Tag Archives: whole genome sequencing

Sasan Amini

NGS in Food Safety: Seeing What Was Never Before Possible

By Sasan Amini
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Sasan Amini

For the past year, Swedish food provider Dafgård has been using a single test to screen each batch of its food for allergens, missing ingredients, and even the unexpected – an unintended ingredient or pathogen. The company extracts DNA from food samples and sends it to a lab for end-to-end sequencing, processing, and analysis. Whether referring to a meatball at a European Ikea or a pre-made pizza at a local grocery store, Dafgård knows exactly what is in its food and can pinpoint potential trouble spots in its supply chains, immediately take steps to remedy issues, and predict future areas of concern.

The power behind the testing is next-generation sequencing (NGS). NGS platforms, like the one my company Clear Labs has developed, consist of the most modern parallel sequencers available in combination with advanced databases and technologies for rapid DNA analysis. These platforms have reduced the cost of DNA sequencing by orders of magnitude, putting the power to sequence genetic material in the hands of scientists and investigators across a range of research disciplines and industries. They have overtaken traditional, first-generation Sanger sequencing in clinical settings over the past several years and are now poised to supplement and likely replace PCR in food safety testing.

For Dafgård, one of the largest food providers in Europe, the switch to NGS has given it the ability to see what was previously impossible with PCR and other technologies. Although Dafgård still uses PCR in select cases, it has run thousands of NGS-based tests over the past year. One of the biggest improvements has been in understanding the supply chain for the spices in its prepared foods. Supply chains for spices can be long and can result in extra or missing ingredients, some of which can affect consumer health. With the NGS platform, Dafgård can pinpoint ingredients down to the original supplier, getting an unparalleled look into its raw ingredients.

Dafgård hopes to soon switch to an entirely NGS-based platform, which will put the company at the forefront of food safety. Embracing this new technology within the broader food industry has been a decade-long process, one that will accelerate in the coming years, with an increased emphasis on food transparency both among consumers and regulators globally.

Transitioning technology

A decade ago, very few people in food safety were talking about NGS technologies. A 2008 paper in Analytical and Bioanalytical Chemistry1 gave an outlook for food safety technology that included nanotechnology, while a 2009 story in Food Safety Magazine2 discussed spectrometric or laser-based diagnostic technologies. Around the same time, Nature magazine named NGS as its “method of the year” for 2007. A decade later, NGS is taking pathogen characterization and food authentication to the next level.

Over the last 30 years, multiple technology transitions have occurred to improve food safety. In the United States, for example, the Hazard Analysis and Critical Control Points (HACCP) came online in the mid-1990s to reduce illness-causing microbial pathogens on raw products. The move came just a few years after a massive outbreak of E. coli in the U.S. Pacific Northwest caused 400 illness and 4 deaths, and it was clear there was a need for change.

Before HACCP, food inspection was largely on the basis of sight, touch, and smell. It was time to take a more science-based approach to meat and poultry safety. This led to the use of PCR, among other technologies, to better measure and address pathogens in the food industry.

HACCP set the stage for modern-era food testing, and since then, efforts have only intensified to combat food-borne pathogens. In 2011, the Food Safety Modernization Act (FSMA) took effect, shifting the focus from responding to pathogens to preventing them. Data from 20153 showed a 30% drop in foodborne-related bacterial and parasitic infections from 2012 to 2014 compared to the same time period in 1996 to 1998.

But despite these vast improvements, work still remains: According to the CDC, foodborne pathogens in the Unites States alone cause 48 million illnesses and 3,000 fatalities every year. And every year, the food safety industry runs hundreds of millions of tests. These tests can mean the difference between potentially crippling business operations and a thriving business that customers trust. Food recalls cost an average of $10M per incident and jeopardize public health. The best way to stay ahead of the regulatory curve and to protect consumers is to take advantage of the new technological tools we now have at our disposal.

Reducing Errors

About 60% of food safety tests currently use rapid methods, while 40% use traditional culturing. Although highly accurate, culturing can take up to five days for results, while PCR and antigen-based tests can be quicker – -one to two days – but have much lower accuracy. So, what about NGS?

NGS platforms have a turnaround of only one day, and can get to a higher level of accuracy and specificity than other sequencing platforms. And unlike some PCR techniques that can only detect up to 5 targets on one sample at a time, the targets for NGS platforms are nearly unlimited, with up to 25 million reads per sample, with 200 or more samples processed at the same time. This results in a major difference in the amount of information yielded.

For PCR, very small segments of DNA are amplified to compare to potential pathogens. But with NGS tools, all the DNA is tested, cutting it into small fragments, with millions of sequences generated – giving many redundant data points for comparing the genome to potential pathogens. This allows for much deeper resolution to determine the exact strain of a pathogen.

Traditional techniques are also rife with false negatives and false positives. In 2015, a study from the American Proficiency Institute4 on about 18,000 testing results from 1999 to 2013 for Salmonella found false negative rates between 2% and 10% and false positive rates between 2% and 6%. Several Food Service Labs claim false positive rates of 5% to 50%.

