Whole

Genome

Sequencing:

Regulatory Applications

March 31, 2020

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Webinar Logistics

• Everyone is muted• Questions will be addressed during the Q&A session

at the end of the presentation• The presentation is being recorded• The recording/slides will be distributed following the

presentation• Adjourn (60 minutes)• There will be 3 important survey questions at the

conclusion of this webinar. Your response is appreciated

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Speaker

Dr. Eric Brown

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Eric W. Brown, Ph.D., M.Sc., FAAM

Director, Division of MicrobiologyOffice of Regulatory Science , Center for Food Safety and Applied Nutrition

US Food and Drug Administration, Washington, DC

“The Promise of Whole Genome Sequencing and The

Continued Advancement of Food Microbiology Deeper

into the 21st Century”

March 31, 2020

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Yes….but Salmonella, E. coli, and Listeria are all still alive and well on Earth.

A simple and profound vision statement?

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“Whole Genome Sequencing Is The Biggest Thing To

Happen To Food Microbiology Since

Pasteur Showed Us How To Culture Pathogens…”Dr. Jorgen Schlundt

Exec Director and Founder

The Global Microbial Identifier

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THE EVOLUTION OF SUBTYPING TOOLS FOR

BACTERIAL PATHOGENS

STAR-GAZING

LIGHT-

TELESCOPE

MODERN

REFRACTION

TELESCOPE

RADIO

TELESCOPE HUBBLE

PATHOGEN

PLATING

BIOTYPING

SPECIATION

SEROTYPING

PFGE WGS

time

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Some perspective on the US food supply

• Tracking and Tracing of food pathogens

• Over 200,000 registered food facilities

–81,574 Domestic and 115,753 Foreign

• More than 300 ports of entry

• More than 130,000 importers and more than 11 million import lines/yr

• In the US there are more than 2 million farms

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Finished Product Processing Facility Farm

Ecologic ReservoirsImport LinesGlobal Point Source

Tracking contamination down and FAST!

SAVES LIVES

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Identifying an Outbreak Vehicle: Lines of Evidence

Three types of evidence used:

– Epidemiologic: association between illness and food exposure

– Traceback: suspected food item links back to a common source of contamination

– Microbiologic/laboratory:pathogen found in the food, farm or facility

www.fda.gov

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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

Representative* Timeline forConventional Approach to Foodborne Illness Investigation

Contaminated food

enters commerce

Source of contamination

identified too late to

prevent most illnesses

CDC

FDA/FSIS

Nu

mb

er

of

Cas

es

Days

*Data is for illustrational purposes and does not represent an actual outbreak

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Outbreak Investigation Timelines

Epidemiology Investigation Regulatory Investigation

Questionnaires

Laboratory results

Patient interviews

Inspection

Sample collection

Laboratory results

Recall time

Regulatory Investigation

Traditional approach

Epidemiology Investigation

WGS added approach

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WGS Surveillance Outcome

www.fda.gov

More outbreaks identified

Fewer Sick PeopleIn

crea

sed

use

of

WG

SIlln

ess Averted

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The Complex and Global Etiology of Foods

Shrimp – India

Cilantro – Mexico

Romaine – Salinas, CA

Cheddar – Wisconsin

Carrots – Idaho

Gruyere – Switzerland

Pecans – Georgia

Sprouts – Chicago

Red Cabbage - NY

Shrimp – Indonesia

Imitation Crab – Alaska

Tuna Scrape – India

Fish Roe – Seychelles

Salmon – Puget Sound

Soy Sauce – China

Rice – Thailand

Seaweed Wrap – CA

Avocado – Mexico

Cucumber – Maryland

Wasabi – Japan

Pepper – Vietnam

Watermelon – Delaware

Blackberries – Guatemala

Blueberries – New Jersey

Pineapple – Guam

Grapes – California

Kiwi – New Zealand

Apples – New York

Pears – Oregon

Cantaloupe – Costa Rica

Honeydew – Arizona

Papaya – Mexico

Banana – Costa Rica

Salad Sushi Fruit platter

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2009

Don’t eat the salamae

Rejected by Science

2010

FOODBORNE OUTBREAK INVESTIGATION:

