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

医院感染是一个重大的公共卫生问题，因为它们严重损害患者， 扰乱正常运营，增加医院成本。要降低这些风险并允许先发制人 回应，迫切需要对医院内的医院病原体进行常规监测 环境(病人、工作人员、通风设备)。随着测序成本的降低，整个基因组 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 成为一个独特的监视签名一直是一个巨大的挑战。事实上，一项为期一年的医院研究 鲍曼不动杆菌表明，在此期间，菌株通过从 多个创立者菌株。因此，目前的岩心比对工具将无法分离克隆再循环 不同泛基因组含量的菌株。此外，大多数比较对齐工具很少执行 耐药组分析，它已被用于多项研究，以分离暴发菌株与 克隆菌株。 我们的第一阶段计划将使用大肠杆菌作为测试用例，以构建第一个集成的耐药组框架 用于医院病原体的常规监测。我们的方法将使用一个独特的潘基因组，它将 将未知菌株的整个基因组内容转化为独特的菌株签名。同样，我们的 电阻组分析将使用潘电阻组，它将翻译未知菌株的电阻组内容 变成了一个独特的抵抗体特征。综合起来，这些应变和耐药组信号将提供一个 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，我们测试了来自一家医院的342多个大肠杆菌菌株。我们的 初步研究表明，我们能够从同一分支内的其他克隆菌株中分离出集群。 这种分离是使用应变和电阻比较独立完成的。 我们的第一阶段目标是：AIM1--开发一种快速识别未知菌株和 将它们分配到一个节点；AIM2--开发一个应变分析框架，分析未知的应变并 建立密切相关菌株簇的列表；Aim3--开发一个抗药性分析框架， 分析未知的菌株，并建立一份密切相关的电阻列表。 我们的云框架将完全在AWS Marketplace中使用商业组件构建。会是 对照FDA GenomeTrakr项目的完整大肠杆菌数据进行评估。

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.