Effective Surveillance Using Multilocus Variable-Number Tandem-Repeat Analysis and Whole-Genome Sequencing for Enterohemorrhagic Escherichia coli O157

Abstract

Due to the potential of enterohemorrhagic Escherichia coli (EHEC) serogroup O157 to cause large food borne outbreaks, national and international surveillance is necessary. For developing an effective method of molecular surveillance, a conventional method, multilocus variable-number tandem-repeat analysis (MLVA), and whole-genome sequencing (WGS) analysis were compared. WGS of 369 isolates of EHEC O157 belonging to 7 major MLVA types and their relatives were subjected to comprehensive in silico typing, core genome single nucleotide polymorphism (cgSNP), and core genome multilocus sequence typing (cgMLST) analyses. The typing resolution was the highest in cgSNP analysis. However, determination of the sequence of the mismatch repair protein gene mutS is necessary because spontaneous deletion of the gene could lead to a hypermutator phenotype. MLVA had sufficient typing resolution for a short-term outbreak investigation and had advantages in rapidity and high throughput. cgMLST showed less typing resolution than cgSNP, but it is less time-consuming and does not require as much computer power. Therefore, cgMLST is suitable for comparisons using large data sets (e.g., international comparison using public databases). In conclusion, screening using MLVA followed by cgMLST and cgSNP analyses would provide the highest typing resolution and improve the accuracy and cost-effectiveness of EHEC O157 surveillance.IMPORTANCE Intensive surveillance for enterohemorrhagic Escherichia coli (EHEC) serogroup O157 is important to detect outbreaks and to prevent the spread of the bacterium. Recent advances in sequencing technology made molecular surveillance using whole-genome sequence (WGS) realistic. To develop rapid, high-throughput, and cost-effective typing methods for real-time surveillance, typing resolution of WGS and a conventional typing method, multilocus variable-number tandem-repeat analysis (MLVA), was evaluated. Nation-level systematic comparison of MLVA, core genome single nucleotide polymorphism (cgSNP), and core genome multilocus sequence typing (cgMLST) indicated that a combination of WGS and MLVA is a realistic approach to improve EHEC O157 surveillance.

Keywords: core genome SNP analysis; core genome multilocus sequence typing; enterohemorrhagic Escherichia coli; multilocus variable-number tandem-repeat analysis; whole-genome sequencing.

FIG 1

Deleted region around mutS in the isolates used in this study. Closed arrows indicate the presence of the gene, and open arrows indicate the absence of the gene. The genes where the junction is located are highlighted in black. The number represents the nucleotide position in the reference strain Sakai.

FIG 2

Phylogenetic tree of all strains used in this study. The tree was constructed by the maximum likelihood method with 1,000 bootstrap replicates (42) using a concatenated SNP alignment after removal of recombinant regions. The color of the branch and of the first ring indicates the MLVA group. The color of the second ring indicates the locus variant (LV) in each MLVA group: SLV, single locus variants; DLV, double locus variants; and TLV, triple locus variants.

FIG 3

Histogram of pairwise SNP distances between the epi-linked isolates.

FIG 4

Box plots of pairwise SNPs stratified by MLVA allele difference. From the pairwise data matrix of all the isolates, two distance values (cgSNP and MLVA allele difference) were generated. This figure illustrates the relationship of the values. The x axis represents the number of MLVA allele differences. In each value, the number of pairwise cgSNP loci was shown as a box plot (y axis). Circles and crosses represent outliers and means, respectively. The first and third quartile and the maximum and minimum values without outliers are shown. Prop., proportion.

FIG 5

Histogram showing the relationship between the number of SNP loci and interval of isolation date. This figure was generated using cgSNP results of the isolate pairs that have zero or one MLVA allele difference.

FIG 6

Box plots of pairwise difference of cgMLST allele stratified by MLVA allele difference. From the pairwise data matrix of all the isolates, two distance values (cgMLST and MLVA allele difference) were generated. This figure illustrates the relationship of the values. The x axis represents the number of MLVA allele differences. In each value, the number of cgMLST allele differences was shown as a box plot (y axis). Circles and crosses represent outliers and means, respectively. The first and third quartile and the maximum and minimum values without outliers are shown. Prop., proportion.

FIG 7

Histogram of pairwise cgMLST allele differences between epi-linked isolates.

FIG 8

Schematic view of EHEC O157 surveillance workflow using MLVA and WGS analyses. The colored ellipses represent EHEC O157 isolates of different MLVA types. The maximum time required for the analysis is shown on the right.