applications of whole genome sequencing (wgs) technology on food safety management: us presentation

U.S. Presentations

Dr. Marc Allard Dr. John Besser

Dr. Stephanie Defibaug-Chavez

John BesserCDC, Enteric Diseases Laboratory Branch

U.S. Nationwide Real-time WGS-based Surveillance

Annual workflow: 60,000-70,000 isolates/yr

PulseNet Database: ~750,000 patterns (2015)

87 laboratories

National Cluster Investigations:30 – 60 monitored per week

State Cluster Investigations:

1,500 – 2,000 per year

Foodborne Disease Surveillance

Farm Transport

Processing

Distribution

Preparation

Disease

surveillance

X

Limit ongoing illness

Fix underlying problems, measure effectiveness of controls

Food monitoring / Genome TrackR

Food Commodities Made Safer Through PulseNet-

Triggered Outbreak Investigations

Ready-to-eat & “ready-to cook” foods

Beef

Spices

Tree nuts

Eggs

Vine vegetables

Leafy greens

Poultry

Peanut products

Sprouts

Mellon

Flour

Deli meats

Cheese and dairy

Listeria Outbreaks and Incidence, 1983-2014

0

1

2

3

4

5

6

7

8

9

0

1

2

3

4

5

6

7

8

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Outbreak

Incidence

Pre-PulseNet

0.3

69

Early

PulseNet

2.3

11

Listeria

Initiative

2.9

5.5

No. outbreaksIncidence

(per million pop)

Era

Outbreaks per year

Median cases per

outbreak

WGS

?

?

LM caseState/Local

Health Agency CDC

PulseNet

FDAUSDA

Nationwide Listeriosis Surveillance System

• Food / animal, environment sampling

CasesInterview Case / Food

questionnaire

Isolates

GenomeTrakR

Isolates

WGS PFGE

WGSPFGE

Case-CaseStudiesNCBI-Genbank (U.S.)

DDBJ (Japan)

EMBL (Europe)

upload

analysis

International Nucleotide Sequence Database Collaboration

WGS

PulseNet WGS Requirements

High resolution strain-typing, high epidemiological concordance

Consolidation of subtyping and reference laboratory workflows

Compatible with epidemiology and regulatory tracking systems

Ability to compare and communicate results locally, nationally, globally

Fast, economical

Local control

Minimal need for local bioinformatics, local high performance computing

Public Health WGS Workflow

Nomenclature server

Calculation engineTrimming, mapping, de novo

assembly, SNP detection,

allele detection

PH databases

Users at CDC and in the

States

Allele databases

External storageNCBI, ENA, BaseSpace

Sequencer

Raw sequences

LIMS

Data pathway

Proposed data pathway

Analysis request

Genus/speciesSerotypePathotypeResistance

7-gene MLSTrMLSTcMLSTwgMLST

hqSNP analysis(v. 7.6)

14

N/A1

6

19

6

4 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 sourceidentified)

Median no. ofcases per cluster

Pre-WGS (Sept 2012–Aug 2013)

WGS Year 1 (Sept 2013–Aug 2014)

WGS Year 2 (Sept 2014–Aug 2015)

Listeria Cluster Metrics Before and After WGS

Note that cluster 1508MLGX6-1WGS counted as solved with 24 cases

Solved Outbreaks: New Food Sources of Listeriosis

Listeria and Caramel Apples

• 35 cases• 12 states• 34 hospitalizations• 7 deaths

Whole-Genome Multilocus Sequence Typing (wgMLST)

wgMLST (<All Characters>)

100

90

80

70

60

50 Key

.

.

.

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.

.

.

.

.

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RunIds

.

CalculationStatuscdc_id

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

Id

CFSAN023708

PNUSAL001035

PNUSAL001167

PNUSAL001188

PNUSAL001177

PNUSAL001180

PNUSAL001146

PNUSAL001157

PNUSAL001168

PNUSAL001182

PNUSAL001131

PNUSAL001196

PNUSAL001154

PNUSAL001166

PNUSAL001151

PNUSAL001153

PNUSAL001186

PNUSAL001195

PNUSAL001135

PNUSAL001185

PNUSAL001040

State ID

USDA_853178331

MN___C2014016179

MN___C2014019515

TX___TXACB1403719

WI___14MP008990

AZ___AZ00023560

MN___C2014019204

CDC__M14-119

MN___C2014019628

CDC__M14-124

TX___TXACB1403543

AZ___AZ00023800

AZ___AZ00023172

CDC__M14-123

WI___14MP008696

AZ___AZ00023398

NM___2014035025

CDC__M14-127

CDC__2014033414

NM___2014035347

CO___HUM-2014016.

