Cornell scientist looks to fill gaps in listeria knowledge
Whole genome sequencing (WGS), also known as genetic fingerprinting, has increasingly been used to trace foodborne disease outbreaks to their sources.
The technology also helps identify where a specific pathogen appears to survive in a food processing facility, indicating a food safety risk.
When it comes to Listeria monocytogenes, interpreting WGS data has been hindered because most of the work has been based on samples from humans and foodborne outbreaks. That leaves a large data gap about how widespread the pathogen and closely related species are in the natural environment.
Martin Wiedmann, Ph.D., of Cornell University, sought to fill in some of those gaps with ambitious research that collected and assayed more than 1,000 samples from locations nationwide. It also marked the first effort to collect listeria from throughout the United States and develop a WGS database.
The goal of his recently completed project — “Listeria whole genome sequence data reference sets are needed to allow for improved persistence assessment and source tracking” — was to gain a better understanding of the distribution and ecology of the various listeria species and subtypes.
Although the resulting expanded dataset of listeria genetic fingerprints isn’t intended to be used as confirmation of a foodborne outbreak source, it may help guide food safety experts in their hunt for an origin, Wiedmann said.
“There’s a fear that if you find listeria in a human and you also find it in a production field, you may say that block caused that human case,” he said. “Just because you find the same fingerprint in two different places doesn’t mean that field caused that outbreak. You need additional data to support that.”
Wiedmann’s project involved dividing the United States into 40 equisized grids that overlaid natural environments, such as state and national parks. About 60 cooperators nationwide volunteered to collect the samples.
Within each grid, they sampled five different natural areas spaced at least 20 miles apart. Altogether, project cooperators collected 1,004 samples that resulted in 1,854 isolates.
Of those, 594 isolates were characterized using whole genome sequencing. Among the isolates were L. monocytogenes as well as 18 other Listeria species. Broken down further, 13 were existing species and five were new species. Overall, 11.8% of the samples tested positive for L. monocytogenes and 31% were positive for Listeria species.
While some species and subtypes were distributed across much of the country, others were more regional. For example, a L. monocytogenes subgroup designated lineage III was found concentrated in the eastern United States.
“Not all Listeria is found everywhere,” Wiedmann said. “We found some regional differences. In general, the likelihood of finding Listeria wasn’t the same across the United States. In some areas, you were more likely to find it, which will help the industry to identify high-risk areas. It was geographical, but there were also climate factors and soil factors associated. Sometimes knowing which of these are found in the environment and in which environment can help if you find Listeria in a processing facility. Is it more likely to come in with the raw material or is it living in my facility?”
As part of the project, Wiedmann and his team also performed WGS of listeria isolated from the produce chain and from preexisting collections.
They then performed a comprehensive analysis of the WGS data to identify genetic differences or epidemiological links among the different organisms.
The results were something akin to an ancestral tree showing likely recent common ancestors that could be used to help interpret WGS data.
A processing facility, for example, may have isolated closely related listeria species nine months apart. By delving deeper into the genetics of the two samples, the facility could determine whether persistence was likely an issue and whether the two shared a common ancestor years ago.