False positives can create a resource-intensive burden on food companies. Reducing false negatives is important for public health as well as isolating and decontaminating the species within a facility. Research has shown that with robust data analytics and sample preparation, an NGS platform can bring false negative and positive rates down to close to zero for a pathogen test like Salmonella, Listeria, or E.coli.

Expecting the Unexpected

NGS platforms using targeted-amplicon sequencing, also called DNA “barcoding,” represent the next wave of genomic analysis techniques. These barcoding techniques enable companies to match samples against a particular pathogen, allergen, or ingredient. When deeper identification and characterization of a sample is needed, non-targeted whole genome sequencing (WGS) is the best option.

Using NGS for WGS is much more efficient than PCR, for example, at identifying new strains that enter a facility. Many food manufacturing plants have databases, created through WGS, of resident pathogens and standard decontamination steps to handle those resident pathogens. But what happens if something unknown enters the facility?

By looking at all the genomic information in a given sample and comparing it to the resident pathogen database, NGS can rapidly identify strains the facility might not have even known to look for. Indeed, the beauty of these technologies is that you come to expect to find the unexpected.

That may sound overwhelming – like opening Pandora’s box – but I see it as the opposite: NGS offers an unprecedented opportunity to protect against likely threats in food, create the highest quality private databases, and customize internal reporting based on top-of-the-line science and business practices. Knowledge is power, and NGS technologies puts that power directly in food companies’ hands. Brands that adopt NGS platforms can execute on decisions about what to test for more quickly and inexpensively – all the while providing their customers with the safest food possible.

Perhaps the best analogy for this advancement comes from Magnus Dafgård, owner and executive vice president at Gunnar Dafgård AB: “If you have poor eyesight and need glasses, you could be sitting at home surrounded by dirt and not even know it. Then when you get glasses, you will instantly see the dirt. So, do you throw away the glasses or get rid of the dirt?” NGS platforms provide the clarity to see and address problem directly, giving companies like Dafgård confidence that they are using the most modern, sophisticated food safety technologies available.

As NGS platforms continue to mature in the coming months and years, I look forward to participating in the next jump in food safety – ensuring a safe global food system.

Common Acronyms in Food Genomics and Safety

DNA Barcoding: These short, standardized DNA sequences can identify individual organisms, including those previously undescribed. Traditionally, these sequences can come from PCR or Sanger sequencing. With NGS, the barcoding can be developed in parallel and for all gene variants, producing a deeper level of specificity.

ELISA: Enzyme-linked immunosorbent assay. Developed in 1971, ELISA is a rapid substance detection method that can detect a specific protein, like an allergen, in a cell by binding antibody to a specific antigen and creating a color change. It is less effective in food testing for cooked products, in which the protein molecules may be broken down and the allergens thus no longer detectable.

FSMA: Food Safety Modernization Act. Passed in 2011 in the United States, FSMA requires comprehensive, science-based preventive controls across the food supply. Each section of the FSMA consists of specific procedures to prevent consumers from getting sick due to foodborne illness, such as a section to verify safety standards from foreign supply chains.

HACCP: Hazard analysis and critical control points. A food safety management system, HACCP is a preventative approach to quantifying and reducing risk in the food system. It was developed in the 1950s by the Pillsbury Company, the Natick Research Laboratories, and NASA, but did not become as widespread in its use until 1996, when the U.S. FDA passed a new pathogen reduction rule using HACCP across all meat and poultry raw products.

NGS: Next-generation sequencing. NGS is the most modern, parallel, high-throughput DNA sequencing available. It can sequence 200 to 300 samples at a time and generates up to 25 million reads per a single experiment. This level of information can identify pathogens at the strain level and can be used to perform WGS for samples with unknown pathogens or ingredients.

PCR: Polymerase chain reaction. First described in 1985, PCR is a technique to amplify a segment of DNA and generate copies of a DNA sequence. The DNA sequences generated from PCR must be compared to specific, known pathogens. While it can identify pathogens at the species level, PCR cannot provide the strain of a pathogen due to the limited amount of sequencing information generated.

WGS: Whole genome sequencing. WGS uses NGS platforms to look at the entire DNA of an organism. It is non-targeted, which means it is not necessary to know in advance what is being detected. In WGS, the entire genome is cut it into small regions, with adaptors attached to the fragments to sequence each piece in both directions. The generated sequences are then assembled into single long pieces of the whole genome. WGS produces sequences 30 times the size of the genome, providing redundancy that allows for a deeper analysis.


  1. Nugen, S. R., & Baeumner, A. J. (2008). Trends and opportunities in food pathogen detection. Analytical and Bioanalytical Chemistry, 391(2), 451-454. doi:10.1007/s00216-008-1886-2
  2. Philpott, C. (2009, April 01). A Summary Profile of Pathogen Detection Technologies. Retrieved September 08, 2017, from https://www.foodsafetymagazine.com/magazine-archive1/aprilmay-2009/a-summary-profile-of-pathogen-detection-technologies/?EMID
  3. Ray, L., Barrett, K., Spinelli, A., Huang, J., & Geissler, A. (2009). Foodborne Disease Active Surveillance Network, FoodNet 2015 Surveillance Report (pp. 1-26, Rep.). CDC. Retrieved September 8, 2017, from https://www.cdc.gov/foodnet/pdfs/FoodNet-Annual-Report-2015-508c.pdf.
  4.  Stombler, R. (2014). Salmonella Detection Rates Continue to Fail (Rep.). American Proficiency Institute.
Gregory Siragusa, Eurofins
Food Genomics

GenomeTrakr: What Do You Know and What Should You Know?

