Bottom-Up Approaches to Synthetic Cooperation in Microbial Communities

Abstract

Microbial cooperation pervades ecological scales, from single-species populations to host-associated microbiomes. Understanding the mechanisms promoting the stability of cooperation against potential threats by cheaters is a major question that only recently has been approached experimentally. Synthetic biology has helped to uncover some of these basic mechanisms, which were to some extent anticipated by theoretical predictions. Moreover, synthetic cooperation is a promising lead towards the engineering of novel functions and enhanced productivity of microbial communities. Here, we review recent progress on engineered cooperation in microbial ecosystems. We focus on bottom-up approaches that help to better understand cooperation at the population level, progressively addressing the challenges of tackling higher degrees of complexity: spatial structure, multispecies communities, and host-associated microbiomes. We envisage cooperation as a key ingredient in engineering complex microbial ecosystems.

Keywords: cheaters; host-microbiome interactions; mutualism; synthetic ecology; synthetic microbial communities.

Figure 1

The complexity of microbial ecosystems classified according to two different components: the structure of the community and the structure of the environment. As the number of species in the community increases, community structure (and hence, the network of interactions) becomes more complex. As the complexity of the environment increases, the ecosystem becomes more heterogeneous, very often unfolding new outcomes for the community. A well-mixed culture with two strains (bottom-left) provides one of the simplest ways to study microbial interactions, yet the outcome of interactions can change if spatial structure is at play (agar surface at the bottom-right). The complexity of the interaction network can increase with the number of community members (top-left), and again, complex environments such a spatially-structured animal gut can interfere with both microbial interactions and community composition (top-right).

Figure 2

Cooperation and mutualism in a synthetic yeast model. (a) Yeast cells cooperate during extracellular digestion of sucrose via secretion of invertase. (b) Preferential access to the public goods leads to coexistence between cooperators (dark gray cells) and cheaters (light gray) in synthetic yeast populations [21]. (c) Auxotrophic yeast strains can engage in mutualism by cross-feeding essential amino acids through the environment (d) Environmental conditions can drive microbial interactions. In a medium lacking amino acids, two auxotrophic strains need to cooperate with each other to survive (obligate mutualism). Instead, competition for available resources becomes the driving interaction when the medium is supplemented with enough amino acids [37].

Figure 3

Spatial structure of cooperators and mutualists. (a) Clustering of cooperators allows survival in challenging environments. A small-sized inoculum of B. subtilis cells reaches extinction in well-mixed conditions as the low number of initial cooperators is unable to perform an effective extracellular digestion of starch to support growth. When the same amount of cells is inoculated in the spatial context of an agar surface, clusters of cooperators arise and grow, since extracellular digestion becomes locally more efficient around these groups of cooperators (adapted from Ratzke and Gore [52]). (b) Competition leads to segregation of phenotypes (top), while mutualism promotes phenotypic intermixing (bottom) in growing yeast colonies (adapted from Muller et al. [44]). (c) Cheaters are progressively left behind as bacterial mutualists expand into available space (top panel, with mutualists in blue and yellow, cheater strain in red), while a three-member mutualistic consortium (bottom; each of the three colors indicating a different mutualistic strain) preserves all its members as the bacterial colony expands (adapted from Amor et al. [51]).

Figure 4

Bottom-up assembly of synthetic communities. Bottom-up approaches to understand microbial community assembly [64,65] aim at predicting the assembly of multispecies communities (right-side panel) based on the features of simpler subsets of the community (two-member cocultures on the left-side panels). In these schemes, dashed lines represent hypothetical time series for monocultures, and solid lines stand for the corresponding cocultures. The four-member mutualistic network on the right panel was inspired by one of the microbial consortia in [64].