Strain-Level Metagenomic Data Analysis of Enriched In Vitro and In Silico Spiked Food Samples: Paving the Way towards a Culture-Free Foodborne Outbreak Investigation Using STEC as a Case Study

Abstract

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample.

Application of a strain-level shotgun metagenomics approach on food samples: resolution of the source of a Salmonella food-borne outbreak

“…In our analysis, all isolates and metagenomics-derived strains were compared using SNP calling and reconstruction of a phylogenetic tree (). SNP calling offers the possibility of comparing the full genome and is considered more suited to use for metagenomics-derived strains [16]. The cluster corresponding to the hotel-school outbreak (represented in blue in ) includes the isolates from patients and suspicious food vehicles obtained by the NRC and NRL, as well as the two inferred strains obtained from direct sequencing of two food samples (suspect meal and sauce) using a shotgun metagenomics approach.…”

“…Obtaining strains from the metagenomics reads is necessary to mimic the recovery and characterization of an isolate with conventional methods. This was done for each metagenomic sample following a previously reported metagenomics strain-level analysis pipeline [16, 17]. After classification of the reads to a database of Salmonella genomes, 1 843 873 and 1 618 032 reads were classified as ASM303203v1 [ S.…”

“…metagenomics-derived strains [16]. The cluster corresponding to the hotel-school outbreak (represented in blue in Fig.…”

“…Until now, only a few studies have investigated the possibility of achieving strain-level characterization for pathogens in food samples; however, these did not link strains obtained from the food samples to isolates from the human cases, a prerequisite for the trace back of the outbreak [12][13][14][15]. We have previously developed such a metagenomics approach to be implemented for food-borne outbreak investigations [16,17] using artificially contaminated samples, targeting the Shiga toxin-producing Escherichia coli (STEC), and we were able to link it back to isolates from humans. This method has, however, not yet been implemented for another pathogen or during a real outbreak.…”

Direct identification and molecular characterization of zoonotic hazards in raw milk by metagenomics using Brucella as a model pathogen

“…We did not find any indications of a second Brucella strain in the milk sample after bioinformatics analysis with ConFindr [ 45 ] and sigma [ 46 ] (data not shown). Since RefSNPer might be unsuitable for distinguishing strains of the same species or genus in a mixed sample, we propose read extraction, for instance, with sparse and sigma as recently reported [ 47 ], before applying RefSNPer. We also tested two different assembly strategies for metagenomic sequence reads.…”