MicroRNA Regulation in the Freeze-Tolerant Heart of Dryophytes versicolor
Bibliographic record
Abstract
Background: Freeze tolerance is an uncommon but highly effective strategy that allows certain vertebrates to survive prolonged exposure to subzero temperatures in a frozen, ischemic state. While past studies have characterized the metabolic and biochemical adaptations involved, including cryoprotectant accumulation and metabolic rate suppression, the contribution of post-transcriptional gene regulation by microRNAs (miRNAs) remains largely unexplored. This study investigated freeze-responsive miRNAs in cardiac tissue of the gray tree frog, Dryophytes versicolor, to better understand the molecular mechanisms that support ischemic survival and tissue preservation. Methods: Adult frogs were subjected to controlled freezing at −2.5 °C, and cardiac tissue was collected from frozen and control animals. Total RNA was extracted and analyzed via small RNA sequencing to identify differentially expressed miRNAs, followed by target gene prediction and KEGG pathway enrichment analysis. Results: A total of 3 miRNAs were differentially expressed during freezing, with significant upregulation of miR-93-5p and let-7b-5p and downregulation of miR-4485-3p. Predicted targets of upregulated miRNAs included genes involved in immune signaling pathways (e.g., cytokine–cytokine receptor interaction), steroid hormone biosynthesis, and neuroactive ligand–receptor interaction, suggesting suppression of energetically costly signaling processes. Downregulation of miRNAs targeting cell cycle, insulin signaling, and WNT pathways indicates possible selective preservation of cytoprotective and repair functions. Conclusion: Overall, these results suggest that D. versicolor employs miRNA-mediated regulatory networks to support metabolic suppression, maintain essential signaling, and prevent damage during prolonged cardiac arrest. This work expands our understanding of freeze tolerance at the molecular level and may offer insights into biomedical strategies for cryopreservation and ischemia–reperfusion injury.
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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".