National Center for Emerging and Zoonotic Infectious Diseases

Whole Genome Sequencing & Food Safety

Peter Gerner-Smidt, MD, DSc Branch Chief

Food Standards Scotland’s first food Conference Edinburgh, March 28, 2018

A foodborne infection

is an infection you get by

ingesting food, which contains

pathogenic microorganisms

But…. Infections caused by foodborne

pathogens are not always foodborne !

May be transmitted:

• From water/the environment

• From person to person

• From contact to animals

Impact of Foodborne Diseases

1 in 6 America

ns

Disease Burden in the US

Economic Impact

48 Million

illnesses 128,000

Hospitalizations

3,000 Deaths

230,000 Deaths

www.who.int/foosafety

550

Million Foodborne

Illnesses

Disease Burden Worldwide

15+ Billion each year

Pathogens

Flynn D 2014

Campylobacter

Listeria

Salmonella

Shigella

Vibrio

E coli

9 Billion $ each year

Foodborne illnesses are

preventable

Researc

h

Agricultu

re

Indust

ry

Local,

State,

Federal

Governm

ent Medi

a

Consume

rs

“You Are Never Alone”

Outbreak surveillance is a

collaboration

• Outbreaks detected, defined and confirmed by the laboratory

• Outbreaks investigated and solved by epidemiologists

• Inspection, tracebacks and regulatory action

Outbreak investigation

Epid

em

iolo

gy EHS-Net

Integrated Surveillance

Walmart Poultry Salmonella Control Initiative

a successful private - public partnership

FoodSafetyNews.com

Walmart Poultry Salmonella Control Initiative

a successful private - public partnership Four Intervention Areas:

Primary Breeder Stock – source from primary breeders who participate in USDA’s National Poultry Improvement Plan (NPIP) for

Breeding Poultry (9 CFR 145.83)

– Salmonella data on a regular basis report on progress to Walmart

– support changes targeted towards continuous reduction of prevalent Salmonella serotypes of human health concern

Bio-control Measures – Directed at major human Salmonella serotypes in housing complex

– vaccination of broiler-breeder (parental) flocks

– adhere to disease prevention best practices associated with bio-security and vector control

Whole Chicken - Process Control – regulatory approved intervention or a combination of interventions between pre-scald to post-chill

– cumulative 4-log10 reduction of Salmonella

– by December 31, 2015

Chicken Parts Intervention – regulatory approved intervention or a combination of interventions post-chill, after cut-up of whole

chickens prior to packaging

– 1-log10 reduction

– by June 30, 2016

The Walmart Poultry Salmonella Control Initiative Works:

Courtesy Walmart- data are preliminary and not for public distribution

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

Oct-14 Aug-15 Jan-16 Mar-16 Jun-16 Mar-17

Salmonella Prevalence in Chicken Parts

Parts National Average

Parts

PulseNet the National Molecular Subtyping Network for Foodborne Disease

Surveillance

Easier and faster to exchange data than to exchange strains

80,000+

Patterns

analyzed

STEC

Salmonella

Vibrio

Listeria

Campylo bacter

Shigella

55,9

72

11,35

9

4,669

35

7

4,29

1

3,476

2014 Data

>80 Active

Labs

State labs

Ag. labs

Federal Partners

Cities & Counties labs

Molecular Surveillance (by PFGE) Saves Lives and Money

Scharff RL et al. 2016. An Economic Evaluation of PulseNet: A Network for Foodborne Disease Surveillance. Am J Prev Med 50:S66-73.

PulseNet moving to Whole Genome Sequencing

Standardized, automated methods to ensure comparability of data generated in different laboratories, save time and resources

PFGE WGS

2005

1996

2010

Modified from Carleton and Gerner-Smidt (ASM Microbe

July 2016)

PFGE

WGS

MLVA

What Is Whole Genome Sequencing ?

A process to determine the full genetic code (sequence of DNA bases) of an organism

“Massive parallel sequencing” The whole genome sequenced in small random pieces

(‘shotgun sequencing’, 25- >1000 bp) multiple times

Following sequencing the DNA base sequence is determined by piecing the overlapping short sequencing together electronically using specialized software

20- several 100 X

Isolate Raw Sequences (‘Reads’)

CCCGGCCCTTTGGCCCTTTGGGAAATCGCCCCAATGGAAATTTGTAGAAGT

PFGE Is Amazing! How Can WGS Possibly Be

Better?