PFGE-AscI-pattern

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16.0012

GX6A16

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

GX6A16.0135

PFGE-ApaI-pattern

GX6A12.0126

GX6A12.0126

GX6A12.0696

GX6A12.0696

GX6A12.0126

GX6A12.0696

GX6A12.0696

GX6A12.0696

GX6A12.0696

GX6A12.0696

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

GX6A12.0349

Outbreak

1411MNGX6-1NOT

1411MNGX6-1NOT

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MNGX6-1

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

1411MLGX6-1WGS

Serotype

.

.

.

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IsolatDate

2014-07-18

2014-09-08

2014-11-06

2014-11-05

2014-11-09

2014-10-29

2014-10-26

2014-11-08

2014-10-17

2014-11-08

2014-10-20

2014-11-02

2014-10-29

2014-10-26

2014-11-08

2014-11-06

2014-10-21

2014-11-07

2014-09-12

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)

Data as of December 9, 2014

PFGE Pattern 1

PFGE Pattern 2

PFGE Pattern 3

Inclusion/exclusion of individual cases in clusters

Greater significance of smaller disease clusters

Stronger hypotheses from food/environment to human illness “matches”

Ruling out clusters

Root cause analysis

Real-time WGS Appears Useful for…..

Successful use of WGS requires integrated real-time surveillance (not just a better lab method)

Acquiring and analyzing exposure data is still the limiting factor of sporadic case-based surveillance

More attention needs to be given to cluster detection and epidemiological analysis methods

Lessons Learned

Projected wgMLST Database Validation and Deployment Timeline

Apr 14 Oct 14 Apr 15 Oct 15 Apr 16 Oct 16 Apr 17 Oct 17 Apr 18 Oct 18 Apr19

Development and internal validation

Deployment


Deployment


Deployment


Deployment


← External validation




External validation →

Cronobacter &Yersinia

Vibrio, Shigella &other diarrheagenicE. coli

Salmonella

Campylobacteraceae& Shiga toxin-producing E. coli (STEC)

Listeria monocytogenes

[email protected]

The findings and conclusions in this presentation are those of the author and do not necessarily represent the views of the Centers for Disease

Control and Prevention

U.S. Nationwide Real-time WGS-based Surveillance

GenomeTrakr: A Pathogen Database

Marc W. Allard, PhDSenior Biomedical Research Services OfficerDivision of [email protected]

Food and Agriculture Organizationof the United Nations (FAO):

Expert workshop on practical applications of Whole Genome Sequencing (WGS) on food safety management. Dec. 7-8, 2015

Eric W. Brown, PhDDirector Division of [email protected]

PFGE identical in red NGS distinguishes geographical structure among closely related Salmonella Bareilly strains

Same PFGEbut not part of the outbreak

Outbreak Isolates2-5 SNPs

SNP phylogeny for S. Bareilly strains

S. Braenderup

GenomeTrakr Fast Facts

First distributed network of labs to utilize WGS for pathogen identification

GenomeTrakr network has sequenced more than 40,000 isolates, and closed more than 100 genomes through November 12, 2015.

Currently sequencing more than 1,000 isolates a month

The need for increased number of well characterized environmental (food, water, facility, etc.) sequences may outweigh the need for extensive clinical samples

GenomeTrakr Labs

• 14 federal labs

• 14 state and university labs

• 1 U.S. hospital lab

• 5 labs outside of the U.S.

• Collaborations with independent academic researchers

• More GenomeTrakr labs coming on-line

27

Nu

mb

er

of

Se

qu

en

ce

s(a

s o

f th

e last day o

f th

e q

uart

er)

Total Number of Sequences in the GenomeTrakr Database

2013 2014 2015

Average Number of SequencesAdded Per Month in 2013 = 184

Average Number of SequencesAdded Per Month in 2014 = 1,049

First sequences uploaded in Feb 2013

Public Health Englanduploads more than 8,000 Salmonella sequences

0

5

10

15

20

25

30

35

40

4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68

Timeline for Foodborne Illness Investigation

Using Whole Genome Sequencing

Contaminated

food enters

commerce

FDA, CDC, FSIS, and States use WGS in

real-time and in parallel on clinical, food,

and environmental samples

Source of contamination

identified early through WGS

combined database queries

Averted Illnesses N

um

be

r o

f C

ase

s

Days

MINIMAL PATHOGEN METADATA

(FOODBORNE OUTBREAKS)

sample_name

organism

strain/isolate

Category (attribute_package)

1a) Clinical/Host-associated

1a1) specific_host

1a2) isolation_source

1a3) host-disease

OR

1b) Environmental/Food/Other

1b1) isolation_source

Countries, Academia, and Food Industry can hold

confidential metadata linked to public records

collection_date

Geographic location

6a) geo_loc_name

OR

6b) lat_lon

collected by

Where

When

Who

What

Immediate impacts of WGS to industry, growers, and distributers, countries, states.