By Gregory Siragusa, Douglas Marshall, Ph.D.
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Gregory Siragusa, Eurofins

This month we are happy to welcome our guest co-authors and interviewees Eric Brown, Ph.D. and Marc Allard, Ph.D. of CFSAN as we explore the FDA’s GenomeTrakr program in a two-part Food Genomics column. Many of our readers have heard of GenomeTrakr, but are likely to have several questions regarding its core purpose and how it will impact food producers and processors in the United States and globally. In Part I we explore some technical aspects of the topic followed by Part II dealing with practical questions.

Part I: The basics of GenomeTrakr

Greg Siragusa/Doug Marshall: Thank you Dr. Allard and Dr. Brown for joining us in our monthly series, Food Genomics, to inform our readers about GenomeTrakr. Will you begin by telling us about yourselves and your team?

Eric Brown/Marc Allard: Hello, I am Eric, the director of the Division of Microbiology at the U.S. Food and Drug Administration at the Center for Food Safety and Applied Nutrition. Our team is made up of two branches, one that specializes in developing and validating methods for getting foodborne pathogens out of many different food matrices and the other branch conducts numerous tests to subtype and characterized foodborne pathogens. The GenomeTrakr program is in the subtyping branch as Whole Genome Sequencing (WGS) is the ultimate genomic subtyping tool for characterizing a foodborne pathogen at the DNA level.

Hello, my name is Marc, I am a senior biomedical research services officer and a senior advisor in Eric’s division. We are part of the group that conceived, evaluated and deployed the GenomeTrakr database and network.

Siragusa/Marshall: Drs. Allard and Brown, imagine yourself with a group of food safety professionals ranging from vice president for food safety to director, manager and technologists. Would you please give us the ‘elevator speech’ on GenomeTrakr?

Brown/Allard: GenomeTrakr is the first of its kind distributed network for rapidly characterizing bacterial foodborne pathogens using whole genome sequences (WGS). This genomic data can help FDA with many applications, including trace-back to determine the root cause of an outbreak as well providing one work-flow for rapidly characterizing all of the pathogens for which the agency has responsibility. These same methods are also very helpful for antimicrobial resistance monitoring and characterization.

Siragusa/Marshall: From the FDA website, GenomeTrakr is described as “a distributed network of labs to utilize whole genome sequencing for pathogen identification.” We of course have very time-proven methods of microbial identification and subtyping, so why do we need GenomeTrakr for identification and subtyping of microorganisms?

Brown/Allard: If all you want to know is species identification then you are correct, there are existing methods to do this. For some applications you need full characterization through subtyping (i.e., Below the level of species to the actual strain) with WGS. WGS of pathogens provides all of the genetic information about an organism as well as any mobile elements such as phages and plasmids that may be associated with these foodborne pathogens. The GenomeTrakr network and database compiles a large amount of new genetic or DNA sequence data to more fully characterize foodborne pathogens.

GenomeTrakr and WGS are a means to track bacteria based on knowing the sequence of all DNA that comprises that specific bacterium’s genome. It can be called the “ultimate identifier” in that it will show relationships at a very deep level of accuracy.

Siragusa/Marshall: Is it an accurate statement that GenomeTrakr can be considered the new iteration of PulseNet and Pulse field gel electrophoresis (PFGE)? Will PulseNet and PFGE disappear, or will PulseNet and GenomeTrkr merge into a single entity?

Brown/Allard: PulseNet is a network of public health labs run by the CDC, with USDA and FDA as active participants. The network is alive and well and will continue subtyping pathogens for public health. The current and historical subtyping tool used by PulseNet for more than 20 years is PFGE. It is expected that CDC, USDA and FDA’s PFGE data collection will be replaced by WGS data and methods. That transformation has already begun. GenomeTrakr is a network of public health labs run by the FDA to support FDA public health and regulatory activities using WGS methods. Starting in 2012, this network is relatively new and is focused currently on using WGS for trace back to support outbreak investigations and FDA regulatory actions. CDC PulseNet has used WGS data on Listeria and collects draft genomes (i.e., unfinished versions of a final genome are used for quicker assembly) of other foodborne pathogens as well, and USDA’s FSIS has used WGS for the pathogens found on the foods that they regulate. All of the data from GenomeTrakr and Pulsenet are shared at the NCBI Pathogen Detection website (see Figure 1).

Sequences, GenomeTrakr
Figure 1

Siragusa/Marshall: Does an organism have to be classified to the species level before submitting to GenomeTrakr?

