mRNA m<sup>6</sup>A methylation in wood frog brain is maintained during freezing and anoxia
Bibliographic record
Abstract
Abstract Freeze tolerance is an adaptive strategy that wood frogs ( Rana sylvatica ) use to survive the subzero temperatures of winter. It is characterized by a variety of metabolic and physiological changes that facilitate successful freezing and anoxia. As both mRNA regulation and posttranslation protein modification have been implicated in freeze tolerance, we hypothesized that posttranslational RNA regulation is also involved in coordinating freeze‐thaw cycles and metabolic rate depression. As such, we investigated the most abundant RNA modification, adenosine methylation (N 6 ‐methyladenosine; m 6 A) in wood frog brains during 24 h periods of freezing and anoxia. This was followed by an examination of levels of RNA methyltransferases, demethyltransferases, and the readers of RNA methylation. Despite relative levels of methylation on mRNA remaining constant throughout freezing and anoxia, a significant increase in relative abundance of m 6 A methyltransferases METTL3 and METTL14 was observed. In addition, we investigated the effect of m 6 A RNA methylation on mRNA triaging to stress granules and report a significant increase in stress granule markers TIAR and TIA‐1 in both freezing and anoxia. Our findings are the first report of RNA posttranslational regulation during metabolic rate depression in the wood frog brain and suggest that the dynamic RNA methylation observed is not directly linked to mRNA regulation during periods of extreme metabolic reorganization, warranting future investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".