Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations.
Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations.
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DOI：
10.3389/fmicb.2018.01482
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发表时间：
2018
影响因子：
5.2
通讯作者：
Strain E
中科院分区：
文献类型：
--
作者：
Pightling AW;Pettengill JB;Luo Y;Baugher JD;Rand H;Strain E
全基因组序列（WGS）分析通过实现食源性细菌的高分辨率分型，彻底改变了食品安全行业。更高的分辨率使调查人员能够在疾病爆发和监管活动期间快速准确地确定污染源。世界各地的政府机构和行业利益相关者现在都在定期分析WGS数据。虽然研究人员已经发表了许多研究，评估WGS数据分析的来源归因的有效性，但缺乏解释WGS分析的指导。在这里，我们提供了一个框架，用于解释食品和药物管理局食品安全和应用营养中心（CFSAN）使用的WGS分析。我们基于CFSAN调查人员的经验，与政府和行业合作伙伴的合作和互动，以及对已发表文献的评估，建立了这个框架。研究人员面临的一个基本问题是，两种或两种以上的细菌是否来自同一污染源。分析人员经常计算两个或多个基因组序列之间的核苷酸差异[单核苷酸多态性（SNP）]的数量，以测量遗传距离。然而，单独使用SNP阈值来评估细菌是否来自同一来源可能会产生误导。从食物、环境或临床样品中分离的细菌是细菌种群的代表。这些种群受到可以改变基因组序列的进化力量的影响。因此，解释食源性细菌的WGS分析需要更复杂的方法。在这里，我们提出了一个框架来解释WGS分析，结合SNP计数与系统发育树拓扑结构和自举支持。我们还阐明了WGS、流行病学、追溯和其他证据在形成调查结论中的作用。最后，我们提出的例子，说明了这个框架的应用到现实世界的情况。
Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration’s Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.
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影响因子：
3.9
作者：
Duchêne S;Holt KE;Weill FX;Le Hello S;Hawkey J;Edwards DJ;Fourment M;Holmes EC
通讯作者：
Holmes EC
影响因子：
6
作者：
Kruse, H
通讯作者：
Kruse, H
影响因子：
1.6
作者：
Lambert, Dominic;Pightling, Arthur;Blais, Burton
通讯作者：
Blais, Burton
DOI：
10.1056/nejmoa1615910
发表时间：
2017-11-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Crowe SJ;Bottichio L;Shade LN;Whitney BM;Corral N;Melius B;Arends KD;Donovan D;Stone J;Allen K;Rosner J;Beal J;Whitlock L;Blackstock A;Wetherington J;Newberry LA;Schroeder MN;Wagner D;Trees E;Viazis S;Wise ME;Neil KP
通讯作者：
Neil KP
影响因子：
9.4
作者：
Chen Y;Luo Y;Pettengill J;Timme R;Melka D;Doyle M;Jackson A;Parish M;Hammack TS;Allard MW;Brown EW;Strain EA
通讯作者：
Strain EA