Mitochondria, metabolic control and microRNA: Advances in understanding amphibian freeze tolerance
Bibliographic record
Abstract
Winter survival for many animal species depends freeze tolerance, a capacity to endure the conversion of as much as 65-70% of total body water into extracellular ice while reorganizing metabolism to provide cells with cryoprotection against insults that include prolonged ischemia and hyperosmotic stress. Natural freeze tolerance involves not just de novo preservation mechanisms such as synthesis of high levels of cryoprotectants or novel proteins that manage ice formation, but also requires attention to and co-ordination of many cellular processes. The present review examines recent studies of the freeze-tolerant wood frog (Rana sylvatica) that probed previously unexplored areas of metabolic adaptation for freezing survival, with a particular emphasis on mitochondria. Post-translational controls on enzyme function play a prominent role in resculpting metabolic responses of the wood frog to freezing including reversible phosphorylation control over fuel processing at the pyruvate dehydrogenase locus and modulation of antioxidant defense enzymes (Mn-SOD, catalase). Enzymes involved in mitochondrial nitrogen metabolism (glutamate dehydrogenase, carbamoyl phosphate synthetase) are also differentially regulated during freezing but by different post-translational modifications including ADP-ribosylation, lysine acetylation or glutarylation. The action of microRNAs in mediating post-translational controls on gene expression aid the suppression of energy-expensive (cell cycle) or destructive (apoptosis) processes in the frozen state while also providing storage of transcripts that will be immediately available for repair or reactivation of metabolic processes after thawing. The effects of low temperature in strengthening mRNA-microRNA interactions can also provide a passive mechanism of metabolic suppression in the frozen state.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".