Design of synthetic bacterial communities for predictable plant phenotypes

Specific members of complex microbiota can influence host phenotypes, depending on both the abiotic environment and the presence of other microorganisms. Therefore, it is challenging to define bacterial combinations that have predictable host phenotypic outputs. We demonstrate that plant–bacterium binary-association assays inform the design of small synthetic communities with predictable phenotypes in the host. Specifically, we constructed synthetic communities that modified phosphate accumulation in the shoot and induced phosphate starvation–responsive genes in a predictable fashion. We found that bacterial colonization of the plant is not a predictor of the plant phenotypes we analyzed. Finally, we demonstrated that characterizing a subset of all possible bacterial synthetic communities is sufficient to predict the outcome of untested bacterial consortia. Our results demonstrate that it is possible to infer causal relationships between microbiota membership and host phenotypes and to use these inferences to rationally design novel communities.
Author summary Symbiotic microbes influence host development and health, but predicting which microbes or groups of microbes will have a helpful or harmful effect is a major challenge in microbiome research. In this article, we describe a new method to design and predict bacterial communities that alter the plant host response to phosphate starvation. The method uses plant–bacterium binary-association assays to define groups of bacteria that elicit similar effects on the host plant. By constructing partially overlapping bacterial communities, we demonstrated that it is possible to modify phosphate accumulation in the plant shoot and the induction of plant phosphate starvation genes in a controlled manner. We found that bacterial colonization of the plant root does not predict the capacity to produce this phenotype. We evaluated the predictive performance of different statistical models and identified one best able to predict the behavior of untested communities. Our work demonstrates that studying a subset of all possible bacterial communities is sufficient to anticipate the outcome of novel bacterial combinations, and we establish that it is possible to deduce causality between microbiome composition and host phenotypes in complex systems.