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

项目摘要 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，然而使用现有的方法比较病原体基因组这样的树是困难的或不可能的 以获得多种疾病的治疗。这是因为感染性病原体的系统发育树不一定 相当于传输树。对于许多病原体来说，感染人群可能藏匿着大量 核苷酸多样性，这不是一个或几个分离物的基因组所充分描述的，这是 预计会误导重建传播链的尝试。另一个可供推断的数据来源 传播是“共有的稀有变异”：存在多个核苷酸的多态位点 感染，并且在少数病例之间共享。理由是这些反映了一种 传播瓶颈，允许通过多个基因型，因此相同的变异部位是 在不相关的案件中不太可能被偶然发现。模拟演化的初步模拟 传播网络上的病原体表明，这种方法大大优于现有的方法。这是 最近对包括流感和艾滋病毒在内的病毒病原体的研究进一步支持了共享稀有病毒的研究 变异体与宿主网络，但这些方法尚未通过实验测试，或应用于细菌。 这项拟议的研究使用深度测序来分析三种细菌种群中共享的稀有变异 病原体：轮状柠檬酸杆菌在小鼠体内的实验性传播，MRSA的纵向队列研究 高负担环境下的传播和结核病暴发。传输的初步数据 实验表明，在相对较短的传输链(20)上出现了多种多态 动物)。MRSA研究将使用从美国陆军约600名新兵身上收集的4个身体部位的样本 正在接受基本培训，并将测试共享稀有变体是否更有可能在 反映主机网络的密切联系。这可以用来确定某些身体部位是否 很可能传播，在车厢样本中发现的变异可以与皮肤和 软组织感染，以确定哪个身体部位是可能的来源。新的10X基因组学平台， 通过标记单个分子可以提高分辨率的基本策略，将进行试验以测试它是否 进一步区分潜在的来源。最后，对两个深部层序数据进行了回顾分析 并将对已识别的结核病暴发进行分析，以开发出推断是否存在未采样链接的方法， 然后可以将其应用于合作者预期收集和测序的样本。加在一起 这项研究计划将提供对宿主内部感染过程的无与伦比的洞察， 这将通知接触者追踪并帮助识别传输链中缺失的环节，允许新的方法 用于研究风险因素，并允许更好地估计用于疾病建模的参数。

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.