Foodborne pathogens (FBPs) cause foodborne diseases (FBDs) either directly (by infectious agents) or indirectly (by toxic metabolites, i.e., bacterial toxins and mycotoxins; EMAN, 2015; Martinovic et al., 2016) and can have devastating health and economic consequences in both developed and developing countries (Pires et al., 2012; EFSA, 2014; ECDC, 2015a; Henao et al., 2015). A major fraction of FBDs are diarrheal diseases, with particularly high impact on children (Pires et al., 2015). Typical FBPs are bacteria and several viruses, but also parasites and some fungi can cause FBDs.

Microbiological analyses of foods are carried out for verification and control, surveillance, investigation of disease outbreaks or sporadic cases, or for research (Figure 1). Time and labor consuming culture dependent methods, including enrichment and/or selective steps, are often used. Selective enrichment may be crucial to capture the species of interest. Isolation of the pathogen of interest is an optimal starting point for further characterization and research, and contributes to ensure that public health agencies can perform their basic mandate of FBD surveillance and response (Forbes et al., 2017). Over the last decades traditional culture dependent methods have gradually been complemented with molecular analytical methods. Speed and costs are the main drivers of this development. Concerns about the possible lack of necessary sensitivity, specificity or correspondence between molecular findings and presence or absence of viable, pathogenic microorganisms are among factors restraining this development. Polymerase chain reaction (PCR) analysis of enriched samples from food is established for a range of FBPs improving the efficiency of screening of samples, but viable microorganisms are, in most cases, still required for definitive confirmation of positive samples. High throughput sequencing (HTS) based workflows now gradually emerge as options also for routine applications to FBP detection and characterization (Figure 1; Table 1).

HTS can generate thousands to millions of sequence reads, and up to several hundred billion base pairs (bp) of sequence information per sample. The read length, error rate and number of reads and sequenced bases vary substantially. Selective amplification (targeted) and non-selective, random (shotgun) approaches exist. The number of high quality genomes for the most important food pathogens is already high and rapidly growing, in part benefiting from the relatively small genome sizes of most microorganisms (≤100 Mbp). In cases where a sequenced reference genome is unavailable it is necessary to perform de novo sequencing and genome assembly (Figures 2, 3). De novo assembly to obtain a draft quality genome based on high quality, short-read (< 250 bp) sequence data from single, cultured isolates is complex, but can be (semi-)automated (Emond-Rheault et al., 2017). Closing a genome often requires highly skilled bioinformaticians and long sequence reads (up to several kbp) or PCR and Sanger sequencing of the unknown gaps in scaffolds (Goodwin et al., 2015; Loman et al., 2015; Rhoads and Au, 2015). Even this can sometimes be (semi-)automated (Emond-Rheault et al., 2017). Analysis of re-sequencing data employing a mapping strategy (Figures 2, 3) is less complex, and can be (semi-)automated. It is, however, time and computer intensive and interpretation of the mapped data can be quite challenging (Goodwin et al., 2015; Loman et al., 2015; Rhoads and Au, 2015). Alignment-independent comparisons using statistic (probabilistic) approaches (k-mers; Figures 2, 3) can be much faster and automated and emerge as an attractive option at the cost of detailed resolution (Ondov et al., 2016). The optimal HTS strategy is therefore purpose dependent. Large sequence databases including thousands of completely sequenced genomes are now available, facilitating identification of common as well as rare but potentially important genes by mapping of sequence reads to the database sequences. Low error rate, long reads and high coverage facilitate sequence assembly (Figures 2, 3). Altogether, this has expanded our ability to gain a more comprehensive insight into the genetics of individual strains or species, and the microbiota (microbial species composition) and microbiome (functional gene pool of microbiota) of a very broad spectrum of sample types, including environmental, food and clinical samples.

Tremendous progress in HTS technology developments has been made during the recent decade and this review will not discuss per se the sequencing technologies, because several excellent reviews are available (Loman and Pallen, 2015; Goodwin et al., 2016). Understanding advantages and disadvantages associated with the different HTS platforms, (in particular the read length, read number, sequencing error rate and costs) may, however, help readers to better understand the choices made in the following cited examples.

