Regulatory changes contribute to the adaptive enhancement of thermogenic capacity in high-altitude deer mice
Bibliographic record
Abstract
In response to hypoxic stress, many animals compensate for a reduced cellular O(2) supply by suppressing total metabolism, thereby reducing O(2) demand. For small endotherms that are native to high-altitude environments, this is not always a viable strategy, as the capacity for sustained aerobic thermogenesis is critical for survival during periods of prolonged cold stress. For example, survivorship studies of deer mice (Peromyscus maniculatus) have demonstrated that thermogenic capacity is under strong directional selection at high altitude. Here, we integrate measures of whole-organism thermogenic performance with measures of metabolic enzyme activities and genomic transcriptional profiles to examine the mechanistic underpinnings of adaptive variation in this complex trait in deer mice that are native to different elevations. We demonstrate that highland deer mice have an enhanced thermogenic capacity under hypoxia compared with lowland conspecifics and a closely related lowland species, Peromyscus leucopus. Our findings suggest that the enhanced thermogenic performance of highland deer mice is largely attributable to an increased capacity to oxidize lipids as a primary metabolic fuel source. This enhanced capacity for aerobic thermogenesis is associated with elevated activities of muscle metabolic enzymes that influence flux through fatty-acid oxidation and oxidative phosphorylation pathways in high-altitude deer mice and by concomitant changes in the expression of genes in these same pathways. Contrary to predictions derived from studies of humans at high altitude, our results suggest that selection to sustain prolonged thermogenesis under hypoxia promotes a shift in metabolic fuel use in favor of lipids over carbohydrates.
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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 | 0.001 |
| 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".