High-order interactions distort the functional landscape of microbial consortia

Abstract

Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degrading soil bacteria depend on the separate functional contributions from each species and their interactions. Filtering our results through the theory of biochemical kinetics, we show that this simple function is additive in the absence of interactions among community members. For about half of the combinatorially assembled consortia, the amylolytic function is dominated by pairwise and higher-order interactions. For the other half, the function is additive despite the presence of strong competitive interactions. We explain the mechanistic basis of these findings and propose a quantitative framework that allows us to separate the effect of behavioral and population dynamics interactions. Our results suggest that the functional robustness of a consortium to pairwise and higher-order interactions critically affects our ability to predict and bottom-up engineer ecosystem function in complex communities.

Metabolic rules of microbial community assembly

“…Interestingly, the main driver of taxonomic variability among replicates was the dominant member of the respirator group (a sub-dominant species). Amelioration of competition between two fermenter strains in the presence of one (but not the other) dominant respirator points to the subtle role that high-order interactions may play in community assembly (Billick and Case 1994;Mickalide and Kuehn 2019;Sanchez 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019;Levine et al 2017;Grilli et al 2017) . This is also in line with previous observations of the potential importance of sub-dominant bacteria in shaping the composition of microbial communities ) .…”

Dissimilarity-Overlap Analysis of Replicate Enrichment Communities

“…Models of dynamical ecosystems vary in their level of description, which is typically chosen to capture the specific phenomena under study. A detailed population dynamics model of microbial communities would have to include mechanistic microbial interactions (due to cross-feeding [13,14] , direct secretion of substances such as bacteriocins, antibiotics, or extracellular enzymes [15,16] , or competition for the same nutrient), spatial structure of the particular habitat [17] , and environment-or host-microbiome interactions. Building such detailed models can be daunting due to (i) a huge number of model parameters which need to be inferred from experimental data; and (ii) many environmental variables (such as the concentrations of bacteriocins and nutrients) are hard to measure in real time.…”

Artificially selecting microbial communities using propagule strategies

“…cellulose), it is also of value for industrial applications (de Souza and de Oliveira Magalhães 2010; Xu et al 2016) . In our experiments, we used soluble starch as a substrate, as its hydrolysis has been recently characterized as an ecological function at the community-level (Sanchez-Gorostiaga et al 2019) that is relatively straightforward and inexpensive to characterize in high-throughput (Fuwa 1954) .…”

“…One approach has been to engineer communities from the bottom-up, by mixing species with known functionality towards a desired community function (Gilbert et al 2003;Regot et al 2011;Minty et al 2013;Smith et al 2013;Wang et al 2016;Eng and Borenstein 2019) . An important challenge of this approach is the combinatorial explosion in the number of interactions in the community, with high-order interactions posing significant challenges for the predictability of even mid-size consortia (Gould et al 2018;Mickalide and Kuehn 2019;Sanchez-Gorostiaga et al 2019;Senay et al 2019) . In addition, ecological and evolutionary processes can also act against the engineered community function (Goldman and Brown 2009) .…”