Genome-Enabled Molecular Subtyping and Serotyping for Shiga Toxin-Producing Escherichia coli

Abstract

Foodborne pathogens are a major public health burden in the United States, leading to 9.4 million illnesses annually. Since 1996, a national laboratory-based surveillance program, PulseNet, has used molecular subtyping and serotyping methods with the aim to reduce the burden of foodborne illness through early detection of emerging outbreaks. PulseNet affiliated laboratories have used pulsed-field gel electrophoresis (PFGE) and immunoassays to subtype and serotype bacterial isolates. Widespread use of serotyping and PFGE for foodborne illness surveillance over the years has resulted in the accumulation of a wealth of routine surveillance and outbreak epidemiological data. This valuable source of data has been used to understand seasonal frequency, geographic distribution, demographic information, exposure information, disease severity, and source of foodborne isolates. In 2019, PulseNet adopted whole genome sequencing (WGS) at a national scale to replace PFGE with higher-resolution methods such as the core genome multilocus sequence typing. Consequently, PulseNet's recent shift to genome-based subtyping methods has rendered the vast collection of historic surveillance data associated with serogroups and PFGE patterns potentially unusable. The goal of this study was to develop a bioinformatics method to associate the WGS data that are currently used by PulseNet for bacterial pathogen subtyping to previously characterized serogroup and PFGE patterns. Previous efforts to associate WGS to PFGE patterns relied on predicting DNA molecular weight based on restriction site analysis. However, these approaches failed owing to the non-uniform usage of genomic restriction sites by PFGE restriction enzymes. We developed a machine learning approach to classify isolates to their most probable serogroup and PFGE pattern, based on comparisons of genomic k-mer signatures. We applied our WGS classification method to 5,970 Shiga toxin-producing Escherichia coli (STEC) isolates collected as part of PulseNet's routine foodborne surveillance activities between 2003 and 2018. Our machine learning classifier is able to associate STEC WGS to higher-level serogroups with very high accuracy and lower-level PFGE patterns with somewhat lower accuracy. Taken together, these classifications support the ability of public health investigators to associate currently generated WGS data with historical epidemiological knowledge linked to serogroups and PFGE patterns in support of outbreak surveillance for food safety and public health.

Development of Single Nucleotide Polymorphism (SNP)-Based Triplex PCR Marker for Serotype-specific Escherichia coli Detection

“…SNPs are one of the most useful molecular markers because of their stability and abundance in all organisms. The advent of whole-genome sequencing (WGS) technologies [7,8], wider open-source websites, i.e., PubMLST [9], and the availability of reference genome sequences of many bacteria has allowed for the wider implementation of SNP-based detection [10][11][12]. Nucleotide substitutions during DNA replication of bacteria, with mutation rates of approximately 10 −9 changes per nucleotide per generation, are important biologically informative markers in bacteria [13,14].…”

“…Currently, there several molecular techniques are commonly used for food-borne disease surveillance and the subtyping of E. coli, such as the PulseNet [37,38], Multilocus Sequence Typing [39], and Multilocus Variable-Number Tandem-Repeat Analysis [40] techniques. PulseNet is based on the characterization of whole bacterial genomes using enzymatic digestion patterns that are separated by pulsed-field gel electrophoresis [12,20,38]. This technique can be biased because of the subjective interpretation of band patterns [37,41].…”