Original Research ARTICLE
Comparative Analysis of Tools and Approaches for Source Tracking Listeria monocytogenes in a Food Facility Using Whole-Genome Sequence Data
- 1Cornell University, United States
- 2Nestlé Research Center, Switzerland
As WGS is increasingly used by food industry to characterize pathogen isolates, users are challenged by the variety of analysis approaches available, ranging from methods that require extensive bioinformatics expertise to commercial software packages. This study aimed to assess the impact of analysis pipelines (i.e., different hqSNP pipelines, a cg/wgMLST pipeline) and the reference genome selection on analysis results (i.e., hqSNP and allelic differences as well as tree topologies) and conclusion drawn. For these comparisons, whole genome sequences were obtained for 40 Listeria monocytogenes isolates collected over 18 years from a cold-smoked salmon facility and 2 other isolates obtained from different facilities as part of academic research activities; WGS data were analyzed with three hqSNP pipelines and two MLST pipelines. After initial clustering using a k-mer based approach, hqSNP pipelines were run using two types of reference genomes: (i) closely-related closed genomes (“closed references”) and (ii) high-quality de novo assemblies of the dataset isolates (“draft references”). All hqSNP pipelines identified similar hqSNP difference ranges among isolates in a given cluster; use of different reference genomes showed minimal impacts on hqSNP differences identified between isolate pairs. Allelic differences obtained by wgMLST showed similar ranges as hqSNP differences among isolates in a given cluster; cgMLST consistently showed fewer differences than wgMLST. However, phylogenetic trees and dendrograms, obtained based on hqSNP and cg/wgMLST data, did show some incongruences, typically linked to clades supported by low bootstrap values in the trees. When a hqSNP cutoff was used to classify isolates as “related” or “unrelated”, use of different pipelines yielded a considerable number of discordances; this finding supports that cut-off values are valuable to provide a starting point for an investigation, but supporting and epidemiological evidence should be used to interpret WGS data. Overall, our data suggest that cgMLST-based data analyses provide for appropriate subtype differentiation and can be used without the need for preliminary data analyses (e.g., k-mer based clustering) or external closed reference genomes, simplifying data analyses needs. hqSNP or wgMLST analyses can be performed on the isolate clusters identified by cgMLST to increase the precision on determining the genomic similarity between isolates.
Keywords: Listeria monocytogenes (L. monocytogenes), Whole genome sequence (WGS), source tracking, high quality single nucleotide polymorphism (hqSNP), whole genome MLST (wgMLST), core genome MLST (cgMLST), BioNumerics, CFSAN pipeline, Lyve-SET, Smoked salmon
Received: 03 Jan 2019;
Accepted: 15 Apr 2019.
Edited by:Michael Gänzle, University of Alberta, Canada
Reviewed by:Jinshui Zheng, Huazhong Agricultural University, China
Alexandre LECLERCQ, Institut Pasteur, France
Copyright: © 2019 Orsi, Jagadeesan, Baert and Wiedmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Renato H. Orsi, Cornell University, Ithaca, United States, email@example.com
Dr. Martin Wiedmann, Cornell University, Ithaca, United States, firstname.lastname@example.org