The Contribution of High-Order Metabolic Interactions to the Global Activity of a Four-Species Microbial Community

Abstract

The activity of a biological community is the outcome of complex processes involving interactions between community members. It is often unclear how to accurately incorporate these interactions into predictive models. Previous work has shown a range of positive and negative metabolic pairwise interactions between species. Here we examine the ability of a modified general Lotka-Volterra model with cell-cell interaction coefficients to predict the overall metabolic rate of a well-mixed microbial community comprised of four heterotrophic natural isolates, experimentally quantifying the strengths of two, three, and four-species interactions. Within this community, interactions between any pair of microbial species were positive, while higher-order interactions, between 3 or more microbial species, slightly modulated community metabolism. For this simple community, the metabolic rate of can be well predicted only with taking into account pairwise interactions. Simulations using the experimentally determined interaction parameters revealed that spatial heterogeneity in the distribution of cells increased the importance of multispecies interactions in dictating function at both the local and global scales.

Pairing off: a bottom‐up approach to the human gut microbiome

“…The predictions from pairwise experiments were significantly better compared to predictions based on single‐species growth. This finding agrees with work from our group (Friedman et al , ) and others (Guo & Boedicker, ) showing that pairwise outcomes are adequate for predicting multispecies community assembly and community‐level metabolic rates. In contrast to some theoretical work and empirical evidence (Bairey et al , ; Grilli et al , ; Mayfield & Stouffer, ), these results suggest that higher‐order interactions may play a relatively minor role in driving community assembly and that a network of pairwise interactions is an adequate description of the system.…”

Deciphering microbial interactions in synthetic human gut microbiome communities

“…By contrast, history‐dependent responses were not detected in the synthetic consortium of eight soil isolates and coexistence was the prevalent community behavior (Friedman et al , ). On timescales of minutes, members of the freshwater isolate community did not display negative interactions that diminished cellular redox activity (Guo & Boedicker, ). Here, we interrogated the pairwise community dynamic behaviors in a 12‐member anaerobic community spanning four distinct phyla and observed frequent positive interactions and stable species coexistence.…”

“…Microbial interactions have been probed in several synthetic ecologies of varying complexity and diversity including 18 Streptomyces strains (Wright & Vestigian, 2016), four-member freshwater isolates (Guo & Boedicker, 2016), and eight soil bacterial isolates composed of six strains from the Pseudomonas genus (Friedman et al, 2017). A significant fraction of the Streptomyces pairwise communities displayed frequent history-dependent and rare coexistence behaviors, which could be attributed to numerous mutual inhibitory pairwise interactions (Wright & Vestigian, 2016).…”

Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections

“…Moreover, higher-order interactions between more than two bacteria could potentially affect community stability (20), and the complexity of such interactions may limit the predictability of simple pairwise models (55). However, a recent study found that pairwise interactions largely predict the population dynamics of larger communities (56).…”