Enzymatic Regulation of Hepatic Carbohydrate Metabolism in Freeze-tolerant Wood Frogs, Rana sylvatica
Bibliographic record
Abstract
Wood frogs (Rana sylvatica) are a widely researched species, one of just a few vertebrates that can endure natural whole body freezing.These frogs can survive months of sub-zero temperatures during winter, even when 65-70% of their total body water is frozen as extracellular ice.However, this freezing results in cessation of blood circulation, heartbeat, and breathing, leading to limited oxygen supply.Wood frogs must depend on anaerobic glycolysis for energy production during this time.Two of the basic mechanisms underlying freeze tolerance in wood frogs are metabolic rate depression (MRD) and the production of high concentrations of glucose as a cryoprotectant by the liver.This thesis investigates the regulation of key enzymatic checkpoints in hepatic carbohydrate metabolism in wood frogs.The research revealed the downregulation of pyruvate kinase (PK) during freezing, leading to inhibition of glycolysis.The study also shed light on the suppression of fructose-1,6-bisphosphate (FBPase) and citrate synthase (CS), that inhibit flux through gluconeogenesis and the tricarboxylic acid cycle (TCA), respectively.Control of these enzymes likely supports MRD during the severe winter months.Moreover, glycerol-3-phosphate dehydrogenase (G3PDH), an enzyme linking lipid and carbohydrate metabolism, was upregulated despite the hypometabolic conditions of the frozen state.This upregulation of G3PDH activity likely plays a vital role in supporting the metabolic survival strategies of wood frogs.Overall, this thesis uncovered an intricate yet synchronized network of enzymes that support MRD and initiate hepatoprotective mechanisms allowing wood frogs to endure prolonged freezing and maintain cellular homeostasis.
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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".