1. Case studies on the use of WGS in foodborne disease surveillance and outbreak investigations
Steffany Cavallo, MPH (Tennessee)
Carlota Medus, PhD, MPH (Minnesota)
Matt Wise, MPH, PhD (CDC)
Molecular Epidemiology Webinar Series, 6/29/2017

2. Introduction Molecular Epidemiology Webinars
Access other webinars and modules on NYS COE website:
Integrated Food Safety Centers of Excellence
Core activity: training other public health staff
Today’s webinar: WGS case studies
Tennessee (Steffany Cavallo)
Minnesota (Carlota Medus)
CDC (Matt Wise)
1 – WGS for epidemiologists
2 – WGS data analysis methodology – what epis need to know
3 – hqSNP, wgMLST, and the WGS alphabet soup – what epis need to know
4 – Case studies on use of WGS in foodborne disease surveillance and outbreak investigation
Modules:
Genetics & Evolution 101
Intro to next gen sequencing methodologies
Introduction to sequence analysis
Tree building 101

3. Implementation of wgs in tennessee
Tennessee Integrated Food Safety Center of Excellence
Implementation of wgs in tennessee

4. Tennessee Population: 6.6 million Tennessee Department of Health (TDH)
7 Regional Health Departments
6 Metropolitan Health Departments
2 State Public Health Laboratories
1 County Public Health Laboratory
Combination: Bit of a mix of centralized and decentralized state
Currently our main public health lab is located in Nashville and is the only lab that preforms whole genome sequencing

5. Whole Genome Sequencing in TN
Began December 2014
Listeria Initiative
5 Illumina MiSeq Sequencing instruments
1 additional instrument on the way
4 laboratorians certified
2 new employees being trained

6. Specimen received at SPHL
WGS Process
Specimen received at SPHL
WGS Performed
Analysis
NYS
CDC
Real Time
Listeria
E. coli Non-O157
Salmonella*
Upon Request
Salmonella
Campylobacter
Hepatitis C
Shigella
ARLN
Future Activities
Gonorrhea
Organisms:
*350 isolates behind true real time

7. Epidemiology-Laboratory Liaison
Position developed to improve/enhance communication between epidemiologists and laboratorians
Main point of contact between Central Office Epidemiologists and Laboratorians
Weekly visits to SPHL in Nashville, TN
Create a PFGE cluster report in BioNumerics
Assign identified clusters to epidemiologists
WGS prioritization, analysis/interpretation

8. Three WGS Examples
1.) Retrospective analysis: understanding genomic diversity for common TN strains
2.) Use during acute outbreaks
Identification of additional cases
3.) Use for long-term outbreak identification
REDUNDANT – MOVE TO END

9. Example 1: 2014 S. Enteritidis Pattern JEGX01. 0004 and JEGX01
Example 1: 2014 S. Enteritidis Pattern JEGX and JEGX (SE Pattern 4 and 5)
PFGE WGS
JEGX
113 isolates sequenced
69 isolates fit into 16 clusters
SNPs < 5
Same thing for Newport summer 2017
JEGX

10. Example 2: S. Enteritidis Pattern 4, June 2016

11. Example 2: S. Enteritidis Pattern 4
6/7 ate at Restaurant A on Friday, May 13
Analytic study implicates hangar steak dish (RR: 4.5)
Local eggs from Farm X, distributed throughout 2 counties
WGS supports epidemiology
Enhanced surveillance, June - August
9 cases identified (age, geography, eating patterns)
8 ate at Restaurant B
Source eggs from Farm X

12. Example 2: Epi-curve Summary, n=32

13. Example 2: WGS Results Heat Map Phylogenetic Tree 0-3 SNPs
*Next closest isolate is 30 SNPs away
0 SNPs
Restaurant B
Restaurant A
JEGX

14. Example 3: S. Enteritidis Pattern 4, March 2017
March 7 – Increase of SE Pattern 4 isolates on the weekly PFGE cluster report
March 31– WGS results available
The PFGE cluster report is created weekly and serves as the main tool for cluster identification.
This is the actual excerpt of the report that shows 11 cases of Salmonella Enteritidis, all of which were added in a 3 week time frame. We don’t expect our surge of Salonella cases typically until the first week of June, so having several new cases in february and march made us a little uneasy.
I have terrible vision, and so for those who also have terrible vision or maybe sitting in the back of the room, I don’t expect you to see all the small details on this slide, but I do want to point out that each row represents 1 isolate and there are 10 columns that have very basic laboratory and epi data that allow us to quickly compare cases in a cluster for common relationships. For instance we review collection date which we use as an idicator for onset date, assuming that cases with similar dates may have gotten ill around the same time. We also review if cases are similar in age, gender and geogrophy and another thing we noticed with these cases is that several of them resided in one county.
Whole genome sequencing (WGS) was requested because Salmonella Enteritidis Pattern 4 is the most common pattern in TN and nationally, which makes it difficult to distinguish sporadic cases from true clusters of illness. WGS analysis was completed on 3/31/2017 and linked only 3 of the cases together with a difference of 0 Single Nucleotide Polymorphisms (SNPs); the other 8 cases were deemed sporadic.
This is a perfect example of how wgs is a powerful tool in including and excluding cases in a cluster. No doubt, with out WGS all these cases would have been linked together, making finding a common source impossible.
So, that’s great, now we only have 3 cases to look into and can ignore all the other pattern 4s.

15. WGS Identifies a 2 year outbreak
Example 3: WGS Results
Heat Map
2015 & 2016
Phylogenetic Tree
However this higher resolution also allowed us to link our cases with 6 historical cases with collection dates in 2015 and 2016.
So as you can see at the bottom we have our 3 most recent 2017 cases, but we also have 6 additional cases with a maximum of 3 snps difference.
At this point we were all a little stumped as whether to include the older cases, so we reached out to CDC for guidance and was advised that all cases within a 10 SNP range should be considered related.
Reasons for prolonged outbreak:
interepretation
2017
WGS Identifies a 2 year outbreak

16. Example 3: Epidemiology
Interviewed cases from
6/9 reporting eating a Restaurant A in Memphis TN
Addressed food safety deficiencies in the restaurant
Opportunities for cross-contamination
Unknown source:
Locally sourced product?
Environmental contamination?
Swabs negative

17. TN Conclusions WGS is very helpful Challenges
Directing resources for cluster investigations
‘White noise’ reduction for common PFGE patterns
Supportive data for challenging hypotheses
Epidemiologic information is still essential!
Helps to have epi-lab liaison (with extra-large computer monitors)
Challenges
Analysis and interpretation
No in-house analysis capacity
Timeliness
Some challenges may be addressed with CDC implementation of wgMLST

18. Thank you! Acknowledgements TDH Foodborne Epidemiology
Lisha Constantine-Renna
John Dunn
Katie Garman
Naomi David
Jane Yackley
TN Public Health Laboratory
Christina Moore
Rhonda Kellum
Nathan Britt
Zachary Perry
New York State Wadsworth Laboratories
William J Wolfgang
Pascal Lapierre
Thank you!