Coordination among the various agencies and laboratories responsible for food safety is an ongoing challenge. Coordination and standardization of laboratories and methods related to food authenticity testing can be even more challenging. As noted in the Elliott Review into the Integrity and Assurance of Food Supply Networks (conducted following the 2013 horsemeat incident):
“Official controls of food authenticity require a wide range of analytical and molecular biological techniques, many with exacting instrumentation requirements and in-depth scientific interpretation of the datasets generated. No single institution…could field the complete range of such techniques with the required expertise.”
One of the recommendations in Elliott Review was the establishment of an “Authenticity Assurance Network” to facilitate standardized approaches to food authenticity testing. This network would also enable better coordination among government departments related to policies, surveillance and criminal investigation around food fraud. The Food Authenticity Network (FAN) was subsequently established in 2015 by the U.K. government and serves as a repository for news and information on best practices for food authenticity testing methods and food fraud mitigation. At the heart of FAN, there is a network of laboratories that provide authenticity testing, which are designated as Food Authenticity Centers of Expertise (CoE). A contact person is named for every CoE so that stakeholders can communicate with them regarding food authenticity testing. There is a call currently open for UK Food Authenticity Centres of Expertise, so take a look and see if your laboratory fits the requirements.
Over the past four years, FAN has grown to more than 1,500 members from 68 countries/territories and in 2019, more than 12,000 unique users accessed information on the network’s website.
The site currently hosts 101 government reports, 77 standard operating procedures (SOPs), 16 survey reports, and 22 reports on nitrogen factors (which are used for meat and fish content calculations). Importantly, the site also includes a section on food fraud mitigation, which signposts some of the world’s leading services, guidance and reports aimed at preventing fraud from occurring.
FAN posts periodic newsletters with updates on funded projects, research reports, government activity, upcoming conferences, and other news of interest related to assuring the integrity of food. The latest newsletter has just been issued.
In its efforts to create a truly global network, as well as reaching out to the international food community, FAN is collaborating with other governments. In 2019, Selvarani Elahi gave presentations on FAN in Ghana and Vietnam, and discussions are currently taking place with the Ghana Food and Drugs Administration and the International Atomic Energy Agency about creating bespoke country-specific pages. In 2018, FAN was recognized at a Codex Alimentarius Commission meeting as being a “leading example of an integrity network.” Discussions are also in progress with multiple Codex Member countries.
FAN is an open access platform and membership is free (you can sign up here). The benefits of membership include access to closed discussion fora on the site, customizable email alerts, and options to communicate with other network members, as well as a monthly highlights email that rounds up the month’s activities in one convenient location.
Anyone who has attended a food safety conference in the last few years has experienced some type of whole genome sequencing (WGS) presentation. WGS is the next big thing for food safety. The technology has been adopted by regulatory agencies, academics, and some food companies. A lot has been said, but there are still some questions regarding the implementation and ramifications of WGS in the food processing environment.
There are a few key acronyms to understand the aspects of genomics in food safety (See Table I below).
Pulse Field Gel Electrophoresis
Technique using restriction enzymes and DNA fragment separation via an electronic field for creation of a bacterial isolate DNA fingerprint; PFGE is being replaced by WGS at CDC and other public health laboratories
Whole Genome Sequencing
The general term used for sequencing—a misnomer—the entirety of the genome is not used, and depends on the analytical methodology implemented
Next Generation Sequencing
NGS is the next set of technology to do WGS and other genomic applications
Single Nucleotide Polymorphisms
A variation in a single nucleotide that occurs in specific position of an organism’s genome; Used in WGS as a methodology for determining genetic sameness between organisms
Multilocus sequence typing
A methodology for determining genetic sameness between organisms; Compares internal fragment DNA sequences from multiple housekeeping genes
16s RNA sequencing
A highly conserved region of the bacterial genome used for species and strain identification
Joseph Heinzelmann will be presenting: Listeria Testing Platforms: Old School Technology vs New Innovative Technology during the 2016 Food Safety Consortium | LEARN MOREIn 1996, the CDC established the PulseNet program for investigating potential foodborne illness outbreaks. PulseNet has relied on using bacterial DNA fingerprints generated via PFGE as comparisons for mapping potential sources and spread of the outbreaks. Due to a number of advantages over PFGE, WGS is quickly becoming the preferred method for organism identification and comparison. Moving to WGS has two critical improvements over PFGE: accuracy and relatedness interpretation. Like PFGE there are nuances when defining the difference between two very closely related organisms. However, instead of defining restriction enzymes and comparing the number of bands, the language changes to either single nucleotide polymorphisms (SNP) or the number of alleles. The other important aspect WGS improves is the ability to determine and interpret the relatedness of organisms more broadly. The frequent Listeria outbreaks and incidence from 1983-2015 provide an insight to what the future might hold with WGS implementation.1 The incidence report shows the increased ability to quickly and more accurately define relatedness between clinical cases creates a link of potential cases much faster.
