Identification and Characterization of
Foodborne Pathogens by Whole Genome
Sequencing: A Shift in Paradigm
Peter Gerner-Smidt, MD, ScD
Enteric Diseases Laboratory Branch
EFSA Scientific Colloquium No 20
Use of Whole Genome Sequencing (WGS) of foodborne pathogens for public health protection
Parma, Italy, 16-17 July 2014
National Center for Emerging and Zoonotic Infectious Diseases
Division of Foodborne, Waterborne, and Environmental Diseases
Current Methods of Characterizing Foodborne
Pathogens in a Public Health Laboratory










Growth characteristics
Phenotypic panels
Agglutination reactions
Enzyme immuno assays (EIAs)
PCR
DNA arrays (hybridization)
Sanger sequencing
DNA restriction
Electrophoresis (PFGE, capillary)
Each pathogen is characterized by methods that are specific to
that pathogen in multiple workflows
 Separate workflows for each pathogen
 TAT: 5 min – weeks (months)
Why Move Public Health
Microbiology to WGS?
• Consolidation of workflows in the lab
• More efficient outbreak detection, investigation & control
• Precise and flexible case definition
– More outbreaks will be detected and solved when they are
small
– Scarce epi-resources may be focused
• More efficient surveillance of sporadic infections
• Source attribution analysis of sporadic disease
• Focus on pathogens of particular public health
importance:
– Virulence – Resistance - Emerging pathogens - Rapidly
spreading clones/ traits- Vaccine preventable diseases
WGS in Public Health:
The tools must be
• Simple
• Public health microbiologists are NOT
bioinformaticians
• Standard desktop software
• Comprehensive
• All characterization in one workflow
• Work in a network of laboratories
• Free sharing and comparison of data between labs
• Central and local databases
Genomics for Diagnostics and
Surveillance is a Global Issue


How can we implement genomics fast and efficiently
at the global level?
When to share data?
 Surveillance WGS should be shared in real-time




What is the minimal IT-infrastructure?
What technical gaps need to be filled?
QA/QC
Political and ethical barriers
Generating succes stories:
Proof-of-Concept Study on the Use of Real-Time
Whole Genome Sequencing in Conjunction with
Enhanced Surveillance for Listeriosis
• Collaboration among the public health departments
in the states, CDC, FDA, USDA, and NCBI
• International component: Developing and refining
bioinformatics ‘pipelines’ with partners
in Belgium. Canada, Denmark, England, and France
Why Listeria monocytogenes?






Illness is rare but serious, costly, and commonly
outbreak associated
Controllable
Current subtyping methods are not ideal
 Not highly discriminatory
 No evolutionary relationships
Listeria genome is fairly small and relatively easy to
sequence and analyze
Strong epidemiology surveillance (Listeria Initiative)
Strong regulatory component
Approach:

Sequence all clinical isolates in the U.S. during one
year as close to real-time as possible in parallel with
current surveillance
 PulseNet PFGE, strain characterization at CDC, interview of
case-patients


Evaluate data on a weekly basis
Follow up on clusters detected
 Both PFGE and WGS defined clusters

Upload sequences to NCBI (Genbank), a public
database, as the sequences are generated
 With metadata that do NOT identify state or isolation date but
with link to the PulseNet database
Listeria Whole Genome Sequencing
Surveillance in Numbers
(8 months into the project, June 2014)





~ 690 clinical isolates sequenced in real-time
~ 75 environmental and historical clinical isolates
sequenced
470 food isolates sequenced by FDA/GenomeTrakr
13 clusters identified by PFGE & WGS
3 clusters identified only by WGS
Listeria Whole Genome Sequencing Surveillance
Lessons Learned:



Possible to perform WGS in real-time
Historical data are critical for cluster determination
WGS provides more resolution than PFGE
 One PFGE cluster could not be confirmed by WGS
• Saving resources by interviewing only patients relevant to the cluster
 Suspected source disproved by WGS in one cluster

A common source identified in one cluster until now
 Difficult to obtain relevant exposure information from patients


A sporadic case was linked to a lettuce recall by WGS
Many good analytical approaches yielding (almost) the same
information
WGS Minimum Spanning Tree of Listeria monocytogenes
Connection between lettuce recall and a sporadic case?
Courtesy Public Health Agency of Canada & the Canadian Food Inspection Agency
Lessons learned:
WGS may identify clusters not evident by PFGE
Cluster 1403MLGX6-1
0.1
0.1
0.0
0.3
0.0
0.3
0.5
1.5
0.6
2.1
IL isolate 2
18
20
41
NYC25isolate 21
18
20
41
FL isolate
25
2
18
20
41
NYC25isolate 22
20
41
NYC25
isolate 23
18
20
41
201325isolate 2– Nearest Neighbor 41
LMO_14
LMO_13
LMO_12
LMO_11
LMO_10
LMO_7
wgMLST
LMO_6
LMO_5
LMO_4
LMO_1
100
wgMLST (<All Characters>)wgMLST
99

