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

利用单分子测序对异质样品中复杂的植物病原体进行鉴定和定量

• 批准号: BB/V017608/1
• 负责人: Richard Harrison
• 金额: $ 19.2万
• 依托单位: National Institute of Agricultural Botany
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV017608%2F1
• 关键词: Identification; quantification; complex; plant; pathogens

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.

该项目旨在生成原理数据的证明，这将允许通过多种新型DNA测序和生物信息学方法的结合来开发快速的现场测定法，以鉴定特定的植物病原体。植物病原体的准确和快速诊断仍然是我们防御天气和土壤传播疾病的关键弱点。在速度和特异性之间通常会有权衡，基于现场的检测系统通常仅限于属或物种水平。 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.他们通常需要多层次序列键入才能识别其特定的植物宿主，这需要多个SNP特异性测定（例如Taqman或Kasp）或DNA测序方法来识别特定的Pathovar相关SNP。没有可以捕获标识所需信息的复杂性的现场准备方法。我们的项目旨在将DNA文库构造中的最新发展与实时DNA分子鉴定相结合，以提供一种特定的定量方法，以将植物病原体识别为病原体水平，尽管该方法对其他疾病环境具有更广泛的适用性。这种方法将首次实时识别Pathovar级信息，从而为引起植物病原体疾病的概率分配，导致植物病原体疾病，并对混合样本中的总丰度进行估计。此外，由于我们应用的方法仅是部分选择性的，因此可以（再次以定量方式）捕获整个样品的组成，从而估算样品中其他微生物生物学剂的绝对丰度和相对丰度。我们的方法取决于为单分子测序牛津纳米孔平台开发的两种技术的组合。第一个创新是使用“读取”或“自适应”测序，该测序扫描了长期读取的前150base，并在实时查询中，用于一个或多个感兴趣的生物的目标序列数据库。为了生成有关目标样本的更多信息，只有超出初始150base的样品被测序超出了最初的150base。这意味着在单个样本中进行了靶向测序和不靶向测序，从而估算了估计生物体的总体丰度，以及靶基生物的特定丰度。第二个取DNA并将唯一鉴定的分子（UMI）带到样品中的一部分分子。然后，几个PCR的循环生成这些UMI标记的分子的副本，可以在测序后进行准确的共识鉴定，同时保留有关样品中分子相对比例的关键信息。虽然在此阶段纯粹是一项试点研究，但我们的最终野心是为了提供一种快速，低成本的方法，可用于在复杂的现实世界中迅速识别病原体，在复杂的现实世界中，样品通常是次优质量的，并且诊断时间通常至关重要。