Mapping the inner workings of the microbiome: genomic- and metagenomic-based study of metabolism and metabolic interactions in the human microbiome

Abstract

The human gut microbiome is a major contributor to human metabolism and health, yet the metabolic processes that are carried out by various community members, the way these members interact with each other and with the host, and the impact of such interactions on the overall metabolic machinery of the microbiome have not yet been mapped. Here, we discuss recent efforts to study the metabolic inner workings of this complex ecosystem. We will specifically highlight two interrelated lines of work, the first aiming to deconvolve the microbiome and to characterize the metabolic capacity of various microbiome species and the second aiming to utilize computational modeling to infer and study metabolic interactions between these species.

Figure 1. A culture-independent metagenomic pipeline vs. a culture-based genomic pipeline

Microbiome untangling can be accomplished at several different levels, crossing over from the metagenomic pipeline into the single-species genomic pipeline at various stages as discussed in the text. Specifically, microbial cells can be physically isolated and cultured or sequenced using single-cell genomics. Shotgun metagenomic short reads can also be assembled de novo into genomes or large contigs using binning and metagenomic-based assembly. Finally, community-level functional profiles can be mathematically deconvolved into species specific functional profiles. Ultimately, the functional capacity of various community members can be characterized, facilitating species-level modeling and analysis.

Figure 2. Alternative multi-species modeling frameworks

(A) Simple network-based models can be used to predict the metabolic niche of each species (e.g., using the algorithm introduced in Borenstein et al., 2008). The potential for competition and syntrophy between a pair of species can be inferred by comparing their predicted niches (Levy and Borenstein, 2013). (B) A common compartmentalization scheme in multi-species constraint-based models includes a separate compartment for each species, a shared medium compartment, and explicit shuttle reactions. Community objective is often defined as a weighted sum of the species’ biomass (see, for example, Stolyar et al., 2007). (C) A dynamics-based multi-species model, as introduced, for example, in Chiu et al., 2014. The growth of each species is independently optimized according to nutrient availability and allocated resources. This optimization step is used to infer the growth rate of each species, as well as uptake and secretion rates. These are used to update the composition of the shared medium and of the community. By iterating this process, community temporal dynamics can be tracked.