Within microbial communities consisting of multiple species, member species interact with each other and display “community functions”, defined as biochemical activities not achievable by member species in isolation. One way to improve community functions within a multispecies microbial community is to identify and modify interactions. However, this becomes a very challenging task given the myriad compounds each species can release, and the immense number of combinations of possible species and genotypes.
Artificial selection has been practiced for centuries to shape the properties of individual organisms, providing Darwin with a powerful argument for his theory of natural selection. Properties of whole ecosystems can also be shaped by artificial selection procedures. Ecosystems initiated in the laboratory vary phenotypically and a proportion of the variation is heritable, despite the fact that the ecosystems initially are composed of thousands of species and millions of individuals. Artificial ecosystem selection illustrates an important role for complex interactions in evolution, and treats communities as though they were discrete units by applying selection on community-level properties.
Artificial selection of whole communities can be carried out over cycles to improve community functions, but is hindered by the complexity of communities used in these selection attempts. Dr. Wenying Shou in the Basic Sciences division, and members of her laboratory took on the task of simulating artificial selection on communities with two defined species whose phenotypes can be modified by random mutations. Specifically, they sought to improve a “costly” community function, which is defined by the reduced fitness of any community member contributing to that community function. Their work was published in a recent issue of PLoS Biology.
Led by Dr. Li Xie, a postdoc in the Shou lab, the authors simulated artificial community selection using a commensal 2-species community that converts substrates to a valued product. Four stages of community selection were simulated. Through this, they demonstrated critical measures that make community selection effective, and show that their work is applicable to mutualistic communities, as well as communities whose member species may not coexist.
Dr. Xie explained the significance of their work: “We are trying to answer the question “can we breed microbial communities like we breed animals and plants?” Our work shows that it is possible to improve desirable community traits through artificial community selection, which doesn’t require detailed understanding of the microbial community or techniques of genetic engineering. However, improvement could be easily stalled unless various aspects of selection are carefully considered. Our work also contributes to how we might think about community selection in the natural environment.” Indeed, given that microbes can co-evolve with each other and with their host.
This work further demonstrates the importance of the careful design of the selection regimen prior to launching a selection experiment. Some studies have proposed that complex microbial communities such as the gut microbiota could serve as a unit of selection. The authors suggest that should selection for a costly microbial community function occur in nature, certain mechanisms including suppressing nonheritable variations in community function and exerting an appropriate strength of intercommunity selection may be necessary.
As for what lies ahead, Dr. Xie revealed what is next to come: “On the one hand, we will adapt population genetics theories that are developed for artificial selection on individuals for artificial selection on groups and communities. This will give us more insight about how different evolutionary processes influence the outcome of selection. On the other hand, we will perform artificial selection experiments on a 2-species cross-feeding Saccharomyces cerevisiae community. This will allow us to compare our theoretical prediction with experimental outcome, either confirming our theory or motivating a better one.”
Xie L, Yuan AE, Shou W. 2019. Simulations reveal challenges to artificial community selection and possible strategies for success. PLoS Biol. Jun 25;17(6):e3000295 [Epub ahead of print]
This work was supported by the National Institutes of Health, the Fred Hutchinson Cancer Research Center and the W.M. Keck Foundation.