Navigating Microbiological Food Safety in the Era of Whole-Genome Sequencing

Abstract

The epidemiological investigation of a foodborne outbreak, including identification of related cases, source attribution, and development of intervention strategies, relies heavily on the ability to subtype the etiological agent at a high enough resolution to differentiate related from nonrelated cases. Historically, several different molecular subtyping methods have been used for this purpose; however, emerging techniques, such as single nucleotide polymorphism (SNP)-based techniques, that use whole-genome sequencing (WGS) offer a resolution that was previously not possible. With WGS, unlike traditional subtyping methods that lack complete information, data can be used to elucidate phylogenetic relationships and disease-causing lineages can be tracked and monitored over time. The subtyping resolution and evolutionary context provided by WGS data allow investigators to connect related illnesses that would be missed by traditional techniques. The added advantage of data generated by WGS is that these data can also be used for secondary analyses, such as virulence gene detection, antibiotic resistance gene profiling, synteny comparisons, mobile genetic element identification, and geographic attribution. In addition, several software packages are now available to generate in silico results for traditional molecular subtyping methods from the whole-genome sequence, allowing for efficient comparison with historical databases. Metagenomic approaches using next-generation sequencing have also been successful in the detection of nonculturable foodborne pathogens. This review addresses state-of-the-art techniques in microbial WGS and analysis and then discusses how this technology can be used to help support food safety investigations. Retrospective outbreak investigations using WGS are presented to provide organism-specific examples of the benefits, and challenges, associated with WGS in comparison to traditional molecular subtyping techniques.

FIG 1

First-, second-, and third-generation sequencing platforms are currently available, and each has distinct advantages and disadvantages. First-generation sequencers, including the ABI capillary sequencer, are characterized by high accuracy and a relatively long read length; however, these systems are not amenable to high-throughput sequencing. Second-generation platforms include MiSeq, HiSeq, and NextSeq from Illumina, as well as Ion Torrent from Thermo Fisher Scientific. These platforms use massively parallel sequencing to achieve high throughput and have high base-calling accuracy; however, sequencing reads are short and this can result in split contigs in repetitive regions during sequence assembly. Third-generation sequencers, including PacBio from Pacific Biosciences and MinION, PromethION, and SmidgION from Oxford Nanopore Technologies, are able to sequence single-molecule templates, which results in very long read length at a high throughput. Third-generation sequencers have a very high error rate relative to the other technologies. Currently, the optimal sequencing platform is highly dependent on the desired applications.

FIG 2

Analysis of SNP variation within a genome sequence can be used to compare isolates on a phylogenetic basis and draw conclusions about the relatedness of strains. However, there are various methods of detecting SNPs, and different methodologies can result in different conclusions. After sequencing, genomes can be assembled by either a reference-guided or a de novo approach. A common method of SNP detection based on a reference-guided assembly is to assemble sequencing reads to the reference genome and then detect SNPs based on nucleotide differences between the reference and the assembly. After de novo assembly, a commonly used approach is to use a concatenated sequence of each of the core genes in a genome and call SNPs based on this pseudoreference genome. The optimal SNP detection approach will depend on the desired application of the data. Reference-guided assembly is a much less computationally intensive method. However, in a reference-guided assembly, reads from regions in the genome being assembled that are not present in the reference genome will be discarded. In addition, regions present in the reference genome that are not present in the genome being assembled will result in alignment gaps.

FIG 3

Pangenome, core genome, and accessory genome are commonly used terms in genomics. Pangenome refers to all of the genes that occur in a given phylotype. For example, each gene identified in any L. monocytogenes genome is part of the L. monocytogenes pangenome. In this visualization, genes 1 to 7 are part of the pangenome. The core genome is defined as all of the genes that are present in all of the members of a given phylotype. In this example, genes 1, 2, and 7 are all part of the core genome. The core genome will always be smaller than the pangenome. The term accessory genome is used to refer to genes present in an organism, or group of organisms, that are unique to that organism or group. Accessory genes are part of the pangenome but not the core genome. In the image, genes 4 and 6 are part of the accessory genome of group 2, gene 5 is the accessory genome of group 1, gene 3 is the accessory genome of group 4, and group 1 does not have any accessory genes.

FIG 4

Past, present, and potential future workflows of pathogen detection by WGS and traditional subtyping techniques. Although times are approximate and vary according to the pathogen being analyzed, in all instances, WGS offers substantial time savings in comparison to traditional techniques. Future techniques using culture-independent metagenomic and high-throughput RNA sequencing technologies have the potential to offer faster detection times. SNV, SNP variation.