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Animal Frontiers: The Review Magazine of Animal Agriculture logoLink to Animal Frontiers: The Review Magazine of Animal Agriculture
. 2018 Nov 9;8(4):33–41. doi: 10.1093/af/vfy024

Use of omics methods for the advancement of food quality and food safety

Peter W Cook 1,2, Kendra K Nightingale 3,✉
PMCID: PMC6952017  PMID: 32002228

Implications

  • Advancing food quality and food safety is critical for improving global food security and public health metrics.

  • Omics tools (i.e., genomics, transcriptomics, proteomics, and metabolomics) are robust tools to gain further insight into microbial communities along the food chain and their implications for human and animal health.

  • Whole genome sequencing facilitates early detection of foodborne illness outbreaks, including smaller clusters of related illnesses spanning over longer time periods, and microbial source tracking.

  • Omics approaches can provide insight into adaptation of microorganisms to specific niches along the food continuum and other strain-specific characteristics that affect human and animal health, including virulence genes and antimicrobial resistance.

Overview on Omics Approaches for Advancing Food Quality and Food Safety

Food quality and food safety are critical components to maintaining a safe and economically sustainable food supply, and are important to improving food security across the globe (WHO, 2015). Maintaining and improving the microbiological quality as well as ensuring the safety of food require identifying the presence of known and unknown microorganisms, including spoilage and pathogenic organisms that have been introduced throughout the food chain. Utilization of omics-based techniques (i.e., genomics, transcriptomics, proteomics, and metabolomics; see Figure 1) can broaden the scope of sampling programs and can increase detection of a broad range of issues, including pathogen detection, foodborne illness outbreak detection, microbial source tracking investigations, niche adaptation, antimicrobial resistance, and product shelf-life. Using omics techniques can improve food quality, food safety, and subsequently public health metrics by developing better screening and subtyping tools for both known and unknown pathogens. Better tools can then be generated for tracing specific bacterial strains to their origin and our understanding of these pathogens can be improved to prevent future contamination events, including identification and remediation of strains that persist in a given niche such as the processing plant environment (Miller et al., 2013; Zhou et al., 2016; Forbes et al., 2017). Omics techniques extend the range of information that is collected from a single system. For instance, genomics, the study of all DNA in an organism, increases the number of genes that can be compared across strains (Jackson et al., 2016), whereas metagenomics, the study of all microorganisms present in a system, can identify the presence of known and unknown microorganisms in the overall system (Forbes et al., 2017). The investigation of these strains or systems using omics-based approaches still requires well-characterized databases and a deep understanding of the individual organisms and genes. Improvements in food quality and food safety have been and will continue to be the direct result of integrating these approaches into routine sampling and testing schemes and microbial source tracking investigations (Jackson et al., 2016).

Figure 1.

Figure 1.

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Word cloud showing various state-of-the-art “omics” technologies.

The simple biological central dogma (DNA replication, transcription to mRNA, and translation to protein) is the starting point for expanding these omics techniques and can provide a robust picture of the biological or environmental system under study. Figure 2 shows a description of these processes. The development of motifs, or patterns, that can be associated with specific protein functionality can then be used to search and identify a protein’s functionality based on sequence similarity across many different strains or species. This application of specific knowledge (i.e., studying a single gene in a single organism) to a broader context increases the pace at which novel and known mechanisms of virulence, antibiotic resistance, or enzymatic ability can be identified (Chen et al., 2012; Zhao et al., 2014; Jia et al., 2017). Initial studies that sequenced individual genes and probed their function in the cell through extensive combined genetic and phenotypic characterization provide this vital information for understanding how genes are regulated, and how gene sequences correlate to protein structure and function. Understanding the structural differences between transmembrane, extracellular, or intracellular proteins can provide insight into their function without necessarily performing additional laboratory experiments (Krogh et al., 2001).

Figure 2.

Figure 2.

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DNA is transcribed to mRNA and mRNA is translated to protein. New technologies (e.g., “omics” technologies) have been developed to study changes in DNA, mRNA, and protein under varying physiological and environmental conditions.

