Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules

Abstract

The human microbiome plays a key role in human health and is associated with numerous diseases. Metagenomic-based studies are now generating valuable information about the composition of the microbiome in health and in disease, demonstrating nonneutral assembly processes and complex co-occurrence patterns. However, the underlying ecological forces that structure the microbiome are still unclear. Specifically, compositional studies alone with no information about mechanisms of interaction, potential competition, or syntrophy, cannot clearly distinguish habitat-filtering and species assortment assembly processes. To address this challenge, we introduce a computational framework, integrating metagenomic-based compositional data with genome-scale metabolic modeling of species interaction. We use in silico metabolic network models to predict levels of competition and complementarity among 154 microbiome species and compare predicted interaction measures to species co-occurrence. Applying this approach to two large-scale datasets describing the composition of the gut microbiome, we find that species tend to co-occur across individuals more frequently with species with which they strongly compete, suggesting that microbiome assembly is dominated by habitat filtering. Moreover, species' partners and excluders exhibit distinct metabolic interaction levels. Importantly, we show that these trends cannot be explained by phylogeny alone and hold across multiple taxonomic levels. Interestingly, controlling for host health does not change the observed patterns, indicating that the axes along which species are filtered are not fully defined by macroecological host states. The approach presented here lays the foundation for a reverse-ecology framework for addressing key questions concerning the assembly of host-associated communities and for informing clinical efforts to manipulate the microbiome.

Fig. 1.

An illustration of model-based prediction of species interaction. The metabolic network of each species is reconstructed with nodes representing metabolites and edges connecting substrates to products. The shaped nodes represent exogenously acquired nutrients (seeds). (A) Evaluating metabolic competition. The brackets indicate the four and five seed nutrients exogenously acquired by the ellipse- and rectangle-shaped species, respectively. The two metabolites enclosed in a dashed contour denote shared nutrients for which the two species may compete. Accordingly, in this illustration, the competition index experienced by the first species in the presence of the second is 2/4, whereas the competition index of the second species in the presence of the first is 2/5. (B) Evaluating metabolic complementarity. The compounds enclosed in a dashed contour denote nutrients required by the second species that can be synthesized by the first species. In this example, the complementarity index of the second species in the presence of the first is 3/5.

Fig. 2.

Partner species have higher metabolic competition than excluder species. Each bar represents a target species with the bar height representing the difference between the mean competition index with its partners and the mean competition index with its excluders. In total, 82% of species have higher metabolic competition index with partners (blue bars).

Fig. 3.

Habitat filtering in the gut microbiome across varying phylogenetic distances. (A) Heat map of ranked co-occurrence score, binned by phylogenetic distance (x axis) and metabolic competition index (y axis). The color of each bin represents the mean co-occurrence of all pairwise associations within it. Evidently, even among species pairs with a given phylogenetic relatedness, mean co-occurrence tends to increase with metabolic competition (red regions at the top of the heat map). (B) Average metabolic competition index and SE of partners and excluders vs. phylogenetic relatedness. At any level of phylogenetic relatedness, species have more similar nutritional profiles (significantly higher metabolic competition index) with partners than with excluders (P < 0.05 in all bins, one-tailed Mann–Whitney U test; see also Table S3E).