IV. Challenges and Opportunities of WGS for Illness Detection and Response

The implementation of WGS as the new standard subtyping method in PulseNet began in spring 2019. CDC has worked for several years to increase laboratory and epidemiological capacity for the transition from PFGE to WGS. As of April 26, 2019, 50 states including 64 labs have converted to BioNumerics 7.6, and 48 states including 55 labs are WGS Analysis Certified for Listeria, Salmonella, Escherichia, and Campylobacter. In addition, there have been numerous trainings, webinars, and meetings to prepare for the transition to WGS including

• 50-State OutbreakNet calls
• 50-State Laboratory calls
• PulseNet Webinars
• PulseNet/OutbreakNet Regional meetings
• CSTE’s Advanced Molecular Detection (AMD) Molecular Epidemiology training
• MiSeq/BioNumerics training
• Integrated Food Safety Centers of Excellence WGS training (BioNumerics and WGS interpretation) webinars
• CoE “Office hours” (live consultations for WGS facilitated by CoE staff)

Discussion/Response

Discussion

The Working Group’s discussion included the following observations:

• Concerns exist regarding the future need to triage cluster investigations as states transition to WGS due to the potential increased epidemiological workload and due to reductions in federal funding awarded to states for testing.
• Efforts are underway to create a hierarchical nomenclature consisting of a sequence of five or six numbers (allele codes) that will indicate the relatedness of isolates. Outbreaks will still have outbreak codes, but allele codes will be the WGS equivalent of PFGE patterns. In addition, epidemiologists and laboratorians will still be able to use WGS trees to examine data, but allele codes will be used more often.
• WGS predicts resistance of all sequenced isolates, not only those submitted to CDC for phenotypic resistance testing. WGS will help to rapidly identify known antimicrobial resistance (AR) genes, which will enable public health response in near real time and enable isolates and resistance determinants to be compared globally. In addition, AR surveillance for multistate outbreaks will become timelier and more representative, especially for single-state outbreaks. NARMS will continue to receive 1 in 20 surveillance pathogens to detect novel resistance.
• Although Campylobacter is not routinely sequenced, it has been sequenced to help with AR patterns in the past. Campylobacter isolate sequencing will also be impacted by the increased use of CIDTs by clinical laboratories.
• Concerns were expressed regarding BioNumerics’ global usability and upkeep. Efforts are underway to create a software that is open-source, as well as prepare for metagenomics.
• A concerted effort by federal partners is needed to create a single/joint pipeline for states to submit human, animal, and environmental data. Data currently go through the National Center for Biotechnology Information (NCBI) and then to the PulseNet database.
• WGS helps define “clades of concern”—groups of closely related strains that persist for years. These clades of concern have caused repeated outbreaks from similar sources, more sporadic cases over time, are often multidrug resistant, and may not appear as classic time-place-person outbreaks. Defining clades of concern can provide focus for intensive investigation, traceback, and environmental assessment and new prevention measures by industry and regulatory partners.

Response

Based on these questions, the Working Group highlighted the following possible responses: