food safety
From PFGE to NGS: Transforming
the Enterics Reference Bench
by Kristy Kubota, MPH, senior specialist, Food Safety
One of the greatest technological transformations in public health microbiology
is taking place. Public health laboratories (PHLs) across the country are moving
toward whole genome sequencing for subtyping and characterization of bacteria
and viruses. Whole genome sequencing (WGS) using next generation sequencing
(NGS) platforms is faster than conventional characterization methods for
bacteria and cheaper than current workflows when the sequence generated can
be used for both characterization and subtyping.
P
ulseNet, the national molecular subtyping network for foodborne disease surveillance, has utilized pulsed-field
gel electrophoresis (PFGE) as the primary method for laboratory-based surveillance of enteric pathogens for 20
years. In the summer of 2013, the Division of Foodborne, Waterborne and Environmental Diseases, Enteric Diseases
Laboratory Branch along with local and state public health PulseNet laboratories and federal partners (CFSAN/FDA and
FSIS/USDA), began a pilot project to subtype all Listeria monocytogenes isolates by WGS for real-time detection of listeriosis
clusters in the United States.
The project prompted PHLs to begin adopting NGS technologies for routine subtyping of bacterial isolates of foodborne
pathogens. It is anticipated that NGS technologies will eventually replace current workflows to predict virulence and
serotype, and to identify bacterial isolates at the genus to sub-species and clonal levels. Given that many phenotypic tests
used for these purposes have been around for over a century, these developments are revolutionary.
Changing workflows from multiple phenotypic and molecular tests into a single workflow will be a challenge, but the vast
information generated will open the doors to a deeper understanding of the biology of pathogens and the role they play in
foodborne outbreaks.
Interpreting WGS Data
Several tools and software packages now available allow scientists to analyze whole genome sequences after the raw
sequences have been assembled. The Center for Genomic Epidemiology has developed a suite of open access tools
that allow users to input genomic sequences and obtain information regarding acquired virulence (VirulenceFinder),
antimicrobial resistance genes (ResFinder) and in silico serotyping
(SeroTypeFinder). The National Institutes of Health, National Center
for Biotechnology Information (NCBI), offers Pathogen Detection,
which allows public health agencies to rapidly characterize WGS
data for Campylobacter, E. coli, Shigella, Listeria and Salmonella.
Pathogen Detection also provides tools for the identification of
acquired antimicrobial resistance genes and some phylogenetic
tree analysis using kmer-based and SNP-based methods. A
cautionary note: Despite the availability of these tools, many
laboratories may require the support of bioinformaticians to
analyze and interpret their data.
Changing workflows from multiple
phenotypic and molecular tests into a
single workflow will be a challenge,
but the vast information generated
will open the doors to a deeper
understanding of the biology of
pathogens and the role they play
n foodborne outbreaks.
PulseNet laboratories use BioNumerics to analyze and upload
routine PFGE data for surveillance activities and will continue to
employ BioNumerics version 7.5 for analysis of WGS data using
whole genome multi-locus sequence typing (wgMLST). This gene-by-gene method has proven to be highly discriminatory
in subtyping of isolates and demonstrated to be better in some foodborne epidemiological investigations than PFGE.
BioNumerics allows users to analyze WGS data with simple built in tools to identify virulence factors and serotype E. coli
and Salmonella in addition to predicting an isolate’s antimicrobial resistance phenotype. Furthermore, since BioNumerics
software functions as a database, metadata can be aligned with analyzed sequence data for easy interpretation and
generation of reports.
14
LAB MATTERS Summer 2016
PublicHealthLabs
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