A method for the culture-free discovery and host affiliation of novel viruses from metagenomic samples

一种从宏基因组样本中无需培养地发现新型病毒并确定其宿主归属的方法

• 批准号: 10259447
• 负责人: Ivan Liachko
• 金额: $ 82.04万
• 依托单位: PHASE GENOMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-08 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259447
• 关键词: Affect; Antimicrobial Resistance; Bacteria; Bacteriophages; Biology; Biotechnology; Cells; Chemistry; Communicable Diseases; Communities; Complex; Computer software; Computing Methodologies; Consumption; DNA; DNA Sequence; Data; Data Analyses; Databases; Development; Disease; Ecosystem; Feces; Genome; Genomics; Health; Hi-C; Horizontal Gene Transfer; Human; Human Microbiome; Internet; Libraries; Ligation; Lytic; Measurement; Metabolism; Metagenomics; Methodology; Methods; Microbe; Microbiology; Online Systems; Outcome; Phase; Planets; Play; Population; Predatory Behavior; Preparation; Reagent; Recovery; Reporting; Research; Resistance to infection; Retinal blind spot; Role; Safety; Sampling; Satellite Viruses; Series; Shapes; Site; Technology; Therapeutic; Time; Viral; Viral Genome; Virus; Virus-like particle; Visualization; Work; base; computational platform; contig; crosslink; data quality; experimental study; fecal transplantation; genome analysis; improved; in vivo; interest; metagenome; microbial; microbial community; microbial host; microbiome; microorganism; next generation sequencing; novel; novel strategies; novel virus; particle; resistance gene; restriction enzyme; tool; transmission process; user-friendly; vector; web portal; web-based tool

ABSTRACT In this application, we propose to develop a novel reagent kit and accompanying analytic software platform that employs Hi-C technology for a user-friendly method to assemble viral genomes from metagenomic samples and associate these viruses with their microbial hosts. This product enables culture-free discovery of phages and quantitative measurement of their host range directly in mixed microbial communities. Bacteriophages are viruses that infect bacteria; they shape microbial ecosystems through predation on hosts and through horizontal gene transfer. They are also an important vector in the transmission of anti-microbial resistance. Despite their potent impact on microbial biology, only a tiny proportion of phage genomes are represented in public databases in part because of the difficulty of isolating phage whose hosts are not culturable laboratory settings. This limitation in our ability to understand viral biology has important impacts on emerging biotechnology applications including fecal microbiota transplantation and phage therapy as well as wide-ranging effects on microbiological research. We propose to develop a novel computational approach combined with improved experimental methods to deconvolute viral genomes and perform host attribution from whole microbiome samples without culturing. At the core of this approach is utilization of proximity ligation methods (Hi-C) which physically associates DNA sequences present within intact microbes. This provides direct physical evidence of the contiguity of viral genome sequences and host affiliation information that is not achievable by any other method. The outcome of this direct-to-Phase II proposal would be a combination of kit and user-facing computational platform that would empower both the sophisticated next-generation-sequencing biologist and the sequencing novice to ask important questions about viruses in their microbial community of interest. Aim 1 focuses on computational methods improve the recovery of viral genomes from metagenomic data. Aim 2 develops methodologies to enhance viral Hi-C chemistry. These two aims work together synergistically enhance data quality. Finally, Aim 3 develops a computational platform for data analysis through a web portal where results could be visualized directly via web browser or raw data downloaded for further exploration. Upon completion we will have developed a first-in-class commercial platform to discover viral genomes and identify their hosts from complex microbial communities.

摘要 在本申请中，我们提出开发一种新的试剂盒和附带的分析软件平台， 采用Hi-C技术，以用户友好的方法从宏基因组样本中组装病毒基因组， 将这些病毒与它们的微生物宿主联系起来。该产品可实现无培养发现大肠杆菌， 直接在混合微生物群落中定量测量其宿主范围。 噬菌体是感染细菌的病毒;它们通过捕食宿主来塑造微生物生态系统 通过水平基因转移。它们也是传播抗微生物药物的重要载体。 阻力尽管它们对微生物生物学有着强大的影响，但只有一小部分噬菌体基因组是 在公共数据库中出现，部分原因是难以分离宿主不可培养的噬菌体 实验室设置。我们理解病毒生物学能力的这种局限性对新兴的 生物技术应用，包括粪便微生物群移植和噬菌体治疗，以及广泛的 对微生物研究的影响。 我们建议开发一种新的计算方法结合改进的实验方法， 解卷积病毒基因组，并在不培养的情况下从整个微生物组样品进行宿主归属。在 该方法的核心是利用邻近连接方法（Hi-C）， 存在于完整微生物中的序列。这提供了直接的物理证据， 基因组序列和宿主关系信息，这是任何其他方法都无法实现的。 这个直接进入第二阶段的建议的结果将是一个工具包和面向用户的计算工具的组合。 该平台将为成熟的下一代测序生物学家和测序人员提供支持 初学者询问有关他们感兴趣的微生物群落中病毒的重要问题。目标1侧重于 计算方法改进了从宏基因组数据中恢复病毒基因组。目标2开发 增强病毒Hi-C化学的方法。这两个目标协同工作，增强数据 质量.最后，Aim 3通过一个门户网站开发了一个用于数据分析的计算平台， 可以通过网络浏览器直接可视化或下载原始数据以供进一步探索。完成后 我们将开发出一个一流的商业平台来发现病毒基因组并识别其宿主 复杂的微生物群落。