5 cases, 3 PFGE patterns, 3 states
All patients originally from former USSR or Poland
98

11
19
11
8
37
11
19
11
8
37
11
19
11
8
37
11
19
11
8
37
11
19
11
8
37
11
19
11
8
Lessons Learned:
WGS May Identify Clusters Not Evident by PFGE
1403MLGX6-1WGS
hqSNP tree
How different are WGS profiles of isolates belonging to the same outbreak?
2013T-30500.00
77
2013T-30420.00
Salmonella serovar Heidelberg
PFGE pattern JF6X01.0022 single
state outbreaks in summer 2013
2013T-31110.00
2013T-3112910.01
77
0.01
0.002013T-311074
0.00
0.00
74 2013T-31090.00
0.00
2013T-304087 0.01
100
2-8 SNPs
2-16 SNPs
AL funeral
0.03 2013T-3059-A
0.00
2013T-30580.01
0.32
105-113 SNPs
2013T-30410.02
0.19
0.18
0.14
0
Sporadic case, NC
2013AM-0488
410.02
0.00
100 2013AM-0486
0.01
0.14
0.04
NY daycare
2013K-1067-
Sporadic case, NE
2013K-1038
0.00
0.19
100
2013K-10360.02
2013K-1040930.01
0.02
2013K-10390.00
0.05
4-5 SNPs
2013AM-0487
0.01
0.00
75
2013K-1037-
Sporadic case, OH
3-6 SNPs
(3-9 SNPs)
CO family gathering
2012-2013 Salmonella
serovar Heidelberg multistate
outbreak associated with
chicken from manufacturer X
PFGE patterns
Pattern 122
Pattern 672
Pattern 45
Pattern 22
Pattern 326
Pattern 41
Pattern 258
** Chicken from manufacturer X
*** Clinical isolate from 2011
89 2012K-1420
0.00251
84
0.00779 2012K-1421
0.03113
0.00481
2013K-1635
42
0.01289
2012K-1747
0.00251
0.00251
2012K-1549**
98
0.05323
100
0.02825
2012K-1550**
6-46 SNPs
0.03381 0.02869
2012K-1417
0.01266
87
2012K-1255
0.01005
0.00763
2012K-1256
0.04386
84
2012K-1254
0.02581
0.00701
2013K-1633**
84 0.02798
2012K-1315***
0.00707 0.07858
2013K-1649**
100
95
0.02262
21 SNPs
0.04630
2013K-1650
0.01637
0.03288
87
100 2013K-0982
0.00319
0.01024 0.06016
2013K-1636
0.01701
7-60 SNPs
2013K-1361
0.03682 0.05173
2013K-0983
75 0.06983
2013K-1638
100
0.00807
0.01738
19 SNPs
0.08289
2013K-1639
0.03360
2013AM-0303
0.02883
100
2013K-0979
0.02834
0.11613
2013K-1275
95
0.01792
6-35 SNPs
2013K-0573**
0.02671
0.03086
72
2013K-0574
0.02037
0.00248
2013K-0980**
0.00000
0.03478
2013K-1274
0.02286
6-62 SNPs
To SNP or Not to SNP?
in public health

