FOOD SAFETY
Leveraging Bioinformatics Tools to Dive Deep into Clostridium perfringens
by Robyn Randolph , senior specialist , Food Laboratory Accreditation and Adom Yusuf , associate specialist , Food Safety
Bill Wolfgang is usually knee-deep in whole genome sequencing ( WGS ) data while working in the Bacteriology Laboratory in the New York Department of Health ’ s Wadsworth Center . This work covers many different pathogens , from
Salmonella to
Mycobacterium species to
Staphylococcus . One day , when he was reviewing laboratory data on a
Clostridium perfringens sample he began to wonder : why don ’ t we sequence our isolates of this pathogen ?
While C . perfringens doesn ’ t get the publicity afforded to common foodborne pathogens such as Salmonella or
Listeria , this bacterium is one of the most common causes of foodborne illness in the country . There is currently no national surveillance system for C . perfringens — these cases are investigated locally . Wadsworth laboratory scientists confirm that a sample is C . perfringens and the epidemiologists start the outbreak investigation — they typically find a linkage to a contaminated product , and that ’ s it . “ Why bother with sequencing ?” was the traditional line of thinking .
Changing the Narrative
Wadsworth Center has long been a contributor to
PulseNet and , more recently , to
GenomeTrakr , two surveillance networks that use the power of whole genome sequencing to link human illnesses with contaminated product . Wolfgang and his team wondered if , by not sequencing C . perfringens , they could be missing critical information that could verify New York ’ s illness case clustering . They decided to investigate this hypothesis that WGS-based subtyping can benefit epidemiological investigations of C . perfringens , as it has with other enteric organisms .
The laboratory retrospectively analyzed 52 patient and food isolates associated with 13 foodborne outbreaks , and 24 sporadic isolates collected over the past decade . They built phylogenetic trees using both NCBI Pathogen Detection and GalaxyTrakr ’ s CFSAN single nucleotide polymorphism ( SNP ) pipeline . This analysis helped determine if the phylogenetic clustering aligned with the epidemiologically defined outbreaks .
Surprising Results
They found that the genomic clustering lined up well with the epidemiologic clustering . The phylogenetic trees generated from both the SNP pipeline and Pathogen Detection agreed with one another . They discovered a restricted SNP distance between 0-20 SNPs within a cluster that is associated with an outbreak , which can help identify a sample that should be included in a cluster from a sporadic case . Out of the 13 outbreaks associated with these isolates , there were four outbreaks that had samples included epidemiologically that were not genetically related , as well as two “ outbreaks ” where none of the associated samples were genetically related . This WGS data was of great
value to their epidemiological colleagues , helping to better define outbreaks and hopefully find related cases and sources of contamination more quickly .
For Wolfgang and his team , this successful project relied on the great support received from GenomeTrakr and those supporting the GalaxyTrakr SNP pipeline . Their willingness to quickly and efficiently answer questions or issues that occurred allowed Bill ’ s team to successfully move forward . Their findings were published in the January 2021 issue of Journal of Clinical Microbiology . 1 Based on this project , Wadsworth plans to use GalaxyTrakr tools during future C . perfringens investigations to assist their epidemiologists and protect public health . g
Reference :
1 . Carey J , Cole J , Venkata SLG , Hoyt H , Mingle L , Nicholas D , Musser KA , Wolfgang WJ . Determination of Genomic Epidemiology of Historical Clostridium perfringens Outbreaks in New York State by Use of Two Web-Based Platforms : National Center for Biotechnology Information Pathogen Detection and FDA GalaxyTrakr . J Clin Microbiol . 2021 Jan 21 ; 59 ( 2 ): e02200-20 . doi : 10.1128 / JCM . 02200-20 . PMID : 33177125 ; PMCID : PMC8111140 .
14 LAB MATTERS Fall 2022