FUNCTIONAL GENOMICS OF ADAPTATION TO HYPOXIC COLD-STRESS IN HIGH-ALTITUDE DEER MICE: TRANSCRIPTOMIC PLASTICITY AND THERMOGENIC PERFORMANCE
Bibliographic record
Abstract
In species that are distributed across steep environmental gradients, adaptive variation in physiological performance may be attributable to transcriptional plasticity in underlying regulatory networks. Here we report the results of common-garden experiments that were designed to elucidate the role of regulatory plasticity in evolutionary adaptation to hypoxic cold-stress in deer mice (Peromyscus maniculatus). We integrated genomic transcriptional profiles with measures of metabolic enzyme activities and whole-animal thermogenic performance under hypoxia in highland (4350 m) and lowland (430 m) mice from three experimental groups: (1) wild-caught mice that were sampled at their native elevations; (2) wild-caught/lab-reared mice that were deacclimated to low-elevation conditions in a common-garden lab environment; and (3) the F(1) progeny of deacclimated mice that were maintained under the same low-elevation common-garden conditions. In each experimental group, highland mice exhibited greater thermogenic capacities than lowland mice, and this enhanced performance was associated with upregulation of transcriptional modules that influence several hierarchical steps in the O(2) cascade, including tissue O(2) diffusion (angiogenesis) and tissue O(2) utilization (metabolic fuel use and cellular oxidative capacity). Most of these performance-related transcriptomic changes occurred over physiological and developmental timescales, suggesting that regulatory plasticity makes important contributions to fitness-related physiological performance in highland deer mice.
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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 | 0.000 |
| 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".