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

该项目旨在通过多种新型DNA测序和生物信息学方法的结合，产生原理证明数据，从而允许开发用于鉴定特定植物病原体的快速现场分析。准确和快速诊断植物病原体仍然是我们防御空气和土壤传播疾病的一个关键弱点。通常在速度和特异性之间进行权衡，基于现场的检测系统通常仅限于属或种水平。对于许多重要的病原体系统来说，这是一个问题，这些病原体在物种复合体中具有宿主特异性病原体或特殊形式（ff.spp.），如镰刀菌、黄萎病菌和丁香假单胞菌，因为它们在环境中含量丰富，并且具有致病性和非致病性谱系，通常使用标准的“DNA条形码”引物集在系统发育上无法区分。它们通常需要多位点序列分型来识别其特定的植物宿主，这需要多种snp特异性测定（例如Taqman或KASP）或DNA测序方法来识别特定的病原相关snp。目前还没有现成的方法可以捕捉到识别所需信息的复杂性。我们的项目旨在将DNA文库建设的最新进展与实时DNA分子鉴定相结合，以提供一种特定的、定量的方法来鉴定植物病原体的致病水平，尽管该方法在其他疾病环境中具有更广泛的适用性。这种方法将首次允许实时识别病原体水平信息，为植物病原体致病因子产生身份的概率分配，而且还可以估计混合样本（例如植物叶片，土壤等）中的总丰度。此外，由于我们采用的方法只是部分选择性的，因此可以捕获整个样品的组成（再次以定量方式），从而可以估计样品中其他微生物物种生物制剂的绝对丰度和相对丰度。我们的方法取决于为牛津纳米孔平台开发的两种单分子测序技术的结合。第一个创新是使用“读至”或“自适应”测序，它扫描长序列的前150个碱基，并实时查询一个或多个感兴趣生物的目标序列数据库。只有具有阳性ID的样本才会在最初测序的150个碱基之外进行测序，以便生成更多关于目标样本的信息。这意味着目标和非目标测序都在单个样本中进行，从而可以估计生物体的总体丰度，以及目标生物体的特定丰度。第二种方法是提取DNA，并将一个独特的分子识别（UMI）与样本中一定比例的分子连接起来。几个PCR循环然后产生这些uni标记的分子的副本，允许准确的共识鉴定，在测序时，同时保留有关样品中分子相对比例的关键信息。虽然在这个阶段纯粹是一项试点研究，但我们的最终目标是为这项研究提供一种快速、低成本的方法，可用于在复杂的、现实世界的情况下快速识别病原体，在这些情况下，样品往往质量不佳，诊断时间往往很关键。

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.