title: Analysis of Salmonella enterica serovar Typhimurium variable-number tandem-repeat data for public health investigation based on measured mutation rates and whole-genome sequence comparisons.
abstract: Variable-number tandem repeats (VNTRs) mutate rapidly and can be useful markers for genotyping. While multilocus VNTR analysis (MLVA) is increasingly used in the detection and investigation of food-borne outbreaks caused by Salmonella enterica serovar Typhimurium (S. Typhimurium) and other bacterial pathogens, MLVA data analysis usually relies on simple clustering approaches that may lead to incorrect interpretations. Here, we estimated the rates of copy number change at each of the five loci commonly used for S. Typhimurium MLVA, during in vitro and in vivo passage. We found that loci STTR5, STTR6, and STTR10 changed during passage but STTR3 and STTR9 did not. Relative rates of change were consistent across in vitro and in vivo growth and could be accurately estimated from diversity measures of natural variation observed during large outbreaks. Using a set of 203 isolates from a series of linked outbreaks and whole-genome sequencing of 12 representative isolates, we assessed the accuracy and utility of several alternative methods for analyzing and interpreting S. Typhimurium MLVA data. We show that eBURST analysis was accurate and informative. For construction of MLVA-based trees, a novel distance metric, based on the geometric model of VNTR evolution coupled with locus-specific weights, performed better than the commonly used simple or categorical distance metrics. The data suggest that, for the purpose of identifying potential transmission clusters for further investigation, isolates whose profiles differ at one of the rapidly changing STTR5, STTR6, and STTR10 loci should be collapsed into the same cluster.
authors: Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne, Parkville, Victoria, Australia.
authors: Karolina
authors: K
authors: Dimovski
authors: Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia.
authors: Hanwei
authors: H
authors: Cao
authors: Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia.
authors: Odilia L C
authors: OL
authors: Wijburg
authors: Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia.
authors: Richard A
authors: RA
authors: Strugnell
authors: Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia.
authors: Radha K
authors: RK
authors: Mantena
authors: Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne, Parkville, Victoria, Australia.
authors: Margaret
authors: M
authors: Whipp
authors: Microbiological Diagnostic Unit Public Health Laboratory, University of Melbourne, Parkville, Victoria, Australia.
authors: Geoff
authors: G
authors: Hogg
authors: Department of Microbiology and Immunology, University of Melbourne, Parkville, Victoria, Australia Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, Victoria, Australia kholt@unimelb.edu.au.
authors: Kathryn E
authors: KE
authors: Holt
mesh_terms: D016000:Cluster Analysis; D004269:DNA, Bacterial / Q000235:genetics*; D016680:Genome, Bacterial; D005838:Genotype; D018598:Minisatellite Repeats; D017720:Molecular Epidemiology / Q000379:methods; D058889:Molecular Typing / Q000379:methods*; D059645:Mutation Rate; D012480:Salmonella Infections / Q000453:epidemiology* / Q000382:microbiology; D012486:Salmonella typhimurium / Q000145:classification / Q000235:genetics*
chemical_list: D004269:DNA, Bacterial