Comparative metabolomics analysis reveals high-altitude adaptations in a toad-headed viviparous lizard, Phrynocephalus vlangalii
Bibliographic record
Abstract
Extreme environmental conditions at high altitude, such as hypobaric hypoxia, low temperature, and strong UV radiation, pose a great challenge to the survival of animals. Although the mechanisms of adaptation to high-altitude environments have attracted much attention for native plateau species, the underlying metabolic regulation remains unclear. Here, we used a multi-platform metabolomic analysis to compare metabolic profiles of liver between high- and low-altitude populations of toad-headed lizards, Phrynocephalus vlangalii, from the Qinghai-Tibet Plateau. A total of 191 differential metabolites were identified, consisting of 108 up-regulated and 83 down-regulated metabolites in high-altitude lizards as compared with values for low-altitude lizards. Pathway analysis revealed that the significantly different metabolites were associated with carbohydrate metabolism, amino acid metabolism, purine metabolism, and glycerolipid metabolism. Most intermediary metabolites of glycolysis and the tricarboxylic acid cycle were not significantly altered between the two altitudes, but most free fatty acids as well as β-hydroxybutyric acid were significantly lower in the high-altitude population. This may suggest that high-altitude lizards rely more on carbohydrates as their main energy fuel rather than lipids. Higher levels of phospholipids occurred in the liver of high-altitude populations, suggesting that membrane lipids may undergo adaptive remodeling in response to low-temperature stress at high altitude. In summary, this study demonstrates that metabolic profiles differ substantially between high- and low-altitude lizard populations, and that these differential metabolites and metabolic pathways can provide new insights to reveal mechanisms of adaptation to extreme environments at high altitude.
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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 | 0.000 |
| 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".