http://tiny.cc/faowgsworkshop Applications of genome sequencing technology on food safety management-United States of America. Presentation from the FAO expert workshop on practical applications of Whole Genome Sequencing (WGS) for food safety management - 7-8 December 2015, Rome, Italy.

1. U.S. Presentations
Dr. Marc Allard Dr. John Besser
Dr. Stephanie Defibaug-Chavez

2. John Besser
CDC, Enteric Diseases Laboratory Branch
U.S. Nationwide Real-time WGS-based
Surveillance

3. Annual workflow: 60,000-70,000 isolates/yr
PulseNet Database: ~750,000 patterns (2015)
87 laboratories

4. National Cluster Investigations:
30 – 60 monitored per week
State Cluster
Investigations:
1,500 – 2,000 per year

5. 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

6. 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

7. 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. outbreaks
Incidence
(per million pop)
Era
Outbreaks per year
Median cases per
outbreak
WGS
?
?

8. LM case
State/Local
Health Agency CDC
PulseNet
FDA
USDA
Nationwide Listeriosis Surveillance System
• Food / animal,
environment
sampling
Cases
Interview Case / Food
questionnaire
Isolates
GenomeTrakR
Isolates
WGS PFGE
WGS
PFGE
Case-Case
StudiesNCBI-Genbank (U.S.)
DDBJ (Japan)
EMBL (Europe)
upload
analysis
International Nucleotide Sequence
Database Collaboration
WGS

9. 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

10. Public Health WGS Workflow
Nomenclature server
Calculation engine
Trimming, mapping, de novo
assembly, SNP detection,
allele detection
PH databases
Users at CDC
and in the
States
Allele databases
External storage
NCBI, ENA, BaseSpace
Sequencer
Raw sequences
LIMS
Data pathway
Proposed data pathway
Analysis request
Genus/species
Serotype
Pathotype
Resistance
7-gene MLST
rMLST
cMLST
wgMLST
hqSNP analysis
(v. 7.6)

11. 14
N/A
1
6
19
6
4 4
21
6
9
3
0
5
10
15
20
25
No. of clusters
detected
No. of clusters
detected sooner
or only by WGS
No. of outbreaks
solved
(food source
identified)
Median no. of
cases 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

12. Solved Outbreaks: New Food Sources of Listeriosis

13. Listeria and Caramel Apples
• 35 cases
• 12 states
• 34 hospitalizations
• 7 deaths

14. Whole-Genome Multilocus Sequence
Typing (wgMLST)
wgMLST (<All Characters>)
100
90
80
70
60
50
Key
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
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

15.  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…..

16.  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

17. 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 Apr
19 Development and
internal validation
Deployment
Development and
internal validation
Deployment
Development and
internal validation
Deployment
Development and
internal validation
Deployment
Development and
internal validation
← External validation
← External validation
← External validation
← External validation
External validation →
Cronobacter &Yersinia
Vibrio, Shigella &
other diarrheagenic
E. coli
Salmonella
Campylobacteraceae
&
Shiga toxin-producing
E. coli (STEC)
Listeria
monocytogenes

18. Jbesser@cdc.gov
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

19. GenomeTrakr: A Pathogen Database
Marc W. Allard, PhD
Senior Biomedical Research
Services Officer
Division of Microbiology
Marc.Allard@fda.hhs.gov
Food and Agriculture Organization
of 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, PhD
Director
Division of Microbiology
Eric.Brown@fda.hhs.gov

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

21. Same PFGE
but not part of
the outbreak
Outbreak Isolates
2-5 SNPs
SNP phylogeny for S. Bareilly
strains

22. 22

23. S. Braenderup

25. 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

26. 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. 27
NumberofSequences
(asofthelastdayofthequarter)
Total Number of Sequences in the GenomeTrakr Database
2013 2014 2015
Average Number of Sequences
Added Per Month in 2013 = 184
Average Number of Sequences
Added Per Month in 2014 = 1,049
First sequences uploaded
in Feb 2013
Public Health England
uploads more than 8,000
Salmonella sequences

29. 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
NumberofCases
Days

30. 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

31. 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.

