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PUBLIC HEALTH FOOD SAFETY PUBLIC HEALT
APPLICATIONS
TH FOOD SAFETY ALT
SS FOR WHOLE GENOME CATIONSS OR WHOLE GEFO
SEQUENCING (WGS)
National Center for Emerging and Zoonotic Infectious Diseases
Division of Foodborne, Waterborne, and Environmental Diseases
Chief, Enteric Diseases Laboratory Branch
4th Asia-Pacific International Food Safety Conference,
Penang, Malaysia, October 11- 13, 2016
Peter Gerner-Smidt, MD ScD
WGS Works!
The Experience In The United States
Non-regulatory
CDC
q Disease surveillance
q Outbreak detection and investigation
q Analyzing burden, trends, and effectiveness of prevention efforts
q Attribution to sources
q Education and training
q Information for policy
Regulatory
FDA and USDA
q Inspection
q Enforcement
q Investigating farm and production facilities
q Product recall
q Product traceback
q Risk assessment and management
Shared Mission—Different Roles
Surveillance By WGS In The U.S.
q Integrated Surveillance
§ Began September 2013 with Listeria
q Since 2014 almost all multi-state outbreaks by any
foodborne pathogen are investigated by WGS
Public Health Agency of Canada
Listeria Cluster Metrics Before and After WGS
14
N/A
2
6
19
65
4
21
6
9
3
0
5
10
15
20
25
No. of clustersdetected
No. of clustersdetected sooneror only by WGS
No. of outbreakssolved (food
source identified)
Median no. ofcases per cluster
Pre-WGS (Sep 2012- Aug 2013)
WGS Year 1 (Sep 2013- Aug 2014)
WGS Year 2 (Sep 2014- Aug 2015)
13
20
98
No. cases linkedto food source
Courtesy: Amanda Conrad, Outbreak Preparedness and Response Branch
WGS For Outbreak Investigations2014 U.S. Caramel Apples Listeria Outbreak
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)
PFGE Pattern 1
PFGE Pattern 2
PFGE Pattern 3
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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 .
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Data are preliminary and subject to change
How WGS Influenced Outbreak Investigations
q Improved case definitions in outbreaks
§ Apparent PFGE clusters are not single-source outbreaks or are
pseudo-clusters
§ Isolates with same PFGE patterns may be unrelated
§ Isolates with different PFGE patterns may be related
q Increase confidence in the link between human and
product isolates
q Link historical cases to a current outbreak
investigation
q Characterize the ecology of long-term pathogen
reservoirs in the food chain
WGS In Public Health In A Global Perspective
Foodborne Pathogens Know No Borders
q A foodborne infection on one continent may have
its source on a different continent
q International outbreaks are common
q Subtyping methods and analyses must be
standardized at the global level to ensure global
comparability of data• Outbreak investigations fast
http://www.kidsmaps.com/
WGS in Public Health:
The analytical tools must be
• Simple
• Public health microbiologists are NOT
bioinformaticians
• Standard desktop software
• Comprehensive
• All characterization incl. analysis in one workflow
• Working in a network of laboratories- STANDARDIZED
• Everybody analyze data the same way
• Free sharing and comparison of data between labs
– Robust QA/QC
“Transforming Public Health Microbiology –PulseNet and Beyond”
q Replacing traditional microbiology with WGS
§ For Food Safety Reference Labs:
o Cost-efficient consolidation of multiple workflows: Identification –
serotyping – virulence profiling – antimicrobial resistance
characterization – subtyping
Partners In System Development
International Partners:
PulseNet International eCDC
EFSA Statens Serum Institut
