveil the genetic basis of freeze tolerance adaptation in ectothermic vertebrates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".