Earlier intervention means:

1) Reduced amount of recalled product;

2) fewer sick patients which means fewer lawsuits;

3) less impact overall and minimal damage to brand recognition.

Impacts to industry, growers, and distributers (continued).Regular testing throughout network:

1) identifies specific suppliers that are introducing contaminants;

2) identifies whether contaminant is resident to a facility or transient;

3) knowledge of where contaminant is coming from allows industry to fix the problem based on scientific evidence.Shift costs to the supplier who has introduced the contaminant.

How often is the root cause of the problem left unresolved

to occur again at a later date?

33

Background: CFSAN SNP Pipeline

http://snp-pipeline.rtfd.org

Shttps://github.com/CFSANBiostatistics/snp-pipeline

https://pypi.python.org/pypi/snp-pipeline

Davis S, Pettengill JB, Luo Y, Payne J, Shpuntoff A, Rand H, Strain E. (2015) CFSAN SNP Pipeline: an automated method for constructing SNP matrices from next-generation sequence data. PeerJ Computer Science 1:e20 https://dx.doi.org/10.7717/peerj-cs.20

Intended for use by bioinformaticists (Linux)

http://snp-pipeline.rtfd.org/

https://github.com/CFSANBiostatistics/snp-pipeline

https://pypi.python.org/pypi/snp-pipeline

https://dx.doi.org/10.7717/peerj-cs.20

Molecular Epidemiology and Ecology of

Multi-drug Resistance (MDR) Salmonella

in Tanzania

Julius MedardusSokoine University of agriculture

Wondwossen A. [email protected]

mailto:[email protected]

ICOPHAI GenomeTrakr partnership

FDA GenomeTrakr partnership

924 isolates submitted

to FDA-CFSAN

• Brazil (4)

• Ethiopia (401)

• Kenya (86)

• Mexico (63)

• Tanzania (64)

• Thailand (60)

• U.S. –OSU (247)

37

Tanzania

• WGS- 45 food animal isolates completed

• All Unknown STs

• Plasmid types- ColRNAI, IncI1, IncI2, IncFII, ColpV2

(total 10)- Others?

• Kentucky (16/ 45) and Not conforming with any

known type (n=8)

• Pending- HM and biocide tolerance genes/ efflux

system…

• Comparison with isolates of human origin?

Whole Genome Sequencing Program (WGS)

http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/default.htm#trakr

GenomeTrakr

• State and Federal laboratory network

collecting and sharing genomic data

from foodborne pathogens

• Distributed sequencing based network

• Partner with NIH

• Open-access genomic reference

database• http://www.ncbi.nlm.nih.gov/bioproject/183844

• Can be used to find the contamination

sources of current and future outbreaks

http://www.ncbi.nlm.nih.gov/bioproject/183844

For more information:

For information about joining the GenomeTrakr network as a sequencing lab, providing isolates to a current member lab for sequencing, or using the GenomeTrakr database as a research tool, please contact FDA at [email protected]

ORA OCC OFS OC OAO OFVM/SRSC CFSAN CDERCBER CDRH CVM NCTR FDA CHIEF SCIENTIST OIP OARSASCIENCE BOARD IAS FFC FERN JIFSAN ADVISORY COMMITTEE IFSHMOFFETT CENTER CIO DAUPHIN ISLAND CFSAN-OCD CORE WESTERN CENTER

INTERNAL FDA STAKEHOLDERS

FDLIGMAVaFSTFCDCFBIPULSENET-LATIN AM.AM. ACAD MICROBIOLASMFSISARSUNIV VERMONTMINN DOHAZ DOHUNIV FLVA DOHWA DOHTX DOHNY AG LABIRISH FSANOVA SE UNIVIGS BALTIMOREINFORM MEETINGHONGKONG POLYT U

NISTITALIAN FSAEFSAWHO-FOOD SAFETT DIR.WHO-GFNCDC-EUEMERGING INFECTIOUS DIS CONFDANISH TECH UNIVNM STATE UNIV/ NM DOHCARLOS MALBRAN INST/ARGST COULD UNIV/FOOD MICROSENASICAGMINY DOH/WADSWORTH CENTUNIV HAMBURGCHINA CDCNESTLEFERA-UKMD DOHIAFPAPHLAFDOBELGIUM

VaTechUS ARMYUS NAVYMELBOURNE FSA (AUS)UNIV NEBRASKAPUBLIC HEALTH ENGLANDDHSDELMARVA TASKFORCEPENN STATE FOOD SCIENCEPROD MAN ASSOCILLUMINAUNIV IRELAND/DUBLIN COLLEGE