There are an estimated 3.6 million cases of bacterial foodborne illness each year in the United States, and an estimated 10.3 million cases globally (Scallan et al., 2011; Havelaar et al., 2015). The incidence and cost of foodborne illness is shown in Figure 3. Foodborne illness and food product recalls bear a significant emotional and economic burden on society globally. Foodborne illnesses affect the population at large by increasing healthcare costs and decreasing the quality and length of life for sick individuals. Estimating the number of quality-adjusted life years, or healthy years lost in terms of quality or death, can provide a measurement for determining the full impact of outbreaks and the resulting illnesses. The monetary value (see Figure 4) and the loss in quality-adjusted life years have been estimated for each of the 14 common foodborne pathogens with the combination of Listeria monocytogenes, Salmonella enterica, and shiga toxin producing Escherichia coli (STEC) amounting to more than US$6 billion annually and more than 27,000 quality-adjusted life years lost to foodborne illnesses in the United States alone (Batz et al., 2012; Hoffmann et al., 2012). In 2010, Ivanek et al. estimated the marginal benefit per prevented case of listeriosis for food processing companies to be between US$4.4 and US$8.8 million, whereas the marginal cost per prevented case of listeriosis for food processing companies to be between US$0.5 and US$1 million (Ivanek, et al., 2005). This imbalance of the cost benefit ratio could be leveraged to increase support from governmental agencies and the food industry for the reduction of listeriosis, and potentially all other pathogens overall.

Figure 3.

Figure 3.

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Incidence and cost of foodborne illness.

Figure 4.

Figure 4.

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Cost of various foodborne pathogens. Source: USDA Economic Research Service.

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. Bacteria can be routinely introduced into a food processing facility through a variety of mechanisms, including raw materials, water, equipment/utensils, and workers. Once inside a food processing facility, bacteria can be transferred to various surfaces and postlethality-exposed product by cross-contamination of raw products or contaminated equipment (as reviewed by Pérez-Rodríguez et al., 2008). Food safety controls (i.e., cleaning and sanitation programs, process interventions and management systems, and robust environmental monitoring) are important techniques for food processing facilities to reduce spoilage organisms and control pathogens. Although the application of efficacious food safety controls appears to control low-level contamination, when these food safety systems fail a sporadic high-level contamination event may result in an outbreak of foodborne illness.

Foodborne pathogens, such as STEC, S. enterica, and L. monocytogenes, are commonly identified along the food chain continuum and are capable of causing disease in both humans and animals. These pathogens must be able to survive a significant number of different stressors to be transmitted throughout the food continuum, including large temperature fluctuations, high salt concentrations, low water activity, limited free nutrients, oxidative stresses, and disinfectants. Many of these stresses are intentionally incorporated into food processing as interventions as well as in cleaning and sanitation practices to decrease bacterial loads, and prevent additional growth of spoilage and pathogenic microorganisms. Animals that symptomatically or asymptomatically carry and shed pathogens can introduce pathogens into environments associated with food production, processing, and handling, which may result in loading the human food chain with pathogens ultimately increasing the incidence of human foodborne and animal disease (McDaniel et al., 2014).

Molecular Subtyping of Food-Associated Microorganisms

Current gold standard methods of bacterial subtyping include phenotypic methods such as serotyping and band-based molecular methods such as pulse field gel electrophoresis (PFGE). Although these current gold standard methods are rapidly being augmented with omics approaches, information obtained from gold standard methods is still clinically and epidemiologically relevant. Serotyping and pulse field gel electrophoresis differentiate strains of a given bacterial species beyond the species level–based antibody/antigen interactions and endonuclease restriction of total bacterial DNA followed by fragment separation, respectively. Based on serotyping data, STEC has been shown to be comprised of more than 180 different serotypes, S. enterica includes more than 2,500 serotypes, and L. monocytogenes isolates can be differentiated into 13 serotypes (Seeliger and Höhne, 1979; Uzzau et al., 2000; Stenutz et al., 2006). However, approximately 91.7% of STEC outbreaks in the United States during the last 18 yr have been attributed to only seven serogroups, including the O157:H7 serotype and the six regulated non-O157 serogroups (i.e., O103, O111, O121, O145, O26, and O45) or the “big 6” non-O157 STEC as reported by the CDC (https://wwwn.cdc.gov/foodborneoutbreaks/). In addition, only a small fraction of all S. enterica subtypes are regularly isolated from human clinical cases and animals, and all of these serotypes belong to S. enterica subspp. enterica (as reviewed in Uzzau et al. (2000)). Only three of the 13 L. monocytogenes serotypes (i.e., 1/2a, 1/2b, and 4b) appear to be responsible for majority (approximately 90%) of human listeriosis cases (McLauchlin, 1990; Maury et al., 2016), and reviewed in Orsi et al. (2011). Serotyping can provide subspecies discrimination and relevant historical clinical reference information; however, a significantly deeper level of strain discrimination is necessary to facilitate outbreak detection, outbreak investigations, and other microbial source tracking studies. Pulse field gel electrophoresis demonstrates increased discriminatory power over serotyping and is still the current gold standard for differentiating bacterial pathogens by PulseNet at the CDC. However, this technique can be laboratory-dependent and requires a significant amount labor (https://www.cdc.gov/pulsenet/pathogens/pfge.html). In addition, some types of pulse field gel electrophoresis are very common in general and a shared subtype between a human clinical and food isolate does not necessarily imply that the food was responsible for disease without supporting epidemiological information. This technique does not have sufficient discriminatory capability to differentiate isolates belonging to certain Salmonella serotypes such as Salmonella Enteritidis and Salmonella Newport.