Brown/Allard: Yes, species-level identification is part of the minimal metadata (all of the descriptors related to a sample such as geographic origin, lot number, sources, ingredients etc.) required to deposit data in the GenomeTrakr database. This allows initial QA/QC metrics to determine if the new genome is labeled properly.

Siragusa/Marshall: After an isolate is identified to the species level, would you describe to the reader what the basic process is going from an isolated and speciated bacterial colony on an agar plate to a usable whole genome sequence deposited in the GenomeTrakr database?

Brown/Allard: The FDA has a branch of scientists who specialize in ways to isolate foodborne pathogens from food. The detailed methods used ultimately end up in the Bacteriological Analytical Manual (BAM) of approved and validated methods. Once a pathogen is in pure culture then DNA is extracted from the bacterial cells. The DNA is then put into a DNA sequencing library, which modifies the DNA to properly attach and run sequencing reactions depending on the specific sequencing vendor used. The sequence data is downloaded from the sequencing equipment and then uploaded to the National Center for Biotechnology Information (NCBI) Pathogen Detection website. The database is publicly open to allow anyone with foodborne pathogens to upload their data and compare their sequences to what is available in the database.

Siragusa/Marshall: Suppose a specific sequence type of a foodborne bacterial pathogen is found and identified from a processing plant but that the plant has never had a positive assay result for that pathogen in any of its history of product production and ultimate consumption. If an outbreak occurred somewhere in the world and that same specific sequence type were identified as the causative agent, would a company be in anyway liable? Could one even make an association between the two isolates with the same sequence type isolated at great distances from open another?

Brown/Allard: The genetic evidence from WGS supports the hypothesis that the two isolates shared a recent common ancestor. If, for example, the isolate from the processing plant and the outbreak sample where genetically identical across the entire genome, the prediction is that the two samples are connected in some way that is currently not understood. The genetic matches guide the FDA and help point investigations to study the possible connections. This might include additional inspection of the processing plant as well as linking this to the typical epidemiological exposure data. Sometimes due to the indirect nature of how pathogens circulate through the farm to fork continuum and the complex methods of trade, no connection is made. More commonly, these investigative leads from genetic matches help the FDA establish direct links between the two bacterial isolates through a shared ingredient, shared processing, distribution or packaging process. The genetic information and cluster helps the FDA discover new ways that the pathogens are moving from farm to fork. We are unaware of any example where identical genomes somehow independently arose and were unrelated. This is counter to molecular evolutionary theory anyway. Genetic identity equals genetic relatedness and the closer two isolates are genetically to each other, the more recent that they shared a common ancestor. With regard to liability, this is a topic beyond the scope of our group, but genomic data does not by itself prove a direct linkage and that is why additional investigations must follow any close matches.

Siragusa/Marshall: We know that SNPs (Single Nucleotide Polymorphisms or single base pair differences in the same location in a genome) are commonly used to distinguish clonality of bacteria with highly similar genomes. Are there criteria used by GenomeTrakr bioinformaticists that are set to help define what is similar, different or the same?

Brown/Allard: As the database grows with more examples of diverse serotypes or kinds of foodborne pathogens, the FDA WGS group is observing common patterns that can be used as guidance to define what is same or different. For example, closely related for Salmonella and E. coli are usually in the five or fewer SNPs, and closely related for Listeria is 20 or fewer SNPs using the current FDA validated bioinformatics pipeline. These values are not set in stone but should be considered more like guidance for what FDA and GenomeTrakr have observed already from earlier case studies that have already been collected and examined. Often, a greater number (e.g., 21-50) of SNP differences have been observed between strains isolated in some outbreaks. Any close match might support or direct an outbreak investigation if there is evidence that suggests that a particular outbreak looks most closely like an early case from a specific geographic location. WGS data helps investigators focus their efforts toward and international verses domestic exposure or possible country of origin. Even more divergent WGS linkages, when SNPs are greater than 50-100, often connect to different foods or different geographic locations that would lead investigators away from the source of an outbreak as the data provides both inclusivity as well as exclusivity.

When two strains have more than 50–100 SNPs, different food or geographic sources of those strains can be incorrectly linked resulting in investigators pursuing an incorrect source.

Siragusa/Marshall: Can SNPs be identified from different agar-plate clones of the same strain (i.e., Different colonies on the same plate)?

Brown/Allard: Since understanding the natural genetic variation present in foodborne pathogens is the basis to understanding relatedness, the FDA conducted validation experiments on growing then sequencing colonies from the same plate, colonies from frozen inocula, thawing and plating, as well as running the same DNAs on different instruments and with different sequencing technicians. The FDA’s work with Salmonella enterica Montevideo sequencing as well as ongoing proficiency testing among laboratories shows that the same isolate most often has no differences, although some samples have 1-2 SNP differences. Genetic differences observed in isolates collected by FDA inspectors all related to a common outbreak generally have more genetic differences, and this appears to be dependent on the nature of the facility and the length of time that the foodborne pathogen has been resident in the facility and the selective pressure to which the pathogen was exposed to in a range from 0–5 SNPs different.