Illumina sequencing is currently the prevailing HTS technology and also offers the highest fidelity. It provides very large data sets of relatively short reads (100–300 bp) at an error rate per sequenced base of approximately 1%. Roche 454 (phased out in 2016) was the previously dominating technology and provided smaller data sets of longer reads (≤700 bp) with higher error rates. Much of the initial HTS literature reports on Roche 454 data (Liu et al., 2012; Mayo et al., 2014; van Dijk et al., 2014). Long read sequences, up to several kbp, can be obtained using other platforms (see below) but the error rates are high (5–40%). All HTS technologies allow for assembly of good quality draft bacterial genomes with up to 100 contigs. A fully closed genome sequence is usually obtained through application of different technologies combining the accuracy of short reads with the ability of long reads to span gaps in assembly scaffolds (Tallon et al., 2014). Due to the capacity of the Single Molecule Real Time sequencing technology of Pacific Biosciences to provide reads of 1–10 kbp, this technology has recently been popular for de novo assembly of completely closed genomes, long repetitive sequences, plasmids and bacteriophages (Rhoads and Au, 2015). The Oxford Nanopore MinION has competitive potential, as it is claimed to be able to provide even longer reads in real-time at low costs (Goodwin et al., 2015; Loman et al., 2015; Quick et al., 2015). The short read technologies generally provide significantly higher coverage than the long read technologies, at much lower costs per sequenced base.

Foods, feeds, clinical and environmental samples harbor complex and diverse microbial communities. We use the expression “metagenomics” for analysis of such samples by HTS. For further distinction we use the term “shotgun metagenomics” for whole genome sequencing (WGS) or whole-sample-DNA based metagenomics (Figure 4). Targeted amplicon analysis of various ribosomal RNA coding DNA (rDNA) and other conserved markers is, as suggested by Marchesi and Ravel (2015), denoted “metataxonomics” throughout this review. “Metabarcoding” is a related term sometimes used in the literature (e.g., Jones et al., 2013; Staats et al., 2016). Shotgun metagenomics, and “metatranscriptomics” (i.e., shotgun sequencing of RNA transcripts) enables full genome or transcriptome sequencing, respectively, in a complex sample. RNA based shotgun metagenomics (i.e., sequencing of all RNA in the sample) and metatranscriptomics are often combined into a single approach “RNAseq”. We therefore only refer to metatranscriptomics or RNA based shotgun metagenomics when we need to distinguish from RNAseq.

Metataxonomics is generally more sensitive than shotgun metagenomics, due to enrichment of targets by amplification. Metataxonomics is, due to the targeted enrichment, prone to bias and may fail to detect novel variants of relevant targets, e.g., 16S rDNA with mismatches in the PCR primer binding motifs or genes involved in previously unknown but relevant biosynthetic pathways. Shotgun metagenomics on the other hand presumably has a low bias, is independent of a priori knowledge of target sequences, and can be used to monitor alterations in the microbiome that may not be evident from the composition of the microbiota. RNAseq is biased by the reverse transcription used to synthesize cDNA prior to sequencing, possibly affecting detectability of RNA viruses. Metatranscriptomics is further biased by RNA transcription rates. RNAseq is otherwise comparable to shotgun metagenomics. The main drawbacks of shotgun metagenomics are: several logs higher (inferior) limit of detection (LOD; Figure 4) and complex data analyses (bioinformatics) that, at present, are difficult to automate/standardize. The composition of the sample's microbiota and/or the causative agent is typically not well known in advance (limiting the possibility to apply targeted approaches such as metataxonomics), and relevant reference sequences may be lacking from public databases (limiting the possibility to perform mapping and assign function to sequences).

Many FBPs are well-studied and the use of genomic data and large scale WGS have become important for studies in epidemiology, evolution, surveillance and outbreak investigations. On August 16th 2017, there were 104,667 (84,726) genome assemblies and 8,119 (6,286) complete bacterial genomes (number in brackets was 7 months earlier) available from National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/genome/browse/). The contribution of bacteriophages to bacterial genome size, evolution and virulence is very significant (Brüssow et al., 2004; Salmond and Fineran, 2015). Shotgun metagenomics can provide new insights into these aspects and the possible relevance of phages to FBDs (Nieuwenhuijse and Koopmans, 2017).

