Norovirus transfer between foods and food contact materials

Human infective noroviruses (NoVs) are a worldwide leading cause of foodborne illness and are frequently spread via infected food handlers preparing and manipulating food products such as deli sandwiches. The objective of the current study was to determine the efficiencies whereby NoV could be transferred between surfaces associated with the preparation of manually prepared foods such as deli sandwiches. Nonfood surfaces included gloves and stainless steel discs, and boiled ham, lettuce, and a sandwich bun were the ingredients of the deli sandwich. Both NoV GII.4 and the murine NoV 1 (MNV-1, a cultivable human NoV surrogate) were included in the presented study. Transfer of NoV GII.4 and MNV-1 between surfaces was performed by pressing an inoculated donor surface against an acceptor surface. To evaluate the effect of subsequent contact, donor surfaces were pressed a second time to an identical acceptor surface. Subsequently, NoV GII.4 and MNV-1 were detected using real-time reverse transcription PCR assays and plaque assays, respectively. Transfer of both viruses from gloves to stainless steel was inefficient, and virus transfer from food products to stainless steel occurred with inure variability for NoV GII.4 than for MNV-1. Virus transfer from the stainless steel discs to the gloves was substantially more efficient than from the gloves to the stainless steel. NoV GII.4 and MNV-1 transfer from food products to the doves occurred with varying efficiencies, although this variation was more evident for NoV GII.4. The MNV-1 inoculum was significantly less efficiently transferred to the acceptor surface at the second contact, which was not the case for NoV GII.4. The obtained transfer efficiency data may provide insights into the transfer of NoV during preparation of foods and can be included in risk assessment models describing the transmission of NoVs in this context

Comparative Analysis of Tools and Approaches for Source Tracking Listeria monocytogenes in a Food Facility Using Whole-Genome Sequence Data

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

The GEN-ERA toolbox: unified and reproducible workflows for research in microbial genomics.

peer reviewed[en] BACKGROUND: Microbial culture collections play a key role in taxonomy by studying the diversity of their strains and providing well-characterized biological material to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (e.g., yeasts). FINDINGS: This public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenomic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. TECHNICAL DETAILS: Nextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository (https://github.com/Lcornet/GENERA). All the workflows are based on Singularity containers to increase reproducibility. TESTING: The toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the Gloeobacterales order, the first group to diverge in the evolutionary tree of Cyanobacteria. CONCLUSION: The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be used by all researchers interested in microbial taxonomy, as exemplified by our case study on Gloeobacterales