bioRxivEcology
Synchrony and idiosyncrasy in the gut microbiome of wild baboons
J. R. Bjork, M. R. Dasari, K. Roche, L. Grieneisen, T. J. Gould, J.-C. Grenier, V. Yotova, N. Gottel, D. Jansen, L. R. Gesquiere, J. B. Gordon, N. H. Learn, T. L. Wango, R. S. Mututua, J. K. Warutere, L. i. Siodi, S. Mukherjee, L. B. Barreiro, S. C. Alberts, J. A. Gilbert, J. Tung, R. Blekhman, E. Archie
Abstract
Human gut microbial dynamics are highly individualized, making it challenging to link microbiota to health and to design universal microbiome therapies. This individuality is typically attributed to variation in host genetics, diets, environments, and medications, but it could also emerge from fundamental ecological forces that shape microbiota more generally. Here we leverage extensive gut microbial time series from wild baboons--hosts who experience little interindividual dietary and environmental heterogeneity--to test whether gut microbial dynamics are synchronized across hosts or largely idiosyncratic. Despite their shared lifestyles, baboon microbiome dynamics were only weakly synchronized. The strongest synchrony occurred among baboons living in the same social group, likely because group members range over the same habitat and simultaneously encounter the same sources of food and water. However, this synchrony was modest compared to each hosts personalized dynamics. Indeed, host-specific factors, especially host identity, explained 10 times the deviance in longitudinal microbial dynamics, compared to factors shared across hosts. These results contribute to mounting evidence that highly idiosyncratic gut microbiomes are not an artifact of modern human environments, and that synchronizing forces in the gut microbiome (e.g., shared environments, diets, and microbial dispersal) are often not strong enough to overwhelm drivers of microbiome personalization, including host genetics, priority effects, horizontal gene transfer, and functional redundancy.
- organism: non-human primate
- assay: metagenomics
- research focus: ecology & biodiversity
- approach: statistical model
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