Abstract
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.
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modified by normalizing curves in temperature range 81.0–83.6 °C (dark horizonal line) relative to S. Bareille to generate Clusters D-F (right panel). b Original HRM patterns of Salmonella strains in HRM Cluster 2 were modified by normalizing curves in temperature ranges 79.6–83.6 °C and 85.0–87.3 °C (green horizonal line) relative to S. Bareille to generate modified Clusters A-C (left panel) and Clusters G-L (right panel)
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Data Availability
Original data of HRM melting curves used for performing DTW algorithm analysis and source codes are available in the supplementary information file.
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Acknowledgements
The study was supported by the school of Medical Science, University of Phayao, Grant No. 25634988. The authors thank Associate Professor Suphak Mahatthontanahak for research cooperation, Asanai Leng-Ee for geographical illustration of northern Thailand and Professor Emeritus Torpong Sanguansermsri, Thalassemia Unit, University of Phayao, for use of the high resolution DNA melting curve analysis facility, and Professor Emeritus Prapon Wilairat for critical reading and English editing of the manuscript.
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The study was conducted with the ethical approval from the Institutional Animal Care and Use Committee, University of Phayao (IACUC-UP), reference no. 62–02-04–001.
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Wisittipanit, N., Pulsrikarn, C., Wutthiosot, S. et al. Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand. World J Microbiol Biotechnol 36, 103 (2020). https://doi.org/10.1007/s11274-020-02874-7
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DOI: https://doi.org/10.1007/s11274-020-02874-7