WGS Is More Than Subtyping Replacing Traditional Microbiology with

WGS

o Cost-efficient consolidation of multiple workflows into one: Identification – serotyping – virulence profiling – antimicrobial resistance characterization – subtyping

o Cost: (100- ) 150- 200 US $/ bacterial strain

Reference Characterization by WGS: ’One Shot’ Characterization Of STEC

Genus/Species: Escherichia coli

Serotype: O104:H4

Pathotype: Shiga toxin-producing and enteroaggregative E. coli (STEC/EAEC)

Virulence profile: stx2a, aggR, aggA, sigA, sepA, pic, aatA, aaiC, aap

Sequence Type: ST678

Allele code: 102.45.26.35.3

Antimicrobial resistance genes: blaTEM-1, blaCTX-M-15, strAB, sul2, tet(A)A, dfrA7

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 State A funeral

4-5 SNPs

State B daycare

Unrelated Sporadic case

Unrelated Sporadic case

3-6 SNPs (3-9 SNPs)

State C Church Supper

Unrelated Sporadic case

105-113 SNPs

WGS Higher Resolution Than PFGE

Typical tight clustering in point-source outbreaks

14

N/A

2

6

19

6.7 6.3

4

0

2

4

6

8

10

12

14

16

18

20

No. of clustersdetected

No. of clustersdetected sooneror only by WGS

No. of outbreakssolved (food

source identified)

Median no. ofcases per cluster

PFGE (1-year pre-WGS) 3-Year average WGS

13

53

No. cases linkedto food source

Real-time WGS Improves Laboratory Surveillance Listeria Metrics

Courtesy Amanda Conrad, CDC Outbreak Response & Preparedness Branch

Salmonella ser. I 4,[5],12:b:-

1712MLJKX-1 (JKXX01.1478)

Monoclonal: Typical point

souce

The methods used in the analysis of this sequence data

are preliminary and remain under validation. Please email pulsenet@cdc.gov if you plan

to use/distribute this phylogeny further.

2018-

03-22

Settings: LyveSET 1.1.4f used with reads trimmed using fastx_trimmer 5 bases from 3’ ends before mapping by SMALT. R2 reads of PNUSAS032540 and PNUSAS032740 trimmed 15 bases from 3’ ends. SNPs were called using Varscan at > 20x coverage, > 95% read support, and < 5 bp apart. Reference: Draft genome of str. PNUSAS028219 (42 contigs) without prophage masking

PNUSAS028151

PNUSAS035064

PNUSAS032422

PNUSAS035402

PNUSAS035363

PNUSAS032537

PNUSAS032389

PNUSAS035217

PNUSAS034936

PNUSAS030561

PNUSAS033901

PNUSAS033100

PNUSAS032541

PNUSAS032542

PNUSAS035651

PNUSAS034276

PNUSAS034279

PNUSAS036129

PNUSAS034010

PNUSAS035063

PNUSAS032539

PNUSAS032540

PNUSAS034719

PNUSAS030750

PNUSAS035439

PNUSAS032543

PNUSAS035911

PNUSAS032740

PNUSAS032157

PNUSAS034845

PNUSAS031173

PNUSAS035061

PNUSAS028219

PNUSAS034559

PNUSAS035466

PNUSAS032163

PNUSAS032538

PNUSAS032545

PNUSAS034571

PNUSAS032544

PNUSAS034980

PNUSAS035062

PNUSAS035675

PNUSAS034591

100

100

0 – 2

SNPs

76 - 109

SNPs

56 SNPs 1199 –

1289 SNPs

• Salmonella outbreak 2017- 18 • ~ 90 cases as of March 23 • 4 serotypes

• I 4,5,12:b:- • Thompson • Okatie • Javiana

• New vehicle: herbal supplement: kratom

Kratomonline.org Kratompowders.org

Kratomguides.com

1712MLJKX-1 Salmonella ser. Thompson – (JP6X01.0684)

2018-03-22

The methods used in the analysis of this sequence data are

preliminary and remain under validation. Please email

pulsenet@cdc.gov if you plan to use/distribute this phylogeny

further.

LyveSET 1.1.4f used with reads trimmed using fastx_trimmer 5 bases from 3’ ends before mapping by SMALT. SNPs were called using Varscan at > 20x coverage, > 95% read support, and < 5 bp apart. PacBio assembly of str. 2013K-0463 with prophage regions masked at 87735-908183, 1963137-1971305, 2386740-2436933, 3249575-3263912, and 4084799-4101913.

