An Integrated Pan Genome-Resistome Platform for Nosocomial Pathogen Surveillance in Hospitals

用于医院病原体监测的综合泛基因组-抗药性平台

• 批准号: 9255896
• 负责人: Srini S Iyer
• 金额: $ 22.37万
• 依托单位: ICBIOME, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-02 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9255896
• 关键词: Address; Area; Award; Bacteremia; Benchmarking; Bioinformatics; Biological Markers; Blood; Clinical; Complex; Data; Data Analytics; Databases; Diagnostic; Diagnostic tests; Disease Outbreaks; Economic Burden; Environment; Environmental air flow; Equipment; Escherichia coli; Evaluation; Event; Feedback; Genes; Genome; Genomics; Hospital Costs; Hospitals; Infection; Left; Methodology; Monitor; Nodal; Nosocomial Infections; Patients; Phase; Process; Public Health; Reporting; Resolution; Risk; Siblings; Speed; System; Testing; Time; Translating; United States National Institutes of Health; Urinary tract infection; Virulent; Work; base; cloud based; comparative; cost; genome analysis; genome sequencing; genomic data; operation; pan-genome; pathogen; research study; response; screening; tool; transmission process; whole genome

Nosocomial hospital infections are a significant public health concern as they severely harm patients, disrupt normal operations, and increase hospital costs. To reduce these risks and allow for preemptive responses, there is a critical need for routine surveillance of nosocomial pathogens within a hospital environment (patients, staff, ventilation equipment). With the decreasing costs of sequencing, whole genome sequencing is increasingly becoming viable as a diagnostic tool. However, translating its genomic information into a distinctive surveillance signature has been a significant challenge. Indeed a one-year hospital study of A. baumannii showed that strains continuously evolved during that period through complex mixing from multiple founder strains. As such, current core-alignment tools will be unable to separate clonal re-circulating strains that differ in their Pan Genome content. Moreover, most comparative alignment tools rarely perform resistome analysis, which has been employed in multiple research studies to separate outbreak strains from clonal strains. Our Phase 1 proposal will use E. coli as a test case to build the first integrated strain-resistome framework for routine surveillance of nosocomial pathogens. Our approach will employ a unique Pan Genome that will translate the entire genome content of the unknown strain into a distinctive strain signature. Likewise, our resistome analysis will employ a Pan Resistome that will translate the resistome content of the unknown strain into a distinctive resistome signature. Taken together, these strain and resistome signatures will provide a comprehensive set of genome-markers that will be used to connect newly sequenced strains in the event of a new infection. To confirm our approach, we tested over 342 E. coli strains from a single hospital. Our preliminary studies show that we are able to separate clusters from other clonal strains within the same clade. This separation was done independently using both strain and resistome comparisons. Our Phase 1 aims are: Aim1--Develop a screening framework that rapidly identifies unknown strains and assigns them to a node; Aim2--Develop a strain analysis framework that analyzes the unknown strain and establishes a list of closely-related strain clusters; Aim3--Develop a resistome analysis framework that analyzes the unknown strain and establishes a list of closely-related resistomes. Our cloud framework will be built completely in AWS marketplace using commercial components. It will be evaluated against the complete set of E. coli data from the FDA GenomeTrakr project.

医院感染是一个重要的公共卫生问题，因为它们严重伤害患者， 扰乱正常运营，增加医院成本。为了减少这些风险，并允许先发制人 回应，有一个迫切需要例行监测医院内的病原体 环境（患者、工作人员、通风设备）。随着测序成本的降低， 测序作为一种诊断工具正变得越来越可行。然而，翻译它的基因组信息 变成一个独特的监视信号是一个巨大的挑战。事实上，一项为期一年的医院研究， A.鲍曼尼指出，在这一时期，菌株通过复杂的混合从 多个创始人菌株。因此，目前的核心比对工具将无法分离克隆再循环 不同的菌株在其泛基因组内容。此外，大多数比较对齐工具很少执行 耐药基因组分析，已用于多项研究，以分离爆发菌株， 克隆菌株 我们的第一阶段提案将使用E。大肠杆菌作为测试案例，构建第一个整合的菌株-耐药基因组框架 用于医院病原体的常规监测。我们的方法将采用独特的泛基因组， 将未知菌株的整个基因组内容翻译成独特的菌株特征。同样，我们的 耐药基因组分析将使用泛耐药基因组，其将翻译未知菌株的耐药基因组内容 变成了一个独特的耐药基因。总之，这些菌株和耐药基因组特征将提供 一套全面的基因组标记，将用于连接新测序的菌株， 新的感染为了证实我们的方法，我们测试了超过342 E。大肠杆菌菌株。我们 初步研究表明，我们能够从同一进化枝内的其他克隆菌株中分离出簇。 使用菌株和耐药基因组比较独立地进行该分离。 我们的第一阶段目标是：目标1--开发一个快速识别未知菌株的筛选框架， 目标2--开发一个应变分析框架，分析未知应变， 建立密切相关的菌株群列表;目标3--开发耐药基因组分析框架， 分析未知菌株并建立密切相关的耐药基因列表。 我们的云框架将完全在AWS Marketplace中使用商业组件构建。将 与E.来自FDA GenomeTrakr项目的大肠杆菌数据。