1. Whole Genome Sequencing aka “WGS” - utility in foodborne illness outbreak detection and investigations Dan Rice FDA ORA – Pacific Regional Lab Northwest

2. Foodborne illness in the US  We have one of the safest food supplies in world but burden of illness still high  Estimated 1 in 6 Americans (48 million people) sick annually with foodborne illness  128,000 hospitalizations  3,000 deaths  Annual laboratory confirmed cases in US  Campylobacter – 43,696  Salmonella – 41,930  E. coli O157 – 3,704  Shiga-toxin producing E. coli (STECs) – 1,579  Listeria monocytogenes – 808

3. JAMA June 18, 2014 311(23): 2374 We still have work to do….

4. http://www.cspinet.org/new/200910061.html http://www.nextgenerationfood.com/news/risky-food-list/ The 10 Riskiest Foods

5. PulseNet  Network of public health labs  Perform standardized protocols of PFGE on:  Salmonella enterica  Campylobacter ssp.  E. coli O157 and other Shiga-toxin producing E. coli (STECs)  Listeria monocytogenes  Shigella spp.  Data sharing in private network

6. PFGE Patterns of L. monocytogenes isolates associated w/alfalfa sprouts

7. L. monocytogenes - Outbreaks and Incidence, 1978-1997 Before PulseNet (20 years) 1978-1997 5 outbreaks Median 69 cases/outbreak 1989: hot dogs detected as source 1985: large cheese outbreak No. outbreaks Incidence (per million pop) SOURCE: John Besser (CDC)

8. L. monocytogenes - Outbreaks and Incidence, 1978-2003 Before PulseNet (20 years) 1978-1997 5 outbreaks Median 69 cases/outbreak PulseNet’s first years (6 years) 1998-2003 14 outbreaks Median 11 cases/outbreak 1998: PulseNet began 1989: hot dogs detected as source 1985: large cheese outbreak No. outbreaks Incidence (per million pop) SOURCE: John Besser (CDC)

9. L. monocytogenes - Outbreaks and Incidence, 1978-2012 Before PulseNet (20 years) 1978-1997 5 outbreaks Median 69 cases/outbreak PulseNet’s first years (6 years) 1998-2003 14 outbreaks Median 11 cases/outbreak Listeria Initiative & PulseNet (9 years) 2004-2012 28 outbreaks Median 5.5 cases/outbreak No. outbreaks Incidence (per million pop) SOURCE: John Besser (CDC)

10. Changes in technology (1983-2014) 1983 First Cell Phone: Weighed 2.5lbs and could only be used for 20min before the battery died. Use: phone calls; not widely adopted until late 1990’s/early 2000’s Apple iPhone 6: Up to 24hr of phone talk time; up to 16 days of standby time; weighs 4.55 oz; 128GB on board storage; Use: Phone calls, texts, web browsing, fitness tracking, photo/videos, GPS tracking, books, music, movies, games, and the list keeps growing….

11. Why replace PFGE with WGS?  PFGE served practical public health function but data are qualitative  Whole genome sequencing (WGS) reveals complete DNA make-up of organism, better resolution both within and between species.  Public health labs now using WGS to perform foodborne pathogen identification during foodborne illness outbreaks

12. Why replace PFGE with WGS?  Whole genome sequencing performs same function as PFGE but also differentiates strains of foodborne pathogens, no matter what the species  Used to determine important information such as;  Serotype  Virulence attributes  Antibiotic resistance  Other novel markers  Technology works on all microorganisms, ideal for laboratories that support public health

13. Why develop a WGS based network?  Tracking and Tracing of food pathogens  Faster identification of the food involved in the outbreak  Global travel  Global food supply  IT infrastructure exists

14.  WGS is high resolution  3-5 million data points are collected for each isolate vs. 12 – 24 agarose gel bands  WGS analyses statistically robust  Unlike PFGE patterns, WGS data analyzed in evolutionary context. Accurate and stable genetic changes within pathogen genomes enable ID of specific common sources of outbreak strains (farms, processing plants, food types, and geographic regions).  Source Tracking is Key Application PFGE v/s WGS

