A cloud-based WGS platform for routine surveillance of plasmid-borne carbapenem resistance

基于云的 WGS 平台，用于常规监测质粒携带的碳青霉烯类耐药性

• 批准号: 9409442
• 负责人: Srini S Iyer
• 金额: $ 22.34万
• 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: 2017-08-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9409442
• 关键词: Adoption; Algorithms; Antibiotics; Award; Benchmarking; Bioinformatics; Biological Markers; Clinical; Complex; Data Analytics; Databases; Diagnostic; Drug Monitoring; Drug resistance; Enterobacteriaceae; Escherichia coli; Evaluation; Event; Exposure to; Face; Future; Generic Drugs; Genes; Genomics; Horizontal Gene Transfer; Hospitals; Hybrids; Infection Control; Inherited; Klebsiella pneumonia bacterium; Left; Location; Manuals; Methodology; Monitor; Movement; Patients; Phase; Plasmids; Positioning Attribute; Protocols documentation; Publishing; Structure; System; Testing; Translating; United States National Institutes of Health; Validation; base; carbapenem resistance; cloud based; design; genetic element; genome sequencing; indexing; novel strategies; pathogen; preference; pressure; resistance gene; whole genome

Plasmid-borne carbapenem resistance has become a serious global threat in hospital settings. As hospitals face a dwindling number of treatment options, there is a great urgency to track the movement of carbapenem-resistant plasmids and the associated resistant genes. While whole genome sequencing has great diagnostic potential, plasmids pose a unique set of challenges as they lack a clear, well-defined structure and contain a wide range of repetitive genetic elements. Additionally, plasmids routinely undergo complex rearrangements during patient colonization, driven by environmental pressures such as continual exposure to antibiotics. Finally, plasmid movements through horizontal gene transfer frequently results in further diversification, predominantly from host-specific selection pressures. As a result of these challenges, tracking the movement of plasmids through whole genome sequencing often requires painstaking manual bioinformatics approaches. In this proposal, with an initial focus on Enterobacteriaceae plasmids that carry the KPC gene, we look to develop an automated diagnostics platform that will allow hospitals to closely monitor drug-resistant plasmids. Our platform will provide infection control units a searchable cloud-based database of all their drug- resistant plasmids, their association with various strains, and the various points of entry within the hospital. Our approach uses two different alignment methodologies for plasmid identification: one methodology is designed to rapidly identify the generic content of the plasmid; the other methodology is designed to uniquely quantify the transposon regions around resistance genes. Our proposal aims are: 1) Develop a plasmid framework that quantifies plasmids with both global and transposon biomarker sequences; 2) Develop a plasmid-strain alignment framework that will search new strains for plasmid biomarker sequences and then identify other strains that contain similar global biomarker sequences; 3) Develop a transposon-alignment framework that will search new strains for KPC-gene carrying transposon sequences and then identify other strains with similar biomarker sequences. Our approach was tested against two closely-related plasmids at a single hospital. By first establishing key biomarkers, we were able to rapidly identify over 240 strains from NCBI that had inherited the same plasmids over a four-year period and across three species. Our algorithm was also able to identify a key HGT event for one of the plasmids, which was independently documented in a published study. Our cloud-based plasmid diagnostics framework will be implemented by processing over 4,000 Enterobacteriaceae strains from NCBI. Validations will be made using manual bioinformatics protocols.

质粒携带的碳青霉烯耐药已成为医院环境中的严重全球威胁。作为 医院面临的治疗选择越来越少，有一个非常紧迫的跟踪运动， 碳青霉烯类耐药质粒和相关耐药基因。虽然全基因组测序 尽管质粒具有巨大的诊断潜力，但由于缺乏清晰、明确的结构，它们构成了一系列独特的挑战 并含有大量重复的基因成分此外，质粒通常经历复杂的 在患者定植期间的重新排列，由环境压力驱动，例如持续暴露于 抗生素最后，质粒通过水平基因转移的移动经常导致进一步的突变。 多样化，主要来自宿主特异性选择压力。由于这些挑战， 质粒在全基因组测序中移动通常需要艰苦的手工操作 生物信息学方法。 在这个提议中，我们首先关注携带KPC基因的肠杆菌质粒， 开发一个自动化诊断平台，使医院能够密切监测耐药 质粒。我们的平台将为感染控制单位提供一个可搜索的基于云的数据库， 耐药质粒，它们与各种菌株的关联，以及医院内的各种进入点。 我们的方法使用两种不同的比对方法进行质粒鉴定：一种方法 设计用于快速鉴定质粒的通用内容;另一种方法设计用于独特地 量化抗性基因周围的转座子区域。我们的建议目标是：1）开发质粒 用全局和转座子生物标志物序列定量质粒的框架; 2）开发一个 质粒-菌株比对框架，该框架将搜索新菌株的质粒生物标志物序列，然后 鉴定含有类似全局生物标志物序列的其他菌株; 3）开发转座子比对 该框架将搜索携带转座子序列的KPC基因的新菌株，然后鉴定其他 具有相似生物标志物序列的菌株。 我们的方法在一家医院针对两种密切相关的质粒进行了测试。通过首先建立 关键的生物标志物，我们能够快速识别超过240株从NCBI遗传相同的 质粒在四年的时间里跨越三个物种。我们的算法还能够识别一个关键的HGT 其中一种质粒的事件，在一项已发表的研究中独立记录。 我们基于云的质粒诊断框架将通过处理4000多个 肠杆菌科菌株来自NCBI。将使用手动生物信息学方案进行验证。