Another major category of molecular subtyping methods involves sequencing specific gene regions of the bacteria to be subtyped, including individual genes, multiple genes, or the whole genome. For example, multilocus sequence typing (MLST) can distinguish strains based on the allelic type of a set of selected genes that are present in the core genome of a species (e.g., housekeeping genes), for instance, housekeeping genes (Feil and Enright, 2004). To accomplish this, the gene or a fraction of the gene is sequenced and the resulting nucleotide data are then compared with a database of previously identified allelic types. If the query sequence matches one of the reference sequences uniquely, then the query sequence is assigned to that allelic type. Clonal complexes have been defined as group organisms sharing 100% similarity of allelic types in six of seven housekeeping genes tested by a MLST protocol (Feil et al., 2004). MLST protocols have been developed that target different regions for the same organism. For instance, MLST schemes have been developed for L. monocytogenes using housekeeping genes only, genes from the prfA virulence gene complex only, and a mixture of housekeeping, virulence, and stress-response genes (Salcedo et al., 2003; Ward et al., 2004; Nightingale et al., 2005). The higher resolution subtyping that molecular techniques such as MLST have been shown to provide has been used for source-associated clustering of L. monocytogenes isolates (Nightingale et al., 2006). This study showed that isolates from the same source (i.e., human, animal, or food) are more closely related to each other than isolates from different sources, supporting niche adaptation of L. monocytogenes isolates to a human or animal host or nonhost environment. Once the DNA sequence–based subtyping information has been collected, the sequence data can then be used for additional bioinformatics to understand the molecular evolution and ecology of a set of isolates (den Bakker et al., 2008; Ragon et al., 2008).

Whole Genome Sequencing of Food-Associated Microorganisms

Recent advancements in sequencing technology (Next Generation Sequencing) have made available several omics-based techniques that can be used to improve food quality and food safety by providing rapid detection and high-resolution subtyping. The genome of an organism can be used to provide strain-level resolution of the pathogen-causing disease and to track the pathogen to its source to limit additional exposure in humans and animals (den Bakker et al., 2014; Deng et al., 2016). Bacterial subtyping by whole genome sequencing (WGS) has been performed using several different platforms that vary in the length of fragments that can be sequenced and the correctness or quality of the nucleotide attribution for each read. Shorter reads can significantly limit the ability of assembling a complete genome de novo, without a reference sequence, due to the presence of repetitive regions that can occur in the bacterial genome. Longer reads can be used to sequence across these repetitive regions with adequate coverage for de novo assembly. However, current technology limits the quality of the reads generated by long read technologies relative to short read technologies. High-quality draft (i.e., incomplete or unclosed) genomes generated using current platforms have been used for regular subtyping of several organisms and have been shown to identify the source of sporadic cases or smaller clusters of related cases over longer periods of time (Jackson et al., 2016). High-quality draft genomes can be used in multiple subtyping schemes based on previous MLST structures, novel MLST subtyping schemes, specifically on the number of single-nucleotide polymorphism (SNP) differences (Zhang et al., 2004; Cody et al., 2017; Saltykova et al., 2018).

Core genome MLST focuses only on the genes that are present in all organisms identified in the species, whereas the whole genome MLST subtyping scheme uses the pan-genome, or both core and accessory genes. L. monocytogenes WGS data have shown that using either the core genome or the whole genome MLST structures results in similar phylogenetic trees (Chen et al., 2016; Jackson et al., 2016; Moura et al., 2016). As WGS becomes more routinely performed in laboratories, including the rate at which isolates can be sequenced, assembled, annotated, and compared through bioinformatics pipelines increases, 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 (Gilmour et al., 2010; den Bakker et al., 2011, 2014; Chen et al., 2017).

