Identification and quantification of complex plant pathogens within heterogenous samples harnessing single molecule sequencing

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This project aims to generate proof of principle data that will allow the development of rapid in-field assays for the identification of specific plant pathogens through the combination of multiple novel DNA sequencing and bioinformatics approaches. Accurate and rapid diagnosis of plant pathogens remains a key weakness in our defence against aerial and soil-borne diseases. There is often a trade off between speed and specificity, with field based detection systems often limited to genus or species level. This is a problem for many important pathogens systems with host-specific pathovars or formae speciales (ff.spp.) within species complexes, such as Fusarium, Verticillium and Pseudomonas syringae as these are abundant in the environment and have both pathogenic and non-pathogenic lineages that are often phylogenetically indistinguishable using standard 'DNA barcoding' primer sets. They often require multi-locus sequence typing in order to identify their specific plant host, which requires either multiple SNP-specific assays (e.g. Taqman or KASP) or DNA sequencing approaches to identify specific pathovar associated SNPs. There are no field-ready approaches that can capture the complexity of this information required for identification. Our project aims to combine recent developments in DNA library construction with real time DNA molecule identification in order to provide a specific, quantitative method to identify plant pathogens to the pathovar level, though the method has much broader applicability to other disease settings. This approach will, for the first time, allow the identification of pathovar-level information in real time, generating a probabilistic assignment of identity for the plant pathogen disease causing agent, but also an estimate of the total abundance within a mixed sample, e.g. plant leaf, soil etc. Moreover, as the method that we apply is only partially selective, the composition of the whole sample can be captured (again in a quantitative manner) allowing the estimation of both the absolute and relative abundance of other microbial species biological agents within the sample. Our approach hinges on the combination of two techniques developed for the single molecule sequencing Oxford Nanopore platform. The first innovation is the use of 'read-until' or 'adaptive' sequencing, which scans the first 150bases of a long read and in real-time queries a database of target sequences for one or more organisms of interest. Only samples with a positive ID are sequenced beyond the initial 150bases sequenced in order to generate more information about the target sample. This means both targeted and untargeted sequencing is taking place within a single sample, allowing both overall abundance of organisms to be estimated, along with specific abundance of the target organisms. The second takes DNA and ligates a unique molecular identified (UMI) to a proportion of molecules in a sample. A few cycles of PCR then generate copies of these UMI-tagged molecules allowing accurate consensus identification, upon sequencing while retaining the crucial information about the relative proportions of molecules in the sample. While at this stage purely a pilot study, our ultimate ambition is for this study to provide a rapid, low-cost method that can be used to identify pathogens rapidly in complex, real-world situations, where samples are often of suboptimal quality and where time to diagnosis is often critical.