Siragusa/Marshall: Regarding the use of WGS to track strains in a particular processing plant, is it possible that within that closed microenvironment that strains will evolve sufficiently so that it becomes unique to that source?

Brown/Allard: Yes, we have discovered multiple examples of strains that have evolved in a unique way that they appear to be specific to that source. Hospitals use the same practice to understand hospital-acquired infections and the routes of transmission within a hospitals intensive care unit or surgery. Food industry laboratories as well as FDA investigators could use WGS data in a similar way to determine the root cause of the contamination by combining WGS data with inspection and surveillance. The FDA Office of Compliance uses WGS as one piece of evidence to ask the question: Have we seen this pathogen before?

Siragusa/Marshall: The number of sequences in the GenomeTrakr database is approaching 120,000 (~4,000 per month are added). Are the sequences in the GenomeTrakr database all generated by GenomeTrakr Network labs?

Brown/Allard: The sequences labeled as GenomeTrakr isolates at the NCBI biosample and bioproject databases are the WGS efforts supported by the U.S. FDA and USDA FSIS. GenomeTrakr is a label identifying the FDA, USDA FSIS and collaborative partner’s efforts to sequence food and environmental isolates. Additional laboratories, independent and beyond formal membership in the GT network, upload WGS data to the NCBI pathogen detection website of which GenomeTrakr is one part. CDC shares WGS data on primarily clinical PulseNet isolates and USDA FSIS shares WGS foodborne pathogens for foods that they regulate. Numerous international public health laboratories also upload WGS data to NCBI. The NCBI pathogen detection website includes all publicly released WGS data for the species that they are analyzing, and this might include additional research or public health data. The point of contact for who submitted the data is listed in the biosample data sheet, an example of which can be seen here.

Siragusa/Marshall: Once sequences are deposited into the GenomeTrakr database, are they also part of GenBank?

Brown/Allard: The majority of the GenomeTrakr database is part of the NCBI SRA (sequence read archive) database, which is a less finished version of the data in GenBank. GenBank data is assembled and annotated, which takes more time and analysis to complete. Once automated software is optimized and validated, NCBI likely will place all of the GenomeTrakr data into GenBank. Currently, only the published WGS data from GenomeTrakr is available in GenBank. All of the GenomeTrakr data is available in SRA both at GenomeTrakr bioprojects and in the NCBI pathogen detection website.

Readers, look for the Part II of this column where we continue our exploration with Drs. Brown and Allard and ask some general questions about the logistics surrounding GenomeTrakr. As always, please contact either Greg Siragusa or Doug Marshall with comments, questions or ideas for future Food Genomics columns.

About the Interviewees

Marc W. Allard, Ph.D.

Marc Allard, FDAMarc Allard, Ph.D. is a senior biomedical research services officer specializing in both phylogenetic analysis as well as the biochemical laboratory methods that generate the genetic information in the GenomeTrakr database, which is part of the NCBI Pathogen Detection website. Allard joined the Division of Microbiology in FDA’s Office of Regulatory Science in 2008 where he uses Whole Genome Sequencing of foodborne pathogens to identify and characterize outbreaks of bacterial strains, particularly Salmonella, E. coli, and Listeria. He obtained a B.A. from the University of Vermont, an M.S. from Texas A&M University and his Ph.D. in biology in from Harvard University. Allard was the Louis Weintraub Associate Professor of Biology at George Washington University for 14 years from 1994 to 2008. He is a Fellow of the American Academy of Microbiology.

Eric W. Brown, Ph.D.

Eric Brown, FDAEric W. Brown, Ph.D. currently serves as director of the Division of Microbiology in the Office of Regulatory Science. He oversees a group of 50 researchers and support scientists engaged in a multi-parameter research program to develop and apply microbiological and molecular genetic strategies for detecting, identifying, and differentiating bacterial foodborne pathogens such as Salmonella and shiga-toxin producing E. coli. Brown received his Ph.D. in microbial genetics from The Genetics Program in the Department of Biological Sciences at The George Washington University. He has conducted research in microbial evolution and microbial ecology as a research fellow in the National Cancer Institute, the U.S. Department of Agriculture, and as a tenure-track Professor of Microbiology at Loyola University of Chicago. Brown came to the Food and Drug Administration in 1999 and has since carried out numerous experiments relating to the detection, identification, and discrimination of foodborne pathogens.


Persistent Strain of Salmonella Triggering Dozens of Recalls

By Food Safety Tech Staff
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The recalls involving powdered milk continue to pile up.

Since December, more than a dozen products containing powdered milk have been recalled due to the risk of Salmonella, including mini eclairs and cream puffs, mac & cheese products, chocolate-covered pretzels, potato chips, seasonings and white peppermint Hostess Twinkies.

Back in November, FDA seized more than 4 million pounds of dry nonfat milk powder and buttermilk powder produced by Valley Milk Products, LLC. The agency used whole genome sequencing to make the connection between the samples that were collected in the facility—Salmonella strains were found from samples taken in 2016 and back to 2010. FDA identified it as a persistent strain of the pathogen.