Public Health England
Institut Pasteur DTU
Academia GMI
PHAC
U.S. Partners:
PulseNet OutbreakNet
FDA/CFSAN-CVM Genome Trakr
Academia
USDA /FSIS-ARS
NIH
Other
CDC and State
programs
Foodborne Disease
Branches1. We neither have the
capacity nor the
knowledge to make the
WGS transformation
alone
2. What we do must be
in sync with what
others do to ensure
national and
international
comparability of data
WGS for Reference Characterization
q Genus/species ID
q Serotype
q Virulence profile
q Antimicrobial resistance
q Plasmid profile
q …. more
https://cge.cbs.dtu.dk/services/
‘One Shot’ Characterization Of STEC by WGS
ANI
SerotypeFinder
Virulence Finder
7-gene MLST
ResFinder
Unique phylogenetic
relevant strain identifier
Explanation of Virulence and Resistance Markers:
wgMLST profile: 102.45.26.35.3
Resistance Prediction By WGS Is Very Accurate ResFinder ~ Salmonella
Phenotype
Resistant Not
Resistant
Gen
oty
pe Resistant 1129 37 1166
Not
Resistant13 1830 1843
1142 1867 3009
Measure Value (%) 95% CI
Sensitivity 98.9 98.1-99.4
Specificity 98.0 97.3-98.6
Kappa coefficient 0.97 (very good) 0.96-0.98
NARMS, 2015 - Data are preliminary and subject to change
Two WGS Analytical Approaches• Nucleotide level comparison: hqSNP
• Gene- gene comparison: cg/wgMLST
hqSNP cg/wgMLST
Epidemiological concordance High High
Stable nomenclature (No) Yes
SpeedSlow SNP calling,
slow analysis
Slow allele calling,
fast analysis
Local computing requirements Medium-High Low
Local bioinformatics expertise Yes No
Reference used to perform analysis
Sequence of
closely related
annotated strain
Allele database
Internationally standardization No Yes
Cost of analysis (Expensive) Cheap
Subtyping: Two Approaches- Same Resultsyping: TwgMLST
ppSalmonella
pppa Newport (JJPX01.0061) hqSNP
wgMLST (<All Characters>)
0510
15
20
25
30
PNUSAS004011
PNUSAS003864
2016K-0812
2016K-0773
PNUSAS003566
PNUSAS003862
2016K-0753
PNUSAS004088
PNUSAS003895
PNUSAS003945
PNUSAS004171
PNUSAS003948
PNUSAS004087
PNUSAS004175
PNUSAS003712
PNUSAS004009
PNUSAS003946
2016K-0863
PNUSAS004173
PNUSAS003947
PNUSAS004089
2016K-0865
2016K-0862
2016K-0772
PNUSAS004172
PNUSAS003505
PNUSAS004174
PNUSAS003689
PNUSAS004090
PNUSAS004176
PNUSAS003731
PNUSAS003005
2016K-0767
2016K-0792
2016K-0790
2016K-0791
PNUSAS003732
PNUSAS003863
2016K-0794
PNUSAS004048
PNUSAS003949
PNUSAS004010
PNUSAS003950
2016K-0695
PNUSAS003467
PNUSAS003776
PNUSAS003271
PNUSAS003777
2016K-0614
2016K-0746
2016K-0864
2016K-0793
2016K-0852
Clade B:
0 – 3 alleles;
median 2
Clade C:
2 – 8 alleles;
median 6
Clade A:
0 – 4 alleles;
median 2
PNUSAS003947
PNUSAS004087
PNUSAS003948
2016K-0863
2016K-0865
PNUSAS004171
PNUSAS004175
PNUSAS004009
PNUSAS003946
PNUSAS003712
PNUSAS004173
PNUSAS004089
2016K-0862
2016K-0753
PNUSAS003945
PNUSAS003895
PNUSAS004088
PNUSAS003864
PNUSAS003862
PNUSAS004011
2016K-0812
PNUSAS004174
PNUSAS003566
PNUSAS003505
PNUSAS004172
2016K-0772
PNUSAS003271
2016K-0793
2016K-0614
2016K-0746
2016K-0864
PNUSAS003777
2016K-0852
PNUSAS003950
PNUSAS004010
2016K-0695
PNUSAS003467
PNUSAS004176
PNUSAS004090
PNUSAS003732
2016K-0794
PNUSAS003863
PNUSAS003949
PNUSAS003776
PNUSAS003005
PNUSAS003731
2016K-0767
2016K-0792
2016K-0791
2016K-0790
88
23
95
97
94
85
51
53
24
99
96
91
51
55
98
100
96
100
100
54
82
70
36
29
29
12
8
14
0
61
100
94
59
80
76
66
65
64
56
71
95
100
100
100
91
100
96
0.0002
Clade A: 0
SNP
Clade B: 0-1
SNPs
Clade C: 1-7
SNP
Two WGS Analytical Approaches
• cg/wg MLST is the preferred public health standard
– May be globally standardized
• Direct comparison of data generated in different labs
– Do not require bioinformatics expertise
– Is NOT computer intensive