However, there are still some clear issues with depending solely on WGS data to define outbreaks without the use of food consumption information. For instance, L. monocytogenes with identical WGS genome sequences have been found in multiple independent food–associated environments (Stasiewicz et al., 2015). The SNP analysis from which these comparisons are made can be dependent on the choice of reference sequence and assembly pipeline (Pightling et al., 2014). WGS can also be used to discriminate between persistent and reintroduced strains of L. monocytogenes, providing facilities that process and handle food with information on the difference between cleaning and sanitation failures and continuous reintroduction/recontamination from incoming raw materials and other sources (Stasiewicz et al., 2015). The high discriminatory power of WGS and metagenomics will be useful for identifying outbreaks and cases that may be the result of coinfections or multiple pathogens. More than one strain of L. monocytogenes has been cultured from both a case of invasive listeriosis and an outbreak associated with cantaloupe (Tham et al., 2002; Lomonaco et al., 2013).

In addition to the determination of evolutionary history, WGS can provide valuable information on the virulence potential and antibiotic resistance by providing information on all of the genes in the genome (Chen et al., 2012; Jia et al., 2017). This also includes any genetic differences in gene content, mobile genetic elements, or horizontal gene transfer events (Milillo et al.,2009; 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 allelic types in silico (Doumith et al., 2004; Inouye et al., 2014; Zhang and Knabel, 2005). For instance, genetic markers for serotype identification can be used in place of antibody/antigen interactions and simultaneously provide increased discrimination between serotypes (Zhang and Knabel, 2005). 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 E. 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). Sequencing the entire genome of the organism provides the ability to investigate the mechanisms responsible for bacterial survival in the environment and coordination of bacterial pathogenesis for different subtypes. 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.

The use of WGS and the other omics-based techniques can provide a drawdown in the scientific workload required to investigate or solve complicated problems due to the large amount of information that can be generated by each of the omics tools. For instance, routine surveillance of food animals resulted in the identification of commensal E. coli resistant to the antibiotic colistin (Liu et al., 2016b). After confirming conjugation could result in the transfer of resistance to another E. coli strain, whole plasmid sequencing was performed to identify potential genes that could be responsible for the antimicrobial resistance (Liu et al., 2016b). The plasmid sequence generated was then compared with closely related plasmids that do not have resistance to the antibiotic (Liu et al., 2016b). At this point, multiple candidate resistance genes were selected and mutated in an attempt to find the gene conferring resistance.

Other Omics Approaches for Food-Associated Microorganisms

The transcriptome, or all available mRNA, can be investigated by growing the resistant strain in broth with and without the antibiotic. The identification of differentially expressed genes under the antibiotic cultivation condition would provide additional support for several of the candidate genes involved in resistance. In a similar fashion, gene knockout mutants of known transcriptional regulators can be used with whole transcriptomes to define the regulon of these transcriptional regulators by recognizing differentially expressed genes (McGann et al., 2008; Oliver et al., 2009; Liu et al., 2016a). However, others have shown that the correlation between transcript level and protein expression is less than perfect in eukaryotic cells (Zhang et al., 2014), but that in prokaryotes it can be partially accounted for using specific models (Gedeon and Bokes, 2012). The full proteome would provide additional evidence of a candidate gene or suggest a different mechanism that may be responsible for resistance by eliminating any uncorrelated increased transcription expression that may be present for some genes. Metabolomics has been used to determine the specific known and unknown enzymatic targets of various antimicrobials (Vincent et al., 2016).

These omics techniques come with a series of advantages and disadvantages that when used individually that cannot be ignored; however, it gives the researcher a place to start from a biological perspective as opposed to a stochastic one. Merging multiple levels of omics-based techniques into a single data set that can represent a single environmental change could provide an incredibly detailed understanding of how a bacterium or pathogen survives in the environment. Other tools are available for looking at multiple sources of omics-based data at one time (Eichner et al., 2014). Visual analysis software will be key for enabling biologists to begin looking at more complex relationships between bacterial pathogens, the environment, and their eukaryotic hosts. Although continuing to build on the basic-level research that has given rise to these techniques is important, all of these omics techniques and the information obtained can provide a positive and rapid feedback loop for understanding more of the biological processes involved in antibiotic resistance and pathogenesis. Specifically, expanding the level of transcriptomic, proteomic, and metabolomic information for bacterial pathogens and the host environment could provide new targets for limiting serious infections, and decreasing the morbidity and mortality rate of all bacterial pathogens. All of the genes, transcripts, and proteins in a bacterium represent a large and complex data set to sift through. Each of the different omics data types provide a different layer and can confirm assumptions made at prior levels.