WGS also provides key practical changes for outbreaks and recalls in the food industry. Sequencing provides a much faster response time and therefore means the outbreaks of foodborne illness decrease, as does the number of cases in each outbreak. As the resolution of the outbreaks increases, the number of outbreaks identified increases. The actual number of outbreaks has likely not increased, but the reported number of outbreaks will increase due increased resolution of the analytical method.
WGS continues to establish itself as the go-to technology for the food safety agencies. For example, the USDA food safety inspection service recently published the FY2017–2021 goals. The first bullet point under modernizing inspection systems, policies and the use of scientific approaches is the implementation of in-field screening and whole genome sequencing for outbreak expediency.
Agencies and Adoption
The success of FDA and CDC Listeria project provides a foundation for implementation of WGS for outbreak investigations. The three agencies adopting WGS for outbreak investigations and as replacement for PulseNet are the CDC, FDA and USDA. However, there are still questions on the part of the FDA for when WGS is utilized, including under what circumstances and instances the data will be used.
In recent public forums, the FDA has acknowledged that there are situations when a recall would be a potential solution based on WGS results in the absence of any clinical cases.2 One critical question that still exists in spite of the public presentations and published articles is a clear definitions of when WGS surveillance data will be used for recall purposes, and what type of supporting documentation a facility would need to provide to prove that it had adequate controls in place.
A key element is the definition between agencies for sameness or genetic distance. The FDA and FSIS are using a SNP approach. A sequence is generated from a bacterial isolate, then compared with a known clinical case, or a suspected strain, and the number of different SNPs determines if the strains are identical. The CDC is using the Multilocus sequence typing (MLST) approach.
Simple sequence comparisons are unfortunately not alone sufficient for sameness determination, as various metabolic, taxa specific and environmental parameters must also be considered. Stressful environments and growth rates have significant impact on how quickly SNPs can occur. The three primary pathogens being examined by WGS have very different genetic makeups. Listeria monocytogenes has a relatively conserved genomic taxa, typically associated with cooler environments, and is gram positive. Listeria monocytogenes has a doubling time of 45–60 minutes under enrichment conditions.3 These are contrasted with E. coli O157:H7, a gram negative bacteria, associated with higher growth rates and higher horizontal gene transfer mechanisms. For example, in an examination of E. coli O104, and in research conducted by the University in Madurai, it showed 38 horizontal gene elements.4
These two contrasting examples demonstrate the complexity of the genetic distance question. It demonstrates a need for specific definitions for sameness within a microbiological taxa, and with potential qualifiers based on the environment and potential genetic event triggers. The definitions around SNPs and alleles that define how closely related a Listeriamonocytogenes in a cold facility should be vastly different from an E. coli from a warm environment, under more suitable growth conditions. Another element of interest, but largely unexplored is convergent evolution. In a given environment, with similar conditions, what is the probability of two different organisms converging on a nearly identical genome, and how long would it take?
MLST vs. SNP
As previously stated, the three agencies have chosen different approaches for the analytical methodology: MLST for CDC and SNP of the FDA and USDA. For clarity, both analytical approaches have demonstrated superiority over the incumbent PFGE mythology. MLST does rely on an existing database for allele comparison. A SNP based approach is supported by a database, but is often used in defining genetic distance specifically between two isolates. Both approaches can help build phylogenetic trees.
There are tradeoffs with both approaches. There is a higher requirement for processing and bioinformatics capabilities when using a SNP based approach. However, the resolution between organisms and large groups of organisms is meaningful using SNP comparison. The key take away is MLST uses a gene-to-gene comparison, and the SNP approach is gene agnostic. As mentioned in Table 1, both approaches do not use every A, T, C, and G in the analytical comparisons. Whole genome sequencing in this context is a misnomer, because not every gene is used in either analysis.
Utilizing WGS for companies as a preventive measure is still being developed. GenomeTrakr has been established as the data repository for sequenced isolates from the FDA, USDA, CDC and public health labs. The data is housed at the National Center for Biotechnology Information (NCBI). The database contains more than 71,000 isolates and has been used in surveillance and outbreak investigations. There is a current gap between on premise bioinformatics and using GenomeTrakr.
The FDA has stated there are examples where isolates found in a processing facility would help support a recall in the absence of epidemiological evidence, and companies are waiting on clarification before adopting GenomeTrakr as a routine analysis tool. However, services like NeoSeek, a genomic test service by Neogen Corp. are an alternative to public gene databases like GenomeTrakr. In addition to trouble shooting events with WGS, NeoSeek provides services such as spoilage microorganism ID and source tracking, pathogen point source tracking. Using next generation sequencing, a private database, and applications such as 16s metagenomic analysis, phylogenetic tree generation, and identification programs with NeoSeek, companies can answer critical food safety and food quality questions.
