Artificially selecting bacterial 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.

Publication
Evolution