Detection of stable community structures within gut microbiota co-occurrence networks from different human populations

PeerJ - Tập 6 - Trang e4303
Matthew Jackson1, Marc Jan Bonder2, Zhana Kuncheva3, Jonas Zierer1,4, Jingyuan Fu2,5, Alexander Kurilshikov2, Cisca Wijmenga6,2, Alexandra Zhernakova2, Jordana T. Bell1, Tim D. Spector1, Claire J. Steves1
1Department of Twin Research & Genetic Epidemiology, King's College London, London, United Kingdom
2University Medical Center Groningen, Department of Genetics, University of Groningen, Groningen, Netherlands
3Department of Mathematics, Imperial College London, London, United Kingdom
4Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany
5University Medical Center Groningen, Department of Pediatrics, University of Groningen, Groningen, Netherlands
6K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway

Tóm tắt

Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.

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