WGS_id Key SourceType IsolatDate Outbreak SourceSite

PNUSAS031605 KY___17-66544339-1939 Human 12/15/2017 1712MLJKX-1 Unknown

PNUSAS032798 MN___I2018000921-1 Human 1/17/2018 1712MLJKX-1 Stool

PNUSAS034058 OH___2018000468 Human 1/26/2018 1712MLJKX-1 Stool

PNUSAS034847 VA___R180202147 Human 1/30/2018 1712MLJKX-1 Stool

PNUSAS035625 MI___CL18-165035 Human 2/11/2018 1712MLJKX-1 Stool

PNUSAS036031 UT___1267404-2 Food 2/15/2018 1712MLJKX-1 Kratom

PNUSAS036173 MI___CL18-174130 Human 2/20/2018 1712MLJKX-1 Stool

PNUSAS036195 TX___TXAML1800215 Human 1/28/2018 1712MLJKX-1 Stool

PNUSAS036299 WI___18MP001687 Human 1/29/2018 1712MLJKX-1 stool

PNUSAS036362 CA___M18X00331 Human 2/6/2018 1712MLJKX-1 Stool

PNUSAS036363 CA___M18X00387 Human 2/12/2018 1712MLJKX-1 Stool

PNUSAS036400 GA___18C0075848 Human 2/13/2018 1712MLJKX-1 Stool

PNUSAS036837 or___18030802215 Food 2/22/2018 1712MLJKX-1 Kratom Powder

PNUSAS036838 or___18030802216 Food 2/22/2018 1712MLJKX-1 Kratom Powder

PNUSAS034058

PNUSAS036400

PNUSAS035625

PNUSAS036031

PNUSAS036299

PNUSAS036363

PNUSAS031605

PNUSAS036362

PNUSAS032798

PNUSAS036838

PNUSAS036837

PNUSAS036173

PNUSAS034847

PNUSAS036195

99

97

97

97

100

PN

US

AS

03

16

05

PN

US

AS

03

27

98

PN

US

AS

03

40

58

PN

US

AS

03

48

47

PN

US

AS

03

56

25

PN

US

AS

03

60

31

PN

US

AS

03

61

73

PN

US

AS

03

61

95

PN

US

AS

03

62

99

PN

US

AS

03

63

62

PN

US

AS

03

63

63

PN

US

AS

03

64

00

PN

US

AS

03

68

37

PN

US

AS

03

68

38

PNUSAS031605 0 38 39 38 30 30 32 33 21 32 30 26 45 45

PNUSAS032798 38 0 51 41 41 41 37 44 30 37 41 38 50 50

PNUSAS034058 39 51 0 47 41 42 42 43 32 43 42 27 57 57

PNUSAS034847 38 41 47 0 40 37 29 40 29 34 39 35 46 47

PNUSAS035625 30 41 41 40 0 30 33 37 21 34 31 28 46 46

PNUSAS036031 30 41 42 37 30 0 33 33 20 34 32 27 47 47

PNUSAS036173 32 37 42 29 33 33 0 37 22 29 34 30 42 42

PNUSAS036195 33 44 43 40 37 33 37 0 24 37 35 33 47 48

PNUSAS036299 21 30 32 29 21 20 22 24 0 23 21 20 36 36

PNUSAS036362 32 37 43 34 34 34 29 37 23 0 34 31 43 43

PNUSAS036363 30 41 42 39 31 32 34 35 21 34 0 24 47 47

PNUSAS036400 26 38 27 35 28 27 30 33 20 31 24 0 43 43

PNUSAS036837 45 50 57 46 46 47 42 47 36 43 47 43 0 0

PNUSAS036838 45 50 57 47 46 47 42 48 36 43 47 43 0 0

0 SNPs

20 –32

SNPs

27 SNPs

29 SNPs

This cluster could not have been detected by WGS alone

20- 57 SNPs

PRODUCT TESTING-DRIVEN (“RETROSPECTIVE”) OUTBREAK INVESTIGATIONS

PulseNet Clusters In 2016* With Or Without Isolates From Potential Sources Available At

The Time Of Detection

118

33

9 6

18

2 2 1 0

20

40

60

80

100

120

140

Salmonella STEC Listeria Campylobacter

No food/Environmentalisolates

With Food/Environmentalisolate

Listeria clusters detected by WGS The other organisms by PFGE

2016*: Until 9/19/2016

14% of PulseNet clusters has a non-clinical ‘match’ at the time of detection

wgMLST (<All Characters>)

10

0

99

98

.

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..