NCBI/NIHGSRS GLOBAL SUMMITFAO/OIEPUBLIC HEALTH CANADACFIAHEALTH CANADAINTL VTEC MEETINGCPS-GAAOACUNITED FRESHCOLUMBIA

HAWAII DOHCA DOHALASKA DOHSOUTH DAK UNIVUNIV GAUNIV IOWA/DOHUNIV CHILEBRAZILOSU VETNETTURKEYMEXICOIEHSILLAKERNEW ENG BIOLABPACIFIC BIOCLC-BIO/QIAGENCON-AGRADUPONTAGILENTUC-DAVISHARVARD MEDINFORM MEETINGTHAILAND

Food Safety and Inspection Service:

42


WGS for Food Safety Management: FSIS Perspective

Stephanie Defibaugh-Chavez, Ph.D.Senior Microbiologist, Science Staff

Office of Public Health ScienceUS Department of Agriculture, FSIS

FAO WGS Meeting – December 2015

43

Food Safety and Inspection Service:Food Safety and Inspection Service:

• FSIS is the public health agency in the U.S. Department of Agriculture responsible for ensuring that the nation's commercial supply of meat, poultry, and processed egg products is safe, wholesome, and correctly labeled and packaged

• Regulates more than 6,000 slaughter and processing establishments nationwide

• Verifies safety of approximately 100 billion pounds of product annually

44

FSIS Mission


• Improved resolution for foodborne illness investigations– Improved strain discrimination, illness cluster detection, and case

classification

• Supports FSIS mission goals– Effectively use science to understand foodborne illness and emerging

microbiological trends– Identification of environmental harborage or recurrences of pathogens

in FSIS-regulated establishments/products to further support the inspection and verification process

• Alignment of pathogen surveillance with our domestic public health and regulatory partners– Collaborative efforts with US Food and Drug Administration Center for

Food Safety and Applied Nutrition (FDA-CFSAN), the US Centers for Disease Control and Prevention (CDC), the US National Institutes of Health National Center for Biotechnology Information (NCBI), and also state/local health partners/laboratories

45

Whole Genome Sequencing at FSIS: Benefits


• FSIS continues to build capacity for WGS of isolates obtained from FSIS sampling programs– Expect full capacity with 6 sequencers by FY 2017– Goal is to sequence around 5000 isolates per year

• FSIS considers available WGS analyses in addition to PFGE and epidemiological information to further understand the relationship between clinical and food isolates

• FSIS is part of an interagency collaboration with CDC, FDA, and NCBI (Gen-FS) to harmonize efforts for implementation of WGS for food safety purposes within the US

46

WGS at FSIS: Current Status and Short Term Plans


• Product/Source type (Ready to eat product, raw meat/poultry, environmental swab, etc.)

• Year sample was collected• State where sample was collected• Subtyping information when available

– Salmonella – serotype and PFGE data– Adulterant STECs - O-group and PFGE data– Campylobacter – species and PFGE– Listeria monocytogenes - PFGE

• Metadata and sequence data is immediately available for upload to NCBI

47

WGS at FSIS: Data Sharing (Metadata and sequence data)


• Data storage and transmission– Massive volume of data generated

– FTP and other IT-related security issues

• Laboratory considerations– Scope of ISO 17025 accreditation (sequence quality)

– Need for high-throughput sequencing capacity for real-time applications

• Bioinformatics– Interpretation of strain relatedness

• hqSNP, wgMLST, k-mer

• Incorporating epidemiological and other metadata in analyses

48

WGS at FSIS: Challenges


• Case definitions: FSIS depends on its public health partner (CDC/States) for case definitions, the descriptions of the outbreak strain(s) and the subtyping method used to define the strain(s)

• Higher resolution subtyping and evolving strains: Food and environmental samples collected as part of an outbreak investigation may span a period of time longer than the outbreak – genetic drift should be considered

• Using WGS for regulatory decisions: FSIS is exploring how to interpret and apply the case definitions established by our public health partners that include WGS criteria to FSIS surveillance and investigative results

49

WGS at FSIS: Challenges


• BAX speciation – Campylobacter

• Molecular Serotype – Salmonella

• Pulse Field Gel Electrophoresis– Salmonella– Campylobacter– Adulterant STECs– Listeria monocytogenes

• Antimicrobial Susceptibility Testing– Salmonella– Campylobacter– E. coli– Enterococcus

50

WGS at FSIS: Future Considerations

A single WGS workflow could potentially consolidate all analyses


51

Example: Retrospective WGS analysis

51

Primary pattern A

Primary pattern B

Secondary pattern CSecondary pattern DSecondary pattern E

• FSIS food and environmental samples from one investigation were compared to clinical isolates with an epidemiological link to the establishment where sampling occurred

• The isolates from the investigative sampling had 2 different primary PFGE patterns and 3 different secondary PFGE patterns

• WGS was able to show high similarity (0-5 SNP differences) between differing primary PFGE patterns and primary/secondary combinations


Questions?

52

Dr. Stephanie [email protected]

mailto:[email protected]

applications of whole genome sequencing (wgs) technology on food safety management: us presentation

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