Effects of hibernation on two important contractile tissues in tibetan frogs, Nanorana parkeri: a perspective from transcriptomics and metabolomics approaches
Bibliographic record
Abstract
BACKGROUND: In response to seasonal cold and food shortage, the Xizang plateau frogs, Nanorana parkeri (Anura: Dicroglossidae), enter a reversible hypometabolic state where heart rate and oxygen consumption in skeletal muscle are strongly suppressed. However, the effect of winter hibernation on gene expression and metabolic profiling in these two tissues remains unknown. In the present study, we conducted transcriptomic and metabolomic analyses of heart and skeletal muscle from summer- and winter-collected N. parkeri to explore mechanisms involved in seasonal hibernation. RESULTS: We identified 2407 differentially expressed genes (DEGs) in heart and 2938 DEGs in skeletal muscle. Enrichment analysis showed that shared DEGs in both tissues were enriched mainly in translation and metabolic processes. Of these, the expression of genes functionally categorized as "response to stress", "defense mechanisms", or "muscle contraction" were particularly associated with hibernation. Metabolomic analysis identified 24 and 22 differentially expressed metabolites (DEMs) in myocardium and skeletal muscle, respectively. In particular, pathway analysis showed that DEMs in myocardium were involved in the pentose phosphate pathway, glycerolipid metabolism, pyruvate metabolism, citrate cycle (TCA cycle), and glycolysis/gluconeogenesis. By contrast, DEMs in skeletal muscle were mainly involved in amino acid metabolism. CONCLUSIONS: In summary, natural adaptations of myocardium and skeletal muscle in hibernating N. parkeri involved transcriptional alterations in translation, stress response, protective mechanisms, and muscle contraction processes as well as metabolic remodeling. This study provides new insights into the transcriptional and metabolic adjustments that aid winter survival of high-altitude frogs N. parkeri.
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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".