Abstract
In a microbial community, associations between constituent members play an important role in determining the overall structure and function of the community. The human gut microbiome is believed to play an integral role in host health and disease. To understand the nature of bacterial associations at the species level in healthy human gut microbiomes, we analyzed previously published collections of whole-genome shotgun sequence data, totaling over 1.6 Tbp, generated from 606 fecal samples obtained from four different healthy human populations. Using a Random Forest Classifier, we identified 202 signature bacterial species that were prevalent in these populations and whose relative abundances could be used to accurately distinguish between the populations. Bacterial association networks were constructed with these signature species using an approach based on the graphical lasso. Network analysis revealed conserved bacterial associations across populations and a dominance of positive associations over negative associations, with this dominance being driven by associations between species that are closely related either taxonomically or functionally. Bacterial species that form network modules, and species that constitute hubs and bottlenecks, were also identified. Functional analysis using protein families suggests that much of the taxonomic variation across human populations does not foment substantial functional or structural differences.
Highlights
Of bacterial association networks in human gut microbiomes across healthy populations, and if so, are there conserved associations?
The Random Forest Classifier (RFC) was able to distinguish between cohorts with an F1-score > 0.85 for all tested prevalence thresholds (0%, 20%, 40%, 50%, 60%, 80%, 90%, 100%), but demonstrated the highest F1-score at the 90% threshold, even though less than 10% of the original species remained (Supplemental 1)
We explored the variability in signature species relative abundance between samples using principal components analysis (PCA) applied to the Centered Log-Ratio (CLR)-transformed data (Fig. 1c)
Summary
Of bacterial association networks in human gut microbiomes across healthy populations, and if so, are there conserved associations?. We use a machine learning algorithm to identify a set of signature species that can accurately distinguish between the different healthy populations Using these signature species, we construct networks by employing a glasso method that incorporates a b ootstrapping[40] approach to reduce the number of false positive edges inferred[41]. We construct networks by employing a glasso method that incorporates a b ootstrapping[40] approach to reduce the number of false positive edges inferred[41] We analyze these networks to assess the theoretical ecology, and potential importance of species within healthy human gut microbial communities
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