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Understanding and predicting PATHOgen COMmunities

Periodic Reporting for period 2 - PATHOCOM (Understanding and predicting PATHOgen COMmunities)

Okres sprawozdawczy: 2023-01-01 do 2024-06-30

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.
Natural populations of the plant Arabidopsis thaliana have been extensively characterized for ecological variables across two plant life cycles from fall 2021 to spring 2023. The characterization of microbiota and pathobiota in and on these plants as well as on companion species and soil is ongoing, as is the genetic characterization of the plants. A subset of plants is also being processed for gene expression analyses, to determine the effects of microbial colonization on the immune status of wild plants.

Both plant and microbe strains are being barcoded in preparation for massive infection trials. The efficiency and power of the barcoding system has been demonstrated in pilot experiments, and an instrument for massively parallel bacterial infections by spraying has been developed.
Previous work has largely ignored the impact of interactions between pathogens, the pathobiota. This project will provide the first large-scale experimental data set accurately describing the range of interactions, including higher-order interactions, that drive complex microbial communities in the plant phyllosphere.
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