1. Epidemiologist Supervisor Foodborne Diseases Unit
Using Whole Genome Sequencing for Surveillance and Outbreak Investigations, Minnesota
Carlota Medus, PhD, MPH
Epidemiologist Supervisor
Foodborne Diseases Unit

2. Use of WGS for Surveillance in Minnesota
Goals of this presentation:
Show Minnesota experience using WGS as part of routine surveillance
Compare the utility of WGS and PFGE as part of routine surveillance, using 2017 Salmonella Enteritidis (SE) data as an example
Outbreak examples, showing the role of WGS
Goals of this presentation are to
-Show the Minnesota experience using WGS as part of routine surveillance
-Compare the utility of WGS and PFGE as part of routine surveillance, using 2017 SE data as an example
-And to show some outbreak examples, showing the role of WGS

3. Use of WGS for Surveillance in Minnesota
Whole genome sequencing (WGS) for SE surveillance/cluster detection since 2014
WGS for S. Typhimurium (STm) surveillance/cluster detection since 2017
SE and STm isolates sequenced at MDH, hqSNP analysis at Wadsworth Center, New York State Department of Health
All other Salmonella and STEC are sequenced, but only analyzed on request. All Listeria isolates are also sequenced and analyzed at CDC
Subtyping using pulsed-field gel electrophoresis (PFGE) continues
We started using whole genome sequencing for S. Enteritidis (SE) surveillance/cluster detection in MN in 2014.
This year, we started using WGS for S. Typhimurium (STm) sureveillance as well;
- Just like Steffany from TN just described, SE and STm isolates sequenced at MDH, hqSNP analysis at Wadsworth Center, New York State Department of Health.
Additionally, all other Salmonella and STEC are sequenced, but only analyzed on request; for example, if we are investigating an outbreak and want grater discrimination than PFGE.
All Listeria isolates are also sequenced and analyzed at CDC
And subtyping using pulsed-field gel electrophoresis (PFGE) continues.

4. Laboratory-Epidemiology Communication
Interpret the data and identifies clusters
Communicate clusters to epidemiologists
Provide access to the data to epidemiologists
Epidemiology
Share epidemiological data with the laboratory
Communicate with if changes needed
Lab and Epi jointly decided what to call a cluster
An essential element of conducting surveillance is how do epidemiologists obtain results from the public health laboratory. But even before we even get to the communication piece, we had to decide who would take responsibility for identifying clusters. We jointly decided that it made the most sense for the laboratory to interpret the data, identify clusters, and communicate clusters to epidemiologists(just like we do for PFGE), , and to also provides access to the data to epidemiologists.
Epidemiologist share epidemiological data with the laboratory, communicate with if changes needed.

5. Laboratory-Epidemiology Communication
Shared drive
hqSNP trees
Heat maps
Spreadsheet
Line list of result interpretation
notification of new clusters
Previous webinars covered hqSNP trees and heat maps, so I won’t show them here. But let me show you the other ways our laboratory communicates with the epis.

6. Lab ID for closely related isolates Lab ID PFGE pattern Cluster ID
Instead of always looking at trees and heat maps, this is one way the laboratory shares data with epi: a spreadsheet that includes the clinical isolate lab IDs, PFGE pattern, it lists how closely related is the closest isolate sequence. It includes a list of closely related isolates, and a cluster id.
Lab ID for closely related isolates
Lab ID
PFGE pattern
Cluster ID
Number of SNPs

7. This is what we rely on the most, and from the lab letting us know that we have a cluster or clusters, including the specimen ids and the number of SNPs.
Note that the 2 isolates in cluster 13 have different PFGE patterns

