Directed Evolution of Microbial Communities

Abstract

Directed evolution is a form of artificial selection that has been used for decades to find biomolecules and organisms with new or enhanced functional traits. Directed evolution can be conceptualized as a guided exploration of the genotype-phenotype map, where genetic variants with desirable phenotypes are first selected and then mutagenized to search the genotype space for an even better mutant. In recent years, the idea of applying artificial selection to microbial communities has gained momentum. In this article, we review the main limitations of artificial selection when applied to large and diverse collectives of asexually dividing microbes and discuss how the tools of directed evolution may be deployed to engineer communities from the top down. We conceptualize directed evolution of microbial communities as a guided exploration of an ecological structure-function landscape and propose practical guidelines for navigating these ecological landscapes.

Keywords: artificial ecosystem selection; collective community functions; directed evolution; microbial communities; structure–function landscape.

Figure 1

Methods of top-down engineering above the individual organism. (a) Any biological system can be subject to artificial selection as long as it exhibits variation along a trait of interest (z), and that trait is heritable, i.e., can be reliably passed into descendants derived from it in a subsequent generation. (b) Typical workflow for an enrichment approach to engineering communities from the top down. Multiple enrichment communities are set up by inoculating habitats from a species pool. Typically, the environment is selective for the desired function. The enrichment communities can be stabilized by serial passaging. Then, a severe bottleneck (dilution-to-extinction) is applied to subsample from the stable communities and find simpler communities that maintain the function, and the best among those is selected. (c–d) A depiction of the two main methods of artificial population-level selection, representing their original application in animal populations (40, 43, 111, 112). The methods shown are (c) the propagule method and (d) the migrant pool method, together with (e) a random selection control and (f) the no-selection control.

Figure 2

Limitations of artificial selection at the level of communities. (a) Schematic illustrating the conflict between heritable variation and selection. As the top-performing communities get selected, the worst-performing communities get purged from the metacommunity, and as a result, F_mean increases, and the amount of heritable variation decreases over generations (G). After multiple rounds of selection, and without any novel variants being introduced, the heritable variation is exhausted, and selection has nothing upon which to act. Variation can be replenished by, for instance, introducing migrants from a species pool, which may allow communities to reach new function peaks (F_max). (b) Microbial community growth in serial batch culture (without selection). Communities are initially seeded from a highly diverse species pool into a new habitat (infant community) and then allowed to grow for an incubation time t (at which point they are an adult community). Without selection, a small and random fraction of the cells from the adult community are inoculated into a new habitat, forming a new infant. This growth and dilution process is repeated multiple times. (c) Within each batch incubation, the species undergo an ecological succession as they grow and interact with each other. After multiple rounds of serial passage, communities reach a generationally stable equilibrium, which is seen when the species abundance vectors X during (and at the end of) the ith and all subsequent incubation periods are identical, i.e., when X_i(τ) = X_i+j(τ) for all τ ε (0,t) and j ≥ 1. Without such generational stability, community heritability is very low, and the success of ecosystem-level selection at the level of communities is strongly reduced.

Figure 3

Directed evolution as navigation of an ecological structure–function landscape. (a) In this schematic, for simplicity, we project the community function over an ecological space defined by the abundance of just two species (i and j). The depicted ecological dynamics are multistable, and communities converge to one of three different attractors (stable points), colored by red, yellow, and blue circles. This ecological landscape can be navigated through an iterative process of perturbation, stabilization, ranking, and selection. (b–f) Six different methods to create a library of compositional variants of the selected community. The methods shown are (b) coalescence, (c) bottleneck, (d) migration from a pool, (e) species knockin, and (f) species knockout. (g) Altering resource concentration may also be a way to change the fitness of different species within the community and, therefore, to change the composition of generationally stable communities (60).