Applications of Whole Genome Sequencing (WGS) technology on food safety management: US presentation

1. U.S. Presentations Dr. Marc Allard Dr. John Besser Dr. Stephanie Defibaug-Chavez

2. John Besser CDC, Enteric Diseases Laboratory Branch U.S. Nationwide Real-time WGS-based Surveillance

3. Annual workflow: 60,000-70,000 isolates/yr PulseNet Database: ~750,000 patterns (2015) 87 laboratories

4. National Cluster Investigations: 30 – 60 monitored per week State Cluster Investigations: 1,500 – 2,000 per year

5. Foodborne Disease Surveillance Farm Transport Processing Distribution Preparation Disease surveillance X Limit ongoing illness Fix underlying problems, measure effectiveness of controls Food monitoring / Genome TrackR

6. Food Commodities Made Safer Through PulseNet- Triggered Outbreak Investigations Ready-to-eat & “ready-to cook” foods Beef Spices Tree nuts Eggs Vine vegetables Leafy greens Poultry Peanut products Sprouts Mellon Flour Deli meats Cheese and dairy

7. Listeria Outbreaks and Incidence, 1983-2014 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Outbreak Incidence Pre-PulseNet 0.3 69 Early PulseNet 2.3 11 Listeria Initiative 2.9 5.5 No. outbreaks Incidence (per million pop) Era Outbreaks per year Median cases per outbreak WGS ? ?

8. LM case State/Local Health Agency CDC PulseNet FDA USDA Nationwide Listeriosis Surveillance System • Food / animal, environment sampling Cases Interview Case / Food questionnaire Isolates GenomeTrakR Isolates WGS PFGE WGS PFGE Case-Case StudiesNCBI-Genbank (U.S.) DDBJ (Japan) EMBL (Europe) upload analysis International Nucleotide Sequence Database Collaboration WGS

9. PulseNet WGS Requirements  High resolution strain-typing, high epidemiological concordance  Consolidation of subtyping and reference laboratory workflows  Compatible with epidemiology and regulatory tracking systems  Ability to compare and communicate results locally, nationally, globally  Fast, economical  Local control  Minimal need for local bioinformatics, local high performance computing

10. Public Health WGS Workflow Nomenclature server Calculation engine Trimming, mapping, de novo assembly, SNP detection, allele detection PH databases Users at CDC and in the States Allele databases External storage NCBI, ENA, BaseSpace Sequencer Raw sequences LIMS Data pathway Proposed data pathway Analysis request Genus/species Serotype Pathotype Resistance 7-gene MLST rMLST cMLST wgMLST hqSNP analysis (v. 7.6)

11. 14 N/A 1 6 19 6 4 4 21 6 9 3 0 5 10 15 20 25 No. of clusters detected No. of clusters detected sooner or only by WGS No. of outbreaks solved (food source identified) Median no. of cases per cluster Pre-WGS (Sept 2012–Aug 2013) WGS Year 1 (Sept 2013–Aug 2014) WGS Year 2 (Sept 2014–Aug 2015) Listeria Cluster Metrics Before and After WGS Note that cluster 1508MLGX6-1WGS counted as solved with 24 cases

