Emergent Biosynthetic Capacity in Simple Microbial Communities

Abstract

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity – instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a “Goldilocks” principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.

Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota

“…Overall, the average metabolic distance was 0.48, which is consistent with other reports of metabolic and functional distances between microbes based on the presence of KEGG enzymes and KEGG orthology annotations 23,24 . Metabolic pathway enrichment was detected at different taxonomic levels (Fig.…”

“…Even though most interactions were observed over a wide range of metabolic distances, positive interactions, in which the growth rates of one or both microbes are neutral or enhanced in the pairwise simulation, occurred only among metabolically distant organisms (Supplementary Fig. 7), in agreement with previous computational studies 23,24 .…”

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

“…The importance of collective effects is evidenced by the metabolic behavior of microbes growing together. For example, work by Chiu et al [24] suggests that emergent biosynthetic capacity is relatively common; that is, when grown in two-species culture, some bacteria are predicted to produce metabolites that neither species produces when grown individually. In another example, when exposed to one another, the microbes Eubacterium rectale and Bacteroides thetaiotaomicron alter their nutrient utilization in the gut [25].…”

“…A model that did not take into account the nutrient environment would not have been able to predict these interactions. Furthermore, Chiu et al [24] have shown that emergent biosynthetic capacity is common by using dFBA to predict instances among a large collection of two-species communities (30% of 1,400 pairings resulted in at least one emergent metabolite). Intriguingly, they found emergent biosynthesis was most common when two community members were neither too similar nor too different functionally and phylogenetically.…”

Constructing and Analyzing Metabolic Flux Models of Microbial Communities

“…These ecological approaches work well to explain species dynamics, metabolite dynamics, and the overall ecology of a system, but they lack mechanistic detail at the level of individual genes. In contrast, bottom-up approaches assemble metabolic pathways for individual community members, or for the community as a whole, to model the biochemical interactions taking place within the microbiome [20][21][22][23]. These metabolic modeling approaches provide extensive mechanistic detail to support their predictions, but their complexity makes the explanation of large-scale species dynamics challenging.…”