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

16. Is WGS a viable solution? Cost Increasing ease of operation Database longevity Comparable times to conventional pipelines Sample prep –Identical for all pathogens Cost savings –Resistance, subtyping, virulence factors, more… New applications –tracking, regulatory/compliance actions, historical trends, more… Illumina Miseq 454 $70/genome in 2014 $40/genome in 2015 w/ Illumina NextSeq Technology

17. Timeline for traditional approach to foodborne illness investigation using PFGE Contaminated food enters commerce Identify contaminated food and confirm that product or environmental sample PFGE pattern match clinical sample pattern Identify illnesses and get PFGE pattern from clinical samples Source of contamination identified too late to prevent most illnesses CDC FDA/FSIS Number of Cases Days

18. Timeline for foodborne illness investigation using WGS Contaminated food enters commerce Local, state and federal agencies 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 Number of Cases Days

19. From C. Darwin's, “On the Origin of Species” - 1859  “It is obvious that the Galapagos Islands would be likely to receive colonists, whether by occasional means of transport or by formerly continuous land, from America; and the Cape de Verde Islands from Africa; and that such colonists would be liable to modification;— the principle of inheritance still betraying their original birthplace" With WGS, we now have the ability to discern those birthplaces…

20. I Detection (species) II Identification (serotype) III Traceback (subtype) Is a pathogen there? What kind of pathogen is it? Is it part of the outbreak? Next-Generation Sequencing qPCR/amp- based tech Maldi-TOF MS/X-MAP Investigating Food Contamination Events with OMICS Approaches Getting to the information needed faster and with more precision

21. Health and economic impact of active WGS-based surveillance  Comparison of 2 related Salmonella contamination events  Similar facilities – broad domestic distribution  Nut butter 1 WGS not used: 42 cases and 10 hospitalizations with estimated 1,260 unreported illnesses (Fall 2012)  Nut butter 2 WGS used: – 4 confirmed cases, 1 hospitalization (Summer 2014)  WGS informed investigation prevented significant illness and hospitalizations

22. Current status  WGS network reliable – efficient, provides very good location specificity of outbreaks  FDA GenomeTrkr program sequenced >15,000 Salmonella and > 4,000 Listeria monocytogenes. Current rate about 1 genome per hour.  Need for increased number of well-characterized environmental (food, water, facility, etc.) sequences may outweigh need for extensive clinical isolates  Highly successful partnership between FDA, CDC and local/state public health labs on real-time tracking of FB illness outbreaks

23. Lessons learned  WGS works – demonstrates value whenever used.  Use in tracebacks and to limit scope of food contamination events is unprecedented – numerous offshoot food safety applications exist (i.e., compliance, quality assurance, risk assessment)  Development of international open source databases promote WGS- based sentinel surveillance on a global scale  WGS more than just an “Epi-tool” - provides information on AMR, virulence, serotype, and other critical factors in one assay, including historical reference to pathogen emergence  WGS international/global ramifications to policy making (trade, commerce)

24. WGS for the national interest  Well established in foodborne illness systems – could extend into other areas of infectious disease (Ebola, MERS, Chikungunya, TB, etc.)  Provides sentinel surveillance on a national/global scale for antimicrobial resistance with real-time capacity for AMR monitoring  Ability to examine historical context and root cause analysis – ID novel biomarkers and historical acquisition of those markers.  Potential to dramatically reduce health care costs in the US – help find “patient zero” swiftly and accurately.

25. Barriers to Moving Forward  Culture independent diagnostic assays – reducing clinical isolates going into PHLs – still need an isolate to perform WGS  Capacity building (funding and training)  Issues surrounding data and metadata release into the public domain  Data handling – terabytes or more/isolate

26. What’s next?  Metagenomics………..

27. Questions? Acknowledgements: -Dr. Eric Brown, FDA CFSAN -Dr. Brian Sauders, NY State Food Laboratory Who kindly shared much of the material for this presentation 27