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.
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.