– BUT few MLST allele databases are currently available in public
domain
• hqSNP is best suited for analysis when specific
information that is not provided by cg/wgMLST is needed
– Difficult to standardize
• Comparison of data generated in different labs will require analysis in each
lab after sharing raw sequence data
– Bioinformatics expertise and high capacity computing required
– BUT easy to set up with resources available on the internet
The Gene-Gene (MLST) approach is scalablePFGE indistinguishable isolates from an outbreak and unrelated to the outbreak (2013)
wgMLST (MLST loci)
01
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PNUSAL000528
PNUSAL000983
PNUSAL001651
PNUSAL001687
PNUSAL000453
PNUSAL000014
PNUSAL000019
PNUSAL000452
PNUSAL000018
PNUSAL000157
PNUSAL000024
PNUSAL000031
PNUSAL000294
PNUSAL000435
PNUSAL000515
PNUSAL000299
PNUSAL000116
PNUSAL000053
PNUSAL000313
PNUSAL000082
PNUSAL000286
PNUSAL000512
PNUSAL000102
PNUSAL000249
PNUSAL000184
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.2574
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1307MNGX6-1
1312MLGX6-1
1307MNGX6-1
1312MLGX6-1
1307MNGX6-1
1307MNGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1312MLGX6-1
1312MLGX6-1
1307MNGX6-1
1307MNGX6-1
1312MLGX6-1
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publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
ST825
wgMLST (Core loci)
010
20
30
40
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PNUSAL000528
PNUSAL000299
PNUSAL000014
PNUSAL000184
PNUSAL001651
PNUSAL001687
PNUSAL000116
PNUSAL000512
PNUSAL000294
PNUSAL000435
PNUSAL000515
PNUSAL000157
PNUSAL000453
PNUSAL000452
PNUSAL000983
PNUSAL000286
PNUSAL000019
PNUSAL000018
PNUSAL000024
PNUSAL000031
PNUSAL000053
PNUSAL000313
PNUSAL000082
PNUSAL000102
PNUSAL000249
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.2574
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
.
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publicST6
publicST6
publicST6
ST825
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
wgMLST (<All Characters>)
020
40
60
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PNUSAL000528
PNUSAL000299
PNUSAL000014
PNUSAL000184
PNUSAL001651
PNUSAL001687
PNUSAL000116
PNUSAL000512
PNUSAL000294
PNUSAL000435
PNUSAL000515
PNUSAL000157
PNUSAL000453
PNUSAL000452
PNUSAL000983
PNUSAL000286
PNUSAL000018
PNUSAL000031
PNUSAL000053
PNUSAL000019
PNUSAL000024
PNUSAL000313
PNUSAL000082
PNUSAL000102
PNUSAL000249
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A16.0016
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.2574
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
GX6A12.0003
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1312MLGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
1307MNGX6-1
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publicST6
publicST6
publicST6
ST825
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
publicST6
7-gene and cgMLST schemes: Institut Pasteur; wgMLST scheme: Institut Pasteur & CDC
UPGMA
cgMLST: 1,748 loci wgMLST: 4,808 loci
7-gene MLST (Institut Pasteur)
WGS Data Flow
Allele & Allele code
DatabasesAllele names,
Allele code (strain names)
NO Metadata
Temporary storage,
QA/QC, Data
extractionTrimming, mapping, de novo
assembly, SNP detection,
allele detection
NO Metadata
Public Health databases
Extensive Metadata
End users in
national/ local
laboratories
External storageNCBI, ENA,
Limited
Metadata
Sequencer
Raw sequences
LIMS
Tem
QA/
Raw sequences
7-gene MLST ST
cgMLST Allelic profile
wgMLST Allele Code
(SNPs)
Genus/species
Serotype
Pathotype
Virulence
Resistance
How Close Is Close?