WGS analysis of foodborne salmonellae case study

This investigation focused on

Salmonella Montevideo samples

associated with red and black pepper

used in the production of Italian-style

spiced meats in a New England

processing facility. This manufacturer

was implicated in a major salmonellosis

outbreak that affected more than 272

people in 44 states and the District of

Columbia.

15-20x shot gun sequencing

35 pure culture isolates

from patients, foods and

Environmental samples.

Concatenate 40 variable genes for

Phylogenetic analysis

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PFGE-SpeI

JIXS18.0001

PFGE-BlnI

JIXA26.0012

PFGE-XbaI

JIXX01.0011

During the S. Montevideo outbreak, all isolates

were indistinguishable by 1st, 2nd, and 3rd enzyme

PFGE.

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Environmental sampling combined with WGS can help point to root cause of the contamination

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“It’s the seagulls!”

Everyone likes to blame the seagulls.

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The VES

The DelMarVa:~170 miles long~<12 miles wide at its most narrow point

~5,454 sq miles~pop. 685,000~plant/animal ag; comm. fishing; tourism beaches

~colonized 1631 by DutchWest India Co.

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Environmental surveys

It’s really is the water!

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WA

OR

CA

MT

ID

NV

AZ

UT

WY

CO

NM

TX

OK

KS

NE

SD

ND

MN

IA

MO

AR

LA

MSAL

GA*

FL

SC*TN

NC

IL

WIMI

OH*

IN

KY

WV VA

PA

NY

ME

VTNH

NJ*DE

MD*

Washington D.C.

MA

CT

RI

AK

HI

Environmental/Surface water sampling

(*) Scan of the state in agricultural areas

-5 sites per state, 4 samplings over 1 summer

-Wadeable streams

Some studies were multiple years longitudinal

efforts to the same sites at regular intervals

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Why Develop a WGS based Network?

• Tracking and Tracing of food pathogens• Insufficient resolution of current tools

-matching clinical to environmental

• Faster identification of the food involved in the outbreak

• Limited number of investigators vs. facilities and import lines

• Global travel

• Global food supply

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GenomeTrakr• First distributed sequencing based network

• State and Federal laboratory network collecting and sharing genomic data from foodborne pathogens

• Partner with NIH/NCBI for storage and serving data

• Partner with CDC for human real-time surveillance

• Partner with FSIS/USDA to better cover the food supply

• Partner with the food industry to expand use to industry

• Partner with international organizations to expand use worldwide

• Open-access genomic reference database

Resource costs in a WGS lab network

Labs to generate

WGS data

Sequence storage,

data provider & analysis

Network administration

and management

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Clinical

Samples

Food and

Environmental

Samples

Maximum

WGS Benefit

Importance of a Balanced Approach

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December 31, 2019 GenomeTrakr Numbers

Species Total Isolates

Salmonella enterica 249,281

E.coli and Shigella 95,045

Campylobacter jejuni 47,371

Listeria monocytogenes 30,525

Vibrio parahaemolyticus 2,861

Cronobacter 713

Vibrio vulnificus 384

Clostridium botulinum 303

Clostridium perfringens 250

Total 426,733

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Role of WGS in investigations

Pointing to potential sources of contamination

Defining scope of contamination and illness

Effectiveness of cleaning and sanitization

Providing a piece of the information used in

regulatory action

Root cause

How do we use the GenomeTrakr information?Example of Listeria in sprouts using a phylogenetic

perspective.

WGS and Phylogeny identifies novel linkages

for outbreak detection and infectious control.

>36,000 clusters

examined daily

For clusters of <50

SNPs

Post inspection determines root cause of contamination event.

