TECHNOLOGY IN THE FOOD SAFETY WORLD: TOOLS SUCH AS WHO GENOME - - PowerPoint PPT Presentation

TECHNOLOGY IN THE FOOD SAFETY WORLD: TOOLS SUCH AS WHO GENOME SEQUENCING – FRIEND OR FOE?

Room 314 | December 5 2017

CEUs – New Process

Certified Crop Advisor (CCA)

• Sign in and out of each session you attend.
• Pickup verification sheet at conclusion of each

session.

• Repeat this process for each session, and

each day you wish to receive credits. Pest Control Advisor (PCA), Qualified Applicator (QA), Private Applicator (PA)

• Pickup scantron at the start of the day at first

session you attend; complete form.

• Sign in and out of each session you attend.
• Pickup verification sheet at conclusion of each

session.

• Turn in your scantron at the end of the day at

the last session you attend.

Sign in sheets and verification sheets are located at the back of each session room.

• Tim Birmingham, Almond Board
• f California, moderator
• Jesse Miller, NSF International
• Maria Hoffmann, FDA Center for

Food Safety and applied Nutrition

3

AGENDA

Ne xt Ge ne r ation Se que nc ing – T he T e c hnology and its Applic ations – F r ie nd or F

• e ?

Je sse D. Mille r , Ph.D. Dir e c tor Applie d Re se ar c h Ce nte r NSF Authe nT e c hnologie s

Age nda

Ne xt Ge ne r ation Se que nc ing

5

Me thods Applic ations and E xample s

6

Ne xt Ge ne r a tion Se que nc ing

Pr

• c e ss o f e xtr

ac ting ge ne tic mate r ial and r e ading the “c o de ”.

L e ts g o Ba c k in T ime ………….1952.

Ho w do we Ana lyze DNA? Se q ue nc ing Ba c kg ro und

 RNA se q ue nc ing wa s first to b e de ve lo pe d (diffe re nt

me tho ds)

 1965: Ro b e rt W. Ho lle y se q ue nc e d tRNA fro m Sac c haro myc e s  1976: Wa lte r F

ie rs’ la b first to c o mple te RNA-b a se d g e no me (MS2 b a c te rio pha g e )

 Big b re a kthro ug h wa s DiDe o xy Se q ue nc ing (Sa ng e r

Se q ue nc ing )

 1977: I

nve nte d b y F re dric k Sa ng e r (No b e l Prize 1958, 1980)

 1977: F

irst to se q ue nc e DNA-b a se d g e no me (PhiX b a c te rio pha g e )

 T

e rme d the “Cha in T e rmina tio n Me tho d”

 Di-De o xy Nuc le o tide s a re la b e le d with fluo ro pho re s

Use d to b e ra dio la b e le d. Pro b a b ly mo uth pipe tte d to o !

 T

he se Di-De o xy NT Ps (ddNT Ps) te rmina te the e xte nsio n re a c tio n whe n inc o rpo ra te d via PCR

 T

he e nd o f e a c h fra g me nt ha s a fluo re sc e nt sig na l

 Curre nt me tho d is to run thro ug h a c a pilla ry g e l to size a nd

• rde r

Use d to b e a po lya c ryla mide g e l

 Ca pture the sig na l se q ue nc e a nd tra nsla te to nuc le o tide

b a se s

Wha t is Ne xt-Ge n Se q ue nc ing ?

 T

e rm use d fo r se q ue nc ing tha t ha s a hig he r thro ug hput tha n tra ditio na l Sa ng e r se q ue nc ing

 No w E

nc o mpa sse s ma ny pla tfo rms – T he rmo F ishe r, I llumina , Pa c ific Bio sc ie nc e s, Oxfo rd Na no po re

 Ca n be Whole Ge nome Se que nc ing , 16S rRNA

Me ta g e nomic s, Shotg un Me ta g e nomic s, T a rg e te d Ge ne Se que nc ing , RNA- SE Q

 1st Ge n – Sa ng e r, ABI

(3130xl)

 2nd Ge n – 454, I

llumina (So le xa ) a nd T he rmo F ishe r (Ma ssive ly Pa ra le ll Se q ue nc e rs – Sho rt Re a d)

 3rd Ge n – Pa c ific Bio sc ie nc e s, Oxfo rd (L

• ng Re a d

Se q ue nc e rs)

BASE S T O BYT E S

Ho w is NGS Diffe re nt tha n T ra ditio na l Se q ue nc ing ?

