WGS Works! Shared Mission Different Roles APPLICATIONS SEQUENCING (WGS) Non-regulatory. Regulatory CDC. FDA and USDA. Peter Gerner-Smidt, MD ScD

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1 PUBLIC HEALTH FOOD SAFETY APPLICATIONS FOR WHOLE GENOME SEQUENCING (WGS) Peter Gerner-Smidt, MD ScD Chief, Enteric Diseases Laboratory Branch 4 th Asia-Pacific International Food Safety Conference, Penang, Malaysia, October 11-13, 216 National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases WGS Works! The Experience In The United States Shared Mission Different Roles Non-regulatory CDC q Disease surveillance q Outbreak detection and investigation q Analyzing burden, trends, and effectiveness of prevention efforts q Attribution to sources q Education and training q Information for policy Regulatory FDA and USDA q Inspection q Enforcement q Investigating farm and production facilities q Product recall q Product traceback q Risk assessment and management

2 Surveillance By WGS In The US Listeria Cluster Metrics Before and After WGS q Integrated Surveillance Began September 213 with Listeria q Since 214 almost all multi-state outbreaks by any foodborne pathogen are investigated by WGS Pre-WGS (Sep 212- Aug 213) WGS Year 1 (Sep 213- Aug 214) WGS Year 2 (Sep 214- Aug 215) Public Health Agency of Canada No of clusters detected N/A No of clusters detected sooner or only by WGS No of outbreaks solved (food source identified) Median no of cases per cluster No cases linked to food source Courtesy: Amanda Conrad, Outbreak Preparedness and Response Branch WGS For Outbreak Investigations 214 US Caramel Apples Listeria Outbreak How WGS Influenced Outbreak Investigations Allele differences at node: median [min max] (>5,8 loci analyzed by BioNumerics software) 1,628 [ 1,694] 89 [89 89] 5 [1 114] 4 [1 6] 3 [ 1] 4 [ 44] 214L L L L L L L L L L L L L L L L-6713 PFGE 214L-6577 Unrelated isolates (hot dog and patient) Highly-related patient isolate; different PFGE pattern Cluster 1 ( 6 allele differences) Cluster 2 ( 1 allele differences) Unrelated patient isolate (Sept 214) Not closely related (minimum 1,628 allele differences) PFGE Pattern 1 PFGE Pattern 2 PFGE Pattern 3 q Improved case definitions in outbreaks Apparent PFGE clusters are not single-source outbreaks or are pseudo-clusters Isolates with same PFGE patterns may be unrelated Isolates with different PFGE patterns may be related q Increase confidence in the link between human and product isolates q Link historical cases to a current outbreak investigation q Characterize the ecology of long-term pathogen reservoirs in the food chain Data are preliminary and subject to change

3 Foodborne Pathogens Know No Borders WGS In Public Health In A Global Perspective q A foodborne infection on one continent may have its source on a different continent q International outbreaks are common q Subtyping methods and analyses must be standardized at the global level to ensure global comparability of data Outbreak investigations fast WGS in Public Health: The analytical tools must be Simple Public health microbiologists are NOT bioinformaticians Standard desktop software Comprehensive All characterization incl analysis in one workflow Working in a network of laboratories- STANDARDIZED Everybody analyze data the same way Free sharing and comparison of data between labs Robust QA/QC Transforming Public Health Microbiology PulseNet and Beyond q Replacing traditional microbiology with WGS For Food Safety Reference Labs: o Cost-efficient consolidation of multiple workflows: Identification serotyping virulence profiling antimicrobial resistance characterization subtyping

4 Genotype Partners In System Development WGS for Reference Characterization International Partners: PulseNet International ecdc EFSA Statens Serum Institut Public Health England Institut Pasteur DTU Academia GMI PHAC US Partners: PulseNet OutbreakNet FDA/CFSAN-CVM Genome Trakr Academia USDA /FSIS-ARS NIH q Genus/species ID q Serotype q Virulence profile q Antimicrobial resistance q Plasmid profile q more 1 We neither have the capacity nor the knowledge to make the WGS transformation alone Foodborne Disease Branches Other CDC and State programs 2 What we do must be in sync with what others do to ensure national and international comparability of data One Shot Characterization Of STEC by WGS Resistance Prediction By WGS Is Very Accurate ResFinder ~ Salmonella ANI Phenotype SerotypeFinder Virulence Finder 7-gene MLST Resistant Not Resistant wgmlst profile: ResFinder Unique phylogenetic relevant strain identifier Resistant Not Resistant Explanation of Virulence and Resistance Markers: Measure Value (%) 95% CI Sensitivity Specificity Kappa coefficient 97 (very good) NARMS, Data are preliminary and subject to change

