Genome Typing and Epidemiology of Human Listeriosis in New Zealand, 1999 to 2018

Abstract

This study describes the epidemiology of listeriosis in New Zealand between 1999 and 2018 as well as the retrospective whole-genome sequencing (WGS) of 453 Listeria monocytogenes isolates corresponding to 95% of the human cases within this period. The average notified rate of listeriosis was 0.5 cases per 100,000 population, and non-pregnancy-associated cases were more prevalent than pregnancy-associated cases (averages of 19 and 5 cases per annum, respectively). ABSTRACT This study describes the epidemiology of listeriosis in New Zealand between 1999 and 2018 as well as the retrospective whole-genome sequencing (WGS) of 453 Listeria monocytogenes isolates corresponding to 95% of the human cases within this period. The average notified rate of listeriosis was 0.5 cases per 100,000 population, and non-pregnancy-associated cases were more prevalent than pregnancy-associated cases (averages of 19 and 5 cases per annum, respectively). WGS data was assessed using multilocus sequencing typing (MLST), including core-genome and whole-genome MLST (cgMLST and wgMLST, respectively) and single-nucleotide polymorphism (SNP) analysis. Thirty-nine sequence types (STs) were identified, with the most common being ST1 (21.9%), ST4 (13.2%), ST2 (11.3%), ST120 (6.1%), and ST155 (6.4%). A total of 291 different cgMLST types were identified, with the majority (n\u2009=\u2009243) of types observed as a single isolate, consistent with the observation that listeriosis is predominately sporadic. Among the 49 cgMLST types containing two or more isolates, 18 cgMLST types were found with 2 to 4 isolates each (50 isolates in total, including three outbreak-associated isolates) that shared low genetic diversity (0 to 2 whole-genome alleles), some of which were dispersed in time or geographical regions. SNP analysis also produced results comparable to those from wgMLST. The low genetic diversity within these clusters suggests a potential common source, but incomplete epidemiological data impaired retrospective epidemiological investigations. Prospective use of WGS analysis together with thorough exposure information from cases could potentially identify future outbreaks more rapidly, including those that may have been undetected for some time over different geographical regions.