Integrated analysis of transcriptome and metabolome data reveals insights for molecular mechanisms in overwintering Tibetan frogs, Nanorana parkeri
Bibliographic record
Abstract
Nanorana parkeri (Anura, Dicroglossidae) is a unique frog living at high altitude on the Tibetan plateau where they must endure a long winter dormancy at low temperatures without feeding. Here, we presented a comprehensive transcriptomic and metabolomic analysis of liver tissue from summer-active versus overwintering N. parkeri, providing the first broad analysis of altered energy metabolism and gene expression in this frog species. We discovered that significantly up-regulated genes (2,397) in overwintering frogs mainly participated in signal transduction and immune responses, phagosome, endocytosis, lysosome, and autophagy, whereas 2,169 down-regulated genes were mainly involved in metabolic processes, such as oxidation-reduction process, amino acid metabolic process, fatty acid metabolic process, and TCA cycle. Moreover, 35 metabolites were shown to be differentially expressed, including 22 down-regulated and 13 up-regulated in winter. These included particularly notable reductions in the concentrations of most amino acids. These differentially expressed metabolites were mainly involved in amino acid biosynthesis and metabolism. To sum up, these findings suggest that gene expression and metabolic processes show adaptive regulation in overwintering N. parkeri, that contributes to maintaining homeostasis and enhancing protection in the hypometabolic state. This study has greatly expanded our understanding of the winter survival mechanisms in amphibians.
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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 | 0.001 | 0.001 |
| 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".