P2-121 Investigating the Epidemiology of Resistance and Virulence Genes in Listeria monocytogenes Using Bionumerics® 7.6

Tuesday, July 11, 2017
Exhibit Hall (Tampa Convention Center)
Katleen Vranckx , Applied Maths NV , Sint-Martens-Latem , Belgium
Kyle Kingsley , Applied Maths Inc. , Austin , TX
Katrien De Bruyne , Applied Maths NV , Sint-Martens-Latem , Belgium
Hannes Pouseele , Applied Maths NV , Sint-Martens-Latem , Belgium
Introduction:   Listeria monocytogenes (Lmo), although an uncommon cause of illness in the general population, is an important pathogen in pregnant patients, neonates, elderly individuals, and immunocompromised individuals. Following considerable cost reductions, complete Lmo genome sequencing has dramatically increased the number of publically available genomes on the Sequence Read Archive (SRA) of NCBI. Rapid and automated processing of whole genome sequencing (WGS) data ensures a reliable and easy to follow workflow in routine molecular surveillance, reducing the time needed to detect and contain an outbreak.

Purpose: In this study, we compared two subsequent pipelines for high resolution WGS-based molecular typing.

Methods:  First, the BioNumerics® calculation engine was used to apply whole genome multilocus sequence typing (wgMLST) to WGS data from 10,000+ isolates, after which the alleles are downloaded to the BioNumerics®7.6 database and cluster analysis is performed. Second, clusters of closely related isolates from different food sources are further characterized by whole genome single-nucleotide polymorphism analysis (wgSNP) on the calculation engine. SNP variants are detected by mapping the WGS reads to a reference chosen from within the cluster to maximize the resolution.

Results:  We demonstrate that wgMLST is suitable for the analysis of very large (and growing) datasets, making it an appropriate technique for outbreak surveillance. The added resolution of wgSNP against an internal reference sequence increases the confidence in the detected clusters and supports epidemiologists in their source tracking efforts.

Significance: This software platform performs high-throughput wgMLST and wgSNP analyses whose results can be validated against traditional data such as MLST or PFGE. The platform’s integrated pipelines can provide robust, portable, high resolution and cost-effective molecular typing for food safety and public health monitoring programs.