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Interpreting WGS in the regulatory env.

Manuscript: Pightling et al. 2018. Frontiers in microbiology.

39Manuscript: Pightling et al. 2018. Frontiers in microbiology.

Interpreting WGS in the regulatory env.

Listeria monocytogenes isolates collected from a food

processing facility during a single inspection

Escherichia coli isolates implicated in a 2016 flour outbreak

Salmonella enterica isolates collected from a food processing

facility and closely related clinical isolates

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Field 1

Field 2

Processing facility

Scenario 1 (pass through)

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Field 1

Field 2

Processing facility

Scenario 2: Resident

Contamination

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Facility Match Probability P(F|D<=d)

SNP Distance

Pro

babili

ty

SNP

cutoffSal. Lis.

0 0.82 0.94

5 0.78 0.89

10 0.72 0.86

15 0.68 0.79

20 0.65 0.70

Yu Wang, James B. Pettengill, Arthur Pightling, Ruth Timme, Marc Allard,

Errol Strain, and Hugh Rand (2018) Genetic Diversity of Salmonella and

Listeria Isolates from Food Facilities. Journal of Food Protection: December

2018, Vol. 81, No. 12, pp. 2082-2089.

Predictive Power of WGS

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From inspections we have combined facility and

genomic information from 5,321 Listeria and 6,351

Salmonella isolates collected by the FDA to

characterize common origin P between those isolates.

As we predicted, if two isolates are from different

facilities, the probability that they are genetically close

is rather low [P(D < 20) = 0.00016 for Salmonella and

0.00042 for Listeria].

Predictive Power of WGS

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“Bypassing a Food Vehicle

Altogether”

“Linking up halfway across the

world”

S. Braenderup in nut butter (2014):

S. Tennessee in peanut butter paste (2007/2009) & S. Agona in dry cereal

(1998-2008): “Probing back in time”

S. Enteritidis in shell eggs (2010): “Sourcing down to the

farm”

S. Montevideo in salami (2009): “Sorting through the

ingredients”

“A COMPASS THAT POINTS TRUE NORTH and a TELESCOPE FOR THE DEEPEST OF SPACE”

S. Bareilly in tuna (2012):

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“The right tool for the right job”

“The right key to open the lock”

Applications of WGS in the Food

Safety Environment

Delimiting scope and traceback of food contamination events (Track-N-

Trace)

Quality control for FDA testing and surveillance (Confidence in Regulatory

Actions)

Preventive control monitoring for compliance standards (the “repeated

event” project)

…

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SeqSero

Salmonella Serotyping by Whole Genome Sequencing

•Reads (paired-end & interleaved)

•Reads (paired-end)

•Reads (single-end)

•Genome Assembly

*The following formats are supported for raw reads input: .fastq.gz(preferred), .fastq and .sra.

Please select your input file:

*The following formats are supported for raw reads

input: .fastq.gz(preferred), .fastq and .sra.

Please select the first reads file:

Please select the second reads file:

*The following formats are supported for raw reads input: .fastq.gz(preferred), .fastq and .sra.

Please select your input file:

*The FASTA format is supported for genome assembly input.

Please select your input file:

Salmonella Serotyping

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strains ST serotype stx1 type stx2 type eae type espA espB espJ espK gad astA nleA nleB nleC tir pssA air tccP cif espF espI efa1 ehxA espP etpD toxB katP subA saa sab

CFSAN046715 11 O157:H7 - a gamma-1 + + + + + + + + + + + + + - - - - + + + + + - - -

FDA00009839 11 O157:H7 - a gamma-1 + + + + + + + + + + + + + - - - - + + + - + - - -

CFSAN046724 21 O26:H11 a - beta-1 + + + + + + + + + + + + + + + - + + + - - + - - -

IEH-NGS-ECO-00076 21 O26:H11 a - beta-1 + + + + - - + + + + + + + + + - + + + - + + - - -

