Identification and Characterization of

Foodborne Pathogens by Whole Genome

Sequencing: A Shift in Paradigm

National Center for Emerging and Zoonotic Infectious Diseases

Division of Foodborne, Waterborne, and Environmental Diseases

Peter Gerner-Smidt, MD, ScDEnteric 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

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 SurveillanceLessons 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

5 cases, 3 PFGE patterns, 3 states

All patients originally from former USSR or Poland

0.0

0.0

0.3

0.3

0.1

0.5

0.1

0.6

1.5

2.1

wgMLST (<All Characters>)100

99

98

wgMLST

LM

O_

1

LM

O_

4

LM

O_

5

LM

O_

6

LM

O_

7

LM

O_

10

LM

O_

11

LM

O_

12

LM

O_

13

LM

O_

14

LM

O_

15

LM

O_

16

LM

O_

17

LM

O_

18

2 18 20 41 11 19 11 8 37 21 22 13 4

25 2 18 20 41 11 19 11 8 37 21 22 13 4

25 2 18 20 41 11 19 11 8 37 21 22 13 4

25 2 20 41 11 19 11 8 37 21 22 13 4

25 2 18 20 41 11 19 11 8 37 21 22 13 4

25 2 41 11 19 11 8 21 22 13 109

NYC isolate 1

IL isolate

FL isolate

NYC isolate 2

NYC isolate 3

2013 isolate – Nearest Neighbor

wgMLST

1403MLGX6-1WGS

Lessons Learned:

WGS May Identify Clusters Not Evident by PFGE

hqSNP tree

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-

77

74

7791

74

87

100

41

100

0

75

100

93

0.00

0.00

0.00

0.01

0.00

0.00

0.01

0.00

0.01

0.02

0.19

0.14

0.02

0.01

0.01

0.00

0.02

0.01

0.00

0.00

0.00

0.000.01

0.00

0.03

0.32

0.14

0.00

0.18

0.00

0.04

0.19

0.02

0.05

Salmonella serovar Heidelberg PFGE pattern JF6X01.0022 single state outbreaks in summer 2013

2-8 SNPs

2-16 SNPsAL funeral

4-5 SNPs NY daycare

Sporadic case, NC

Sporadic case, NE

3-6 SNPs(3-9 SNPs)

CO family gathering Sporadic case, OH

105-113 SNPs

How different are WGS profiles of isolates belonging to the same outbreak?

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

100

75

100

100

89

84

98

42

100

87

84

84

95

87

100

95

72

0.00000

0.02286

0.02037

0.03086

0.01792

0.02834

0.02883

0.06983

0.01738

0.03360

0.05173

0.00319

0.01701

0.02262

0.03288

0.07858

0.04386

0.01005

0.01266

0.01289

0.00251

0.03113

0.00251

0.05323

0.02869

0.02581

0.02798

0.03478

0.08289

0.00807

0.06016

0.04630

0.00779

0.00481

0.02825

0.00251

0.03381

0.00763

0.00701

0.00707

0.01637

0.01024

0.03682

0.11613

0.02671

0.00248

0.02

2012-2013 Salmonellaserovar Heidelberg multistate outbreak associated with chicken from manufacturer 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 manufacturer X*** Clinical isolate from 2011

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 but…

Sufficiently discriminatory?

No soft cheese exposure

It took days to

NY – no soft cheese exposure

TX - no exposure info

Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers Cheese outbreakHigh confidence core SNP

It took days to generate this tree using our own

bioinformatics pipeline

Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers Cheese outbreakHigh confidence core SNP

Historical isolates from the plant environment added to the comparison (courtesy FDA/CFSAN)

CF

SA

N004365

CF

SA

N004359

CF

SA

N004361

CF

SA

N004360

CF

SA

N004358

CF

SA

N004353

2010L

-1790

CF

SA

N004348

CF

SA

N004363

2013L

-5298

CF

SA

N004355

CF

SA

N004356

CF

SA

N004352

CF

SA

N004369

2013L

-5214

2011L

-2809

CF

SA

N004377

CF

SA

N004375

CF

SA

N004374

CF

SA

N004373

CF

SA

N004349

CF

SA

N004364

2013L

-5275

2013L

-5223

2013L

-5337

CF

SA

N004354

2013L

-5357

CF

SA

N004372

CF

SA

N004370

CF

SA

N004371

CF

SA

N004368

CF

SA

N004350

CF

SA

N004357

2013L

-5283

CF

SA

N004366

CF

SA

N004376

CF

SA

N004362

CF

SA

N004351

2012L

-5487

2013L

-5121

2013L

-5284

2012L

-5105

2012L

-5274

2013L

-5374

2013L

-5301

100

100

100

100

100

100

100

100

0.0

05 Red= epi-related clinical isolates

Blue= retrospective clinical controls or not outbreak relatedGreen= historical environmental isolates from the plant

It took additional hours to generate this tree

using our own bioinformatics pipeline

It took less than 5 minutes to draw this

tree using wgMLST

Whole Genome Sequencing of Listeria monocytogenes during the Crave Brothers Cheese outbreak

Gene – Gene Approach:

Fixed set of genes (‘loci’) leading to typing schemes on different levels

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

eMLST cMLST wgMLST

MLST

Genus/Species

Serotype

AR

Genes That May Be Targeted In a

Gene-Gene Analytical Approach

Core (c) genes (‘present

in all strains in a species’)

Housekeeping genes for MLST & eMLST

Serotyping genes

Genes for genus/species/subspecies

identification

Virulence genes

Antimicrobial resistance

genes

Pan- genome (wg) (‘all

genes in the whole

population of a species’)