“FDA investigators observed residues on internal parts of the processing equipment after it had been cleaned by the company and water dripping from the ceiling onto food manufacturing equipment. In addition, environmental swabs collected during the inspection confirmed the presence of Salmonella meleagridis on surfaces food came into contact with after being pasteurized.” – FDA news release

To date, no illnesses have been reported.

Pursuit of Clarity for WGS in Food Production Environments

By Joseph Heinzelmann
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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).

PFGE 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
WGS 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
NGS Next Generation Sequencing NGS is the next set of technology to do WGS and other genomic applications
SNP 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
MLST Multilocus sequence typing A methodology for determining genetic sameness between organisms; Compares internal fragment DNA sequences from multiple housekeeping genes
16S 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.

Figure 1: (Permission for use of slide from Patricia M. Griffin, M.D. – Center for Disease Control and Prevention)

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 Listeria monocytogenes 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?


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.

Commercial Applications

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.


  1. Carleton, H.A. and Gerner-Smidt, P. (2016). Whole-Genome Sequencing Is Taking over Foodborne Disease Surveillance. Microbe. Retrieved from https://www.cdc.gov/pulsenet/pdf/wgs-in-public-health-carleton-microbe-2016.pdf.
  2. Institute for Food Safety and Health. IFSH Whole Genome Sequencing for Food Safety Symposium. September 28­–30, 2016. Retrieved from https://www.ifsh.iit.edu/sites/ifsh/files/departments/ifsh/pdfs/wgs_symposium_agenda_071416.pdf.
  3. Jones, G.S. and D’Orazio, S.E.F. (2013). Listeria monocytogenes: Cultivation and Laboratory Maintenance. Curr Proto Microbiol. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3920655/.
  4. Inderscience Publishers. “Horizontal gene transfer in E. coli.” ScienceDaily, 19 May 2015.
  5. Gerner-Smidt, P. (2016). Public Health Food Safety Applications for Whole Genome Sequencing. 4th Asia-Pacific International Food Safety Conference. Retrieved from http://ilsisea-region.org/wp-content/uploads/sites/21/2016/10/Session-2_2-Peter-Gerner-Smidt.pdf.

New Whole Genome Sequencing Test Monitors Threat of Pathogens

By Maria Fontanazza

Today food companies will have access to a new whole genome sequencing (WGS) test that could help them prevent dangerous pathogens from getting into their products. Released by Clear Labs, the test takes a detailed approach to identifying pathogen strains in samples, providing information about their geography and from which food groups they originate.

In an exclusive interview with Food Safety Tech, Mahni Ghorashi, co-founder of Clear Labs, explains how he expects the company’s new test, which has a five- to seven-day turnaround time, will offer companies with a more accurate yet less expensive alternative to protecting consumers by actively monitoring their supply chain for emerging pathogens.

Food Safety Tech: What differentiates this WGS test from current available solutions?

Mahni Ghorashi: No one has been able to provide the food industry with modern whole genome sequencing techniques for food safety. What we’re releasing is a quantum leap in terms of what’s been available on the market today. Whole genome sequencing has been largely siloed to regulatory bodies like FDA and CDC to trace outbreaks and inform investigations—the technologies and techniques that they’re using are actually fairly old; they’re some of the original WGS techniques that emerged on the next-gen sequencing platform. We’ve taken the most advanced techniques on the NGS platform for human disease exploration and personalized medicine and adapted them for food industry.

What gives our WGS test a competitive advantage over legacy-based methods is two fold:

1. Clear Labs has a 2-million+ entry-curated database of genomic information and sequences for the accurate ID of food ingredients (pathogenic organisms and microbiomes). Its accuracy and the confidence level that comes behind our matching is a huge step above anything that’s available in the public domain today.

2. Being able to place pathogenic strain information in the context of overall food ingredients and samples. The whole genome sequencing test we developed has been specifically catered for the food industry, and for food samples in particular, [versus] FDA’s GenomeTrakr, CDC’s PulseNet, or other food safety labs that are offering full genomic sequencing of pathogen strains—they’re using some of the earliest methods to do this. On the NGS platform, we’re able to put those strains in the context of food ingredients and suppliers: Specifically, [matching] bacterial strains with food ingredients [and] suppliers.

Clear Labs, whole genome sequencing

FST: Does this test target specific foods?

Ghorashi: Our platform particularly shines in complex foods. The value of next-gen sequencing and DNA barcoding over PCR-based technologies, which is the gold standard in food safety, is its stability to break down complex food ingredients into all of their known parts, and to look in a universal and unbiased way into food samples. It’s untargeted, so you don’t have know what it is that you’re looking for—and that’s the real power.

FST: What impact do you anticipate for this test, especially in the context of FSMA?

Ghorashi: Our customers are using [the test] for monitoring ingredient supplies and the effectiveness of preventive and sanitary controls [and] to match specific pathogen strains to specific food ingredients. They are using it for proactive testing for FSMA compliance—there’s a lot of movement in this direction and hefty budgets are being allocated to put new preventive controls in place in response to FSMA; whole-genome sequencing will play a big role, and we anticipate large-scale partnerships with agencies and private industry on that front. And the most obvious use case is that it’s being used for techniques to mitigate or reduce the risk of product recall and outbreak.