CFSAN046651 655 O121:H19 - a epsilon-2 + + + + + + + + + + + + + - + + + + - - - - - - -

FDA00010257 655 O121:H19

- a epsilon-2 + + + + + + + + + + + + + - + + + + + - - - - - -

CFSAN046652 677 Ounk:H21 - d - - - - - + - - - - - + + - - - - - - - - - - - - -

CFSAN046748 677 O174:H21 a d - - - - - - - - - - - + + - - - - - + + - - - + + +

CFSAN046713 955 O139:H1 - e - - - - - - - - - - - + + - - - - - - - - - - - - -

CFSAN051539 993 O100:H30 - e - - - - - + + - - - - + + - - - - - - - - - - - - -

CFSAN051526 43 O6:H10 c - - - - - - - - - - - - + + - - - - - - - - - + - - -

CFSAN051527 43 O6:H10 c - - - - - - + - - - - - + + - - - - - - - - - + - - -

eae – 69 (25%), subA – 72

(26%)

stx1- 53 (19%) (variantes a y c)

stx2- 186 (67%) (variantes a, b,

c, d, d/e, e, y g)

stx1+stx2 – 39 (15%)

FSAC: The FDA STEC Advisory Council…

Relies heavily on WGS

Leveraging GenomeTrakr &

NCBI Pathogen Detection

WGS Data to Enhance

• Risk Assessment

• Attribution

• Large-scale

Epidemiology studies

GEN ME

GRAPH R

Lesson: You can predict more

using structured metadata ontologies

for risk assessment.

GenomeGraphR: WGS data integration, analysis,

and visualization for risk assessment and management:

https://fda-riskmodels.foodrisk.org/genomegraphr/

Moez Sanaa, Régis Pouillot, Francisco J Garces-Vega, Errol Strain, Jane M

Van Doren doi: https://doi.org/10.1101/495309 2018.

S. Bareilly CFSAN000189

new genomic island

arsenic resistance operon

about 40 kb

Salmonella Bareilly from Tuna

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GOAL = <5 years have first 25 mapped

Salmonella Adaptations of particular interest to food safety specialists:

(1) Thermal tolerance

(2) Dessication resistance

(3) Osmotic/Ionic tolerance

(4) Quat resistance

(5) Chlorine resistance

(6) Biofilm persistence

(7) Surface adherence

(8) Antibiotic resistance

(9) Antimicrobial resistance

(10) Ecological fitness

(11) Heavy metal resistance

(12) Metabolic persistence

(13) Enhanced hydrophobic fitness

(14) Produce invasiveness

(15) Flower invasiveness

(16) Root system invasiveness

(17) Acid resistance

(18) Surface water fitness

(19) In vivo plant migratory fitness

(20) Soil fitness

(21) Capsaicin resistance

(22) Swarming

(23) Trans-ovarian poultry colonization

(24) Fecal persistence (poultry)

(25) Yolk content invasion

(26) Multidrug resistance

(27) External amoeba harborage

(28) Internal amoeba harborage

(29) Acyl-homoserine lactone (AHL)

(30) KatE stationary-phase catalase

(31) In vivo migratory fitness

(32) RDAR phenotype

(33) The ‘Weltevreden’ type

(34) Persistence within the tomato**

5353

Adaptation of lineage III Newport in tomato using

transcriptomic approach

0

20

40

60

80

100

120

140

160

180

200

Nu

mb

er o

f g

en

es

COG functions

C Energy production and conversion

D Cell cycle control and mitosis

E Amino Acid metabolis and transport

F Nucleotide metabolism and transport

G Carbohydrate metabolism and transport

H Coenzyme metabolis

I Lipid metabolism

J Tranlsation

K Transcription

L Replication and repair

M Cell wall/membrane/envelop biogenesis

N Cell motility

O Post-translational modification, protein

turnover, chaperone functions

P Inorganic ion transport and metabolism

Q Secondary Structure

T Signal Transduction

U Intracellular trafficing and secretion

Y Nuclear structure

R General Functional Prediction only

S Function Unknown

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WGS at FDA:

