Using Whole Genome Sequencing to Provide Insight into the Epidemiology of Resistance and Virulence Genes in Listeria monocytogenes.

Introduction:  Listeria monocytogenes is a ubiquitous organism in the environment and a rare cause of human disease. Although its incidence is at least 100 times lower than those of other foodborne pathogens, such as Campylobacter and Salmonella, listeriosis is characterized by a high case-fatality rate which can exceed 30% percent in outbreak situations. Currently, every isolate in a food or clinical settings is considered problematic; even though some isolates are more likely to persist in a food environment and/or cause human disease. Many virulence and resistance genes have been linked to these features, but no large scale investigation has been conducted on the presence of these factors in isolates from different environments. Therefore, our knowledge on the frequency and importance of known virulence and resistance genes in L. monocytogenes is limited.

Purpose:  As more and more whole genome sequence (WGS) data become available from surveillance, this data can, as shown here, can be used to make an extensive study on the epidemiology of known resistance and virulence genes.

Methods:  Publically available sequence read sets of over 10,000 L. monocytogenes isolates were assembled on the BioNumerics Calculation Engine using SPAdes. A reference database was created with all known virulence and resistance genes, as well as genes determining serovars. This database was used to screen all assembled genomes for the presence of these genes, and to predict the serovar.

Results:  The BioNumerics® 7.6 software and its Calculation Engine offer a powerful platform on which WGS analysis can be performed and validated against traditional typing data, as well as phenotypic data. The genotyping tool provides the possibility to extract virulence- and antibiotic-related genomic signatures from WGS data.

Significance:  The virulence genes and resistance genes could be easily extracted and compared to the available metadata, providing insight in the presence and distribution of these genes within all publicly available NGS data.