title: BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communities.
abstract: Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection.
authors.affiliation: Department of Mathematics & Statistics, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Shafiei
authors.affiliation: Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Katherine A
authors.initials: KA
authors.lastname: Dunn
authors.affiliation: Department of Mathematics & Statistics, Acadia University, Wolfville, Nova Scotia, Canada.
authors.forename: Hugh
authors.initials: H
authors.lastname: Chipman
authors.affiliation: Department of Mathematics & Statistics, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
authors.affiliation: Department of Mathematics & Statistics, Dalhousie University, Halifax, Nova Scotia, Canada; Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
authors.forename: Joseph P
authors.initials: JP
authors.lastname: Bielawski
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D060435:Carnivory / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D060434:Herbivory / Q000502:physiology; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000378:metabolism / Q000382:microbiology; D054892:Metagenome; D064307:Microbiota / Q000235:genetics / Q000502:physiology*; D008954:Models, Biological; D015203:Reproducibility of Results