8. Taylor et al. J Clin Micro Oct 2015
MDH00219
MDH In-vivo, same as E
MDH Sporadic 4/19/01
MDH Sporadic 8/6/12
MDH Sporadic 5/14/01
MDH Sporadic 5/14/01
MDH Sporadic 7/11/00
MDH Sporadic 3/12/01
MDH Sporadic 8/23/00
MDH Sporadic 6/10/13
MDH00237 Sporadic 6/22/11
MDH Sporadic 5/7/11
MDH Sporadic 8/31/2000
MDH Sporadic 12/7/2001
MDH Sporadic 6/10/13
MDH Sporadic 8/22/2000
MDH Sporadic 4/30/2001
MDH Sporadic 6/11/2001
MDH00254
MDH00252
MDH00253
MDH00234
MDH Sporadic 6/21/2001
MDH Sporadic 7/16/2001
MDH Sporadic 7/7/2000
MDH Sporadic- Same time, PFGE, and MLVA as Outbreak 1
MDH00209
MDH00210
MDH00211
MDH In-vivo, same as E
MDH In-vivo, same as E
MDH00223
MDH00220
MDH00218
MDH Sporadic- Same PFGE and time as Outbreak 1
MDH Sporadic 10/17/01
MDH00227
MDH00230
MDH00251
MDH00229
MDH Sporadic 10/3/05
MDH Sporadic, same PFGE and time as Outbreak 5
MDH Sporadic 6/26/12
MDH00249
MDH00250
MDH00246-Sporadic 7/30/12
MDH OH Sample 1
MDH OH Sample 2
MDH Sporadic, same PFGE and time as Outbreak 5
MDH00239
MDH00242
MDH Environmental sample from Outbreak 5
MDH00238
MDH00240
Defined Outbreak Samples
Outbreak 1- Sept 2000
Outbreak 2- May 2001
Outbreak 3- Aug 2001
Outbreak 4- Nov 2003
Outbreak 5- Aug 2008
Outbreak 6- Spring 2014
Outbreak 7- Spring 2014
0-2 SNPs
1SNP
0 SNPs
0-1 SNPs
0-3 SNPs
How did we decide what to call a cluster? We know we are looking at degrees of relatedness with WGS-a continuum. But for identifying clusters prospectively, we needed to decide what to look at-when to initiate a cluster investigation, knowing that we may need to more inclusive once an outbreak is identified, or less inclusive in a particular outbreak.
We conducted a retrospective study- for this study, our lab sequenced clinical isolates from distinct, well-characterized foodborne outbreaks and isolates from sporadic cases. All the isolates within each outbreak were 0-3 SNPs, and very different from non-outbreak isolates.
So we decided that the lab will notify us of everything within 5 SNPs- so that is what we are calling a “cluster”.
Note that we are what we call a WGS cluster is completely independent of PFGE results. In other words, we are not using WGS only when PFGE cluster is identified, we are using sequencing to identify clusters,
Taylor et al. J Clin Micro Oct 2015

9. PFGE Clusters and WGS Clusters, January-April, 2017
WGS Cluster Number
Corresponding PFGE pattern
No. Cases
Cluster 1
.0002 7
Cluster 2
.0004 6
Cluster 3
.0008 2
Cluster 4
.0021 12
Cluster 5 3
Clusters 6
Cluster 7
Cluster 9
.0049
Cluster 10
Cluster 11
.0116 &.1076
Cluster 12
Cluster 13
.0054
Cluster 14
.0042
PFGE Clusters and WGS Clusters,
January-April, 2017
Cluster PFGE Pattern (MN pattern name)
No. Cases
.0004 (SE1) 27
.0002 (SE11) 30
.0005 (SE43) 4
.0021 (SE77) 17
.0049 (SE16) 7
vs.
This slide shows clusters in January-April, 2017: The number of clusters identified by PFGE, and the number of clusters identified using WGS.
On this slide, for PFGE, clusters are loosely defined: a cluster is 2 or more isolates with the same PFGE in 2017 without a time component.
Here they are listed in the order of how common that pattern is in MN.
Patterns 4 and 2 are basically 2 giant never-ending clusters. Pattern 21 is fairly common, but that’s high for us; and also concerning is the pattern 49, which is rare. So the number of cases per cluster ranged from 4 to 30 with a median of 17 cases.
A WGS clusters are defined as 2 or more isolates that are 5 or fewer SNPs different- We identified had 13 clusters, ranging from 2 to 12 cases per cluster, with a median number of 3- note that we are missing cluster 8 (it’s just an error in assigning cluster codes)