Bacterial FBPs are often present in foods in low numbers heterogeneously spread in the product. Consequently, ability to detect very low levels of FBPs in various food sources is important. Current detection methods normally involve one or more enrichment steps, screening (e.g., by PCR), followed by an isolation step. Isolation of a FBP from a food matrix may be challenging due to low recovery of isolates;—a sample can be positive with PCR screening, yet isolation of a corresponding bacterial strain may not be achieved. Some of the most important bacterial FBPs are Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC) causing many outbreaks and sporadic cases with severe or fatal outcome (Crim et al., 2014; Astridge et al., 2015; EFSA, 2015b). These three FBPs are used in the following as illustrative examples. For Salmonella infections contaminated food sources typically include poultry, eggs, swine and ready-to-eat foods, and affect people at all ages (EFSA, 2015b). L. monocytogenes is commonly detected in ready-to-eat foods such as smoked fish and soft cheeses and often affect elderly, immunocompromised patients, pregnant women and have high mortality rate (EFSA, 2015b). The main food vehicles of STEC infections are bovine meat followed by vegetables and juice (EFSA, 2013, 2015b). STEC can cause severe complications like acute kidney failure (hemolytic uremic syndrome) and often affects children under the age of five, elderly and immunocompromised people (Davis et al., 2014).

For outbreak investigation the pathogen must be linked to the correct food product (source of infection). Food producers on the other hand, need to determine if their products or production line is contaminated, how an unwanted pathogen entered their production facilities, and/or if it is a persistent household strain (Figure 1; Table 1). Several strategies can be applied for comparison of isolates, e.g., pulsed-field gel electrophoresis (PFGE), multi-locus variable number of tandem repeats analysis (MLVA) and multi-locus sequence typing (MLST). For many FBPs the traditional typing or subtyping offers too low (phylogenetic) resolution to distinguish closely related but distinct strains. High resolution is required to discriminate parallel outbreaks or to separate sporadic cases from an outbreak, but also to assess if a reemerging contamination problem is caused by a persistent strain or reintroduction of similar strains.

WGS will provide highly discriminatory data for subtyping of strains by single nucleotide polymorphism (SNP) analysis or extended (core genome or whole genome) MLST (cgMLST, wgMLST) for strain comparison for outbreak investigations and surveillance purposes. Among possibilities beyond traditional molecular fingerprinting is the reanalysis of complete genome sequences when subsets (e.g., MLST) provide insufficient information/resolution. Polymorphisms can be investigated with or without mapping of the HTS data to a reference genome (Figures 2, 3). WGS-based analyses can also aid in the identification of other relevant factors such as virulence and antibiotic resistance genes (Joensen et al., 2014; Holmes et al., 2015; Octavia et al., 2015; Forbes et al., 2017). By standardizing the workflow of the actual HTS and bioinformatics analysis, this can take only a few days. However, comparable data and standardized protocols and pipelines are required, a topic discussed in further detail below.

Isolates are often unavailable and may be difficult to obtain. Most foods harbor complex and composite microbial communities. Contaminating pathogens are often heterogeneously dispersed and represent a minority of the microorganisms present in the sometimes complex food sample. Metagenomics approaches offer the opportunity to investigate the composition of microbes in food matrices in toto without selective isolation, including the detection of non-viable and “viable but not cultivable” microbes (Bergholz et al., 2014), and will capture a broader range of the microbial community than classical microbiology. Most of the numerous HTS metagenomics studies report on 16S rDNA analysis, i.e., what we refer to as metataxonomics. This approach has proven useful for identification of bacteria, phylogenetic studies and characterization of bacterial communities in different foods, water and other environments (Mayo et al., 2014; Kergourlay et al., 2015; Tan et al., 2015). However, the 16S rDNA has limited resolution power and cannot be used to detect non-bacterial taxa. Reliable 16S rDNA based classification of bacteria rarely extends beyond phylum, group or genus level (Livezey et al., 2013). A few FBPs can be identified by 16S rDNA sequencing, but only exceptionally at the species level and never to pathotype. All Salmonella species are considered as pathogens (Jarvis et al., 2015; Zhang et al., 2015), but only two of the Listeria species known so far are pathogenic, i.e., L. monocytogenes and L. ivanovii. These species are partly distinguishable based on 16S rDNA sequence. In contrast, 16S rDNA sequences cannot distinguish STEC from non-diarrheagenic or commensal E. coli. For identification of STEC, detection of virulence-associated genes including the Shiga toxin- and intimin-encoding stx and eae genes will be essential for meaningful strain typing. Database limitations (not all relevant taxa and haplotypes represented + possible erroneous sequences and taxonomic annotations) and the common use of only a subsection of the 16S rDNA (missing or covering only some of the inter-strain variation) further reduces the fitness of metataxonomics for FBP detection (Adeolu et al., 2016; Singer et al., 2016).

