DNA Hypomethylation May Contribute to Metabolic Recovery of Frozen Wood Frog Brains
Bibliographic record
Abstract
Transcriptional suppression is characteristic of extreme stress responses, speculated to preserve energetic resources in the maintenance of hypometabolism. In recent years, epigenetic regulation has become heavily implicated in stress adaptation of many animals, including supporting freeze tolerance of the wood frog (Rana sylvatica). However, nervous tissues are frequently lacking in these multi-tissue analyses which warrants investigation. The present study examines the role of DNA methylation, a core epigenetic mechanism, in the response of wood frog brains to freezing. We use immunoblot analysis to track the relative expression of DNA methyltransferases (DNMT), methyl-CpG-binding domain (MBD) proteins and ten-eleven-translocation (TET) demethylases across the freeze-thaw cycle in R. sylvatica brain, including selected comparisons to freeze-associated sub-stresses (anoxia and dehydration). Global methyltransferase activities and 5-hmC content were also assessed. The data show coordinated evidence for DNA hypomethylation in wood frog brains during freeze-recovery through the combined roles of depressed DNMT3A/3L expression driving lowered DNMT activity and increased TET2/3 levels leading to elevated 5-hmC genomic content (p < 0.05). Raised levels of DNMT1 during high dehydration were also noteworthy. The above suggest that alleviation of transcriptionally repressive 5-mC DNA methylation is a necessary component of the wood frog freeze-thaw cycle, potentially facilitating the resumption of a normoxic transcriptional state as frogs thaw and resume normal metabolic activities.
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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".