High‐Altitude Adaptation of Frogs (Case Study: <i>Nanorana parkeri</i> ): From Physiological Phenotypes to AltitudeOmics
Bibliographic record
Abstract
High altitudes are challenging for the animals that inhabit these environments. The Xizang plateau frog (Nanorana parkeri), endemic to the Qinghai-Tibet Plateau and distributed between 2800 and 5100 m, represents an ideal model for studying high-altitude adaptations. Here, we compared environmental differences between high- (4600 m) and low-altitude (3400 m) habitats, characterized the physiological traits of high-altitude frogs, and integrated metabolomic and proteomic data to elucidate adaptive mechanisms to extreme environments. High-altitude habitats exhibited significantly lower water temperatures and dissolved oxygen levels. High-altitude frogs showed a 31%-37% reduction in resting metabolic rate, decreased concentrations of metabolites (glucose and β-hydroxybutyric acid), and 18%-56% lower activities of critical metabolic enzymes. This coordinated metabolic depression is indicative of an energy conservation strategy for surviving at high altitudes. Interestingly, hepatic glycogen (3.1-fold increase) and pyruvate accumulated in high-altitude frogs, suggesting enhanced energy storage and potential antioxidant utilization. Metabolomic profiling further revealed a remodeling of glycerophospholipid, indicating adaptive membrane stabilization. Proteomics analysis identified altered expression of proteins involved in stress response, energy metabolism, and translation, including chaperones (DNAJB6 and DNAJC22) and glutathione peroxidase (GPX4), which may be potential biomarkers for evaluating high-altitude adaptation in ectothermic vertebrates. Collectively, these findings demonstrate that N. parkeri survives in high-altitude environments through a synergistic strategy of metabolic remodeling and protein expression adjustment to optimize energy efficiency and enhance cellular protection. This study provides new insights into the mechanisms by which ectothermic vertebrates adapt to extreme environments.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".