MicroRNA transcriptomics in liver of the freeze‐tolerant gray tree frog (<i>Dryophytes versicolor)</i> indicates suppression of energy‐expensive pathways
Bibliographic record
Abstract
The rapid and reversible nature of microRNA (miRNA) transcriptional regulation is ideal for implementing global changes to cellular processes and metabolism, a necessary asset for the freeze-tolerant gray tree frog (Dryophytes versicolor). D. versicolor can freeze up to 42% of its total body water during the winter and then thaw completely upon more favorable conditions of spring. Herein, we examined the freeze-specific miRNA responses in the gray tree frog using RBiomirGS, a bioinformatic tool designed for the analysis of miRNA-seq transcriptomics in non-genome sequenced organisms. We identified 11 miRNAs differentially regulated during freezing (miR-140-3p, miR-181a-5p, miR-206-3p, miR-451a, miR-19a-3p, miR-101-3p, miR-30e-5p, miR-142-3p and -5p, miR-21-5p, and miR-34a-5p). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis suggests these miRNAs play roles in downregulating signaling pathways, apoptosis, and nuclear processes while enhancing ribosomal biogenesis. Overall, these findings point towards miRNA inducing a state of energy conservation by downregulating energy-expensive pathways, while ribosomal biogenesis may lead to prioritization of critical processes for freeze-tolerance survival.
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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) | 0.002 | 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".