Living the high life: Could gut microbiota matter for adaptation to high altitude?
Bibliographic record
Abstract
At high altitude, the reduced availability of thermal energy and oxygen poses major challenges to organisms. Different species or populations have evolved similar solutions to these challenges, such as blood flow regulation in animals (Bouverot, 1985). Previous studies investigating such convergent adaptations have primarily looked at changes in host genomes (e.g., see Scheinfeldt & Tishkoff, 2010), but have rarely considered the potential role of the gut microbiome in mediating host adaptation. As gut microbes can indirectly regulate host blood pressure (Pluznick, 2014) and energy intake efficiency, it has been hypothesized that they could help maintain normal energy production and/or optimize nutritional assimilation in high-altitude hypoxic environments (e.g., Li & Zhao, 2015). However, it has been hard to (a) show that there is a direct effect of altitude on the gut microbiota, because of the many potential confounding effects of altitude (e.g., diet is correlated to altitude, as well as to the microbiome) and to (b) understand the mechanisms by which the microbiota could mediate host hypoxic and thermoregulatory stresses. In this issue of Molecular Ecology, Suzuki, Martins, and Nachman (2018) show that, independently of diet, taxonomic composition and functions of mouse gut microbiota converge in independent high-altitude environments and propose the intriguing hypothesis that some of these functional convergences might be beneficial to their host.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".