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

对病原体进行深度测序，以使用罕见变异精确定义传播网络

基本信息

批准号：
10196948
负责人：
William Hanage
金额：
$ 55.45万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-26 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10196948
关键词：
Animals Award Bacteria Biological Assay Characteristics Citrobacter rodentium Communicable Diseases Consensus Consensus Sequence Contact Tracing Data Data Set Disease Disease Outbreaks Disease model Drug resistance Enrollment Epidemiology Event Evolution Genetic Genetic Polymorphism Genome Genomics Genotype Goals HIV Individual Infection Infection Control Infectious Agent Influenza Inherited Intervention Investigation Laboratories Link Longitudinal cohort study Methods Mission Mus Mutation Mycobacterium tuberculosis Nucleotides Organism Outcome Output Pathway interactions Phylogenetic Analysis Policy Maker Population Positioning Attribute Process Public Health Research Resolution Risk Factors Sampling Scientist Signal Transduction Site Skin Tissue Soft Tissue Infections Source Swab Technology Testing Training Trees Tuberculosis United States National Institutes of Health Variant Visit Work base carbapenem-resistant Enterobacteriaceae comparative data exchange deep sequencing disorder prevention experimental study flu transmission genomic data genomic platform improved insight interest methicillin resistant Staphylococcus aureus models and simulation novel novel strategies pathogen pathogen genome pathogenic bacteria pathogenic virus programs prospective rare variant recruit simulation single molecule transmission process

项目摘要

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.

项目摘要定义病原体如何通过主机网络传播的传播树对于流行病学，但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来治疗许多疾病。这是因为传染源的系统发育树不一定相当于传输树。对于许多病原体，感染人群可以携带大量的核苷酸多样性，一个或几个分离株的基因组不能充分表征，预计会误导重建传播链的尝试。另一个数据来源来推断传播是“共享的罕见变异”：多态性位点，其中一个以上的核苷酸存在于感染，并在少数病例中共享。理由是，这些反映了一个这是一个传输瓶颈，允许通过一个以上的基因型，所以同一个变异位点是在不相关的案件中不太可能偶然发现。初步模拟模拟传播网络上的病原体表明该方法大大优于现有方法上级。这是最近关于病毒病原体（包括流感和艾滋病毒）的研究进一步支持了这一观点，变种与宿主网络，但这些方法还没有通过实验测试，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有的罕见变异。病原体：鼠中柠檬酸杆菌的实验传播，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，在相对较短的传播链上出现了多种多态性（20 动物）。MRSA研究将使用从美国陆军约600名新兵中收集的4个身体部位的样本接受基本训练，并将测试是否共享的罕见变异将更有可能被发现，密切接触者反映了东道国网络。这可以用来确定一些身体部位是否更可能传播，在运输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 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，以测试它是否进一步区分潜在的来源。最后，对两个深部层序资料进行了回顾性分析，并对已确定的结核病爆发进行分析，以开发推断未抽样联系存在的方法，然后可以将其应用于合作者预期收集和测序的样品。两者合计这项研究计划将为宿主体内的感染过程提供无与伦比的洞察力，这将为接触者追踪提供信息，并帮助识别传播链中遗漏的环节，风险因素的研究，并允许更好地估计疾病建模的参数。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Using viral load and epidemic dynamics to optimize pooled testing in resource-constrained settings.

DOI：
10.1126/scitranslmed.abf1568
发表时间：
2021-04-14
期刊：
Science translational medicine
影响因子：
17.1
作者：
Cleary B;Hay JA;Blumenstiel B;Harden M;Cipicchio M;Bezney J;Simonton B;Hong D;Senghore M;Sesay AK;Gabriel S;Regev A;Mina MJ
通讯作者：
Mina MJ

Inferring bacterial transmission dynamics using deep sequencing genomic surveillance data.

DOI：
10.1038/s41467-023-42211-8
发表时间：
2023-10-31
期刊：
Nature communications
影响因子：
16.6
作者：
Senghore M;Read H;Oza P;Johnson S;Passarelli-Araujo H;Taylor BP;Ashley S;Grey A;Callendrello A;Lee R;Goddard MR;Lumley T;Hanage WP;Wiles S
通讯作者：
Wiles S

Perfect as the enemy of good: tracing transmissions with low-sensitivity tests to mitigate SARS-CoV-2 outbreaks.