Top-down engineering of complex communities by directed evolution

Abstract

Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.

Artificially selecting bacterial communities using propagule strategies†

“…It is important to remark, at this point, that only that variation that is heritable can respond to selection. Those differences in community composition that are due to transient ecological dynamics (i.e., to communities not being in equilibrium), stochastic fluctuations in species abundances, rapid and random fluctuations in environmental conditions, or simply experimental error in determining the function of all communities will not contribute to the success of artificial selection (Blouin et al 2015;Penn and Harvey 2004;Chang et al 2020). Quite the contrary: if strong enough, these nonheritable sources of variation can mask the heritable variation, diminishing heritability and therefore the response to selection (Blouin et al 2015;Chang et al 2020;Sanchez et al 2020).…”

“…Those differences in community composition that are due to transient ecological dynamics (i.e., to communities not being in equilibrium), stochastic fluctuations in species abundances, rapid and random fluctuations in environmental conditions, or simply experimental error in determining the function of all communities will not contribute to the success of artificial selection (Blouin et al 2015;Penn and Harvey 2004;Chang et al 2020). Quite the contrary: if strong enough, these nonheritable sources of variation can mask the heritable variation, diminishing heritability and therefore the response to selection (Blouin et al 2015;Chang et al 2020;Sanchez et al 2020). Therefore, future attempts to apply artificial selection at the community level should take great care to minimize those sources of variation, for instance, as we have recently discussed at length, ensuring that the environment is as stable as possible, and that communities are in a generationally stable state before selection is applied (Chang et al 2020;Penn and Harvey 2004).…”

“…Quite the contrary: if strong enough, these nonheritable sources of variation can mask the heritable variation, diminishing heritability and therefore the response to selection (Blouin et al 2015;Chang et al 2020;Sanchez et al 2020). Therefore, future attempts to apply artificial selection at the community level should take great care to minimize those sources of variation, for instance, as we have recently discussed at length, ensuring that the environment is as stable as possible, and that communities are in a generationally stable state before selection is applied (Chang et al 2020;Penn and Harvey 2004).…”

“…2015; Penn and Harvey 2004; Chang et al. 2020). Quite the contrary: if strong enough, these nonheritable sources of variation can mask the heritable variation, diminishing heritability and therefore the response to selection (Blouin et al.…”

Digest: Optimization of microbial community function through propagule strategies*

“…2019; Chang et al. 2020b). Propagule strategy involves inoculating the offspring generation with the best‐performing communities of the parent generation.…”