Whole Genome Sequencing for Outbreak Detection of Salmonella enterica

Salmonella typhimurium Salmonella bacteria are a common cause of infectious disease in human and animals. Salmonella is classically divided into species S. bongori and S. enterica – which is in turn further divided into more than 2,500 different serotypes. However, only a limited number of serovars that are responsible for most infections. In Europe, the most prevalent S. enterica serovars isolated from humans are Enteritidis and Typhimurium, responsible for over 75% of the human cases of salmonellosis.

In order to understand the epidemiology and implement control programs, reliable and rapid sub-typing is essential. Different typing methods are commonly used as a central part of the detection and investigation of Salmonella outbreaks, for instance, serotyping, phage typing, pulse-field gel electrophoresis (PFGE) and multilocus variable number of tandem repeat analysis (MLVA). PFGE has become the standard for epidemiological investigations of foodborne bacterial pathogens including Salmonella. A drawback of PFGE is that it is unable to separate very closely related strains because the low rate of genetic variation does not significantly impact the electrophoretic mobility of a restriction fragment.

During recent years the cost of whole genome sequencing (WGS) has decreased dramatically and the technology becomes increasingly available for routine use around the world. The speed of sequencing is decreasing from several days or weeks to perhaps hours for a bacterial genome in the near future. This combination of low cost and high speed opens an opportunity for WGS to become very useful and practical in bacterial infection studies including the routine use in diagnostic and public health microbiology.

A new study evaluates WGS for outbreak typing of S. enterica and compares it to results obtained using the conventional typing method, PFGE. The results show 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.

Evaluation of Whole Genome Sequencing for Outbreak Detection of Salmonella enterica. (2014) PLoS ONE 9(2): e87991. doi: 10.1371/journal.pone.0087991
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.

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