Convergence of human and Old World monkey gut microbiomes demonstrates the importance of human ecology over phylogeny
Bibliographic record
Abstract
BACKGROUND: Comparative data from non-human primates provide insight into the processes that shaped the evolution of the human gut microbiome and highlight microbiome traits that differentiate humans from other primates. Here, in an effort to improve our understanding of the human microbiome, we compare gut microbiome composition and functional potential in 14 populations of humans from ten nations and 18 species of wild, non-human primates. RESULTS: Contrary to expectations from host phylogenetics, we find that human gut microbiome composition and functional potential are more similar to those of cercopithecines, a subfamily of Old World monkey, particularly baboons, than to those of African apes. Additionally, our data reveal more inter-individual variation in gut microbiome functional potential within the human species than across other primate species, suggesting that the human gut microbiome may exhibit more plasticity in response to environmental variation compared to that of other primates. CONCLUSIONS: Given similarities of ancestral human habitats and dietary strategies to those of baboons, these findings suggest that convergent ecologies shaped the gut microbiomes of both humans and cercopithecines, perhaps through environmental exposure to microbes, diet, and/or associated physiological adaptations. Increased inter-individual variation in the human microbiome may be associated with human dietary diversity or the ability of humans to inhabit novel environments. Overall, these findings show that diet, ecology, and physiological adaptations are more important than host-microbe co-diversification in shaping the human microbiome, providing a key foundation for comparative analyses of the role of the microbiome in human biology and health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".