Glycogen synthase kinase-3: cryoprotection and glycogen metabolism in the freeze-tolerant wood frog
Bibliographic record
Abstract
The terrestrial anuran Rana sylvatica tolerates extended periods of whole-body freezing during the winter. Freezing survival is facilitated by extensive glycogen hydrolysis and distribution of high concentrations of the cryoprotectant glucose into blood and all tissues. As glycogenesis is both an energy-expensive process and counter-productive to maintaining sustained high cryoprotectant levels, we proposed that glycogen synthase kinase-3 (GSK-3) would be activated when wood frogs froze and would phosphorylate its downstream substrates to inactivate glycogen synthesis. Western blot analysis determined that the amount of phosphorylated (inactive) GSK-3 decreased in all five tissues tested in 24 h frozen frogs compared with unfrozen controls. Total GSK-3 protein levels did not change, with the exception of heart GSK-3, indicating that post-translational modification was the primary regulatory mechanism for this kinase. Kinetic properties of skeletal muscle GSK-3 from control and frozen frogs displayed differential responses to a temperature change (22 versus 4°C) and high glucose. For example, when assayed at 4°C, the K(m) for the GSK-3 substrate peptide was ∼44% lower for frozen frogs than the corresponding value in control frogs, indicating greater GSK-3 affinity for its substrates in the frozen state. This indicates that at temperatures similar to the environment encountered by frogs, GSK-3 in frozen frogs will phosphorylate its downstream targets more readily than in unfrozen controls. GSK-3 from skeletal muscle of control frogs was also allosterically regulated. AMP and phosphoenolpyruvate activated GSK-3 whereas inhibitors included glucose, glucose 6-phosphate, pyruvate, ATP, glutamate, glutamine, glycerol, NH(4)Cl, NaCl and KCl. The combination of phosphorylation and allosteric control argues for a regulatory role of GSK-3 in inactivating glycogenesis to preserve high glucose cryoprotectant levels throughout each freezing bout.
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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".