"The prospects for Nextgen surveillance of pathogens: A view from a Public Health Lab" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by National Institute for Standards and Technology in October 2014 by William Wolfgang, PhD from Wadsworth Center NYSDOH.

1. The prospects for Nextgen surveillance of
pathogens: A view from a Public Health Lab
William Wolfgang
Wadsworth Center NYSDOH
NIST Workshop 10/20/14

2. Public Health Genomics – projects at the
Wadsworth
Project Sample Source PI
Salmonella surveillance Isolate Wolfgang
TB surveillance and drug
resistance
Isolate and
Primary
Escuyer and
Musser
C. Botulinum source tracking Isolate Egan
HCV subtyping Primary
Parker and
Chou
Adenovirus surveillance and
characterization
Isolate
St. George
and Lamson
Rabies virus intra-host evolution Isolate Davis
Mosquito microbiome and
West Nile Virus
Primary
Ciota and
Kramer

3. We collaborate with a number of different
groups on Pathogen sequencing projects
• Global Microbial Identifier (GMI) initiative
• CDC: Listeria monocytogenes initiative and AMD initiative
• FDA: GenomeTrakr initiative
• Minnesota and Washington Departments of Health
• And we hope to do more

4. At the Wadsworth we need standards
• As we translate to new technologies we need to know:
• Faster
• Cheaper
• Better
• For this we need to make accurate and meaningful comparisons.
• To do this we need standards.

5. Why use Nextgen for Salmonella typing?
• PFGE has low discriminatory power.
.
Each year
• 1 million cases Salmonella in US.
• 19,000 hospitalizations and 378 deaths.
• The Wadsworth receives about 1,500/yr.

6. Greater discrimination can be achieved by
Whole Genome Sequencing

7. Proof of principle study on a Salmonella
Enteritidis outbreak
• Sept. 2010 Connecticut Dept. of Health identifies a Salmonella
outbreak in a long term care facility (LTCF).
• Outbreak was linked to cannoli from a Westchester bakery.
• Both NY and CT cases consumed cannoli’s.
• Isolates had the most common PFGE pattern.

8. Retrospective cohort
Key County Date PFGE
IDR1000029153 Cattaraugus 8/10/10 JEGX01.0004
IDR1000031528 Rockland 8/26/10 JEGX01.0004
IDR1000033213 Putnam 9/10/10 JEGX01.0004
IDR1000033369 Putnam 9/10/10 JEGX01.0004
IDR1000033371 Putnam 9/11/10 JEGX01.0004
IDR1000034601 Washington 9/13/10 JEGX01.0004
IDR1000034587 Westchester 9/20/10 JEGX01.0004
IDR1000035417 Putnam 9/22/10 JEGX01.0004
IDR1000035178 Westchester 9/13/10 JEGX01.0004
IDR1000035179 Greenwich CT 9/12/10 JEGX01.0004
IDR1000035180 Westchester 9/12/10 JEGX01.0004
IDR1000035181 Westchester 9/13/10 JEGX01.0004
IDR1000035182 Westchester 9/12/10 JEGX01.0004
IDR1000035183 Greenwich CT 9/16/10 JEGX01.0004
IDR1000036119 9/17/10 JEGX01.0004
IDR1100035184 Westchester 9/16/10 JEGX01.0004
IDR1000036319 Putnam 9/28/10 JEGX01.0004
IDR1000036979 Putnam 10/8/10 JEGX01.0004
IDR1000038792 Nassau 10/29/10 JEGX01.0004
IDR1000034599 Orange 9/15/10 JEGX01.0004
IDR1100006235 Westchester 2/21/11 JEGX01.0004
IDR1100021079 Rockland 7/13/11 JEGX01.0004
IDR1000030147 Out-Of-State 8/22/10 JEGX01.0004
IDR1100003844 Onondaga 2/1/11 JEGX01.0004
IDR1100022186 Yates 7/22/11 JEGX01.0004
IDR1100027690 Erie 9/6/11 JEGX01.0004
IDR1100030508 Madison 10/9/11 JEGX01.0004
IDR1100031312 Suffolk 10/5/11 JEGX01.0004
IDR1100032014 Onondaga 10/22/11 JEGX01.0004
IDR1000028670 Nassau 8/8/10 JEGX01.0004
IDR1000029949 Suffolk 8/16/10 JEGX01.0004
IDR1000033603 Erie 9/14/10 JEGX01.0004
IDR1000034213 Erie 9/13/10 JEGX01.0004
IDR1000037723 Westchester 10/4/10 JEGX01.0004
IDR1000039087 Westchester 10/27/10 JEGX01.0004

9. 7.3 SNPs
11­06235
10­33603
11­27690
10­35183
10­36319 +
10­31528
10­33369 +
10­35417 +
11­32014
10­33213 +
10­34599
10­34587 +
10­35179
11­22186
10­30147
11­30508
10­34601 +
10­37723
10­34213
10­35184
11­03844
10­33371 +
10­35181
10­36119
10­28670
11­21079
10­35178
10­35182
10­29153
11­31312
10­38792 +
10­36979 +
10­29949
10­39087
10­35180
100
100
100
88
85
88
68
100
100
A
B
LTCF
Whole genome Cluster Analysis ( WGCA)
can identify an outbreak cluster not detected
by PFGE
All isolates are PFGE PATTERN 4

10. Implementing WGCA for SE in real-time.
• Evaluate WGCA compared to PFGE.
• Speed - Faster
• Cost - Cheaper
• More Actionable Clusters – Better
• Develop an in house bioinformatics pipeline.
• Develop communication pipeline to epidemiologists.
• Determine cluster parameters that represent an outbreak from a single
source (assign a probability).
• Use data sets to evaluate evolving informatic methods.
• Become proficient (PT programs).