12. Solved Outbreaks: New Food Sources of Listeriosis

13. Listeria and Caramel Apples • 35 cases • 12 states • 34 hospitalizations • 7 deaths

14. Whole-Genome Multilocus Sequence Typing (wgMLST) wgMLST (<All Characters>) 100 90 80 70 60 50 Key . . . . . . . . . . . . . . . . . . . . . RunIds . CalculationStatuscdc_id 2014L-6572 2014L-6716 2014L-6704 2014L-6707 2014L-6684 2014L-6710 2014L-6656 2014L-6724 2014L-6681 2014L-6695 2014L-6677 2014L-6679 2014L-6714 2014L-6723 2014L-6660 2014L-6713 2014L-6577 Id CFSAN023708 PNUSAL001035 PNUSAL001167 PNUSAL001188 PNUSAL001177 PNUSAL001180 PNUSAL001146 PNUSAL001157 PNUSAL001168 PNUSAL001182 PNUSAL001131 PNUSAL001196 PNUSAL001154 PNUSAL001166 PNUSAL001151 PNUSAL001153 PNUSAL001186 PNUSAL001195 PNUSAL001135 PNUSAL001185 PNUSAL001040 State ID USDA_853178331 MN___C2014016179 MN___C2014019515 TX___TXACB1403719 WI___14MP008990 AZ___AZ00023560 MN___C2014019204 CDC__M14-119 MN___C2014019628 CDC__M14-124 TX___TXACB1403543 AZ___AZ00023800 AZ___AZ00023172 CDC__M14-123 WI___14MP008696 AZ___AZ00023398 NM___2014035025 CDC__M14-127 CDC__2014033414 NM___2014035347 CO___HUM-2014016. PFGE-AscI-pattern GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16.0012 GX6A16 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 GX6A16.0135 PFGE-ApaI-pattern GX6A12.0126 GX6A12.0126 GX6A12.0696 GX6A12.0696 GX6A12.0126 GX6A12.0696 GX6A12.0696 GX6A12.0696 GX6A12.0696 GX6A12.0696 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 GX6A12.0349 Outbreak 1411MNGX6-1NOT 1411MNGX6-1NOT 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MNGX6-1 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS 1411MLGX6-1WGS Serotype . . . . . . . . . . . . . . . . . . . . . IsolatDate 2014-07-18 2014-09-08 2014-11-06 2014-11-05 2014-11-09 2014-10-29 2014-10-26 2014-11-08 2014-10-17 2014-11-08 2014-10-20 2014-11-02 2014-10-29 2014-10-26 2014-11-08 2014-11-06 2014-10-21 2014-11-07 2014-09-12 4 [1–6] 89 [89–89] 5 [1–114] 3 [0–10] 4 [0–44] 1,628 [0–1,694] Allele differences at node: median [min–max] (>5,800 loci analyzed by BioNumerics software) Cluster 1 (≤6 allele differences) Cluster 2 (≤10 allele differences) PFGE Unrelated isolates (hot dog and patient) Unrelated patient isolate (Sept. 2014) Highly-related patient isolate; different PFGE pattern Not closely related (minimum 1,628 allele differences) Data as of December 9, 2014 PFGE Pattern 1 PFGE Pattern 2 PFGE Pattern 3

15.  Inclusion/exclusion of individual cases in clusters  Greater significance of smaller disease clusters  Stronger hypotheses from food/environment to human illness “matches”  Ruling out clusters  Root cause analysis Real-time WGS Appears Useful for…..

16.  Successful use of WGS requires integrated real- time surveillance (not just a better lab method)  Acquiring and analyzing exposure data is still the limiting factor of sporadic case-based surveillance  More attention needs to be given to cluster detection and epidemiological analysis methods Lessons Learned

17. Projected wgMLST Database Validation and Deployment Timeline Apr 14 Oct 14 Apr 15 Oct 15 Apr 16 Oct 16 Apr 17 Oct 17 Apr 18 Oct 18 Apr 19 Development and internal validation Deployment Development and internal validation Deployment Development and internal validation Deployment Development and internal validation Deployment Development and internal validation ← External validation ← External validation ← External validation ← External validation External validation → Cronobacter &Yersinia Vibrio, Shigella & other diarrheagenic E. coli Salmonella Campylobacteraceae & Shiga toxin-producing E. coli (STEC) Listeria monocytogenes

18. Jbesser@cdc.gov The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease Control and Prevention U.S. Nationwide Real-time WGS-based Surveillance

19. GenomeTrakr: A Pathogen Database Marc W. Allard, PhD Senior Biomedical Research Services Officer Division of Microbiology Marc.Allard@fda.hhs.gov Food and Agriculture Organization of the United Nations (FAO): Expert workshop on practical applications of Whole Genome Sequencing (WGS) on food safety management. Dec. 7-8, 2015 Eric W. Brown, PhD Director Division of Microbiology Eric.Brown@fda.hhs.gov

