Deep sequencing of pathogens to precisely define transmission networks using rare variants

项目概要 定义病原体如何通过宿主网络传播的传播树对于 流行病学，但使用现有方法比较此类树的病原体基因组是困难或不可能的 以获得许多疾病。这是因为传染源的系统发育树不一定是 相当于传输树。对于许多病原体来说，感染人群可能携带大量病原体 核苷酸多样性，不能通过一个或几个分离株的基因组充分表征，并且 预计会误导重建传输链的尝试。用于推断的替代数据源 传播是“共享的稀有变体”：其中存在多个核苷酸的多态性位点 感染，并且在少数病例中共享。理由是这些反映了 允许通过多个基因型的传输瓶颈，因此相同的变异位点 在不相关的案件中偶然发现的可能性微乎其微。初步模拟建模演化 传播网络上的病原体表明这种方法大大优于现有方法。这是 最近关于流感和艾滋病毒等病毒病原体的研究进一步支持了这一点，这些病原体与共享的罕见病毒相关 与宿主网络的变体，但这些方法尚未经过实验测试，或应用于细菌。 拟议的研究使用深度测序来分析三种细菌群体中共有的罕见变异 病原体：啮齿类柠檬酸杆菌在小鼠体内的实验性传播，耐甲氧西林金黄色葡萄球菌 (MRSA) 的纵向队列研究 高负担环境中的传播和结核病爆发。传输的初步数据 实验表明，在相对较短的传播链（20 动物）。 MRSA 研究将使用从约 600 名美国陆军新兵收集的 4 个身体部位的样本 接受基础训练，并将测试是否更有可能在其中发现共享的稀有变异 反映宿主网络的密切接触者。这可以用来确定某些身体部位是否更 可能会传播，并且可以将运输样本中发现的变异与皮肤和皮肤病例中的变异进行比较 软组织感染以确定哪个身体部位是可能的来源。新的 10X Genomics 平台， 通过标记单分子可以提高分辨率超出基本策略，将进行试验以测试是否可以 进一步区分潜在来源。最后，对两个深度序列数据进行回顾性分析 并对已确定的结核病暴发进行分析，以开发推断未抽样链接存在的方法， 然后可以将其应用于合作者前瞻性收集和测序的样本。合在一起 该研究计划将为宿主体内的感染过程提供无与伦比的见解， 这将为接触者追踪提供信息，并帮助识别传播链中缺失的环节，并允许新的方法 风险因素的研究，并可以更好地估计疾病模型的参数。

Project Summary Transmission trees that define how pathogens have spread through a host network are immensely valuable to epidemiology, yet using existing methods comparing pathogen genomes such trees are difficult or impossible to obtain for many diseases. This is because the phylogenetic tree of the infectious agents is not necessarily equivalent to the transmission tree. For many pathogens the infecting population can harbor substantial nucleotide diversity, that is not adequately characterized by the genomes of one or a few isolates, and which is predicted to mislead attempts to reconstruct transmission chains. An alternative source of data to infer transmission is `shared rare variants': polymorphic sites at which more than one nucleotide is present within the infection, and which are shared among a small number of cases. The reasoning is that these reflect a transmission bottleneck that allows through more than one genotype, and so the same variant site is vanishingly unlikely to be found by chance in unrelated cases. Preliminary simulations modeling evolution of pathogens on a transmission network indicate that this approach is greatly superior to existing methods. This is further supported by recent work on viral pathogens including Influenza and HIV that correlates shared rare variants with host networks, but these methods have not been tested by experiment, or applied to bacteria. The proposed research uses deep sequencing to assay shared rare variants in populations of three bacterial pathogens: experimental transmission of Citrobacter rodentium in mice, a longitudinal cohort study of MRSA transmission in a high burden setting, and tuberculosis outbreaks. Preliminary data from the transmission experiments indicate multiple polymorphisms have arisen over the relatively short transmission chains (20 animals). The MRSA study will use samples from 4 body sites collected from ~600 recruits to the US Army undergoing basic training, and will test whether shared rare variants will be more likely to be found among close contacts reflecting the host network. This can be used to determine whether some body sites are more likely to transmit, and variants found in carriage samples can be compared with those from cases of skin and soft tissue infection to determine which body site is the likely source. The new 10X Genomics platform, which by tagging single molecules can increase resolution beyond the basic strategy, will be trialed to test whether it further discriminates between potential sources. Finally, deep sequence data from two retrospectively analyzed and identified outbreaks of TB will be assayed to develop means to infer the presence of unsampled links, which can then be applied to samples prospectively collected and sequenced by collaborators. Taken together this program of research will provide an unparalleled insight into the processes of infection within the host, which will inform contact tracing and help identify missed links in the transmission chain, allow new approaches to the study of risk factors, and allow better estimates of parameters for disease modeling.