We’ve been able to significantly reduce the price point on whole-genome sequencing, and all of our tests across the board, because we’re intimately familiar with how the inner workings of these platforms and how to best optimize them for scale and cost efficiency. We think the test will be more accurate and leaps and bounds ahead of what’s available, as well as cost competitive. We’re excited about the work we’re doing and its impact on food safety. I don’t think the food industry—retailers and manufacturers—have ever had access to these kind of tools and they’re being made available just in time for FSMA, as the industry moves towards a more proactive approach to food safety and [takes] preventive measures in their supply chains.  Hopefully we’ll soon be living in a world where outbreaks, illness and the financial toll are a thing of a past.

Clear Labs also just released a microbiome test that helps companies associate microbiomes with specific food ingredients.

Mahni Ghorashi: The microbiome test we’ve developed is able to sequence samples from the human gut and from food, and look at how the microorganisms are interacting. Our customers for this test have been large brands that have advanced R&D departments and academic research centers that are looking for how diet research and the microbiome interact together and how new product development can help us move toward personalized diets when it comes to prebiotic and probiotic diets.”

The impact of the microbiome and the correlations between bacteria of the human gut and the bacteria in the food we eat. The prevailing thesis at the moment is that the microbiome has a significant impact on our health when it comes to disease risk and diet, inflammation and mood disorders. We’re seeing very forward thinking brands like Nestle, ConAgra and Mars putting a lot of attention on the impact of the microbiome when it comes the development of new products, [such as] prebiotics and probiotics, or even specific food products as it pertains to the microbiome. We believe that this intersection— nutrigenomics and the personalized diet—is going to be a massive market, and we’re at the early stages of that.

John Besser, Listeria conference

Deadly Outbreaks and the Role of Metagenomics

By Maria Fontanazza
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John Besser, Listeria conference

Americans consume 350 billion pounds of food each year, with one out of six people falling victim to foodborne illness, and 3000 dying. The significant amount of Listeria outbreaks hitting the industry (most recently, the staggering number occurring in produce) has left many food safety and quality assurance professionals searching for better methods of prevention and detection. Using big data, specifically metagenomics, to improve food safety and detect potentially deadly outbreaks is indeed where the future is headed.

DID YOU KNOW? The estimated U.S. cost of one case of Listeriosis is $1.4 million. Listeria is a prime concern due to the high percentage of fatalities that occur as a result of contracting Listeriosis. And what’s worse is the fact that many of the cases are preventable.

During Food Safety Tech’s Listeria Detection & Control Workshop this week, John Besser, Ph.D., deputy chief of CDC’s Enteric Diseases Laboratory Branch, outlined how the agency is leveraging metagenomics to find unrecognized problems in the food supply. Perhaps the most important element of disease surveillance is that it enables the detection of new issues, especially those whose presence was previously unknown.

John Besser, Listeria conference
CDC’s John Besser, Ph.D. discusses genome-based outbreak detection work at the agency. (Click to enlarge)

Pathogen-specific surveillance allows the detection of more outbreaks, which will in turn make the food supply safer, because it will enable industry to understand the root causes of outbreaks and help them address problems much sooner. The CDC is focused on genome-based outbreak detection because of its ability to achieve faster detection—and with greater precision in identifying the source. The method has also helped the agency solve outbreaks with fewer cases occurring, and it concurrently helps rule out sources.

PulseNet, a nationwide database (comprised of 87 labs in the United States) that links cases most likely to share a cause for illness, has prompted food safety improvements across a variety of products, including sprouts, peanut products, leafy greens, flour, melons, eggs and poultry. Combine this capability with the Listeria initiative, which was launched in the mid-2000s, and the CDC has been able to find more (and smaller) outbreaks than ever before. In fact, there’s been a dramatic increase in the number of outbreak cases that have been solved (with the food source being identified). During the pre-whole genome sequencing (WGS) stage (September 2012­–August 2013), only one outbreak was solved; in year one of the WGS project (September 2013–August 2014), four cases were solved; in year 2 of the WGS project (September 2014–August 2015), nine outbreaks were solved. In these respective time periods, the median number of cases per cluster dropped from six to four to three. In addition, the number of cases linked to a food source jumped from 6 to 16 to 93 during this respective time period.

Besser also discussed the role of metagenomics, or the study of total genetic material recovered directly from environmental samples. A couple of years ago, this was science fiction and wasn’t possible, he said. But as we look to the future, metagenomics will become a lot cheaper as computers become more powerful—and at break-neck speed. He referenced IBM Research, who earlier this year announced a project being conducted in conjunction with Mars, Inc. and Biorad for sequencing the food supply chain (calling it the “largest-ever metagenomics study”).

Read Food Safety Tech’s interview with IBM Research about the next-generation sequencing project, “Preventing Outbreaks a Matter of How, Not When”

Metagenomics enables the profiling of communities of microbiomes anywhere in the food supply chain. And the method is fast—it can potentially shave weeks off the process of identifying clusters of interest. In addition, it can increase the value of interviews conducted with patients who have fallen ill (Think about it: Do you remember what you ate two weeks ago? What about a month ago?).

