Detecting Outbreaks with Whole Genome Sequencing

At a glance

Sequencing technologies have revolutionized our ability to decode the DNA of disease-causing bacteria and viruses. The information we learn allows public health professionals to detect outbreaks sooner, including many outbreaks that would previously have gone undetected.

Colorful spheres that represent cases in a foodborne outbreak on a dark background

Decoding the whole genome

For more than 20 years, public health laboratories have used a DNA fingerprinting technology called Pulsed-Field Gel Electrophoresis (PFGE) to detect and track foodborne illness. In recent years, a set of new technologies have revolutionized our ability to decode DNA. Whole genome sequencing (WGS) gives us a much more detailed DNA fingerprint than PFGE. In public health, WGS transformed how epidemiologists and laboratory scientists approach the detection and investigation of outbreaks. This allows public health agencies across the US to detect outbreaks sooner, including many outbreaks that would previously gone undetected.

To show how this works, let's look at one example:
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Salmonella is one of the most common causes of foodborne illness. Outbreak investigations in 2018 identified 149 cases of Salmonella serotype Enteritidis from seven states in a particular region.

Unsorted cases

Over the course of year, several Salmonella outbreaks were identified, either in real time or retrospectively. Finding outbreaks is much easier and faster when related cases (shown in color) can be sorted from non-related cases (shown in grey).

Animated graphic showing different colored circles that each represent one of the 149 cases of Salmonella during the year of 2018.
All 149 cases of Salmonella.

Cases sorted by PFGE

For the most part, PFGE accurately identifies related cases as are part of the same outbreak. However, each of the outbreak clusters include grey non-outbreak cases mixed in. Those unrelated cases complicate the investigation to find the common source.

Animated graphic showing different colored circles clustered using PFGE technology. Though they are roughly sorted into groups, different strains are mixed in which will make it harder to determine the correct source of these outbreaks.
149 cases of Salmonella sorted using PFGE

Cases sorted by WGS

Using WGS, the outbreak cases are more tightly clustered, and stand out clearly from the disconnected, non-outbreak cases. The large (red) cluster in the center was initially connected to a small number of patrons at two different restaurants located in two separate states. Using WGS, investigators identified those cases were actually part of a larger outbreak, involving several patients who had not been to either restaurant.

Animated graphic showing different colored circles clustered using WGS technology. Almost all of the cases are clearly sorted into matching groups.
Cases of Salmonella with Whole Genome Sequencing

The results

Epidemiologic data suggested the cause of the outbreak was shell eggs. Scientists used whole-genome sequencing to verify the source. Salmonella was found in the implicated eggs. The DNA fingerprint of the egg isolate matched the outbreak, confirming the attribution, and leading to a nationwide recall.

How WGS is being used

In the US, through the federally funded AMD Program, public health agencies apply next-generation sequencing in almost every area of infectious disease, such as:

In food safety, CDC works with FDA, USDA, NIH, and state and local public health agencies to quickly intervene in outbreaks and to better understand how to prevent pathogens from getting into the food system in the first place.

In flu, next-generation sequencing enables faster, more effective characterization of viruses to better understand how they emerge and to improve vaccine protection.

In viral hepatitis, next-generation sequencing is invaluable for outbreak investigations.

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Content Source:
National Center for Emerging and Zoonotic Infectious Diseases (NCEZID)