Gene – Gene Approach for Naming

Subtyping in Keep with Phylogeny(concept to be developed)

eMLST cMLST wgMLST7 gene MLST

Isolate A ST24 - e12 - c48 - w214

Isolate B ST24 - e12 - c48 - w352

Isolate C ST24 - e12 - c45 - w132

Isolate D ST31 - e15 - c60 - 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

Nomenclature server

Calculation engineTrimming, mapping, de novo

assembly, SNP detection, allele

detection

PH databases

Closed

End users at

CDC and in

the States

Allele databases

External storageNCBI, ENA, BaseSpace

Sequencer

Genus/species

Serotype

Pathotype

Virulence profile

AST

Lineage

Clone

Sequence type

Allele

Raw sequences

LIMS

Open

OpenOpen

SNPs

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

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)

All characteristics have been determined by whole genome sequencing (WGS)

GENUS/SPECIES:

Real-time Sharing Of

Sequence Data Is The Key To

Successful Surveillance !

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

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

36

WGS Analysis at

CDC

WGS Analysis by Lee Katz, CDC

66 hqSNPs [46-

84]

55 hqSNPs [5-66]

24 hqSNPs [0-94]

43 hqSNPs

21.5 hqSNPs [0-33]

58 hqSNPs [46-58]

2013L-5439

2013L-5527

2013L-5545

2013L-5556

2013L-5203

2013L-5204

2013L-5308

2013L-5473

2013L-5085

2013L-5587

2013L-5333

2013L-5314

2013L-5182

2013L-5622

2013L-5623

2013L-5521

2013L-5345

2014L-6017

2013L-5548

Lettuce isolate

Ohio isolate

2013L-5540

2013L-5359

2013L-5297

2013L-5194

2013L-5560

2014L-6086 0.02

5

hqSNPs

Similar results

Circumstantial

evidence

suggesting

pre-packaged

lettuce is the

likely food

vehicle in a

sporadic case

of listeriosis

12

Canadian Lettuce/OH case patient wgMLSTwgMLST (<All Characters>)

100

99

98

wgMLST

LM

O_

1

LM

O_

3

LM

O_

4

LM

O_

6

LM

O_

7

LM

O_

9

LM

O_

10

LM

O_

11

LM

O_

12

LM

O_

13

LM

O_

14

LM

O_

15

LM

O_

16

LM

O_

17

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 55 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 55 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 26 41 29 27 29 26 30 20 27 28

20 3 5 22 55 41 29 27 29 26 30 20 27 28

20 3 5 22 55 41 29 27 29 26 30 20 27 28

20 3 5 22 55 41 84 27 29 26 30 20 27 28

20 5 22 55 41 29 27 29 26 30 20 27 28

wgMLST

LM

O_

57

95

LM

O_

58

05

LM

O_

58

21

LM

O_

58

37

LM

O_

58

49

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1 2

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

2 2 1 1

WGMLST_LMO0000877

WGMLST_LMO0000931

WGMLST_LMO0000959

WGMLST_LMO0000692

WGMLST_LMO0000994

WGMLST_LMO0000993

WGMLST_LMO0000834

WGMLST_LMO0000916

WGMLST_LMO0000884

WGMLST_LMO0000888

WGMLST_LMO0000811

WGMLST_LMO0000906

WGMLST_LMO0000926

WGMLST_LMO0000686

WGMLST_LMO0001160

WGMLST_LMO0000918

WGMLST_LMO0000675

WGMLST_LMO0000713

WGMLST_LMO0000930

WGLMO0001344

14-3198_S1_L001

WGMLST_LMO0000727

WGMLST_LMO0000901

SRR1016591

SRR1021895

SRR1027086

SRR1041615, SRR98.

SRR1030280

SRR1030279

SRR1001027

SRR1112081

SRR1016925

SRR1016594

SRR988736

SRR1021953

SRR1016605

SRR946033

SRR1166834

SRR1021894

SRR1041609, SRR95.

SRR972406

SRR1016609

SRR972392

SRR1021948

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

done

2013L-5521

2013L-5204

2013L-5587

2013L-5314

2013L-5623

2013L-5622

2013L-5473

2013L-5085

2013L-5712

2013L-5527

2013L-5439

2013L-5545

2013L-5556

2013L-5308

2014L-6017

2013L-5548

2013L-5297

2013L-5345

2013L-5560

2014L-6149

2013L-5359

2013L-5540

PNUSAL000256

PNUSAL000320

PNUSAL000348

PNUSAL000069

PNUSAL000383

PNUSAL000382

PNUSAL000212

PNUSAL000305

PNUSAL000270

PNUSAL000278

PNUSAL000189

PNUSAL000296

PNUSAL000315

PNUSAL000063

PNUSAL000548

PNUSAL000307

PNUSAL000052

PNUSAL000090

PNUSAL000319

PNUSAL000172

CA_lettuce

PNUSAL000104

PNUSAL000291

NY___IDR1300025739

HI___N13-140

WA___19002

SC___LM14-001

WA___19014

WA___19029

MD___MDA13096803

NYC__nyc13-101340416

NY___IDR1300029167

MI___CL13-200750

OH___2013042642

CA___M13X04891

WA___18947

NY___IDR1300016274

MA___14EN0045

CT___344766001

SC___LM13-008

NYC__nyc13-101347849

OH___2013063623

OH___2014001036

MI___CL13-200523

MI___CL13-200753

CN lettuce

OH case

*UPGMA tree containing

PFGE matches to Ohio and

Canadian lettuce isolates

*wgMLST groups these

isolates of interest as well

as supports the general tree

layout seen by hqSNP

analysis