Currently there are several limiting factors surrounding metagenomics: Cost; sequencing read length and error rate; specific software (and pipelines); computing processing power and bandwidth; and the signal-to-noise factor. However, with the rapid rate in which technology has been improving in this space, the high likelihood of these issues being addressed and resolved in the not-so-distant future will present exciting opportunities in outbreak prevention and detection.

Gina Kramer
Food Safety Think Tank

Listeria, the Pesky Bug is Everywhere!

By Gina R. Nicholson-Kramer
Gina Kramer

“When a flower doesn’t bloom you fix the environment in which it grows, not the flower.”  A quote, by Alexander Den Heijer, trainer, speaker, purposologist, that rings true in food safety. When there is a contamination issue in food processing, one must fix the environment in which food is being processed. Safe food is a product of a clean environment.

We have better environmental sampling programs in our food manufacturing plants and processing facilities, and we have sanitation standard operating procedures, so why are we seeing a prevalence of Listeria, and in rising numbers?  I recently sat down with Jeff Mitchell, vice president of food safety at Chemstar, about the recent increase in Listeria outbreaks and how you can rid your facility of the dangerous pathogen.

We’re seeing Listeria—in product recalls and outbreaks—over the last couple of years, and in multiple numbers. Why do you think this is happening?

Jeff Mitchell, Gina Kramer, Listeria
My interview with Jeff Mitchell about the increase in Listeria recalls.  Watch the video

Jeff Mitchell: The distribution of Listeria in the environment has not changed, and the processes that we use for processing food really haven’t changed. What’s changed is the way that we collect data. We have PulseNet now, which gathers information. If someone goes to a medical treatment facility with a foodborne illness, they’re going to investigate that and they’re going to get the whole genome sequencing on the pathogen.

There’s a difference between understanding what transient Listeria is and resident Listeria. I think there are a lot of sanitation efforts being put forth to eliminate the resident populations—those are the populations we’re most concerned about, and they’re the ones that are being related back to a lot of these recalls.

If I have resident Listeria in my facility, why can’t I find it?

Food Safety Tech is organizing a Listeria Detection & Control Workshop, May 31 – June 1, 2016 in St. Paul, MN. LEARN MOREMitchell: Resident populations of Listeria are found in a biofilm—most bacteria aggregate within a biofilm. A biofilm is a survival mode for the bacteria; it protects it from sanitizer penetration. That layer actually masks it from sampling. You could swab a surface or an area and not pick it up, because the biofilm is masking it.

Jeff goes on to discuss the type of sanitation program that companies should have in place to get rid of resident Listeria. You can learn about the steps you need to take in my video interview.

Chipotle Outbreaks Over, but Origin Remains Unknown

By Maria Fontanazza
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The CDC has declared the Chipotle E. coli outbreaks over. As for its origin(s), we may never know. Yesterday the CDC provided its latest and final update regarding the two outbreaks, stating that investigators used whole genome sequencing to dig a bit deeper, and isolates tested from those sickened in the second outbreak (sickened five people in three states) were not genetically related to isolates from the people who fell ill in the initial outbreak (55 sickened in 11 states, with 21 hospitalizations).

“We are pleased to have this behind us and can place our full energies to implementing our enhanced food safety plan that will establish Chipotle as an industry leader in food safety,” said Steve Ells, founder, chairman and co-CEO of Chipotle in a company statement. “We are extremely focused on executing this program, which designs layers of redundancy and enhanced safety measures to reduce the food safety risk to a level as near to zero as is possible. By adding these programs to an already strong and proven food culture, we strongly believe that we can establish Chipotle as a leader in food safety just as we have become a leader in our quest for the very best ingredients we can find.”

While the outbreaks “appear” to be over, the fact that the source will remain a mystery is a bit unsettling. All the CDC can tell us is that the “likely” source was a common meal item or ingredient served at Chipotle Mexican Grill. Regulatory officials simply cannot trace a food or ingredient to the outbreak. “When a restaurant serves foods with several ingredients that are mixed or cooked together and then used in multiple menu items, it can be more difficult for epidemiologic studies to identity the specific ingredient that is contaminated,” according to the CDC’s final update on the outbreak.

The most recent reported illness started on December 1, 2015. No deaths were reported as a result of either of the outbreaks.

Today Chipotle released its Q4 2015 earnings, reporting a 6.8% decrease in revenue ($997.5 million) compared to Q4 2014. However, 2015 revenue increased 9.6% over 2014.

The problems are not over for the restaurant chain either. On January 28, Chipotle was served another subpoena that broadened the scope of the existing DOJ investigation. The company stated the following in a release, “The new subpoena requires us to produce documents and information related to company-wide food safety matters dating back to January 1, 2013, and supersedes the subpoena served in December 2015 that was limited to a single Chipotle restaurant in Simi Valley, California. We intend to fully cooperate in the investigation.”