Metagenomics sequencing (i.e., 16s or shot-gun approaches) of microbial communities, such as agricultural soils, biofilms, or the microflora of the intestinal tract, can provide detailed information on the community distribution and could provide insights into the stability and nutrient requirements of the community. Metagenomics has been used to analyze the effect of subtherapeutic antibiotic use on the microbiome during the finishing diet of cattle to monitor differences in the resistome and changes in the microbiome (Thomas et al., 2017). In addition, strain-level identification of bacterial pathogens contaminating spinach can be generated from shotgun metagenomic sequencing (Leonard et al., 2016). Metatranscriptomics has been used to identify significant microbial differences between diseased and healthy crabs from China (Shen et al., 2017). These differences can be used to assess and improve the continued viability of these crabs as a food source while the etiological agent remains unknown. Metatranscriptomics (i.e., sequencing of the transcriptome of the entire microbial community in a given sample) has also been used to assess the differences in the microbiome from agricultural soils regularly treated with and without pesticides. The pesticide-treated soils had increased levels of transcripts associated with benzoate and heavy metal survival (Sharma and Sharma, 2018). High levels of pesticide use may act as a selection mechanism for bacteria that can survive the increased stress including L. monocytogenes, STEC, and S. enterica (Artz and Killham, 2002; Ratani et al., 2012; Medardus et al., 2014). Knowing the antibiotic-resistance profile of a bacterial isolate or microbial community can provide valuable information about how to treat an infection, or by sequencing all available bacterial DNA in a system, the potential antibiotic resistance of the whole microbiome can be accounted for providing information on the presence of transferable antibiotic-resistance plasmids and common harborage bacteria that persist in a given niche.

Conclusions

Improvements in food quality and food safety are required to maintain the availability of safe and nutritious food and can subsequently increase food security across all socioeconomic levels. Omics techniques can improve current food quality, food safety, and public health metrics by increasing the number and type of contaminants (spoilage organisms and pathogens) detected in a sample using metagenomics and meta-metabolomics. WGS can provide very high-resolution subtyping to differentiate strains to facilitate early detection of foodborne illness outbreaks and increase the likelihood of identifying the source of an outbreak or contamination. Figure 5 provides an overview of surveillance targets and the use of metagenomics to track and identify microorganisms throughout the food production chain. In addition, these processes can be used in conjunction with the standard protocols and procedures currently being implemented in routine testing in food processing facilities. Rapid, accurate, and comprehensive protocols can increase the validation of these protocols across many industries decreasing the investment required to test for pathogens and other contaminates. The additional information acquired through these mechanisms can then be transferred to basic-level research for the improvement of fundamental science improving the predictability and usefulness of these techniques in the future.

Figure 5.

Figure 5.

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Surveillance targets and metagenomics insights for detecting pathogens throughout the food production chain. From: Metagenomic sequencing for surveillance of food- and waterborne viral diseases. Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/Food-and-waterborne-viral-surveillance-targets-for-metagenomic-sequencing-approaches_fig1_313746183 [accessed 4 Sept, 2018].

About the Authors

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Dr. Peter Cook is an Association of Public Health Laboratories Bioinformatics Fellow working in the Center for Disease Control and Prevention, Influenza Division. Dr. Cook earned a B.S. degree in both Microbiology and Food Science from Purdue University. Dr. Cook completed a PhD in 2017 at Texas Tech University and performed postdoctoral research at the Center for Food Safety at the University of Georgia. Dr. Cook’s research has involved the comparison of virulence-attenuated and fully virulent Listeria monocytogenes using whole genome sequencing and transcriptomics, and now focuses on Influenza bioinformatics.

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Dr. Kendra Nightingale is a Professor in the Department of Animal and Food Sciences at Texas Tech University. Dr. Nightingale received her B.S. degree in Agriculture and M.S. degree in Food Science from Kansas State University. Dr. Nightingale completed her PhD and postdoctoral training in the Department of Food Science at Cornell University. Dr. Nightingale’s research program integrates basic and applied sciences to understand the molecular ecology and transmission dynamics of foodborne pathogens along the food continuum. Dr. Nightingale serves as a Scientific Advisor for Food Safety Net Services and International Life Sciences Institute North America (ILSI NA) Technical Committee on Food Microbiology. Dr. Nightingale is also a cofounder of NexGen Innovations, which provides biological solutions to control foodborne pathogens.

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