After receiving input from federal, state, and local regulatory officials, along with industry and trade associations, academia, and consumers, FDA issued its Voluntary National Retail Food Regulatory Program Standards last week. The standards address “what constitutes a highly effective and responsive retail food regulatory program,” according to the document.
The Retail Program Standards include:
Promoting the adoption of science-based guidelines from the FDA Food Code
Promoting improvement of training programs to ensure local, state, tribal, and territorial staff have the necessary skills, knowledge and abilities
Implementing risk-based inspection programs
Developing outbreak and food defense surveillance plans to enable systematic detection and response to foodborne illness or food contamination
The 2015 edition contains new worksheets that are intended to assist regulatory programs in looking at how their programs line up with the 2013 Food Code. This includes helping them assess the consistency and effectiveness of their enforcement activities, and a verification tool to help independent auditors with these self-assessments. Although jurisdictions can use the worksheets and other materials without enrolling in the Retail Program Standards, FDA encourages them to do so, as enrollment allows them to apply for FDA funding. The agency also lists the jurisdictions enrolled in the program here.
Proposed rules under the Food Safety Modernization Act will mandate more inspections, more testing, and better risk-based profiling of food products – both sourced domestically and imported. How is FDA planning to keep pace with these changes? Roberta Wagner, Director, Office of Compliance, for the Center for Food Safety and Applied Nutrition at FDA, provided some insights, while speaking at the recent Food Safety Consortium, organized by Food Safety Tech.
Section 201 under the Food Safety Modernization Act requires the Food and Drug Administration to designate food facilities at high-risk and non high-risk facilities, and accordingly, establish minimum frequency of inspection of these facilities. While high-risk facilities will have to be inspected by FDA once every three years, facilities deemed non high-risk will be inspected once every five years. Wagner described that the following factors have been considered so far for determining if a domestic food manufacturing facility is determined to be high-risk or otherwise:
Whether the facility has been involved in a Class 1 outbreak or recall;
Whether the facility has a history of non-compliance (based on Official – Action Indicated (OAI) or Voluntary Action Indicated (VAI) data);
If the facility has had any significant violations;
Future data considerations (see below);
Type of activity the facility is involved in; and
Date of last inspection.
Future data for consideration of high-risk and non high-risk categorization will include:
Inherent risk factors at product level (for instance is the product bakery goods, or seafood/ fresh produce etc);
Has the facility been linked to an outbreak, recall or adverse event (if so the risk profile gets elevated);
If any sample testing (product or environmental) is positive;
If there’s a history of customer complaints;
Robustness of QA/QC programs and 3rd part audit reports;
Financial viability of the company; and
Food safety culture of the facility/ company.
Foreign facility inspections
Under FSMA, FDA has also been mandated to increase the number of inspections the agency does on foreign facilities, to ensure the safety of imported foods. Wagner explained that FDA currently conducts about 1200 foreign facility inspections a year to determine if those facilities meed FDA regulations. With FSMA rules, FDA will have increased authority to conduct such inspections of foreign faciligies, and look at Foreign Supplier Verification Programs, and Voluntary Qualified Importer Program records, adds Wagner.
Under the new regimen, FDA has been mandated to conduct at least 600 foreign inspections during the first year of FSMA rule implementation. And the target is to double this number every year, for the next five years, taking it to 19,200 inspections by Year 6. Wagner feels this is an impractical number as FDA does not have the resources to do so many foreign inspections. “If we get the Foreign Supplier Verification Program under FSMA rule right, we effectively place the responsibility for ensuring safety of imported foods on the food industry and importers. FDA cannot, and should not be doing this,” she explains.
Risk-based foreign facility site selection
FDA will also adopt a risk-based approach to select foreign facilities for further inspection. This approach will consider:
Food safety risk associated with the sector or commodity;
Risk associated with manufacturing process;
Compliance history of facilities associated with an industry sector commodity in a given country or region (for instance, look at refusal rates for products denied try into the U.S. by country);
Quantity or volume of imported product from country or region;
Robustness of food safety system in the country; and
Portion of resources retained by the facility for compliance, follow up inspections and emergency response situations.
Based on this FDA will continue to diversify the product that it considers high risk, for instance dairy, baby food, candy… Wagner added that economically motivated adulterated continues to be a concern and cause for focus on food products such as oils, honey and dietary supplements.
Wagner also talked about Predictive Risk-based Evaluation for Dynamic Import Compliance Targeting or PREDICT, a risk management tool used by FDA to efficiently and effectively make entry admissibility, decisions that prevent entry of adulterated, mis-branded or otherwise violative imported goods into the U.S., while expediting the entry of non-violative goods. Based on risk scores allocated to different products, this computerized tool targets entries of highest risk for further scrutiny, including field reviews and sampling.
She explained that this dynamic tool, which constantly adapts to different risk situations and products, provides automatic data mining and pattern recognition, provides automated queries of FDA databases including facility registration information, and thus, allows for risk-based allocation of FDA resources.
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