Single Nucleotide Polymorphism (SNP) approaches





Default for phylogenetic analyses of sequence data
Comparative subtyping by nature
Results difficult to communicate
Computationally intensive = SLOW
Gene- gene approach (wgMLST)
 Definitive subtyping
• Leads to naming, tracking over time, easy communication
 Computationally more simple = FAST
 Sufficiently discriminatory?
but…
Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers Cheese outbreak
High confidence core SNP
It took days to
It took days to generate this tree using our own
NY – no soft cheese
exposure
No soft cheese
exposure
bioinformatics pipeline
TX - no exposure info
Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers Cheese outbreak
High confidence core SNP
Historical isolates from the plant environment added to the comparison (courtesy FDA/CFSAN)
0.005
Red= epi-related clinical isolates
It took additional
hours toclinical
generate
this
tree
Blue= retrospective
controls or not
outbreak
related
Green=bioinformatics
historical environmental isolates
from the plant
using our own
pipeline
100
100
100
CFSAN004365
100 CFSAN004359
CFSAN004361
CFSAN004360
CFSAN004358
CFSAN004353
2010L-1790
CFSAN004348
CFSAN004363
2013L-5298
CFSAN004355
CFSAN004356
CFSAN004352
CFSAN004369
2013L-5214
2011L-2809
CFSAN004377
CFSAN004375
100
CFSAN004374
100 CFSAN004373
CFSAN004349
CFSAN004364
2013L-5275
2013L-5223
2013L-5337
CFSAN004354
2013L-5357
CFSAN004372
CFSAN004370
100
CFSAN004371
CFSAN004368
CFSAN004350
CFSAN004357
2013L-5283
CFSAN004366
CFSAN004376
CFSAN004362
CFSAN004351
2012L-5487
2013L-5121
2013L-5284
2012L-5105
2012L-5274
100
2013L-5374
2013L-5301
Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers
Cheese outbreak
It took less than 5 minutes to draw this
tree using wgMLST
Gene – Gene Approach:
 Fixed set of genes (‘loci’) leading to typing schemes
on different levels
MLST
Genus/Species
Serotype
AR
eMLST
cMLST
wgMLST
 Concept of allelic variation, not only point mutations
 Evolutionary distance for events such as recombination
and simultaneous close-range mutations are counted as
one event
 Definitive subtyping
 Leads to nomenclature
 Requires curation
Genes That May Be Targeted In a
Gene-Gene Analytical Approach
Virulence genes
Housekeeping genes for MLST & eMLST
Core (c) genes (‘present
in all strains in a species’)
Pan- genome (wg) (‘all
genes in the whole
population of a species’)
Serotyping genes
Genes for genus/species/subspecies
identification
Antimicrobial resistance
genes
Gene – Gene Approach for Naming
Subtyping in Keep with Phylogeny
(concept to be developed)
7 gene MLST
Isolate A
Isolate B
Isolate C
Isolate D
ST24 ST24 ST24 ST31 -
eMLST
e12 e12 e12 e15 -
cMLST
wgMLST
c48 c48 c45 c60 -
w214
w352
w132
w582
Isolate A and B closely related
Isolate C related to A and B but not as closely as A is to B
Isolate D unrelated to all the other isolates
Providing phylogenetic information in the name is important because isolates from the
same source are more likely to be related than isolates from different sources
Public Health WGS Workflow
End users at
CDC and in
the States
LIMS
Sequencer
External storage
Raw sequences
PH databases
Closed
Genus/species
Serotype
Pathotype
Virulence profile
AST
Lineage
Clone
Sequence type
Allele
NCBI, ENA, BaseSpace
SNPs
Open
Calculation engine
Nomenclature server
Allele databases
Open
Trimming, mapping, de novo
assembly, SNP detection, allele
detection
Open
GENUS/SPECIES:
PATHOTYPE: Shiga toxin producing and Enteroaggregative E. coli (STEC & EaggEC)
VIRULENCE PROFILE: stx2a, aagR, aagA, sigA, sepA, pic, aatA, aaiC, aap
SEQUENCE TYPE:
ST34
ANTIMICROBIAL RESISTANCE GENES: blaTEM-1 , blaCTX-M-15
All characteristics have been determined by whole genome sequencing (WGS)
The strain contains Shiga toxin subtype 2a typically associated with virulent STEC
It does not contain adherence and virulence factors (eae, ehxA) typically associated with virulent STEC
It contains adherence and virulence factors typically associated with virulent EaggEc (aagR, aagA, sigA, sepA,
pic, aatA, aaiC, aap)
This genotype is associated with extremely high (>10%) rates of hemolytic uremic syndrome (HUS)
Real-time Sharing Of
Sequence Data Is The Key To
Successful Surveillance !
Acknowledgements
CDC: John Besser, Kelly Jackson, Amanda Conrad, Lee Katz, Steven Stroika,
Heather Carleton, Zuzana Kucerova, Eija Trees, Katie Roache, Ashley Sabol,
Andrew Huang, Lori Gladney, Maryann Turnsek, Ben Silk, Katie Fullerton, Matthew
Wise, Ian Williams, Duncan MacCannell, Efrain Ribot, Patricia Griffin , Rob Tauxe,
Chris Braden, Dana Pitts, Collette Leaumont, Patti Fields
Public Health Agency of Canada
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.”
National Center for Emerging and Zoonotic Infectious Diseases
Division of Foodborne, Waterborne, and Environmental Diseases
36
2013L-5439

WGS
Analysis at
CDC
Similar results
2013L-5527
2013L-5545
21.5 hqSNPs [0-33]
2013L-5556
2013L-5203
2013L-5204
2013L-5308
2013L-5473
24 hqSNPs [0-94]
2013L-5085
2013L-5587