Where We Started

WGS at FDA:

Where We Are Now

WGS at FDA:

Where We Are Headed

oMolecular Epidemiology

oEnhanced Traceability

oOutbreak Surveillance

oGenomeTrakr

oMicrobiological QA/QC

Antibiotic Resistance

Salmonella Serovar Calls

Metagenomics GAPs

EHEC/STEC Risk

Source-Tracking/Indexing

Quasi-Metagenomics

•Metagenomic CID

•Enhanced PCs/Adaptive

changes

•Attribution and Root

Cause

•MetagenomeTrakr

•Transcriptomics

WGS Evolution at FDA

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Minor et al., 2015.

Risk Analysis. 35(6):1-15

Salmonella spp.

(nontyphoidal)

$5,483,959,000 41%

Campylobacter spp.

$2,963,541,000 22%

Listeria monocytogenes

$2,317,572,000 17%

E. coli O157:H7

$648823 5%

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Canada United States

Incidence of illness 47,028 1,200,000

Costs to adopt WGS $158,340,000 $100,000,000

QUALY lost 469.75 16,782

Total Illness costs $287,770,000 $3,300,000,000

Total net benefit of adopting WGS $90,250,000 $1,000,000,000

Economic IMPACT on Foodborne Salmonella When Using

WGS

*Model assumes 70% reduction in numbers of illnesses due to WGS implementation.; Benefits gained due to earlier detection

and decreased time to recall food items.; United States estimates are adjusted based on increase population size.; Additional

analysis is needed to adjust to US illnesses and US health care costs. Based on Jain et al., 2019 An economic analysis of

salmonella detection in fresh produce, poultry, and eggs using whole genome sequencing technology in Canada. Food Res. Int.

116: 802-809.

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FDA circa 1906 –FOOD SAFETY

PRESENTATION POINTS

Various desktop NGS platforms now exist andare dropping precipitously in price – and per reaction cost making the technology largely accessible for public health applications.

WGS has already become an integral part of the science of food safety, both for morecomprehensive characterization and testing of foods and for providing insight aboutthe scope and sources of outbreaks and other food contamination events. Development of international open source databases will empower WGS for sentinel surveillance work on a global scale.

WGS, as part of a laboratory next-generation analysis pipeline, can augmentfood safety investigations, particularly in cases where strain homogeneity is aproblem, by (i) delimiting the scope of a contamination event; (ii) affirming a cluster from common background genotypes; and (iii) source-tracking by comparative genomics of food and environmental isolates of Salmonella. Recall, sequences are agnostic.

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Acknowledgements

• FDA

• Center for Food Safety and Applied Nutrition

• Center for Veterinary Medicine

• Office of Regulatory Affairs

• National Institutes of Health

• National Center for Biotechnology Information

• State Health and University Labs

• Alaska

• Arizona

• California

• Florida

• Hawaii

• Maryland

• Minnesota

• New Mexico

• New York

• South Dakota

• Texas

• Virginia

• Washington

• USDA/FSIS

• Eastern Laboratory

• CDC

• Enteric Diseases Laboratory

• INEI-ANLIS “Carolos Malbran Institute,”

Argentina

• Centre for Food Safety, University College

Dublin, Ireland

• Food Environmental Research Agency,

UK

• Public Health England, UK

• WHO

• Illumina

• Pac Bio

• CLC Bio

• Other independent collaborators

FDA circa 1906 –FOOD SAFETY

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Q & A

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If your question wasn’t

answered…

Please contact Scott Nichols at snichols@wga.com or one of the trade organization representatives and we would be

happy to respond.

Thank you.

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Brought To

You By:

Whole

Genome

Sequencing:

Regulatory Applications

March 31, 2020