20. PFGE identical in red NGS distinguishes geographical structure among closely related Salmonella Bareilly strains

21. Same PFGE but not part of the outbreak Outbreak Isolates 2-5 SNPs SNP phylogeny for S. Bareilly strains

22. 22

23. S. Braenderup

24. GenomeTrakr Fast Facts  First distributed network of labs to utilize WGS for pathogen identification  GenomeTrakr network has sequenced more than 40,000 isolates, and closed more than 100 genomes through November 12, 2015.  Currently sequencing more than 1,000 isolates a month  The need for increased number of well characterized environmental (food, water, facility, etc.) sequences may outweigh the need for extensive clinical samples

25. GenomeTrakr Labs • 14 federal labs • 14 state and university labs • 1 U.S. hospital lab • 5 labs outside of the U.S. • Collaborations with independent academic researchers • More GenomeTrakr labs coming on-line

26. 27 NumberofSequences (asofthelastdayofthequarter) Total Number of Sequences in the GenomeTrakr Database 2013 2014 2015 Average Number of Sequences Added Per Month in 2013 = 184 Average Number of Sequences Added Per Month in 2014 = 1,049 First sequences uploaded in Feb 2013 Public Health England uploads more than 8,000 Salmonella sequences

27. 0 5 10 15 20 25 30 35 40 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 Timeline for Foodborne Illness Investigation Using Whole Genome Sequencing Contaminated food enters commerce FDA, CDC, FSIS, and States use WGS in real-time and in parallel on clinical, food, and environmental samples Source of contamination identified early through WGS combined database queries Averted Illnesses NumberofCases Days

28. MINIMAL PATHOGEN METADATA (FOODBORNE OUTBREAKS) sample_name organism strain/isolate Category (attribute_package) 1a) Clinical/Host-associated 1a1) specific_host 1a2) isolation_source 1a3) host-disease OR 1b) Environmental/Food/Other 1b1) isolation_source Countries, Academia, and Food Industry can hold confidential metadata linked to public records collection_date Geographic location 6a) geo_loc_name OR 6b) lat_lon collected by Where When Who What

29. Immediate impacts of WGS to industry, growers, and distributers, countries, states.  Earlier intervention means: 1) Reduced amount of recalled product; 2) fewer sick patients which means fewer lawsuits; 3) less impact overall and minimal damage to brand recognition.

30. Impacts to industry, growers, and distributers (continued). Regular testing throughout network: 1) identifies specific suppliers that are introducing contaminants; 2) identifies whether contaminant is resident to a facility or transient; 3) knowledge of where contaminant is coming from allows industry to fix the problem based on scientific evidence. Shift costs to the supplier who has introduced the contaminant. How often is the root cause of the problem left unresolved to occur again at a later date? 33

31. Background: CFSAN SNP Pipeline http://snp-pipeline.rtfd.org Shttps://github.com/CFSANBiostatistics/snp-pipeline https://pypi.python.org/pypi/snp-pipeline Davis S, Pettengill JB, Luo Y, Payne J, Shpuntoff A, Rand H, Strain E. (2015) CFSAN SNP Pipeline: an automated method for constructing SNP matrices from next-generation sequence data. PeerJ Computer Science 1:e20 https://dx.doi.org/10.7717/peerj-cs.20 Intended for use by bioinformaticists (Linux)

32. Molecular Epidemiology and Ecology of Multi-drug Resistance (MDR) Salmonella in Tanzania Julius Medardus Sokoine University of agriculture Wondwossen A. Gebreyes Gebreyes.1@osu.edu

33. ICOPHAI GenomeTrakr partnership

34. FDA GenomeTrakr partnership 924 isolates submitted to FDA-CFSAN • Brazil (4) • Ethiopia (401) • Kenya (86) • Mexico (63) • Tanzania (64) • Thailand (60) • U.S. –OSU (247) 37

35. Tanzania • WGS- 45 food animal isolates completed • All Unknown STs • Plasmid types- ColRNAI, IncI1, IncI2, IncFII, ColpV2 (total 10)- Others? • Kentucky (16/ 45) and Not conforming with any known type (n=8) • Pending- HM and biocide tolerance genes/ efflux system… • Comparison with isolates of human origin?