Circumstantial
evidence
suggesting
pre-packaged
lettuce is the
likely food
vehicle in a
sporadic case
of listeriosis
2013L-5333
2013L-5314
2013L-5182
2013L-5622
2013L-5623
2013L-5521
2013L-5345
2014L-6017
2013L-5548
43 hqSNPs
Lettuce isolate
Ohio isolate
2013L-5540
5
hqSNPs
2013L-5359
2013L-5297
66 hqSNPs [4684]
2013L-5194
2013L-5560
2014L-6086
WGS Analysis by Lee Katz, CDC
55 hqSNPs [5-66]
58 hqSNPs [46-58]
0.02
12
wgMLST (<All Characters>)wgMLST
WGMLST_LMO0000877
SRR1016591
done
2013L-5521
20
3
WGMLST_LMO0000931
SRR1021895
done
3
WGMLST_LMO0000959
SRR1027086
done
2013L-5204
20
2013L-5587
20
WGMLST_LMO0000692
SRR1041615, SRR98.
done
SRR1030280
done
2013L-5314
20
2013L-5623
20
3
WGMLST_LMO0000994
WGMLST_LMO0000993
SRR1030279
done
3
WGMLST_LMO0000834
SRR1001027
done
2013L-5622
20
2013L-5473
20
WGMLST_LMO0000916
SRR1112081
done
3
WGMLST_LMO0000884
SRR1016925
done
2013L-5085
20
2013L-5712
20
WGMLST_LMO0000888
SRR1016594
done
3
WGMLST_LMO0000811
SRR988736
done
2013L-5527
20
2013L-5439
20
WGMLST_LMO0000906
SRR1021953
done
3
WGMLST_LMO0000926
SRR1016605
done
2013L-5545
20
2013L-5556
20
WGMLST_LMO0000686
SRR946033
done
SRR1166834
done
2013L-5308
20
2014L-6017
20
3
WGMLST_LMO0001160
WGMLST_LMO0000918
SRR1021894
done
3
WGMLST_LMO0000675
SRR1041609, SRR95.
done
2013L-5548
20
2013L-5297
20
WGMLST_LMO0000713
SRR972406
done
SRR1016609
done
2013L-5345
20
2013L-5560
20
3
WGMLST_LMO0000930
WGLMO0001344
done
2014L-6149
20
14-3198_S1_L001
done
CN 20
lettuce 3
2013L-5359
20
2013L-5540
20
WGMLST_LMO0000727
SRR972392
done
WGMLST_LMO0000901
SRR1021948
done
3
3
3
3
3
3
3
3
3
3
3
LMO_15
LMO_14
LMO_13
LMO_12
LMO_11
LMO_10
LMO_9
LMO_7
LMO_6
LMO_4
LMO_3
LMO_1
100
99
98
Canadian Lettuce/OH case patient wgMLST
NY___IDR1300025739
29 containing
27
29
26
*UPGMA41 tree
5 PNUSAL000320
22
26
41 HI___N13-140
29
27
29
26
PFGE
matches
to
Ohio
and
5 PNUSAL000348
22
26
41 WA___19002
29
27
29
26
5 PNUSAL000069
22
26
41 SC___LM14-001
29
27isolates
29
26
Canadian
lettuce
PNUSAL000383
WA___19014
5
22
26
41
29
27
29
26
*wgMLST
groups
these
5 PNUSAL000382
22
26
41 WA___19029
29
27
29
26
5 PNUSAL000212
26
41
29
27 as29well
26
isolates
ofMD___MDA13096803
interest
PNUSAL000305
NYC__nyc13-101340416
5
22
26
41
29
27
29
26
as
supports
the
general
tree
PNUSAL000270
NY___IDR1300029167
5
22
26
41
29
27
29
26
layout
by
5 PNUSAL000278
22
26 seen
41 MI___CL13-200750
29 hqSNP
27
29
26
5 PNUSAL000189
22
26
41 OH___2013042642
29
27
29
26
analysis
PNUSAL000296
CA___M13X04891
5
22
26
41
29
27
29
26
30
2
30
2
30
2
30
2
30
2
30
2
30
2
30
2
30
2
30
2
30
2
30
2
5 PNUSAL000315
22
26
5 PNUSAL000063
22
26
41 WA___18947
29
27
29
41 NY___IDR1300016274
29
27
29
26
30
2
26
30
2
5 PNUSAL000548
22
55
PNUSAL000307
5
22
26
5 PNUSAL000052
22
55
41 MA___14EN0045
29
27
CT___344766001
41
29
27
41 SC___LM13-008
29
27
29
26
30
2
29
5 PNUSAL000256
22
26
OH case
5 PNUSAL000090
22
26
PNUSAL000319
5
22
26
5 PNUSAL000172
22
55
5 CA_lettuce
22
55
PNUSAL000104
5
22
55
5 PNUSAL000291
22
55
26
30
2
29
41 NYC__nyc13-101347849
29
27
29
OH___2013063623
41
29
27
29
26
30
2
26
30
2
26
30
2
41 OH___2014001036
29
27
29
26
30
2
41
26
30
2
26
30
2
26
30
2
29
27
29
MI___CL13-200523
41
84
27
29
MI___CL13-200753
41
29
27
29