High-order interactions dominate the functional landscape of microbial consortia

Abstract

Understanding the link between community composition and function is a major challenge in microbial ecology, with implications for the management of natural microbiomes and the design of synthetic consortia. For this purpose, it is critical to understand the extent to which community functions and properties can be predicted from species traits and what role is played by complex interactions. Inspired by the study of complex genetic interactions and fitness landscapes, here we have examined how the amylolytic function of combinatorial assemblages of seven starch-degrading soil bacteria depends on the functional contributions from each species and their interactions. Filtering our experimental results through the theory of enzyme kinetics, we show that high-order functional interactions dominate the amylolytic rate of our consortia, even though this function is biochemically simple, redundantly distributed in the community, and additive in the absence of inter-species interactions. As the community grows in size, the contribution of high-order functional interactions grows too, making the community function increasingly unpredictable. We can explain the prevalence of high order effects and their sign from the redundancy of ecological interactions in the network, in particular from redundant facilitation towards a high-performing community member. Our results suggest that even simple functions can be dominated by complex interactions, posing challenges for the predictability and bottom-up engineering of ecosystem function in complex multi-species communities.

Characterising a stable five-species microbial community for use in experimental evolution and ecology

“…Our study contributes to the growing body of work highlighting the prevalence of indirect interactions in diverse communities (Friedman et al . 2017; Mayfield & Stouffer 2017; Sanchez-Gorostiaga et al . 2019).…”

“…2017) that can alter community dynamics in ways not predicted from models based on pairwise interactions alone (Friedman et al . 2017; Mayfield & Stouffer 2017; Letten & Stouffer 2019; Sanchez-Gorostiaga et al . 2019).…”

Complex yeast-bacteria interactions affect the yield of industrial ethanol fermentations

“…Further understanding microbial interactions is crucial from microbiological, ecological and biotechnological perspectives 5 , but progress is hindered by the enormous complexity of most natural communities. Synthetic microbial communities represent important tools for studying microbial interactions [6][7][8] , as they can be directly manipulated and their responses can be precisely quantified 9,10 .…”

Microbial coexistence through chemical-mediated interactions

“…To overcome the limitations of pairwise fitness models, here we use a mechanistic model framework which incorporates chemical mediators of interspecies interactions (44). This choice is motivated by three factors: 1) indirect interactions where one species affects how strongly other species interact (45)(46)(47)(48) are lost in pairwise interaction models but preserved in mechanistic models. 2) Interactions mediated by chemicals (e.g.…”