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

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 in vivo 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-Pseudomonas
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 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.