Proteomic analysis of Rana sylvatica reveals differentially expressed proteins in liver in response to anoxia, dehydration or freezing stress
Bibliographic record
Abstract
Ectothermic animals that live in seasonally cold regions must adapt to seasonal variation and specific environmental conditions. During the winter, some amphibians hibernate on land and encounter limited environmental water, deficient oxygen, and extremely low temperatures that can cause the whole body freezing. These stresses trigger physiological and biochemical adaptations in amphibians that allow them to survive. Rana sylvatica, commonly known as the wood frog, shows excellent freeze tolerance. They can slow their metabolic activity to a near halt and endure freezing of 65-70% of their total body water as extracellular ice during hibernation, returning to normal when the temperatures rise again. To investigate the molecular adaptations of freeze-tolerant wood frogs, a comprehensive proteomic analysis was performed on frog liver tissue after anoxia, dehydration, or freezing exposures using a label-free LC-MS/MS proteomic approach. Quantitative proteomic analysis revealed that 87, 118, and 86 proteins were significantly upregulated in dehydrated, anoxic, and frozen groups, suggesting potential protective functions. The presence of three upregulated enzymes, glutathione S-transferase (GST), aldolase (ALDOA), and sorbitol dehydrogenase (SORD), was also validated. For all enzymes, the specific enzymatic activity was significantly higher in the livers of frozen and anoxic groups than in the controls. This study reveals that GST, ALDOA, and SORD might participate in the freeze tolerance mechanism by contributing to regulating cellular detoxification and energy metabolism.
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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 | 0.000 |
| 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".