Order among chaos: high throughput MYCroplanters can distinguish interacting drivers of host infection in a highly stochastic system

Abstract

While microbiomes provide diverse benefits for their host, they are notoriously variable in structure and function. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between microbiota, the host and the environment. Here, we describe a novel high throughput plant growth system (MYCroplanters) to test how multiple host, microbiota, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbiome-pathogen model, we found that host genotype and order of arrival predict competition outcome between strains in the rhizosphere, but pathogen and microbiota dose can overwhelm these effects. Regardless of host or inoculation conditions, final microbial community structure emerged as a consistent predictor of host health. We demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host health. Increasing the scale at which we can screen components of microbiome-mediated host benefits will facilitate building microbiome engineering solutions for medicine and agricultural applications.