Abstract
The current routine laboratory practices to investigate food samples in case of foodborne outbreaks still rely on attempts to isolate the pathogen in order to characterize it. We present in this study a proof of concept using Shiga toxin-producing Escherichia coli spiked food samples for a strain-level metagenomics foodborne outbreak investigation method using the MinION and Flongle flow cells from Oxford Nanopore Technologies, and we compared this to Illumina short-read-based metagenomics. After 12 h of MinION sequencing, strain-level characterization could be achieved, linking the food containing a pathogen to the related human isolate of the affected patient, by means of a single-nucleotide polymorphism (SNP)-based phylogeny. The inferred strain harbored the same virulence genes as the spiked isolate and could be serotyped. This was achieved by applying a bioinformatics method on the long reads using reference-based classification. The same result could be obtained after 24-h sequencing on the more recent lower output Flongle flow cell, on an extract treated with eukaryotic host DNA removal. Moreover, an alternative approach based on in silico DNA walking allowed to obtain rapid confirmation of the presence of a putative pathogen in the food sample. The DNA fragment harboring characteristic virulence genes could be matched to the E. coli genus after sequencing only 1 h with the MinION, 1 h with the Flongle if using a host DNA removal extraction, or 5 h with the Flongle with a classical DNA extraction. This paves the way towards the use of metagenomics as a rapid, simple, one-step method for foodborne pathogen detection and for fast outbreak investigation that can be implemented in routine laboratories on samples prepared with the current standard practices.
Highlights
Foodborne diseases represent a major burden worldwide (WHO, 2015)
We present in this study a proof of concept of shotgun metagenomics outbreak investigation performed after Oxford Nanopore Technologies (ONT) sequencing, combined with a new bioinformatics workflow adapted to long reads, to obtain the characterization of the foodborne pathogen at strain level in samples with various strains of the same pathogen (STEC)
Minced beef meat harboring a natural population of commensal E. coli bacteria and artificially contaminated with a low infection dose of Shiga toxin-producing E. coli (STEC) from a previous study (Buytaers et al, 2020) was used to evaluate the performance of MinION and Flongle sequencing compared to Illumina MiSeq sequencing on the same sample
Summary
Foodborne diseases represent a major burden worldwide (WHO, 2015). Foodborne pathogens can cause large outbreaks affecting multiple people sometimes in different regions. In case of an outbreak, the common practice of public health institutions is to investigate human cases and try to relate them to the contaminated food, in order to remove it from the food chain and prevent further contaminations. This process is called source attribution (EFSA, 2019a). Microbial risk assessment and hazard identification are conducted, and the pathogen does not need to be linked to patient’s data, but its characteristics could be added to a database in order to conduct retrospective studies and link related cases or serve as background to detect clusters and putative outbreaks (ECDC and EFSA, 2019)
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