33. 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

34. 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)

35. Molecular Epidemiology and Ecology of
Multi-drug Resistance (MDR) Salmonella
in Tanzania
Julius Medardus
Sokoine University of agriculture
Wondwossen A. Gebreyes
Gebreyes.1@osu.edu

36. ICOPHAI GenomeTrakr
partnership

37. 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

38. 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?

39. 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

40. 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 FoodWGS@fda.hhs.gov

41. ORA OCC OFS OC OAO OFVM/SRSC CFSAN CDER
CBER CDRH CVM NCTR FDA CHIEF SCIENTIST OIP OARSA
SCIENCE BOARD IAS FFC FERN JIFSAN ADVISORY COMMITTEE IFSH
MOFFETT CENTER CIO DAUPHIN ISLAND CFSAN-OCD CORE WESTERN CENTER
INTERNAL FDA STAKEHOLDERS
FDLI
GMA
VaFSTF
CDC
FBI
PULSENET-LATIN AM.
AM. ACAD MICROBIOL
ASM
FSIS
ARS
UNIV VERMONT
MINN DOH
AZ DOH
UNIV FL
VA DOH
WA DOH
TX DOH
NY AG LAB
IRISH FSA
NOVA SE UNIV
IGS BALTIMORE
INFORM MEETING
HONGKONG POLYT U
NIST
ITALIAN FSA
EFSA
WHO-FOOD SAFETT DIR.
WHO-GFN
CDC-EU
EMERGING INFECTIOUS DIS CONF
DANISH TECH UNIV
NM STATE UNIV/ NM DOH
CARLOS MALBRAN INST/ARG
ST COULD UNIV/FOOD MICRO
SENASICA
GMI
NY DOH/WADSWORTH CENT
UNIV HAMBURG
CHINA CDC
NESTLE
FERA-UK
MD DOH
IAFP
APHL
AFDO
BELGIUM
VaTech
US ARMY
US NAVY
MELBOURNE FSA (AUS)
UNIV NEBRASKA
PUBLIC HEALTH ENGLAND
DHS
DELMARVA TASKFORCE
PENN STATE FOOD SCIENCE
PROD MAN ASSOC
ILLUMINA
UNIV IRELAND/DUBLIN COLLEGE
NCBI/NIH
GSRS GLOBAL SUMMIT
FAO/OIE
PUBLIC HEALTH CANADA
CFIA
HEALTH CANADA
INTL VTEC MEETING
CPS-GA
AOAC
UNITED FRESH
COLUMBIA
HAWAII DOH
CA DOH
ALASKA DOH
SOUTH DAK UNIV
UNIV GA
UNIV IOWA/DOH
UNIV CHILE
BRAZIL
OSU VETNET
TURKEY
MEXICO
IEH
SILLAKER
NEW ENG BIOLAB
PACIFIC BIO
CLC-BIO/QIAGEN
CON-AGRA
DUPONT
AGILENT
UC-DAVIS
HARVARD MED
INFORM MEETING
THAILAND

42. Food Safety and Inspection Service:
42

43. Food Safety and Inspection Service:
WGS for Food Safety
Management: FSIS Perspective
Stephanie Defibaugh-Chavez, Ph.D.
Senior Microbiologist, Science Staff
Office of Public Health Science
US Department of Agriculture, FSIS
FAO WGS Meeting – December 2015
43

44. 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

45. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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

46. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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

47. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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)

48. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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

49. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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

50. Food Safety and Inspection Service:Food Safety and Inspection Service:
• 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. Food Safety and Inspection Service:Food Safety and Inspection Service:
51
Example: Retrospective WGS analysis
51
Primary pattern A
Primary pattern B
Secondary pattern C
Secondary pattern D
Secondary 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

52. Food Safety and Inspection Service:
Questions?
52
Dr. Stephanie Defibaugh-Chavez
Stephanie.Defibaugh-Chavez@fsis.usda.gov