Shotgun metagenomics of the entire DNA present in a sample offers a more comprehensive insight into the microbial diversity of a sample with regard to the richness of microbial taxa at all levels, or with regard to the presence of gene families or biomarkers in general (Ferri et al., 2015; Blagden et al., 2016; Ranjan et al., 2016). Shotgun metagenomic approaches used for improved FBP detection have been tested in a few studies. Bioinformatics methods to distinguish two genomes of the same species in a complex sample are needed, but it seems possible to determine whether one or more FBP strains is involved (Leonard et al., 2016). In investigations where it is essential to detect a specific organism assumed present in low numbers in a complex matrix, culture-based bacterial enrichment is still necessary. Then, inevitably, an enrichment bias of the composition of the bacterial community relative to the original sample will emerge.

Complete genome assemblies for 7,409 viruses were available from NCBI on August 16th 2017 (an increase of nearly 500 in 7 months). Viruses lack the genes necessary to transcribe protein-coding genes and replicate and reproduce, and therefore depend completely on the biological machinery of their host cells (Moreira and Lopez-Garcia, 2009). They are highly diverse and lack a common genetic constitution such as genes coding for ribosomal RNAs (Moreira and Lopez-Garcia, 2009), limiting the metataxonomic options. Some viruses can be cultured, but cultivation require advanced protocols, suitable host cells for propagation, and is time-consuming (Rodríguez-Lazaro et al., 2012). Viruses play a dual role in food pathogenesis. Some viruses like bacteriophages can affect the virulence and population structure of microorganisms (Hayes et al., 2017). Other viruses can be FBPs themselves (Newell et al., 2010; EFSA, 2011). For a virus to be transmissible through foods, it must have some environmental stability and remain infectious for some time on or in a food matrix. As viruses are unable to replicate in the food matrix they must have a low infectious dose. Foodborne viruses are commonly shed in large amounts by a fecal route and viral contamination of foods is primarily via human fecal material (Rodríguez-Lazaro et al., 2012). Normal cooking or frying inactivates viruses. Food sources of infections are typically raw foods like fresh produce, soft berry fruits, herbs, shellfish, ready-to-eat products in general and undercooked meat or foods served cold that are contaminated by an infected food-handler post cooking (Halliday et al., 1991; Hedberg and Osterholm, 1993; de Wit et al., 2003; Fiore, 2004; EFSA, 2011, 2015a). Most viruses that can infect through a foodborne route can also utilize a person-to-person infectious route. Many outbreaks, potentially the majority, are simultaneously propagated via combined person-to-person and foodborne infectious routes. Food and water associated transmission is also suspected to enhance the spread of zoonotic viruses and facilitates the occurrence of zoonotic events, e.g., through the handling of bushmeats (Nieuwenhuijse and Koopmans, 2017 and refs. therein). The viral FBP load is often low and heterogeneously dispersed in the food matrix while it is high in clinical patients. It is not trivial to distinguish between foodborne and person-to-person infections, unless initial cases are identified and analyzed. The reporting and surveillance of foodborne viruses is limited, and disease symptoms can be very similar for some viruses and for other viruses differ substantially between infected individuals. All this results in low documentation of the real impact and diversity of foodborne viruses (Nieuwenhuijse and Koopmans, 2017).

The most notable FBP viruses are norovirus (NoV), hepatitis A virus (HAV) and hepatitis E virus (HEV) which are all positive-sense, single-stranded, non-enveloped RNA viruses, and the double-stranded RNA rotavirus (RV) (Newell et al., 2010; EFSA, 2011). Severe acute and Middle East respiratory syndrome (SARS and MERS) and Ebola viruses are examples of other (zoonotic) RNA viruses suspected to be transmissible via food (Newell et al., 2010; Nieuwenhuijse and Koopmans, 2017 and refs. therein). Most FBP viruses are RNA viruses and require special sample processing and nucleic acid extraction methods in contrast to the vast majority of bacteriophages, which are double-stranded DNA viruses.

