Stress-induced antioxidant defense and protein chaperone response in the freeze-tolerant wood frog Rana sylvatica
Bibliographic record
Abstract
Freeze tolerance is an adaptive response utilized by the wood frog Rana sylvatica to endure the sub-zero temperatures of winter. Survival of whole body freezing requires wood frogs to trigger cryoprotective mechanisms to deal with potential injuries associated with conversion of 65-70% of total body water into ice, including multiple consequences of ice formation such as cessation of blood flow and cell dehydration caused by water loss into ice masses. To understand how wood frogs defend against these stressors, we measured the expression of proteins known to be involved in the antioxidant defense and protein chaperone stress responses in brain and heart of wood frogs comparing freezing, anoxia, and dehydration stress. Our results showed that most stress proteins were regulated in a tissue- and stress-specific manner. Notably, protein levels of the cytosolic superoxide dismutase (SOD1) were upregulated by 1.37 ± 0.11-fold in frozen brain, whereas the mitochondrial SOD2 isoform rose by 1.38 ± 0.37-fold in the heart during freezing. Catalase protein levels were upregulated by 3.01 ± 0.47-fold in the brain under anoxia stress, but remained unchanged in the heart. Similar context-specific regulatory patterns were also observed for the heat shock protein (Hsp) molecular chaperones. Hsp27 protein was down-regulated in the brain across the three stress conditions, whereas the mitochondrial Hsp60 was upregulated in anoxic brain by 1.73 ± 0.38-fold and by 2.13 ± 0.57-fold in the frozen heart. Overall, our study provides a snapshot of the regulatory expression of stress proteins in wood frogs under harsh environment conditions and shows that they are controlled in a tissue- and stress-specific manner.
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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".