Abstract
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.
Highlights
The gut microbiome is a complex bacterial community, with its structure determined by many factors including the interactions between its members
We find that operational taxonomic units (OTUs) form similar community structures across all three, and that these communities have similar associations with the health-related host factors of age and body mass index (BMI) in their respective populations
The aim of this study was to compare gut microbiota community structures across populations. To this end comparable OTUs were generated by combining gut microbiota sequencing data from three geographically diverse populations
Summary
The gut microbiome is a complex bacterial community, with its structure determined by many factors including the interactions between its members. Several specialised approaches have been developed to estimate correlations from microbiota data (Faust et al, 2012; Friedman & Alm, 2012; Deng et al, 2012; Gevers et al, 2014; Fang et al, 2015; Kurtz et al, 2015) Whilst these have seen use within the research community (Gevers et al, 2014; Goodrich et al, 2014; Tong et al, 2013; McHardy et al, 2013), correlation metrics for microbiome studies have only recently been compared systematically by Weiss et al (2016) who found that using an ensemble of metrics can improve the precision of co-occurrence detection
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