Leekitcharoenphon, Pimlapas2; Nielsen, Eva M.9; Kaas, Rolf Sommer2; Lund, Ole4; Aarestrup, Frank Møller2
1 Comparative Microbial Genomics, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark2 National Food Institute, Technical University of Denmark3 Division of Epidemiology and Microbial Genomics, National Food Institute, Technical University of Denmark4 Department of Systems Biology, Technical University of Denmark5 Center for Systems Microbiology, Department of Systems Biology, Technical University of Denmark6 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark7 Immunological Bioinformatics, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark8 Statens Serum Institut9 Statens Serum Institut
Salmonella enterica is a common cause of minor and large food borne outbreaks. To achieve successful and nearly ‘real-time’ monitoring and identification of outbreaks, reliable sub-typing is essential. Whole genome sequencing (WGS) shows great promises for using as a routine epidemiological typing tool. Here we evaluate WGS for typing of S. Typhimurium including different approaches for analyzing and comparing the data. A collection of 34 S. Typhimurium isolates was sequenced. This consisted of 18 isolates from six outbreaks and 16 epidemiologically unrelated background strains. In addition, 8 S. Enteritidis and 5 S. Derby were also sequenced and used for comparison. A number of different bioinformatics approaches were applied on the data; including pan-genome tree, k-mer tree, nucleotide difference tree and SNP tree. The outcome of each approach was evaluated in relation to the association of the isolates to specific outbreaks. The pan-genome tree clustered 65% of the S. Typhimurium isolates according to the pre-defined epidemiology, the k-mer tree 88%, the nucleotide difference tree 100% and the SNP tree 100% of the strains within S. Typhimurium. The resulting outcome of the four phylogenetic analyses were also compared to PFGE reveling that WGS typing achieved the greater performance than the traditional method. In conclusion, for S. Typhimurium, SNP analysis and nucleotide difference approach of WGS data seem to be the superior methods for epidemiological typing compared to other phylogenetic analytic approaches that may be used on WGS. These approaches were also superior to the more classical typing method, PFGE. Our study also indicates that WGS alone is insufficient to determine whether strains are related or un-related to outbreaks. This still requires the combination of epidemiological data and whole genome sequencing results.