2014 U.S. Caramel Apples s Listeria a Outbreak
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)
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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 .
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Data are preliminary and subject to change
2012K-1420
2012K-1421
2013K-1635
2012K-1747
2012K-1549**
2012K-1550**
2012K-1417
2012K-1255
2012K-1256
2012K-1254
2013K-1633**
2012K-1315***
2013K-1649**
2013K-1650
2013K-0982
2013K-1636
2013K-1361
2013K-0983
2013K-1638
2013K-1639
2013AM-0303
2013K-0979
2013K-1275
2013K-0573**
2013K-0574
2013K-0980**
2013K-1274
0.02
2012-2013 Salmonella
serovar Heidelberg multistate
outbreak associated with
chicken from producer X
Pattern 122
Pattern 672
Pattern 45
Pattern 22
Pattern 326
Pattern 41
Pattern 258
6-46 SNPs
6-35 SNPs
21 SNPs
7-60 SNPs
19 SNPs
PFGE patterns6-62 SNPs
** Chicken from producer X
*** Clinical isolate from 2011
ALL patients in the tree exposed
to chicken from producer X
2013T-3050-
2013T-3042-
2013T-3111-
2013T-3112-
2013T-3110-
2013T-3109-
2013T-3040-
2013T-3059-A
2013T-3058-
2013T-3041-
2013K-1037-
2013AM-0488
2013AM-0486
2013AM-0487
2013K-1067-
2013K-1038
2013K-1036-
2013K-1040-
2013K-1039-
0.05
Salmonella serovar Heidelberg
PFGE pattern JF6X01.0022 single
state outbreaks in summer 2013
2-8 SNPs
2-16 SNPsState A funeral
4-5 SNPs State B daycare
Unrelated Sporadic case
Unrelated Sporadic case
3-6 SNPs
(3-9 SNPs)
State C Church SupperUnrelated Sporadic case
105-113 SNPs
Same PFGE Pattern – Multiple Outbreaks With Different WGS Profiles
A WGS match between a food isolate and a
clinical isolate does NOT mean that the food
caused the patient’s iIlnessListeria Cluster
They likely share an ancestor somewhere in the food production chain
Epidemiological and traceback information remains critical
How Close Is Close?
q Depends on the question you want answered§ Outbreak? Attribution? Production ecology? Global
Epidemiology?
q Depends on the pathogen
q Depends on the epidemiological context
WGS Is Here To Stay But Issues
Needs to Be Addressed
q Data sharing§ With everyone? With public health partners? With industry?
q Politics & Ethics§ Trade barriers? Liability? Intellectual property?
vs Common good
q How do we implement WGS in lesser
resourced countries?§ Some have no basic surveillance capacity
Acknowledgements
National Center for Emerging and Zoonotic Infectious Diseases
Division of Foodborne, Waterborne, and Environmental Diseases
Disclaimers:
“The findings and conclusions in this presentation are those of the author and do not necessarily
represent the official position of the Centers for Disease Control and Prevention”
“Use of trade names is for identification only and does not imply endorsement by the Centers for
Disease Control and Prevention or by the U.S. Department of Health and Human Services.”
Public Health
Agency of Canada
Center for Genomic Epidemiology, DTU
University of Oxford, M. Maiden, K. Jolly
Public Health England, T. Dallman
