In the last few years, we have seen the regulatory agencies begin to utilize a new detection method that will ultimately be a game changer – whole genome sequencing (WGS). Many of us may have heard of this technique related to cancer research and understanding inherited diseases. As the science has advanced, the complete DNA of microorganisms can be analyzed and minute differences can be identified between DNA strains. This advance capability of determining whether two samples match has implications in foodborne outbreaks and in regulatory verification testing.
Let’s first understand the technology – then it is easier to understand how it can be utilized in the world of food safety.
To better understand the technology, let’s go back to our college genetics class. As you may recall, the genome is an organism’s complete set of DNA. The word “genome” was derived by combining the words “gene” and “chromosome.” (Stretching your brain further, you may recall that genes are segments of DNA and are organized units of chromosomes.)
Today, it is possible to plot out the entire DNA sequence of an organism. This entire DNA sequence is huge. If the human genome was a book, it would have 23 chapters (chromosomes). Within each chapter, there would be between 48 to 250 million letters (A,C,G,T – the “base pairs”) without spaces, totaling over 3.2 billion letters. Microorganisms, such as Salmonella, are a short story in comparison, around 4.5 million letters depending on serotype.
Back to genetics class – genome sequencing analysis is looking for differences at a single nucleotide within a base pair. In essence, the technique looks for differences in the A, T, C or G between members of the same species. The differences are referred to as a single nucleotide polymorphism (SNP); a change of a nucleotide at a single base-pair location on DNA.
Currently, researchers are working to identify which SNPs are important and whether any particular meaning can be assigned to any specific SNPs. For example, can one SNP signify virulence, geographic origin, antibiotic resistance, etc.? In the interim, the entire genome is analyzed in determining whether there is a commonality between two samples (e.g., a human patient sample and a product sample).
GS is continuing to develop, and its widespread use is a question of when, not if. The cost of GS has significantly reduced in the past 12 years, from over $100 million to under $1,000 (for the entire human genome), with further reductions expected. As to bacteria, it is now far less expensive to run GS than to run serotype and two PFGE analyses. The low cost, rapid turn around, and pin point accuracy make GS a very advantageous tool for public health agencies.
