Infectious disease is often the major selective agent in nature, and we cannot understand how populations evolve without understanding their pathogenic microbes. Beyond host immunity, an important factor determining the ability of pathogens to invade a host is how pathogens interact with other pathogens as well as background microbiota. This project investigates these questions in plants. Previous work has largely focused on pairwise interactions between one plant host and one pathogen, leaving a large gap in our understanding of how different types of interactions between microbes, and especially pathogens, determine the outcome of host-pathogen interactions in the real world. This project integrates large-scale field observations of microbes in the plant Arabidopsis thaliana with ultra-high-throughput experimental tests of host-dependent interactions among microbes, allowing experiment-informed modeling of pathogenic microbe-microbe interactions. These models, which will be improved through an iterative process of data collection with synthetic communities, will illuminate how interactions, from pairwise to higher-order, shape microbial community composition and structure. In the final step, the resulting models will be tested against and refined with field data. Together, these efforts will transform the study of plant pathogens by applying deep analyses of microbial interactions in an ecological context to explain patterns in nature. The ultimate goal is to refashion plant-pathogen-microbiome studies into a predictive science.
