Delineating community outbreaks of <em>Salmonella enterica</em> serovar Typhimurium using whole genome sequencing — ASN Events
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  2. Poster Session A
  3. Delineating community outbreaks of Salmonella enterica serovar Typhimurium using whole genome sequencing

Delineating community outbreaks of Salmonella enterica serovar Typhimurium using whole genome sequencing (#225)

Sophie Octavia 1 , Qinning Wang 2 , Mark M Tanaka 1 , Sandeep Kaur 1 , Vitali Sintchenko 2 3 , Ruiting Lan 1
  1. School of Biotechnology and Biomolecular Sciences, University Of New South Wales , SYDNEY , NSW, Australia
  2. Centre for Infectious Diseases and Microbiology–Public Health, Institute of Clinical Pathology and Medical Research, Westmead Hospital, Sydney
  3. Marie Bashir Institute for Infectious Diseases and Biosecurity, The University of Sydney, Sydney, NSW

Introduction. Salmonella enterica serovar Typhimurium is one of the most common foodborne pathogens in humans and farm animals in Australia. The phage type (defined by patterns of susceptibility to lysis by a set of bacteriophages) DT170 has been increasing steadily over recent years and became the most frequent phage type since 2004. In this study, isolates from five retrospective outbreaks (designated as Outbreak 1 to Outbreak 5) that have been investigated epidemiologically were examined using next generation sequencing (NGS).

Results. Fifty seven isolates representing both clinical samples from the infected individuals as well as the environmental samples from contaminated foods were sequenced. Heterogeneity in the S. Typhimurium outbreak isolates was found to be due to single nucleotide polymorphisms (SNPs). Most of the isolates confirmed epidemiologically to be involved in the outbreaks were either identical or differed by one to two SNPs with the exception of Outbreak 1. The mutation process was modelled using known mutation rates to derive a cut-off value for the number of SNP difference to rule-in or rule-out a case being part of an outbreak. For an outbreak with less than one month evolution time, the maximum number of SNP differences between isolates is two and four SNPs using the slowest and the fastest mutation rates respectively. NGS analysis ruled in isolates that were initially not considered to be linked with the outbreak, which increased the total outbreak size by 107%.

Conclusion. Our results showed that NGS increases the resolution of S. Typhimurium outbreak investigations and provides additional molecular evidence for an improved characterisation of community outbreaks of S. Typhimurium. It can also inform more targeted public health response by providing important supplementary evidence to rule-in and rule-out cases of disease associated with foodborne outbreaks of S. Typhimurium.

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