5 Two WGS Analytical Approaches Nucleotide level comparison: hqsnp Gene- gene comparison: cg/wgmlst hqsnp cg/wgmlst Epidemiological concordance High High Stable nomenclature (No) Yes Speed Slow SNP calling, slow analysis Slow allele calling, fast analysis Local computing requirements Medium-High Low Local bioinformatics expertise Yes No Reference used to perform analysis Sequence of closely related annotated strain Allele database Internationally standardization No Yes Cost of analysis (Expensive) Cheap Subtyping: Two Approaches- Same Results wgmlst Salmonella Newport (JJPX61) hqsnp wgmlst (<All Characters>) PNUSAS411 PNUSAS K K-773 PNUSAS3566 PNUSAS K-753 PNUSAS488 PNUSAS3895 PNUSAS3945 PNUSAS4171 PNUSAS3948 PNUSAS487 PNUSAS4175 PNUSAS3712 PNUSAS49 PNUSAS K-863 PNUSAS4173 PNUSAS3947 PNUSAS K K K-772 PNUSAS4172 PNUSAS355 PNUSAS4174 PNUSAS3689 PNUSAS49 PNUSAS4176 PNUSAS3731 PNUSAS35 216K K K K-791 PNUSAS3732 PNUSAS K-794 PNUSAS448 PNUSAS3949 PNUSAS41 PNUSAS K-695 PNUSAS3467 PNUSAS3776 PNUSAS3271 PNUSAS K K K K K-852 Clade C: 2 8 alleles; median 6 Clade A: 4 alleles; median 2 Clade B: 3 alleles; median PNUSAS3947 PNUSAS487 PNUSAS K K-865 PNUSAS4171 PNUSAS4175 PNUSAS49 PNUSAS3946 PNUSAS3712 PNUSAS4173 PNUSAS K K-753 PNUSAS3864 PNUSAS3945 PNUSAS3862 PNUSAS K-812 PNUSAS PNUSAS3895 PNUSAS488 PNUSAS4174 PNUSAS K PNUSAS K K K-864 PNUSAS PNUSAS K-695 PNUSAS3467 PNUSAS K K-852 PNUSAS41 PNUSAS4176 PNUSAS49 PNUSAS K-794 PNUSAS3863 PNUSAS3949 PNUSAS PNUSAS3776 PNUSAS K K K K-79 Clade A: SNP Clade C: 1-7 SNP Clade B: -1 SNPs Two WGS Analytical Approaches cg/wg MLST is the preferred public health standard May be globally standardized Direct comparison of data generated in different labs Do not require bioinformatics expertise Is NOT computer intensive BUT few MLST allele databases are currently available in public domain hqsnp is best suited for analysis when specific information that is not provided by cg/wgmlst is needed Difficult to standardize Comparison of data generated in different labs will require analysis in each lab after sharing raw sequence data Bioinformatics expertise and high capacity computing required BUT easy to set up with resources available on the internet The Gene-Gene (MLST) approach is scalable PFGE indistinguishable isolates from an outbreak and unrelated to the outbreak (213) UPGMA cgmlst: 1,748 loci wgmlst (Core loci) PNUSAL528 PNUSAL299 PNUSAL14 PNUSAL184 PNUSAL1651 PNUSAL1687 PNUSAL116 PNUSAL512 PNUSAL294 PNUSAL435 PNUSAL515 PNUSAL157 PNUSAL453 PNUSAL452 PNUSAL983 PNUSAL286 PNUSAL19 PNUSAL18 PNUSAL24 PNUSAL31 PNUSAL53 PNUSAL313 PNUSAL82 PNUSAL12 PNUSAL249 GX6A wgmlst (MLST loci) ST825 PNUSAL528 PNUSAL983 PNUSAL1651 PNUSAL1687 PNUSAL453 PNUSAL14 PNUSAL19 PNUSAL452 PNUSAL18 PNUSAL157 PNUSAL24 PNUSAL31 PNUSAL294 PNUSAL435 PNUSAL515 PNUSAL299 PNUSAL116 PNUSAL53 PNUSAL313 PNUSAL82 PNUSAL286 PNUSAL512 PNUSAL12 PNUSAL249 PNUSAL184 GX6A ST825 wgmlst (<All Characters>) PNUSAL528 PNUSAL299 PNUSAL14 PNUSAL184 PNUSAL1651 PNUSAL1687 PNUSAL116 PNUSAL512 PNUSAL294 PNUSAL435 PNUSAL515 PNUSAL157 PNUSAL453 PNUSAL452 PNUSAL983 PNUSAL286 PNUSAL18 PNUSAL31 PNUSAL53 PNUSAL19 PNUSAL24 PNUSAL313 PNUSAL82 PNUSAL12 PNUSAL249 7-gene MLST (Institut Pasteur) GX6A wgmlst: 4,88 loci ST825 7-gene and cgmlst schemes: Institut Pasteur; wgmlst scheme: Institut Pasteur & CDC

