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

<b>Abstract</b><br/><p>The likelihood that a host will be susceptible to infection is influenced by the interaction of diverse biotic and abiotic factors. As a result, substantial experimental replication and scalability are required to identify the contributions of and interactions between the host, and the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, microbiota strain identity and dose, and pathogen dose. Elucidating the interactions between these factors <em>in vivo </em>has been challenging because testing combinations of these variables quickly becomes experimentally intractable. 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-<em>Pseudomonas</em> host-microbiota-pathogen model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and microbiota dose can overwhelm these effects. Host susceptibility to infection is therefore driven by complex interactions between multiple factors that can both mask and compensate for each other. However, regardless of host or inoculation conditions, the ratio of pathogen to microbiota emerged as a consistent predictor of disease. Our results demonstrate that high-throughput<em> </em>tools like MYCroplanters can isolate interacting drivers host susceptibility to disease. Increasing the scale at which we can screen drivers of disease outcomes, such as microbiome community structure, will facilitate both disease predictions as well as engineering solutions for medicine and agricultural applications.</p>