Multi-omics Investigation of Freeze Tolerance in the Amur Sleeper, an Aquatic Ectothermic Vertebrate
Bibliographic record
Abstract
Freeze tolerance, the ability of an organism to survive internal ice formation, is a striking survival strategy employed by some ectotherms living in cold environments. However, the genetic bases of this remarkable adaptation are largely unknown. The Amur sleeper (Perccottus glenii), the only known freeze-tolerant fish species, 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 its strategies for surviving freezing. Evolutionary analysis suggested that the Amur sleeper diverged from its closest non-cold-hardy relative about 15.07 million years ago and has experienced a high rate of protein evolution. Transcriptomic and metabolomic data identified a coordinated and tissue-specific regulation of genes and metabolites involved in hypometabolism, cellular stress response, and cryoprotectant accumulation involved in freezing and thawing. Several genes show evidence of accelerated protein sequence evolution or family size expansion were found as adaptive responses to freezing-induced stresses. Specifically, genetic changes associated with cytoskeleton stability, cryoprotectant synthesis, transmembrane transport, and neuroprotective adaptations were identified as potentially key innovations that aid in freezing survival. Our work provides valuable resources and opportunities to unveil the molecular adaptations supporting freeze tolerance 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".