Systems biology of host-microbe metabolomics

Abstract

The human gut microbiota performs essential functions for host and well-being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health-relevant metabolites being considered 'mammalian-microbial co-metabolites'. To systematically investigate this complex host-microbial co-metabolism, a systems biology approach integrating high-throughput data and computational network models is required. Here, we review established top-down and bottom-up systems biology approaches that have successfully elucidated relationships between gut microbiota-derived metabolites and host health and disease. We focus particularly on the constraint-based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host-microbe interactions on the molecular level. We illustrate that constraint-based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host-microbe interactions, yielding, e.g., in potential novel drugs and biomarkers.

Figure 1

Schematic representation of the major network modeling approaches utilized in systems biology analyses of host–microbe interactions. (a) Topological microbiome model. In this approach (e.g., Ref 70), the gut microbiota is treated as a single supra‐organism without species–species boundaries, with nodes representing metabolites and links representing reactions. Topological features of the gut microbial metabolic network, e.g., betweenness centrality (defined as the proportion of shortest paths passing through a node) or neighborhood connectivity (average number of neighbors of a node's neighbors)70 can be elucidated. (b) Constraint‐based microbe–microbe model. In a constraint‐based multispecies model (e.g., Refs 71, 72), metabolic reconstructions targeting two or more individual species are joined in an organism‐resolved manner. Multispecies models allow the prediction of cross‐feeding and mutualistic, commensal, or competitive interactions between microbial species. The tradeoff between two simultaneously growing microbes can be computed (e.g., see Ref 72). (c) Constraint‐based host–microbe community interaction model. In a constraint‐based host–microbe model, a reconstruction of host metabolism is joined with one73 or more74 metabolic networks of representative gut microbes. The setup enables a tractable exchange of host and microbial metabolites and provides outlets for luminal secretion and host secretion into body fluids (e.g., blood and urine). Hence, the host biofluid metabolome can be predicted (see also Figure 2).

Figure 2

Prediction of health‐relevant host body fluid secretion using a constraint‐based modeling framework. Using a constraint‐based modeling framework (Figure 1(c)), the maximal quantitative metabolite secretion was predicted in the presence and in the absence of a model community of 11 microbes. A dietary regime approximating the amounts of protein, carbohydrate, and fat consumed by a typical Western citizen (http://www.ars.usda.gov/) was simulated. Shown are examples for metabolites for which the secretion flux was at least fivefold increased in the presence of the microbe community compared with the ‘germfree’ condition. The complete data analysis is available in Ref 74.

Figure 3

Schematic overview of a pipeline for using constraint‐based models to contextualize high‐throughput metagenomic, metatranscriptomic, metaproteomic, and metabolomic data from human and animal studies.