Viruses, and in particular RNA viruses, evolve rapidly, both via genetic drift and in response to active selection pressures (Holland et al., 1982). Detection and genotyping with PCR approaches, including metataxonomic HTS approaches, can therefore fail. Particular sample processing steps can significantly improve the probability of detection, e.g., by concentration of the viruses and/or removal of interfering substances [reviewed in Hartmann and Halden (2012); see also (EFSA, 2011)]. Detailed understanding of virus stability, inactivation times and temperatures is lacking due to limitations in model systems (Cook, 2013).

On August 16th 2017, there were 2,515 genome assemblies and 29 complete fungal genomes available from NCBI. So far, there are very few examples of application of HTS to studies of epidemiology and virulence of fungal FBPs (Billmyre et al., 2014; Lee et al., 2014; Litvintseva et al., 2014, 2015; Vaux et al., 2014). Only two published studies relate to specific cases of fungal food pathogenesis (Lee et al., 2014; Vaux et al., 2014). Molecular typing of fungal isolates and mycobiotas in relation to FBD is almost exclusively metataxonomic, but usually limited to PCR amplification of one or a few genetic loci followed by Sanger sequencing with exceptional examples of MLST analysis in the published literature (e.g., Byrnes et al., 2010; Desnos-Ollivier et al., 2015; Wang et al., 2015c). The relevance of fungi as FBPs is debated, significantly lower than that of bacteria and viruses, but possibly also understudied.

Plants are commonly infected in the field by “field fungi.” Some of these produce toxic metabolites and may consequently cause disease when plant derived products are consumed (Lee et al., 2015; Stoev, 2015). Typical examples are Fusarium spp. on cereal grains, producing zearalenone, fumonisins and trichothecenes (e.g., deoxynivalenol, T-2 and HT-2 toxin), and Penicillium spp. on fruits, producing patulin. Other fungi infect a broad range of food and feed products during storage (“post-harvest fungi”). Typical examples are Aspergillus spp. and Penicillium spp., producing acute or chronically toxic compounds such as aflatoxins, ochratoxins and citrinin, and multiple antimicrobials with indirect health effects via modulation of the gut microbiota (Gillings et al., 2015; Stoev, 2015). Some mycotoxins are persistent to food processing (EMAN, 2015). They can exacerbate the infections with a wide range of non-fungal and fungal pathogens (Antonissen et al., 2014; Stoev, 2015), and it is hypothesized that mycotoxins may contribute to fungal infections (mycoses; Withlow and Hagler, 2016).

Several fungi are opportunistic, infective FBPs (Clemons et al., 2010; Iriart et al., 2010; Gurgui et al., 2011; Kazan et al., 2011; Benedict et al., 2016). Invasive fungal infections are primarily a problem in immunocompromised people (Brown et al., 2012; Bitar et al., 2014; Benedict et al., 2016). Reported examples of verified foodborne fungal infections are sparse (Benedict et al., 2016), and the problem is perhaps under-investigated. Hitherto, there is only one example of the use of HTS to investigate a fungal FBP outbreak. We suspect that the investigation of several other outbreaks or sporadic cases of fungus associated FBD reported in the literature (Benedict et al., 2016) could have benefited from application of HTS approaches similar to those described for other FBP taxa in this review.

Parasites are a diverse (polyphyletic) group of small animals or animal-like Eukaryotes, and their pathogenic potential is less frequently linked with metabolites and more frequently with their energy consumption and predation on host tissues than bacteria and fungi. World-wide, more than 100 species of foodborne parasites cause disease in humans (Orlandi et al., 2002). Complete or draft genome assemblies were available from NCBI for at least 40 of these species on August 16th 2017. Globalization, i.e., the movement of people, animals and food and feed increases the risk of moving and spreading parasites that are originally endemic, to new countries and hosts (Robertson et al., 2014). The lack of suitable enrichment methods for parasites, as opposed to most of the known bacterial and fungal FBPs, means that recovery/isolation steps are particularly important (Robertson et al., 2014). This also suggests that molecular detection can be useful to monitor presence, distribution and epidemiology, as well as the efficiency of clinical treatments after parasite infections.

Published studies applying HTS technologies to parasites are with few exceptions limited to characterization of genomes and transcriptomes, a topic not covered here. These studies, however, provide for detailed insight into the genetics of adaptations to specialized parasitism, and provide urgently needed reference sequence data for detection and identification purposes and clues to possible target/drug combinations (e.g., Tsai et al., 2013; Foth et al., 2014; Young et al., 2014; Barratt et al., 2015).

