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Updated: Sep 18, 2025

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核和全基因组多位序列类型化方案的验证,用于Shiga-Toxin-ProducingE. 在国家监测网络中检测大肠杆菌 (STEC) 疫情,PulseNet 2.0,美国

Molly M Leeper¹, Morgan N Schroeder¹, Taylor Griswold¹

¹Division of Foodborne, Waterborne, and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA.

|June 27, 2025

概括

全基因组测序 (WGS) 方法,如核心基因组多部位测序类型化 (cgMLST) 是有效的追踪西加毒素产生大肠杆菌 (STEC) 爆发. 高质量的单核酸多态化 (hqSNP) 和全基因组多位置序列类型化 (wgMLST) 为STEC监测提供了进一步的解决方案.

科学领域:

微生物学微生物学
基因组学就是基因组学.
公共卫生公共卫生

背景情况:

在美国,滋果毒素产生大肠杆菌 (STEC) 导致严重的食物传播和动物传播疾病.
全基因组测序 (WGS) 对于STEC检测,监测和疫情调查至关重要.

研究的目的:

为了评估基于WGS的三种STEC爆发分析亚型化方法的一致性:hqSNP,wgMLST (chrom) 和cgMLST.
验证cgMLST用于国家STEC监测,并评估hqSNP和wgMLST对增强分辨率的有用性.

主要方法:

应用hqSNP,wgMLST () 和cgMLST到来自11个疫情的STEC分离物.
评估了使用对对基因组差异,线性回归,图和K-means集群与Silhouette分析的方法一致性.

主要成果:

配对基因组差异显示了方法之间的高度一致性,除了一次与国际旅行相关的爆发.
回归斜率表明方法之间存在不同程度的相关性 (hqSNP与cgMLST:0.432;hqSNP与wgMLST:0.966;cgMLST与wgMLST:1.914).
格拉姆和K-means聚类在爆发组分离方面表现出中度至强度的一致性 (BGIs 0.350.99,CCCs ≥0.88,轮宽度 ≥0.87).

结论:

cgMLST通过PulseNet 2.0系统被验证用于国家STEC疾病集群监测.
hqSNP和wgMLST为STEC亚型和疫情调查提供了宝贵的额外分辨率.

关键词:

埃舍里希亚大肠杆菌 (Escherichia coli) 是一个大肠杆菌.脉冲网2.0 脉冲网2.0 脉冲网2.0 脉冲网史蒂克 (STEC) 是一个国家.cgMLST 的意思 hqSNP 在这里疫情爆发的原因是 wgMLSTST 在线观看整个基因组的测序.

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