Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM curves of amplicons generated from selected Salmonella genome sequences enabled Salmonella spp. to be classified into discrete clusters. Combination of m-HRMa with serogroup identification (ms-HRMa) helped improve assignment of Salmonella spp. into clusters. In addition, a machine learning (dynamic time warping) algorithm (DTW) was employed to provide a simple and rapid protocol for clustering analysis as well as to create phylogeny tree of Salmonella strains (n = 40) collected from home, farms and slaughter houses in northern Thailand. Applications of DTW and ms-HRMa clustering analyses were capable of generating molecular signatures of the Salmonella isolates, resulting in 25 ms-HRM and 28 DTW clusters compared to 14 clusters from a standard HRM analysis, and the combination of both analyses permitted molecular subtyping of each Salmonella isolate. Results from DTW and ms-HRMa cluster analyses were in good agreement with that obtained from enterobacterial repetitive intergenic consensus sequence PCR clustering. While conventional serotyping of Clusters 1 and 2 revealed six different Salmonella serotypes, the majority being S. Weltevraden, the new Salmonella subtyping protocol identified five S. Weltevraden subtypes with S.Weltevreden subtype DTW4-M1 being predominant. Based on knowledge of the sources of Salmonella subtypes, transmission of S. Weltevraden in northern Thailand was likely to be farm-to-farm through contaminated chicken stool. In conclusion, the rapid, robust and specific Salmonella subtyping developed in the study can be performed in a local setting, enabling swift control and preventive measures to be initiated against potential epidemics of salmonellosis.

中文翻译：

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机器学习算法和改良的高分辨率 DNA 熔解曲线分析在泰国北部不同流行病学背景下沙门氏菌分离株的分子亚型中的应用

食用被非伤寒沙门氏菌污染的食物引起的食物中毒。是一个全球性的问题。引入了改进的高分辨率 DNA 熔解曲线分析 (m-HRMa) 以有效区分由选定的沙门氏菌基因组序列生成的扩增子的密切相关的 HRM 曲线。被分类为离散的簇。m-HRMa 与血清群鉴定 (ms-HRMa) 的组合有助于改善沙门氏菌属的分配。成簇。此外，采用机器学习（动态时间扭曲）算法 (DTW) 为聚类分析以及创建从家庭、农场和屠宰场收集的沙门氏菌菌株（n = 40）的系统发育树提供了一个简单而快速的协议在泰国北部。DTW 和 ms-HRMa 聚类分析的应用能够生成沙门氏菌分离株的分子特征，与标准 HRM 分析的 14 个聚类相比，产生 25 个 ms-HRM 和 28 个 DTW 聚类，并且两种分析的组合允许对每个沙门氏菌分离物。来自 DTW 和 ms-HRMa 聚类分析的结果与从肠细菌重复基因间共有序列 PCR 聚类中获得的结果非常一致。虽然集群 1 和 2 的常规血清分型显示六种不同的沙门氏菌血清型，大多数是 S. Weltevraden，但新的沙门氏菌亚型方案确定了五种 S. Weltevraden 亚型，其中 S. Weltevreden 亚型 DTW4-M1 占主导地位。基于对沙门氏菌亚型来源的了解，沙门氏菌的传播。泰国北部的 Weltevraden 很可能通过受污染的鸡粪便进行农场到农场。总之，研究中开发的快速、稳健和特定的沙门氏菌亚型可以在当地环境中进行，从而能够针对沙门氏菌病的潜在流行病启动快速控制和预防措施。