2014L-6390

2014L-6561

2014L-6470

2014L-6268

CFSAN023497

CFSAN023507

CFSAN023482

CFSAN023483

CFSAN023495

CFSAN023480

CFSAN023484

CFSAN023494

CFSAN023503

CFSAN023508

CFSAN023498

CFSAN023499

CFSAN023479

CFSAN023492

CFSAN023085

CFSAN023491

CFSAN023086

CFSAN023478

CFSAN023475

CFSAN023474

CFSAN023506

PNUSAL000870

CFSAN023489

CFSAN023493

CFSAN023500

PNUSAL001024

CFSAN023481

CFSAN023502

CFSAN023504

CFSAN023472

CFSAN023496

CFSAN023473

PNUSAL000957

PNUSAL000730

FCF__869224-4b2

FCF__869224-5b

FCF__869224-2a

FCF__869224-1b

FCF__869224-1c

FCF__873864 S1

FCF__873864 S3

FCF__869223-5c1

FCF__869223-2b

FCF__869222-5c1

MA___14EN4745

MN___C2014015427

FCF__869224-3b1

FCF__869220-3b

FCF__869220-4c

IL___134855

SC___LM14-011

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

.

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2014-07-31

2014-07-31

2014-07-31

2014-07-31

2014-07-31

2014-07-31

2014-07-31

2014-07-31

2014-07-03

2014-08-28

2014-07-31

2014-07-31

2014-07-31

2014-07-08

2014-05-08

State X patient isolate

Patient 1 isolate (State A)

State Y patient isolate

2014 Stone Fruit Outbreak: Concordance Between

Epidemiology and WGS

Patient 2 isolate (State B)

Isolates from recalled stone

fruits

Isolates from recalled stone

fruits

Isolates from recalled stone

fruits

4 [1-7]

3 [0-10]

6 [0-14]

10 [0-43]

11 [0-47]

12 [0-69]

2.5 [0-5]

3 [0-8]

Allele Median [min-max]

We Still Need The Three Legged Stool !

Outbreak investigation

Epid

em

iolo

gy Integrated Surveillance

W

A

O

R

M

T

W

Y

C

O

U

T

AZ

I

D

CA N

V

N

D

S

D

K

S

N

E

T

X

N

M

O

K

M

N

M

O

I

A

LA

A

R

W

I

IN

K

Y

MI

O

H I

L

T

N

G

A A

L

M

S

SC

FL

P

A

N

C

W

V V

A

D

E

M

D

N

J

M

E

V

T

N

Y

N

H

M

A C

T

AK

HI

R

I

Modified: March 16,

2018

WGS certified, BioNumerics 7.6 pilot site

WGS certified

P

R

Tally of certified

labs:

45 states

50 labs

4 federal labs

3 international

labs

PulseNet, WGS and Enhanced Epidemiological Capacity

OutbreakNet Enhanced or FoodCORE

D

C

Area

Laboratories

PulseNet

Central

NY

C

CA

2

CAO

C

CAS

C

LA

C

H

U

Genomes Sequenced and Submitted to PulseNet & SRA, NCBI

(Through September, 2017)

8 85 100 116 129 180 178 188 175

104 200 199 243 213 195 205 216

291 323 339 386

651 719

480 589 540 559

672 644 755

580

946 950

1186

1532

1194 1208

1424

1669 1805

1864

3160

2605

3168 3294

4014

4502

2340

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Oct-

13

Nov-1

3

Dec-1

3

Jan-1

4

Feb-1

4

Mar-

14

Apr-

14

May-1

4

Jun-1

4

Jul-

14

Aug-1

4

Sep-1

4

Oct-

14

Nov-1

4

Dec-1

4

Jan-1

5

Feb-1

5

Mar-

15

Apr-

15

May-1

5

Jun-1

5

Jul-

15

Aug-1

5

Sep-1

5

Oct-

15

Nov-1

5

Dec-1

5

Jan-1

6

Feb-1

6

Mar-

16

Apr-

16

May-1

6

Jun-1

6

Jul-

16

Aug-1

6

Sep-1

6

Oct-

16

Nov-1

6

Dec-1

6

Jan-1

7

Feb-1

7

Mar-

17

Apr-

17

May-1

7

Jun-1

7

Jul-

17

Aug-1

7

Sep-1

7

WGS: New Concerns

• WGS turnaround time issues

• Interpretation concerns o Confusion about the meaning of a

“match” or “mis-match”

o No “absolute” cutoff values (SNPs or allele)

• Food industry concerns o No “statute-of-limitations” on liability

o No precise definition of “outbreak”

o Data sharing

Coming Soon: Using Big Data to Improve Food Safety

• Pathogen characterization direct-from-specimen (faster)

• Compatible with CIDTs

• More information to inform policy

But • Privacy issues • Regulatory hurdles • Data capacity

issues

Acknowledgements

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