Histone Arginine Methylation in the Freeze-tolerant Wood Frog, Rana sylvatica
Bibliographic record
Abstract
The wood frog, Rana sylvatica, is well known for its freeze tolerance ability.To endure winter, frozen frogs switch to a hypometabolic state via transcriptional regulation.Histone methylation is known to play a crucial role in regulating gene transcription.However, histone arginine methylation or demethylation has not previously been studied in the context of freeze tolerance.This thesis presents the first characterization of arginine methylation in a freeze tolerant vertebrate.Overall, levels of protein arginine methyltransferases (PRMTs) and methylated histone residues showed differential regulation over the freeze/thaw-cycle in wood frog liver.All PRMTs and downstream targets showed no changes during freezing, but protein levels of targets associated with transcription activation were elevated during thaw in skeletal muscle.Differential levels of histone demethylases were found in both tissues among the experimental conditions.These results indicate a role for histone methylation in supporting metabolic rate depression and tissue homeostasis during freezing. JMJD6 Jumonji domain-containing protein 6 KCl Potassium chloride KDM1A/LSD1Lysine demethylase 1A/ Lysine-specific demethylase 1 KDM4A Lysine demethylase 4A KDM4B Lysine Demethylase 4B KDM4D Lysine Demethylase 4D KDM4E Lysine Demethylase 4E KDM5B Lysine Demethylase 5B KDM5C Lysine Demethylase 5C KDM7C/PHF2 Lysine Demethylase 7C/PHD finger protein KDMs Lysine demethylases
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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".