Artificially selecting microbial communities using propagule strategies

Abstract

Artificial selection is a promising approach to manipulate the function of microbial communities. Here, we report the outcome of two artificial selection experiments at the microbial community level. Both experiments used "propagule" strategies, in which a set of the best-performing communities are used as the inocula to form a new generation of communities. In both cases, the selected communities are compared to a control treatment where communities are randomly selected. The first experiment used a defined set of strains as the starting inoculum, and the function under selection was the amylolytic activity of the consortia. The second experiment used a diverse set of natural communities as the inoculum, and the function under selection was the cross-feeding potential of the resulting communities towards a reference bacterial strain. In both experiments, the selected communities reached a higher mean and a higher maximum function than the control. In the first experiment this is caused by a decline in function of the control, rather than an improvement of the selected line. In the second experiment, the strong response of the mean is caused by the large initial variance in function across communities, and is the immediate consequence of the spread of the top-performing community in the starting group, whose function does not increase. Our results are in agreement with basic expectations of artificial selection theory, pointing out some of the limitations of community-level selection experiments which can inform the design of future studies.

Steering ecological-evolutionary dynamics during artificial selection of microbial communities

“…This is because 60%-H spiking was so extreme that species composition did not return to the attractor within one cycle ( Figure S11). [26,28,27]. We therefore propose that it is possible to devise appropriate spiking strategies for other types of communities, particularly those with a species composition attractor (i.e.…”

“…Theoretical work predicts that artificial selection of communities can succeed, at least under certain conditions [11,12,13,14,15,16,4]. Experimental work on community selection have yielded variable outcomes [17,18,19,20,21,22,23,24,25,26,27,28]. In some cases, communities indeed responded to selection, presumably driven by changes in the species composition [22,23,24,25] and/or evolution [17,18], although some of these experiments are not conclusive due to lack of a "no selection" control.…”

“…In some cases, communities indeed responded to selection, presumably driven by changes in the species composition [22,23,24,25] and/or evolution [17,18], although some of these experiments are not conclusive due to lack of a "no selection" control. In other cases, selecting for high community function yielded similar outcomes as selecting for low community function or random selection [19,27,28,26], and community function could even decline despite selection [20,25]. For example, selecting marine microbial communities for enhanced chitin-degradation activity was ineffective unless community maturation time was progressively adjusted to prevent undesirable species from taking over [25].…”

“…Thus, to improve a costly community function, inter-community selection must overwhelm intracommunity selection (Figure 1). Indeed, simulations and experiments have shown that artificial community selection could fail unless a selection scheme takes into consideration a variety of factors, including promoting species coexistence, suppressing cheaters, choosing an appropriate number of Adult communities to reproduce, and introducing heritable variations while reducing nonheritable variations in community function [6,15,25,26,28,4].…”

Top-down engineering of complex communities by directed evolution

“…Directed evolution can be used to iteratively optimize the function of microbial communities, through sequential rounds of exploration and selection. Previous approaches to engineer communities from the top-down include enrichment (which is often followed by a perturbation such as a bottleneck, to reduce community complexity)[20,[22][23][24]28,82,83] , and selective breeding by artificial selection[1,[31][32][33][34][36][37][38][39][40][41][42] . The directed evolution approach we have studied here combines components of both approaches: the iterative search that is inherent of the latter, with the idea of building stable consortia and exploring compositional variants of the former.…”