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

Abstract

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, the environment, and biotic factors such as the microbiome. For example, pathogen infection success is known to vary by host genotype, bacterial 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, non-pathogenic bacteria, and pathogen variables predict host health. Using an Arabidopsis-Pseudomonas host-microbe model, we found that host genotype and bacterial strain order of arrival predict host susceptibility to infection, but pathogen and non-pathogenic bacterial 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 non-pathogen emerged as a consistent correlate of disease. Our results demonstrate that high-throughput tools like MYCroplanters can isolate interacting drivers of host susceptibility to disease. Increasing the scale at which we can screen drivers of disease, such as microbiome community structure, will facilitate both disease predictions and treatments for medicine and agricultural applications.

Plant phenotyping relevance

MYCroplantersという植物の高スループット生育・感染評価システム自体を開発・記述し、宿主の感染感受性（病害状態）を測定する方法が研究の中心であるため。

Code and data availability

The paper's Data Availability statement explicitly deposits paper-specific assets: raw plant scan images and 3D printing files in Dryad, and processed data plus analysis/figure-generation code on GitHub. Both are public, actionable, and directly reproduce this paper's plant-phenotyping measurements and analysis.