The presence of a multitude of other taxa and the frequently low abundance of parasites or derived DNA in clinical fecal samples challenge the detectability and may prevent effective sampling and purification of DNA for detection of parasites. Specialized protocols may be required to purify and enrich the parasite relative to a background matrix such as food or feces. Improved sample preparation and enrichment methods are discussed later.

Parasites are Eukaryotes with substantially more genetic similarity to their human and animal hosts than Bacteria. The size of parasite genomes (10–1,000 Mbp) ranges from 10 × the size of bacterial genomes and up to nearly the size of the human genome. New databases are in development, collecting genomic information for various parasites (Martin et al., 2015). The lack of annotated genomes and transcriptomes has been a major obstacle to the effective use of HTS for detection of parasites. Molecular discrimination is dependent on detailed knowledge of genomes and genetic variation. However, many of the parasites are detectable by visual, macro- or microscopic inspection, and this is likely to be a more cost-efficient approach in many instances.

In addition to pathogens naturally associated with the food producing organisms such as gut microbes, dermal yeasts and bacteria, plant pathogenic fungi, etc. many of the most severe food pathogeneses are caused by incidental transfer from animal vectors (pests; Olsen et al., 2001; Jones et al., 2013). The Food and Drug Administration has identified the 22 most common pests contributing to the spread of FBD in the USA (Olsen et al., 2001). Four of these are rodents (mouse and rat species), while the remaining 18 are insects (cockroaches, ants and flies). The traditional approach to detect and identify these is microscopy. Recently it was proposed to use metataxonomics by sequencing of the mitochondrial cytochrome c oxidase subunit I (COI) as a faster, more reliable, sensitive and cost-efficient approach (Jones et al., 2013). COI metataxonomics can be performed using HTS (see Figure 4) and may therefore suit as an attractive approach to screen routinely for presence of these vectors in raw materials for food production, to reduce the risk of introducing pathogens in the production. Vectors other than, or in addition to the 22 identified by Olsen et al. (2001) may be identified as particularly relevant, e.g., in other parts of the world. The introduction of additional sequence targets in an HTS based metataxonomic screening should be feasible (Lammers et al., 2014; Leray and Knowlton, 2015; Arulandhu et al., 2017) despite the limitations of COI and metataxonomic approaches in general (Deagle et al., 2014; Staats et al., 2016; Arulandhu et al., 2017).

The discovery and characterization of biosynthetic gene clusters in microorganisms is radically facilitated with the availability of HTS (Cacho et al., 2015). Secondary metabolites are often toxic, and many of the microorganisms producing them are FBPs. Characterization of the biosynthetic pathways provides a basis for faster detection of agents producing the metabolites, as well as for interception of undesirable biological effects and exploitation of the biosynthetic pathways for production of new bioactive compounds.

Direct shotgun metagenomics on DNA purified from microorganisms isolated without enrichment culturing is an attractive approach. Such an approach applied to clinical, polymicrobial urine samples was found to have comparable identifiability of bacteria as more conventional and much more time consuming approaches (Hasman et al., 2014). The complexity of such urine samples may be comparable to that of some food matrixes. Several approaches to data analysis were taken. First: identification of microorganisms by presence of specific k-mer motifs identified in a database of complete bacterial genomes. Secondly: alignment based subtraction of host-DNA derived reads and estimation of relative bacterial species distribution. Third: mapping of reads against a larger database of complete and draft bacterial, archaeal, fungal, protozoan and viral genomes. Finally, MLST and resistance gene identification performed against relevant databases. In a follow up study, direct shotgun metagenomics on toilet waste samples from long-distance flights was used to identify bacteria and antimicrobial resistance genes (Petersen et al., 2015) by mapping of reads to the abovementioned databases.

Host-specific genetic markers may be present in strains of pathogenic and non-pathogenic taxa, and then hold potential for source tracking (Gomi et al., 2014). See also Franz et al. (2016) for a more detailed discussion on source attribution.