11. Over the past 12 months
• Sequenced all Salmonella Enteritidis (379 genomes).
• All data at NCBI
• Developed an in House pipeline to analyze the data.
• SNP based phylogenetic trees were constructed in real time.
• 63 phylogenetic clusters were reported to epidemiologists.
• 0 to 5 snps differences

12. Data is
analyzed using
a portal
developed by
Informatics core

13. In House Developed Pipeline

14. Current tree
• Is this tree structure
reproduced by other
pipelines?
• Is it reproduced within
our pipeline?
• What are the minimal
sequencing metrics
required to create
reproducible trees?
• What changes would
make this faster-
cheaper-better.

15. Can we develop cluster metrics that give a
probability of linkage to a single source?
• Perform phylogenetic analysis of epidemiologically confirmed
outbreaks.
I. Calculate SNP distance.
II. Examine tree structure.
• Do for many serovars of each species.
• This could be done relatively easily by freezer diving and HiSeq
runs.
• For SE SNP distances appear to be small (0-3 snps)
I. Based on 9 bonafide outbreaks from NY and MN.

16. Large Western NY pattern 5 cluster
• 13 isolates collected over 10
months (0 to 6 snps distance)
• First isolates in fall 2013.
• February 2014, two distinct clades
form.
• Suggests bug has evolved
• Does this cluster represent 1, 2, or
3 sources?
NY-swgs1375_NEW
NY-swgs1355
NY-swgs1333
swgs1224
swgs1018
swgs1008
swgs1079
swgs1223
swgs1226
NY-swgs1339
NY-swgs1360
NY-swgs1387_NEW
swgs1217
NY-swgs1305
NY-swgs1366
NY-swgs1347
NY-swgs1335
swgs1065
NY-swgs1311
7/22/14 Onondaga
7/05/14 Oneida
6/22/14 Seneca
6/3/14 Erie
9/25/13 Niagara
6/11/14 Oswego
5/12/14 Ontario
2/22/14 Oneida
2/22/14 Onondaga
2/21/14 Onondaga
2/6/14 Onondaga
12/12/13 Cattaraugus
10/15/13 Monroe
1 snps

17. One person
two outbreaks
• GC-35 appears 6/9/14
• PFGE pattern 4
• Isolate from food a handler
• Total of 5 cases through 8/7/14
• GC-40 appears 6/30/14
• PFGE pattern 21
• isolate recovered from the same
food handler
JEGX01.0021
JEGX01.0004

18. WGCA is better, but is it faster and cheaper?
metric PFGE WGCA
TAT : extraction to
analysis
2 days 6 days
Cost $69 $294
Technician time 8h 10h
Actionable clusters 3 non-endemic 63

19. We have created a two State Network
• Collaborating with Minnesota.
• Currently no informatics in house.
• We pull their sequences off Basespace.
• Run through our pipeline.

20. Tree from Merged
data
• Does pipeline
used to merge
the data affect
tree structure?
• Do sequence
metrics affect
merged tree
structure?
Travel associated

21. National Genomic Surveillance Machine
• State labs feed the machine by uploading sequences from isolates
received through surveillance.
• Federal and other support for reagents and equipment.
• NCBI to analyze the products of this machine and reports results to
state and federal agencies.

22. Current FDA Genome Trackr
network
State Health labs
• New York
• Florida
• Arizona
• Washington
• Minnesota
• Virginia
• Maryland
FDA labs
• 9 FDA field labs
• CFSAN - MOD1
• CFSAN - Wiley
• IEH (contracting lab)
International labs
• Mexico
• Ireland
• UK (FERA)
• Columbia
Contributors
• Turkey
• Brazil
• Italy

23. NCBI Pathogen Pipeline Salmonella tree
Travel associated

24. Expected Outcomes for WGS surveillance
• Laboratory
• Improve outbreak cluster detection.
• Clusters will be detected more rapidly and from fewer isolates.
• Epi
• Allow identification of clusters within endemic patterns.
• Solve more clusters.
• Public Health
• More efficient identification and removal of pathogen sources.

25. Challenges exist
• Creating a network.
• Increasing amounts of data.
• Metadata: how much should be public?
• In real time?
• What elements?
• Paying
• As sequencing technology and bioinformatics evolve:
• Need to maintain backward compatibility
• Transitioning:
• What to do first.
• Integration with serology and PFGE typing.

26. Standards I would like to see
• Pipeline quality and reproducibility.
• Tree quality and reproducibility.
• Probability metrics that a cluster is from a single source.

27. Summary
• WGS can improve surveillance activities and outbreak traceback.
• It is practical to develop network.
• We need standards.

28. Acknowledgments• Cornell
Martin Wiedmann
Henk den Bakker
• FDA
Eric Brown
Peter Evans
Marc Allard
Errol Strain
Ruth Timme
• Connecticut DOH
Stacey Kinney
John Fontana
• Minnesota DOH
David Boxrud
Angie Jones
Victoria Lappi
• Washington State DOH
Ailyn Perez-Osorio
Zhen Li
• Wadsworth Center Genomics Core
Matt Shudt
Zhen Zhang
Charles MacGowan
Melissa Leisner
Danielle Loranger
Mike Palumbo
Pascal LaPierre
• Kara Michell
• Wadsworth PulseNet Lab
Dianna Bopp
Deb Baker
Lisa Thompson
• NCBI
Bill Klimke
Martin Shumway