Designing Metabolic Division of Labor in Microbial Communities

Abstract

Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A “gedanken” (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment usingin silicogenome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities ofEscherichia colistrains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor.IMPORTANCEUnderstanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia.

Metabolic Modelling Approaches for Describing and Engineering Microbial Communities

“…MultiPlus [114] has two fixed objectives: minimizing the number of reactions and minimizing the exchanged metabolites, in a de novo synthesis pathway; starting from an hypergraph that integrates several GEM models. Following a MILP optimization approach, DOLMN [115] identifies communities able to survive under constraints (e.g. limited number of reactions) that are difficult to identify manually.…”

“…At least 215 and 203 internal reactions to grow, respectively for 2 and 3 strain consortia. Loss one reaction is not compensated with adding one metabolite in the medium (nonlinear boundary) [115] Maximizing -4 E. coli case of study: -GR: 0.736 gDWh −1 -Strains ratio: Ec1-Ec2=50%, Ec3-Ec4=50%. Direct competition Ec1-Ec4 and Ec2-Ec3 -Gut microbiome case of study: values depending on fibre uptake from B. thetaiotaomicron:…”

From Microbial Communities to Distributed Computing Systems

“…Similarly, genome-scale metabolic models have been used to aide in the design of large scale communities by predicting metabolites that can be released by the producer without detriment to fitness, and conditions that encourage the establishment of stable communities (Pacheco et al, 2019). Thommes et al (2019) used genome scale metabolic models of E. coli to compute feasible division of labor strategies that could arise from an initial monoculture through loss of function in genes, giving insight into possible avenues for engineering community formation. Angulo et al (2019) demonstrated a mathematical method for identifying "driver species" in an ecological network.…”

Substrate traits govern the assembly and spatial organization of microbial community engaged in metabolic division of labor

“…In natural environments, microorganisms rarely live autonomously, but interact with other individuals to form complex communities, in which they secrete a variety of toxins to compete with each other, or share metabolites to mutually benefit their survival. Among a variety of modes of microbial interaction, metabolic division of labor (MDOL) is one of the most widespread phenomena, where distinct populations perform different but complementary steps of the same metabolic pathway [1][2][3][4] . MDOL controls numerous ecologically and environmentally important biochemical processes.…”