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

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Authors

Saltykova, Assia
Buytaers, Florence E.
Denayer, Sarah
Verhaegen, Bavo
Pierard, Denis
Roosens, Nancy H.C.
Marchal, Kathleen
De Keersmaecker, Sigrid C.J.

Journal Title

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Publisher

MDPI Publishing

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.

Description

Supplementary Materials: Figure S1. Strain-level metagenomic analysis of enriched minced meat sample (Mm24h) and an enriched minced meat sample that has been spiked with a pathogenic E. coli isolate TIAC1152 (spMm24h) using Sigma and Sparse. Figure S2. Unrooted cgMLST phylogenetic tree of the 728 complete E. coli assemblies used in the reference genome databases of Sigma and Sparse. Figure S3. Detailed report of E. coli virulence gene detection performed on the clusters detected by Sigma and Sparse in the spiked (spMm24h) and unspiked (Mm24h) enriched minced meat samples. Figure S4. Detailed report of E. coli virulence gene detection performed on the in silico spiked metagenomic samples containing the strain TIAC1152 at different coverages. Figure S5. Strain-level analysis of in silico spiked metagenomic samples containing different pathogenic E. coli strains: Sigma. Figure S6. Detailed report of E. coli virulence gene detection performed on the in silico spiked metagenomic samples containing different pathogenic E. coli strains at a ~5x coverage.

Keywords

Public health, Foodborne outbreak investigation, Strain-level metagenomics, Shiga toxin-producing Escherichia coli (STEC)

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Citation

Saltykova, A., Buytaers, F.E., Denayer, S. et al. 2020, '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', International Journal of Molecular Sciences, vol. 21, art. 5688, pp. 1-27.