A major challenge for detection of FBPs in complex matrixes is the recovery of the organism or its genes. Foods are physically and chemically complex matrices hosting complex microbial communities. The extraction and purification of DNA from the samples is of major importance as the presence of inhibitors may hamper the analysis further down the line (Ceuppens et al., 2014; Moore et al., 2015). The sample storage conditions and preparation steps can introduce taxonomic biases linked to recovery (Ceuppens et al., 2015; Menke et al., 2017). Similar or even stronger effects on the microbiome and RNA population of samples are predictable, as also the intraspecific diversity and gene expression can be affected. The size of isolated/purified nucleic acid fragments is also relevant. Longer fragments are usually superior and complementary to shorter fragments for genome assembly. The purity and relative concentration of target isolated nucleic acids affect their detectability and certainly their quantifiability (Holst-Jensen et al., 2003). Different taxa and life stages of potential pathogens require different treatments for detachment from substrates and complex structures, recovery and lysis. Protocols required for extraction and purification of DNA from strongly attaching, biofilm-forming taxa with tough cell walls can cause shearing of nucleic acids of other relevant taxa, yielding undesirably short fragments or totally degraded RNA/DNA. The stability of nucleic acids is a critical parameter, and this topic is reviewed by Ceuppens et al. (2014). Enrichment processes required to achieve the necessary target concentration can also bias the post-enrichment microbial composition in undesirable ways, as illustrated with Salmonella spp. in examples discussed earlier (Ottesen et al., 2013; Jarvis et al., 2015).

Examples of non-culturing based enrichment of particular organisms include size based filtering, buoyancy, affinity based columns or immuno-magnetic separation (Hadfield et al., 2015). Molecular enrichment can be achieved by subtraction hybridization (Galbraith et al., 2004) and various combinations of digestion with restriction enzymes, adapter ligation and sequence specific amplification (Leichty and Brisson, 2014; Arulandhu et al., 2016).

Ability to discriminate viable/infective agents from dead/non-infective agents and to obtain isolates of the agent(s) from food samples for comparison purposes is often important (Ceuppens et al., 2014; Forbes et al., 2017). Molecular analytical methods can potentially circumvent need for enrichment culturing and further selective steps for the detection of many of the most important FBPs, but the issues of viability, recovery and LOD remain critical. Transcriptomics or use of propidium monoazide prior to sequencing are options to discriminate living from dead cells (Weinmaier et al., 2015). There is no harmonized requirement for detection and/or identification of an FBP by HTS, neither with respect to the number of reads, coverage of a specific diagnostic target motif, minimum length of (assembled) contig or maximum number of mismatches. Depending on the choice of actual minimum performance parameters and associated acceptance values it is possible to calculate the probability of detection (POD) of relevant target(s) and perform a statistical comparison of the POD to the actual observations (Holst-Jensen et al., 2016; Spilsberg et al., 2017). This would provide clues to the probability and reliability of findings. POD calculations performed prior to analyses can be useful to assess the cost-efficiency of alternative approaches. HTS is generally not quantitative, and complementary tools are needed to verify the LOD and recovery. Direct sequencing of nucleic acids from a clinical or food derived sample can be faster than culture-dependent analytical approaches. However, successive analysis of HTS data, necessary for interpretation, may be too time-consuming to render HTS really competitive in many cases. For bacteria in particular, the combination of limited enrichment culturing and fast HTS with (semi-)automated bioinformatics is currently the most optimal, realistic option that can provide very detailed information while simultaneously offering sufficient sensitivity and speed. For those FBPs that cannot be enriched by culturing, future pipeline developments may improve the situation, at least for certain types of matrixes and scenarios.

The use of different technologies and methods negatively affect the comparability of results (Junemann et al., 2013), as does lack of harmonized terminology and data interpretation (Lambert et al., 2017; Taboada et al., 2017). Recent studies have tried to overcome this variability in end-point results by establishing standard WGS data sets from outbreaks with Salmonella, STEC and L. monocytogenes (Timme et al., 2015) and a benchmarking dataset consisting of 101 whole genome sequences from one E. coli hypermutator strain (Ahrenfeldt et al., 2017). Use of these data sets is proposed to facilitate standardization and harmonization of bioinformatics pipelines (Timme et al., 2015; Ahrenfeldt et al., 2017).

