The regulation of m<sup>6</sup>A-related proteins during whole-body freezing of the freeze-tolerant wood frog
Bibliographic record
Abstract
Rana sylvatica (also known as Boreorana sylvatica) is one of the few vertebrates that spend extreme winters showing no physiological signs of life. Up to 70% of the total body water of the wood frog freezes as extracellular ice. Survival in extreme conditions requires regulation at transcriptional and translational levels to activate prosurvival pathways. N6-methyladenosine (m6A) methylation is one of the most common RNA modifications, regulating transcript processing and translation by executing important functions that affect regulatory pathways in stress conditions. In the study, regulation of m6A-related proteins in the liver of R. sylvatica was analyzed during 24 h frozen and 8 h thaw conditions. Decreases in the activity of demethylases of 28.44 ± 0.4% and 24.1 ± 0.9% of control values in frozen and thaw tissues, respectively, were observed. Total protein levels of m6A methyltransferase complex components methyltransferase-like 14 and Wilm's tumor associated protein were increased by 1.28-fold and 1.42-fold, respectively, during freezing. Demethylase fat mass and obesity, however, showed a decreasing trend, with a significant decrease in abundance during recovery from frozen conditions. Levels of mRNA degraders YTHDF2 and YTHDC2 also decreased under stress. Overall, increased levels of m6A methylation complex components, and suppressed levels of readers/erasers, provide evidence for the potential role of RNA methylation in freezing survival and its regulation in a hypometabolic state.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".