Biogenesis and Regulation of the Freeze–Thaw Responsive microRNA Fingerprint in Hepatic Tissue of Rana sylvatica
Bibliographic record
Abstract
Background: Freeze-tolerant animals undergo significant physiological and biochemical changes to overcome challenges associated with prolonged whole-body freezing. In wood frog Rana sylvatica (now Lithobates sylvaticus), up to 65% of total body water freezes in extracellular ice masses and, during this state of suspended animation, it is completely immobile and displays no detectable brain, heart, or respirometry activity. To survive such extensive freezing, frogs integrate various regulatory mechanisms to ensure quick and smooth transitions into or out of this hypometabolic state. One such rapid and reversible regulatory molecule capable of coordinating many aspects of biological functions is microRNA. Herein, we present a large-scale analysis of the biogenesis and regulation of microRNAs in wood frog liver over the course of a freeze–thaw cycle (control, 24 h frozen, and 8 h thawed). Methods/Results: Immunoblotting of key microRNA biogenesis factors showed an upregulation and enhancement of microRNA processing capacity during freezing and thawing. This was followed with RT-qPCR analysis of 109 microRNA species, of which 20 were significantly differentially expressed during freezing and thawing, with the majority being upregulated. Downstream bioinformatics analysis of miRNA/mRNA targeting coupled with in silico protein–protein interactions and functional clustering of biological processes suggested that these microRNAs were suppressing pro-growth functions, including DNA replication, mRNA processing and splicing, protein translation and turnover, and carbohydrate metabolism. Conclusions: Our findings suggest that this enhanced miRNA maturation capacity might be one key factor in the vital hepatic miRNA-mediated suppression of energy-expensive processes needed for long-term survival in a frozen state.
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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".