10. 2017 SE hqSNP Tree Isolates in a 90-day time period
Includes closely related (within 10 SNPs) isolates
This is the hqSNP tree showing all SE isolates analyzed in the “last 90 days”, and this one is from early April.
For each new isolate in the tree, it also shows closely related isolates from all years (for us, back to 2014).
This is really hard to look at on this slides, so I’m going to show it top half first, and then second half
0.02

11. Restaurant-associated outbreak
Cluster 9
Cluster 7
Travel to
Jamaica
Cluster 5
Cluster 2
The clusters are numbered in the order in which they were reported to epi. The place where the clusters appear, gives you an impression of how the tree evolved over time- new isolates added based on their relatedness to other isolates on the tree.
The Jamaica travelers stayed at the same resort.
Cluster 4 was a restaurant-associated outbreaks- I won’t describe this outbreak further on this call.
Cluster 4
Restaurant-associated outbreak

12. 2017 SE hqSNP Tree Isolates in a 90-day time period
Includes closely related (within 10 SNPs) isolates
And now let’s look at the bottom half
0.02

13. Travel to Mexico Travel to Dominican Republic Cluster 3 Cluster 6
Among the Mexico travelers, each cluster represented a different destination, and within each cluster, all the cases stayed at the same resort,
All of these clusters were represented by people who traveled to Punta Cana, Dominican Republic.
The Dominican Rep. travelers, all the cases for each cluster at the same resort as the other cases within the cluster, but different resorts between the clusters; except for cluster 5, with cases reporting 3 different resorts
Cluster 1
0.02

14. Cases by week of specimen collection, PFGE alone
No. of Cases
Week of Specimen Collection
Week of Specimen Collection
No. of Cases
WGS cluster cases by week of specimen collection
I’m now just going to focus on pattern 4 cases. Even though we conducted WGS independently of PFGE, it is still useful to compare the two methods.
Note that all the boxes without a dot are Bln pattern 2, and the boxes with a dot are various Bln patterns. Using a second enzyme, Bln, added discrimination, but we still saw a steady stream of cases, difficult to see any patterns
The epi curve at the bottom shows the WGS data for those patter 4 isolates, We had 3 distinct clusters, and 15 unrelated/non-cluster isolates.
The dark red boxes were cases in an outbreak associated with eating Kitfo, an Ethiopian food, made with ground beef from a small retailer,
The second cluster are travelers to Jamaica,
and the third cluster was unsolved (the only cluster we did not solve in this time period).

15. hqSNP Tree for SE Pattern .0004 Isolates
Travel to Jamaica
hqSNP Tree for SE Pattern Isolates
Unsolved
Ethiopian Food
This is the tree for the pattern 4 isolates showing the 3 clusters.
The Jamaica travelers’ isolates are all 0 to 2 snps; the unsolved are all 0 snp, and Kitfo are all 0
Ethiopian food vs 1390=46 snp
hqSNP tree. Reads were trimmed and cleaned with CG-Pipeline run_assemblytrimClean.pl with the options --min_quality ‘15’, --min_avg_quality ‘20’, --bases_to_trim ‘100’. Lyve-SET v.1.1.4f was used for the hqSNP analysis with external reference strain P (Genbank accession: AM933172). Smalt was used for the mapping and SNPs were called with Varscan. Lyve-SET was run with the following options: --min_coverage‘20’, --min_alt_frac‘0.95’, and --allowedFlanking‘5’ bp.

16. Travel to Jamaica Cluster 9 Cluster 7 Cluster 5 Cluster 2 Cluster 4
Now lets look at cluster 9
Cluster 4

17. Cases by week of specimen collection, PFGE alone (n=7)
No. of Cases
Week of Specimen Collection
Week of Specimen Collection
No. of Cases
WGS cluster 9 cases by week of specimen collection
Here is the epi curve using PFGE alone. This is a rare PFGE pattern, so even thought there was a long time period between the January case and the rest of the cases, we wanted to take it into account. If in fact that case was part of the cluster, then it would probably indicate the food vehicle was likely something that was shelf stable or a long production time.
But using WGS, that case was no longer part of the cluster (18 SNP), and the rest of the cases are part of an on-going multi-state investigation. Additional MN cases have been id that are not shown here. The food vehicle is most likely lettuce.
10 MN cases, 149 total.

