Quantifying how host genotype and microbiome composition combine to influence susceptibility to plant disease.

Lead Research Organisation: UK CENTRE FOR ECOLOGY & HYDROLOGY
Department Name: Biodiversity (Penicuik)


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Technical Summary

A key challenge in plant research is to understand how interactions between a plant host and its microbiome affect plant disease incidence and severity. These interactions are driven by genetic variation in the host, environmental conditions, and the dynamics of the microbial community, including the pathogens. Dissecting the relative importance of these components, ideally under controlled experimental conditions, can provide a major advance in understanding plant disease. We propose to use an integrative microbiome approach and focus on Dothistroma septosporum, the pathogen causing Dothistroma needle blight (DNB) in pine trees. However, the methods are applicable to diverse plant host-pathogen systems. DNB is a disease that detrimentally affects >100 economically and environmentally important pine species worldwide. By quantifying a host tree's genetic variation, disease incidence and their microbiome in concert, this project will enable a step change in our understanding of the host-microbiome-pathogen interaction. Our hypothesis is that, in a given environment, host genotype drives foliar microbiome composition and interactions to alter host susceptibility to DNB. To test this hypothesis we will 1) quantify the extent to which host genetic variation explains variation in microbial community composition and susceptibility to DNB, (2) evaluate temporal variation in microbiome composition and function during D. septosporum infection and (3) predict which microbiome community members interact with D. septosporum and which impact on host susceptibility to DNB. We will integrate existing host genotype data from our established common environment progeny-provenance trial for Scots pine with new amplicon and RNA sequencing data. We will apply association genetic analysis and Bayesian network modelling. Our project addresses key challenges within the BBSRC integrative microbiome research priority.


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