36. Whole Genome Sequencing Program (WGS) http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/default.htm#trakr GenomeTrakr • State and Federal laboratory network collecting and sharing genomic data from foodborne pathogens • Distributed sequencing based network • Partner with NIH • Open-access genomic reference database • http://www.ncbi.nlm.nih.gov/bioproject/183844 • Can be used to find the contamination sources of current and future outbreaks

37. For more information:  For information about joining the GenomeTrakr network as a sequencing lab, providing isolates to a current member lab for sequencing, or using the GenomeTrakr database as a research tool, please contact FDA at FoodWGS@fda.hhs.gov

38. ORA OCC OFS OC OAO OFVM/SRSC CFSAN CDER CBER CDRH CVM NCTR FDA CHIEF SCIENTIST OIP OARSA SCIENCE BOARD IAS FFC FERN JIFSAN ADVISORY COMMITTEE IFSH MOFFETT CENTER CIO DAUPHIN ISLAND CFSAN-OCD CORE WESTERN CENTER INTERNAL FDA STAKEHOLDERS FDLI GMA VaFSTF CDC FBI PULSENET-LATIN AM. AM. ACAD MICROBIOL ASM FSIS ARS UNIV VERMONT MINN DOH AZ DOH UNIV FL VA DOH WA DOH TX DOH NY AG LAB IRISH FSA NOVA SE UNIV IGS BALTIMORE INFORM MEETING HONGKONG POLYT U NIST ITALIAN FSA EFSA WHO-FOOD SAFETT DIR. WHO-GFN CDC-EU EMERGING INFECTIOUS DIS CONF DANISH TECH UNIV NM STATE UNIV/ NM DOH CARLOS MALBRAN INST/ARG ST COULD UNIV/FOOD MICRO SENASICA GMI NY DOH/WADSWORTH CENT UNIV HAMBURG CHINA CDC NESTLE FERA-UK MD DOH IAFP APHL AFDO BELGIUM VaTech US ARMY US NAVY MELBOURNE FSA (AUS) UNIV NEBRASKA PUBLIC HEALTH ENGLAND DHS DELMARVA TASKFORCE PENN STATE FOOD SCIENCE PROD MAN ASSOC ILLUMINA UNIV IRELAND/DUBLIN COLLEGE NCBI/NIH GSRS GLOBAL SUMMIT FAO/OIE PUBLIC HEALTH CANADA CFIA HEALTH CANADA INTL VTEC MEETING CPS-GA AOAC UNITED FRESH COLUMBIA HAWAII DOH CA DOH ALASKA DOH SOUTH DAK UNIV UNIV GA UNIV IOWA/DOH UNIV CHILE BRAZIL OSU VETNET TURKEY MEXICO IEH SILLAKER NEW ENG BIOLAB PACIFIC BIO CLC-BIO/QIAGEN CON-AGRA DUPONT AGILENT UC-DAVIS HARVARD MED INFORM MEETING THAILAND

39. Food Safety and Inspection Service: 42

40. Food Safety and Inspection Service: WGS for Food Safety Management: FSIS Perspective Stephanie Defibaugh-Chavez, Ph.D. Senior Microbiologist, Science Staff Office of Public Health Science US Department of Agriculture, FSIS FAO WGS Meeting – December 2015 43

41. Food Safety and Inspection Service:Food Safety and Inspection Service: • FSIS is the public health agency in the U.S. Department of Agriculture responsible for ensuring that the nation's commercial supply of meat, poultry, and processed egg products is safe, wholesome, and correctly labeled and packaged • Regulates more than 6,000 slaughter and processing establishments nationwide • Verifies safety of approximately 100 billion pounds of product annually 44 FSIS Mission

