Advancing Food Quality and Food Safety Through Omics Tools
Maintaining and improving the microbiological quality, as well as ensuring the safety of food, requires identifying the presence of both known and unknown microorganisms. Utilization of -omics based techniques (i.e., genomics, transcriptomics, proteomics and metabolomics) can facilitate detection of a broad range of issues in food quality and food safety. Omics tools can (i) identify microorganisms limiting product shelf-life and (ii) facilitate pathogen detection (i.e., known and unknown for emerging agents) foodborne illness cluster/outbreak detection (i.e., refinement of case definition) as well as microbial source tracking investigations. Omics tools can also provide insight into fundamental biological characteristics of microorganisms such as niche adaptation, including virulence, antimicrobial resistance and resistance to environmental stressors (e.g., acid, heat and desiccation), that allow a microorganism to thrive in specific environments or infect a host. For example, in recent years Salmonella survival in dry environments and low water activity food matrices has emerged as a particular concern and omics tools can elucidate genetic characteristics that facilitate the ability of certain Salmonella serotypes to survive and persist in this niche.
Using omics tools can improve food quality, food safety, and thus subsequently public health metrics, by allowing development of better screening and subtyping tools for both known and emerging pathogens. Better tools can be generated for tracking bacterial strains to their origin and our understanding of these pathogens can be improved to define clusters/outbreaks of foodborne illness in a real-time manner. Combined routine sampling microbiological testing and typing can be employed to prevent future contamination events, including identification of strains that persist in the processing plant environment. Several studies have demonstrated that the environment of a given processing facility tends to be colonized by a few specific microbial strains that can contribute to limiting product shelf-life or inadvertent cross-contamination of food products after mitigation hurdles or the lethality step.
The identification of pathogens in the food processing environment can be difficult due to the complexity of the processing network, heterogeneity of food products, and the presence of normal or natural microflora in the processing plant environment. Food safety controls (i.e., cleaning and sanitation programs, process interventions, management systems and robust environmental monitoring) are important for food processing facilities to reduce spoilage organisms and control pathogens in both the environment and finished product. The application of these aforementioned efficacious food safety control aid in managing low-level contamination. However, when food safety systems fail, a sporadic high-level contamination event may result in a large scale contamination even leading to a product recall or potentially an outbreak of foodborne illness.
Next generation sequencing technologies have made omics based tools widely available to improve food quality and food safety by providing rapid detection and high-resolution subtyping. As WGS becomes more routinely performed in laboratories, including the rate at which isolates can be sequenced, assembled, annotated and compared through bioinformatics pipelines, laboratories have begun to shift from PFGE based surveillance systems to WGS based surveillance and outbreak detection (Moura et al., 2017). For example, the CDC is currently performing WGS on all L. monocytogenes human clinical isolates and the FDA is routinely using WGS in intensified follow-up sampling investigations prompted by recalls or foodborne illness outbreaks. In fact, the use of high quality draft genomes generated by WGS has been shown to be feasible for outbreak detection and provides a similar level of clonal discrimination by SNP analysis in both S. enterica and L. monocytogenes (den Bakker et al., 2011; 2014; Y. Chen et al., 2017). Subtyping by PFGE will be completely replaced by WGS in all U.S. PulseNet laboratories beginning January 15th, 2019. Replacement of PFGE with WGS for Salmonella enterica in Canada has already led to an eight-fold increased detection of salmonellosis clusters many of which have been associated with breaded chicken and low water activity matrices such as flour (J. Besser personal communication).
WGS can provide valuable information on potential phenotypic characteristics (i.e., virulence, antimicrobial resistance and resistance to environmental as well as processing stresses) by detailing all of the genes in the genome (Jia et al., 2017). This also includes any genetic differences in gene content, mobile genetic elements, or horizontal gene transfer events (den Bakker et al., 2010; Hain et al., 2012). Once the genome has been assembled, many of the currently used methods of subtyping can be inferred afterwards using databases and computer programs, such as SRST2, to identify serotypes in silico. In addition, genetic markers have been used to group E. coli into pathotypes to identify strains with specific virulence characteristics. A well-known set of genetic markers for Escherichia coli virulence is the presence or absence of a combination of the eae, stxI and stxII genes. However, these genes alone are not sufficient for pathogenesis as bacterial virulence systems are also a balance of regulation and reaction to secondary signals with some requiring a host response (Arpaia et al., 2011; Camejo et al., 2011). Identification of combinations of additional diarrheagenic E. coli virulence factors (i.e., esp and nle effector genes) could be useful to determine the pathogenic potential of E. coli in food. Analyzing the process of pathogenesis can identify important steps in the pathogenicity and can be used to distinguish between highly virulent and less virulent strains within a given pathogen. RNA-seq can also provide important insight into a microorganisms’ ability to survive and thrive in a given niche through identification of genes that are significantly up or down regulated in a specific environment or upon exposure to conditions used to control microorganisms in food (i.e., ingredients, interventions and sub-lethal concentrations of sanitizers).
Metagenomics has the potential to be applied to several areas of food microbiology. Metagenomics sequencing, through 16s or shot-gun sequencing approaches, of microbial communities can provide detailed information on the relative abundance of phyla and presence/levels of pathogens in a sample. Metagenomics allows identification of taxa that comprise the microbial community in a system and has been used to describe rich microbial populations in many different systems, including humans and animals, soil, food and the environment. Metagenomics has been proposed as a culture independent diagnostic approach to identify pathogens in clinical, food and environmental samples; however, this approach is limited by inability to differentiate DNA sequences from live and dead cells.
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