6 WGS Data Flow LIMS End users in national/ local laboratories Sequencer Public Health databases Extensive Metadata Genus/species Serotype Pathotype Virulence Resistance 7-gene MLST ST cgmlst Allelic profile wgmlst Allele Code (SNPs) Raw sequences How Close Is Close? Allele & Allele code Databases Allele names, Allele code (strain names) NO Metadata Temporary storage, QA/QC, Data extraction Trimming, mapping, de novo assembly, SNP detection, allele detection NO Metadata External storage NCBI, ENA, Limited Metadata 212K US Caramel Apples Listeria Outbreak Allele differences at node: median [min max] (>5,8 loci analyzed by BioNumerics PFGE software) 89 [89 89] 214L ,628 [ 1,694] 5 [1 114] 4 [1 6] 3 [ 1] 4 [ 44] 214L L L L L L L L L L L L L L L L-6577 Unrelated isolates (hot dog and patient) Highly-related patient isolate; different PFGE pattern Cluster 1 ( 6 allele differences) Cluster 2 ( 1 allele differences) Unrelated patient isolate (Sept 214) Not closely related (minimum 1,628 allele differences) Salmonella serovar Heidelberg multistate outbreak associated with chicken from producer X PFGE patterns Pattern 122 Pattern 672 Pattern 45 Pattern 22 Pattern 326 Pattern 41 Pattern 258 ** Chicken from producer X *** Clinical isolate from 211 ALL patients in the tree exposed to chicken from producer X 212K K K K-1549** 212K-155** 212K K K K K-1633** 212K-1315*** 213K-1649** 213K K K K K K K AM K K K-573** 213K K-98** 213K SNPs 6-46 SNPs 7-6 SNPs 19 SNPs 6-35 SNPs 6-62 SNPs Data are preliminary and subject to change 2

7 Same PFGE Pattern Multiple Outbreaks With Different WGS Profiles Salmonella serovar Heidelberg PFGE pattern JF6X22 single state outbreaks in summer T T T T T T SNPs 2-16 SNPs State A funeral A WGS match between a food isolate and a clinical isolate does NOT mean that the food caused the patient s iilness Listeria Cluster 213T T-359-A 213T SNPs Unrelated Sporadic case 213AM AM SNPs State B daycare 213AM T K K-167- Unrelated Sporadic case 213K K K K-139- Unrelated Sporadic case 3-6 SNPs (3-9 SNPs) State C Church Supper They likely share an ancestor somewhere in the food production chain Epidemiological and traceback information remains critical 5 How Close Is Close? q Depends on the question you want answered Outbreak? Attribution? Production ecology? Global Epidemiology? q Depends on the pathogen q Depends on the epidemiological context WGS Is Here To Stay But Issues Needs to Be Addressed q Data sharing With everyone? With public health partners? With industry? q Politics & Ethics Trade barriers? Liability? Intellectual property? vs Common good q How do we implement WGS in lesser resourced countries? Some have no basic surveillance capacity

8 Acknowledgements Center for Genomic Epidemiology, DTU University of Oxford, M Maiden, K Jolly Public Health England, T Dallman Public Health Agency of Canada Disclaimers: The findings and conclusions in this presentation are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention or by the US Department of Health and Human Services National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases