DNA hypomethylation in wood frog liver under anoxia and dehydration stresses
Bibliographic record
Abstract
Wood frogs are freeze-tolerant vertebrates that can endure weeks to months frozen during the winter without breathing and with as much as 65% of total body water frozen as extracellular ice. Underlying tolerances of anoxia and of cellular dehydration support whole body freezing. One pro-survival mechanism employed by these frogs is epigenetic modifications via DNA hypomethylation processes facilitating transcriptional repression or activation. These processes involve proteins such as DNA Methyltransferases (DNMTs), Methyl Binding Domain proteins (MBDs), Ten-Eleven Translocases (TETs), and Thymine Deglycosylase (TDG). The present study evaluates the responses of these proteins to dehydration and anoxia stresses in wood frog liver. DNMT relative protein expression was reduced in liver, but nuclear DNMT activity did not change significantly under anoxia stress. By contrast, liver DNMTs and nuclear DNMT activity were upregulated under dehydration stress. These stress-specific differences were speculated to arise from Post-Translational Modifications (PTMs). DNMT3A and DNMT3B showed increased relative protein expression during recovery from dehydration and anoxia. Further, MBD1 was elevated during both conditions suggesting transcriptional repression. TET proteins showed varying responses to anoxia likely due to the absence of oxygen, a main substrate required by TETs. Similarly, TDG, an enzyme that corrects DNA damage, was downregulated under anoxia potentially due to lower levels of reactive oxygen species that damage DNA, but levels returned to normal during reperfusion of oxygen. Our results indicate differential stress-specific responses that indicate the need for more research in the DNA hypomethylation mechanisms employed by the wood frog during stress.
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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) | 0.002 | 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".