18. Outbreak Examples
Of the 13 WGS clusters, 4 were not travel-associated, 3 were solved
The examples from 2017 surveillance described here showed WGS
Refined the case definition: excluded cases that were not part of the outbreak
A common PFGE pattern (pattern 4) was “split” into multiple outbreaks
Travelers likely represented real outbreaks
Of the 13 WGS clusters, 4 were not travel-associated, 3 were solved.
The examples from 2017 surveillance described here showed WGS
Refined the case definition: excluded cases that were not part of the outbreak
A common PFGE pattern (pattern 4) was “split” into multiple outbreaks
Travelers likely represented real outbreaks

19. Outbreak Examples Stuffed chicken example, 2015
But let me show you one more example, a stuffed chicken outbreak in 2015.

20. Salmonella Enteritidis Cases, Brand A Stuffed Chicken Investigation
by Date of Specimen Collection, May-July 2015
JEGX /JEGA
WGS 0-1 SNPs
JEGX /JEGA
WGS 0 SNPs 8 7
Ate Brand A chicken 6
Number of Cases 5 4
The short version of this outbreak is:
We identified three cases in surveillance with SE 2-enzyme pattern combination 5/37 that were closely related, 0 to 1 SNP, in May and June, All three reported eating the same brand of stuffed chicken products. Products collected from retail were tested. 83% of the products tested positive; including 5 Salmonella serotypes, and 4 PFGE patterns of SE, including pattern 4/2, but not 5/37. All the food isolates were sequenced, and we didn’t find any food isolates that were closely related to the 2 clinical isolates.
Later, in July, two cases of that PFGE pattern combination 4/2 in surveillance were 0 SNPs from product isolates. On interview, the cases reported eating the same product.
In essence, product testing helped us rapidly identify two cases that were part of a polyclonal outbreak.
But this is not the whole story. 3 2 1 4 11 18 25 1 8 15 22 29 6 13 20 27
May
June
July
Week of Specimen Collection

21. Salmonella Enteritidis Cases, Brand A Stuffed Chicken Investigation
by Date of Specimen Collection, May-July 2015
JEGX /JEGA
WGS 0-1 SNPs
JEGX /JEGA
Different sequences 8 7
JEGX /JEGA
WGS 0 SNPs 6
Number of Cases 5
JEGX /JEGA
Different sequences 4
This is what we were seeing in surveillance. The light blue boxes and the dark blue boxes were pattern 5/37. The light blue cases that were not closely related to each other or to the outbreak cases in dark blue by WGS -so 3 out of 8 pattern 5 cases were part of the outbreak. The gray boxes and the teal boxes were pattern 4/2 cases. The teal case-isolates were 0 SNPs, and the gray cases were not closely related to the teal outbreak cases, so 2 out of 22 pattern 4 cases were part of the outbreak. A really small outbreak with 5 cases total of 2 very common PFGE pattern was identified even though they were a high number of PFGE matches at the same time. 3
Ate Brand A chicken products 2 1 4 11 18 25 1 8 15 22 29 6 13 20 27
May
June
July
Week of Specimen Collection

22. Outbreak Examples Stuffed chicken example, 2015
Product testing helped identify additional cases that did not match the initial outbreak cases by PFGE or WGS
WGS identified this really small polyclonal outbreak even though there were a lot of PFGE-matching cases at the same time
Product testing helped identify additional cases that did not match the initial outbreak cases by PFGE or WGS,
WGS identified this really small polyclonal outbreak even though there were a lot of PFGE-matching cases in surveillance at the same time.

23. Acknowledgements Minnesota Dept. of Health, Foodborne Diseases Unit
Kirk Smith
Dana Eikmeier
Josh Rounds
Stephanie Meyer
Amy Saupe
Marijke Decuir
Team Diarrhea
Minnesota Dept. of Health,
Public Health Laboratory
Angie Jones Taylor
Dave Boxrud
Xiong (Sean) Wang & Sequencing Core
Victoria Lappi
Megan Nichols
Enterics lab
PFGE lab
New York Dept. of Health,
Wadsworth Center
Bill Wolfgang
Pascal Lapierre
Samantha Wirth
Bioinformatics Core