Traditional approaches to standardization and validation cannot be directly applied to HTS approaches. There is for example no common cognition as to what is required for “identification” of a FBP, such as the number of organism-specific reads (positive calls), sequence depth (confidence), LOD, etc. for each platform and protocol, or number of differences distinguishing isogenic from non-isogenic isolates (Figure 5). There are, however, several efforts to improve the situation. Guidelines for harmonization of clinical testing by application of HTS have been published and may provide useful guidance also to other sectors including FBP detection (Gargis et al., 2012, 2015; Weiss et al., 2013; Aziz et al., 2015). The Global Microbial Identifier (GMI) was initiated in 2011 (GMI, 2011; Aarestrup et al., 2012). The background for this initiative was the high number of microbiological isolates that are characterized annually with very diverse and expensive typing systems, the increasing number of infectious diseases with global epidemiology requiring rapid detection and identification of microbial agents, the likelihood that microbiological laboratories (primarily clinical) will have DNA sequencers available in the foreseeable future, and that the likely future limiting factor is not the HTS cost but the assembly, processing and data handling in a standardized way to make the information useful (GMI, 2011). In 2015 the GMI launched the first HTS proficiency testing scheme in this respect (GMI, 2015; Moran-Gilad et al., 2015). Recent progress by GMI is reported by Taboada et al. (2017). The World Organization for Animal Health also recently launched its first standards for HTS, bioinformatics and computational genomics (OIE, 2017). Two other examples are the International Organization for Standardization's initiative to standardize WGS for typing and genomic characterization (ISO/TC 34/SC 9/WG 25) and the initiative to standardize the format of HTS derived SNP data (in a cancer context; Pipan and Kunaj, 2015), respectively.

HTS can generate a vast amount of data describing the microbial community of foods. When technical issues such as speed and standardization of laboratory and bioinformatics methods are improved, HTS and especially shotgun metagenomics may find applications in routine analysis of food, also for control purposes, as it has already done in clinical settings. Interpretation of the results is an important issue, for both the agri-food industry and the competent authorities, but also how to act on the results (Lambert et al., 2017; Taboada et al., 2017).

In the quality control of products and production lines, the detection of a pathogen, a genus that includes pathogenic species, and/or specific virulence genes/factors may, but will not necessarily lead to withdrawal of the product or lead to decisions to decontaminate the entire production line. The decision will depend on the specific context, i.e., the specific combination of pathogen marker, how well characterized the pathogen is, the type of product and the intended use. Quantitative data may also play a role, but sometimes are not or cannot be made available. Legal requirements can direct the decision processes. In the European Union (EU), both food safety criteria and process hygiene criteria are listed with details on sampling plans, limits, analytical reference methods and stage where the criterion applies (European Commission, 2005). Similarly, the safety of the food supply chain in the USA is regulated (FDA, 2011).

The application of WGS in microbiological risk assessments of foods is largely unexplored and faces important challenges. The number of hazards increase exponentially when zooming from serovars or serotypes to genotypes. The translation of multidimensional genotypic data into reduced information on phenotypes to ultimately generate a measure of risk that matches the requirements of food safety authorities and policy makers is therefore necessary (Franz et al., 2016). The presence or absence of specific genetic markers in a bacterial isolate or in a sample can direct the acceptance or rejection of a particular product. Furthermore, specific detection of FBP-markers may only be required within a narrow concentration range. For example, the observed presence of the STEC-associated virulence genes stx1 or stx2 in ground beef can lead to rejection or at least to particular caution and further examination to preclude the presence of STEC. We suggest that the observed background level of E. coli can provide a basis for decision in the case of STEC. The presence of E. coli itself is undesirable and un-tolerable background levels of E. coli, e.g., ≥ 10⁴ CFU g⁻¹ should lead to rejection. Thus, we believe it is most critical to ensure that the POD for STEC (markers) is acceptable within the entire range of concentrations at tolerable background levels, e.g., POD ≥ 99% at < 10⁴ CFU E. coli g⁻¹. We suggest that such an approximation can be used to guide both method developments and the enrichment efforts needed to obtain conclusive data. The infectious dose of FBPs vary, and for some FBPs it can be < 100 cells (Tilden et al., 1996; Tuttle et al., 1999; Hall, 2012) and must be considered for this type of calculation.

Recently, it has been questioned if all protists and helminths, historically thought of as “parasites” are indeed parasites (Lukes et al., 2015). Growing evidence of the diverse functional roles that specific bacteria can take on in humans, has forced us to acknowledge that beneficial and pathogenic may be two sides of the same organism, and that it must be understood in the broader contexts of ecology, symbiosis and diversity, among others. Collecting data on the entire microbial communities associated with their hosts may provide for better understanding of the functional interplay between hosts and hosted microorganisms, though so far no clear trend is emerging (Martin et al., 2015). In the future, perhaps, by learning more aided by HTS studies, it will be possible to stimulate beneficial and prevent pathogenic behavior by microorganisms with broad phenotypic potentials.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.