42. Food Safety and Inspection Service:Food Safety and Inspection Service: • Improved resolution for foodborne illness investigations – Improved strain discrimination, illness cluster detection, and case classification • Supports FSIS mission goals – Effectively use science to understand foodborne illness and emerging microbiological trends – Identification of environmental harborage or recurrences of pathogens in FSIS-regulated establishments/products to further support the inspection and verification process • Alignment of pathogen surveillance with our domestic public health and regulatory partners – Collaborative efforts with US Food and Drug Administration Center for Food Safety and Applied Nutrition (FDA-CFSAN), the US Centers for Disease Control and Prevention (CDC), the US National Institutes of Health National Center for Biotechnology Information (NCBI), and also state/local health partners/laboratories 45 Whole Genome Sequencing at FSIS: Benefits

43. Food Safety and Inspection Service:Food Safety and Inspection Service: • FSIS continues to build capacity for WGS of isolates obtained from FSIS sampling programs – Expect full capacity with 6 sequencers by FY 2017 – Goal is to sequence around 5000 isolates per year • FSIS considers available WGS analyses in addition to PFGE and epidemiological information to further understand the relationship between clinical and food isolates • FSIS is part of an interagency collaboration with CDC, FDA, and NCBI (Gen-FS) to harmonize efforts for implementation of WGS for food safety purposes within the US 46 WGS at FSIS: Current Status and Short Term Plans

44. Food Safety and Inspection Service:Food Safety and Inspection Service: • Product/Source type (Ready to eat product, raw meat/poultry, environmental swab, etc.) • Year sample was collected • State where sample was collected • Subtyping information when available – Salmonella – serotype and PFGE data – Adulterant STECs - O-group and PFGE data – Campylobacter – species and PFGE – Listeria monocytogenes - PFGE • Metadata and sequence data is immediately available for upload to NCBI 47 WGS at FSIS: Data Sharing (Metadata and sequence data)

45. Food Safety and Inspection Service:Food Safety and Inspection Service: • Data storage and transmission – Massive volume of data generated – FTP and other IT-related security issues • Laboratory considerations – Scope of ISO 17025 accreditation (sequence quality) – Need for high-throughput sequencing capacity for real- time applications • Bioinformatics – Interpretation of strain relatedness • hqSNP, wgMLST, k-mer • Incorporating epidemiological and other metadata in analyses 48 WGS at FSIS: Challenges

46. Food Safety and Inspection Service:Food Safety and Inspection Service: • Case definitions: FSIS depends on its public health partner (CDC/States) for case definitions, the descriptions of the outbreak strain(s) and the subtyping method used to define the strain(s) • Higher resolution subtyping and evolving strains: Food and environmental samples collected as part of an outbreak investigation may span a period of time longer than the outbreak – genetic drift should be considered • Using WGS for regulatory decisions: FSIS is exploring how to interpret and apply the case definitions established by our public health partners that include WGS criteria to FSIS surveillance and investigative results 49 WGS at FSIS: Challenges

47. Food Safety and Inspection Service:Food Safety and Inspection Service: • BAX speciation – Campylobacter • Molecular Serotype – Salmonella • Pulse Field Gel Electrophoresis – Salmonella – Campylobacter – Adulterant STECs – Listeria monocytogenes • Antimicrobial Susceptibility Testing – Salmonella – Campylobacter – E. coli – Enterococcus 50 WGS at FSIS: Future Considerations A single WGS workflow could potentially consolidate all analyses

48. Food Safety and Inspection Service:Food Safety and Inspection Service: 51 Example: Retrospective WGS analysis 51 Primary pattern A Primary pattern B Secondary pattern C Secondary pattern D Secondary pattern E • FSIS food and environmental samples from one investigation were compared to clinical isolates with an epidemiological link to the establishment where sampling occurred • The isolates from the investigative sampling had 2 different primary PFGE patterns and 3 different secondary PFGE patterns • WGS was able to show high similarity (0-5 SNP differences) between differing primary PFGE patterns and primary/secondary combinations

49. Food Safety and Inspection Service: Questions? 52 Dr. Stephanie Defibaugh-Chavez Stephanie.Defibaugh-Chavez@fsis.usda.gov

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