The gut microbiome regulates thermogenic performance in high-altitude deer mice
Bibliographic record
Abstract
High altitude is one of the most extreme environments inhabited by endotherms, where cold temperatures demand high rates of thermogenesis to maintain body temperature (T b ) but hypoxia constrains aerobic metabolism. Recent findings suggest that the gut microbiome contributes to whole-body thermogenesis, but its significance for coping in cold environments remains poorly understood. We hypothesized that the gut microbiome contributes to adaptive increases in thermogenic performance in deer mice native to high altitude. Mice from populations native to high altitude and low altitude were born and raised to adulthood in common lab conditions. Adults from both populations were then acclimated to warm (25°C) normoxia or cold (5°C) hypoxia (12 kPa O 2 ) for 6 weeks, and a subset of mice in each group were treated with antibiotics to disrupt the gut microbiome. Thermogenic endurance was then measured as the duration that T b could be maintained during acute cold challenge. Antibiotic treatment strongly diminished thermogenic endurance in highlanders, but led to only minor reductions in thermogenic endurance in lowlanders. These differences could not be fully explained by impairments in aerobic heat production by host thermogenic tissues, because antibiotic treatment had only modest effects on cold-induced increases in O 2 consumption rate and the abundance of UCP-1 protein and oxidative phosphorylation complexes in brown adipose tissue. Therefore, our findings suggest that the gut microbiome plays an increased role in regulating thermogenesis in high-altitude deer mice, and that changes in host-microbe interactions contribute to high-altitude adaptation. Funded by NSERC of Canada. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".