Validation of Antimicrobial Resistance Data Analysis and Quality Control Procedures for Whole Genome Sequencing Using Commensal E. coli from Multiple Animal Species

使用来自多种动物物种的共生大肠杆菌验证抗生素耐药性数据分析和全基因组测序质量控制程序

• 批准号: 10424405
• 负责人: Claire R Burbick
• 金额: $ 7.5万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10424405
• 关键词: Validation; Antimicrobial; Resistance; Data; Analysis

1 Project Summary: Veterinary diagnostic laboratories currently lack universal standardized 2 methods and quality control for WGS data analysis with regard to assessing antimicrobial 3 resistance (AMR). As the FDA CVM is currently funding AMR monitoring and whole genome 4 sequencing (WGS) this is a significant gap. Additionally, AMR monitoring is lacking in minor 5 agricultural animal species and companion animals and should be included for holistic AMR 6 monitoring in veterinary medicine. This project addresses these gaps by developing data quality 7 criteria needed for AMR analysis using WGS, developing a bioinformatics pipeline with quality 8 assurance and quality control criteria tailored to veterinary diagnostic laboratory use and 9 providing AMR monitoring in minor agricultural animal species and companion animals. Not 10 only do resistant bacterial infections impact animal health and welfare, but they also have 11 significant potential for causing negative human health consequences through transmission of 12 resistant bacteria or resistance genes through food or animal contact. The use of common 13 classes of antimicrobials in humans and animals increases the likelihood that drug resistance 14 selected for in animal species could impact humans. Therefore, monitoring AMR in animals has 15 the potential to mitigate not only disease in animals but human infections as well. Recent 16 reductions in sequencing cost has provided an opportunity for veterinary diagnostic 17 laboratories to consider utilizing this technology for antimicrobial resistance assessment. 18 Escherichia coli, a microbe commonly harbored in multiple animal species will be used to 19 optimize and validate of existing bioinformatics platforms and bioinformatics quality 20 assurance/quality control procedures related to AMR. During the first year of funding the 21 laboratory will use reference strains of E. coli with known resistance phenotypes and genotypes 22 of interest including resistance to third generation cephalosporins, fluoroquinolones, 23 carbapenems, and aminoglycosides to optimize the sequence data quality assurance/quality 24 control and AMR bioinformatics analysis. In subsequent years, commensal E. coli will be 25 isolated from submissions to WADDL from multiple animal species for performance of 26 phenotypic AST and WGS for continuation of optimization and validation of the process, and 27 expansion of AMR monitoring in veterinary species. A summary of the data will be provided to 28 FDA CVM Vet-LIRN yearly and a standard operating procedure at the conclusion of the study. 29 30

1项目总结：兽医诊断实验室目前缺乏通用标准化 抗菌药物WGS数据分析的方法和质量控制 3抗性(AMR)。由于FDA CVM目前正在资助AMR监测和全基因组 4测序(WGS)这是一个很大的差距。此外，AMR监测在以下方面缺乏 5种农业动物和伴生动物，应包括在整体AMR中 6兽医监测。该项目通过提高数据质量来解决这些差距 使用WGS进行AMR分析所需的7个标准，开发高质量的生物信息学管道 8为兽医诊断实验室使用量身定做的保证和质量控制标准 9提供对小型农业动物物种和伴生动物的AMR监测。不 10抗药性细菌感染只会影响动物的健康和福利，但它们也有 11传播疟疾极有可能对人类健康造成负面影响 12通过食物或动物接触耐药细菌或耐药基因。常用词的使用 13类抗菌药在人类和动物中增加了抗药性的可能性 14个被选为动物物种的物种可能会影响人类。因此，监测动物的AMR具有 15不仅有可能减少动物的疾病，而且有可能减少人类的感染。近期 16测序成本的降低为兽医诊断提供了机会 17个实验室考虑利用这项技术进行抗菌素耐药性评估。 18大肠杆菌，一种常见于多种动物物种中的微生物，将被用于 19优化和验证现有生物信息学平台和生物信息学质量 20与AMR相关的保证/质量控制程序。在资助的第一年， 21实验室将使用已知耐药表型和基因型别的参考菌株 包括对第三代头孢菌素、氟喹诺酮类药物的耐药性， 23种碳青霉烯类和氨基糖苷类药物，优化序列数据质量保证/质量 24对照和AMR生物信息学分析。在接下来的几年里，共生大肠杆菌将被 从提交给世界反兴奋剂机构的多个动物物种中分离出25个 26表型AST和WGS，用于继续优化和验证工艺；以及 27扩大对兽用物种的AMR监测。数据摘要将提供给 每年28次FDA CVM Vet-LIRN，并在研究结束时制定标准操作程序。 29 30