High‐altitude Adaptation and Hypoxia Signaling in Deer Mice
Bibliographic record
Abstract
The hypoxic and cold environment at high altitudes requires that endothermic animals sustain high rates of O 2 consumption for both locomotion and thermogenesis while facing a diminished O 2 supply. Recent evidence suggests that genes in the hypoxia inducible factor (HIF) pathway have been targeted by natural selection and have contributed to evolutionary adaptation to high altitudes in several species. Here, we examine the role of hypoxia signaling in high‐altitude adaptation in deer mice ( Peromyscus maniculatus ). Maximal O 2 consumption (VO 2 max) in hypoxia is augmented in high‐altitude populations, underpinned by increases in pulmonary O 2 extraction, arterial O 2 saturation, cardiac output, and tissue O 2 extraction compared to low‐altitude populations. Highlanders also maintain higher heart rates during deep hypoxia at rest. Associated with the population differences in physiology is extreme allele frequency variation in a non‐synonymous single nucleotide polymorphism in Epas1 , the gene encoding HIF‐2α. Transcriptome scans indicate that these differences in Epas1 allele frequency stem from a history of spatially varying selection between high and low altitudes. RNA‐Seq measurements of gene expression in the left ventricle show that transcriptomic variation in HIF target genes is correlated to VO 2 max. Furthermore, comparisons of the hypoxia response between mice with different Epas1 genotypes (captured from an admixed population on the summit Mount Evans CO) show that the high‐altitude Epas1 allele is associated with higher heart rates in deep hypoxia, but that it has no association with variation in breathing or blood haemoglobin content. Our results therefore suggest that changes in hypoxia signaling contribute to high‐altitude adaptation in deer mice. Support or Funding Information Supported by NSERC of Canada and the USA National Science Foundation
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".