 Se q ue nc ing do ne o n flo wc e lls/ c hips no w. No 2D g e ls

• r c a pilla rie s re q uire d

 MUCH mo re da ta g e ne ra te d (T

e ra b a se s no w, kilo b a se s the n)

 So phistic a te d Bio I

nfo rma tic pro g ra ms e xist to pa rse

• ut the da ta - I

n so me insta nc e s, c a n se q ue nc e a sa mple fo r a ro und $40

MUCH c he a pe r tha n histo ric a l  Ope n so urc e da ta sha ring fo r ma ssive da ta se ts  Clo ud c o mputing c a pa b ility

MORE DAT A CHE APE R PE R BASE INT E RNE T MAKE S COMMS AND ANAL YSIS E ASY

11

Me thods

Cho o se the Right “F it fo r Pur po se ” T

• o l fo r

the Jo b

Pulse -F ie ld Ge l E le c tro pho re sis (PF GE )

 “Go ld Sta nda rd” o f

b a c te ria l DNA fing e rprinting

 Re stric tio n e nzyme s c ut

b a c te ria l DNA in spe c ific lo c a tio ns

 Multi-dire c tio na l g e l

e le c tro pho re sis pro duc e s uniq ue pa tte rn b a se d o n the fra g me nt size s

 Allo ws Co mpa riso ns

b e twe e n o rg a nisms fo r I D

 No t q ua ntita tive  $100-260

Po lyme ra se Cha in Re a c tio n

Se mi Qua ntita tive (With a sta nda rd c urve ) T a rg e ts a re g io n o f g e no me fo r a mplific a tio n

 Po sitive re a c tio n = g e ne is pre se nt

Ca n de te c t se ve ra l ta rg e t g e ne s a t

• nc e (Multiple x)

Che a p! $5-10/ re a c tio n Se ve ra l ho urs to run

I mmuno lo g ic a l Me tho ds

E L I SA L a te ra l F lo w Che a p ($5-10) F a st Ye s/ No a nswe rs

 E

L ISA c a n b e q ua ntita tive

Wha t is Who le Ge no me Se q ue nc ing ?

 Who le Ge no me Se q ue nc ing is the te rm use d fo r

e xtra c tio n o f DNA fro m a n o rg a nism a nd the sub se q ue nt ma pping o f its g e no me .

 T

he g e ne tic c o de (AGCT ) is re a d o n a n instrume nt a nd writte n into a dig ita l file .

 T

ha t dig ita l file c a n b e a sse mb le d (like a line a r puzzle ) to de te rmine the o rde r o f the c o de in the

• rg a nism.

 Onc e yo u ha ve the o rde re d c o de , yo u c a n

a na lyze the da ta a nd ma ke c o mpa riso ns a nd da ta -drive n de c isio ns a b o ut the o rg a nism.

 No t q ua ntita tive  $50/ se q ue nc e – Up to $500 fo r a sse mb ly/ c lo sure

(Ba c te ria )

1 5

YF B

Wha t is 16S/ Sho tg un Me ta g e no mic s?

 16S Se q ue nc ing o n Ne xt-Ge n pla tfo rms fo llo ws a

simila r wo rkflo w, e xc e pt tha t it ta rg e ts a spe c ific g e ne (16S Rib o so ma l RNA) use d to ide ntify b a c te ria

 Me ta g e no mic s a pplie s this c o nc e pt to mixe d

c o nso rtia , re sulting in a pro file o f b a c te ria a b unda nc e (e .g ., yo ur mic ro b io me )

• 1. Se q ue nc e DNA
• 2. Alig nme nt to re fe re nc e da ta b a se s. Cla ssifying

unkno wn b a c te ria in to ta xo no mic g ro ups

• 3. Visua lize in phylo g e ne tic tre e s, pie c ha rts, o r o the r

a na lyse s b a se d o n q ue stio n to b e a ske d

POPUL AT IONS

NGS Be ne fits Ove r Othe r Me tho ds Mo ving F

• rwa rd?

 NGS diffe re ntia tio n (re so lutio n) is unma tc he d  With hig h-thro ug hput e ffic ie nc ie s, NGS is c he a pe r

a nd fa ste r

 NGS e na b le s muc h mo re in-de pth da ta a na lysis, suc h

a s func tio na l g e ne s a nd he re dity

 Co st will c o ntinue to de c re a se  Da ta b a se a va ila b ility a nd po we r will c o ntinue to

inc re a se

 Glo b a l a do ptio n a nd da ta sha ring will inc re a se va lue

18

Applic a tions a nd E xa mple s

Who le Ge no me Se q ue nc ing

 Wha t c a n I

use it fo r?

 E

pide mio lo g y

 Re sista nc e  Stra in le ve l I

D

 Authe ntic ity

 MUCH de e pe r lo o k into g e no me tha n Pulse

F ie ld Ge l E le c tro pho re sis o r RF L P

 L

• o king a t e ve ry b a se , no t just whe re e nzyme s

c ut

YF B

Stra in L e ve l I D

 Is the re va lue in kno wing who yo ur

re sid e nt stra ins a re ?  Ca n yo u e ra dic a te the m mo re e a sily?  Ca n yo u mo dify pro c e sse s a nd

c le a ning re g ime ns?

 Pro a c tivity?

 Va lue in tra nspa re nc y a nd o wne rship?

 Wo rking to wa rd a po sitive so lutio n

 T

hird Pa rty se q ue nc ing  Me ta da ta ho use d b y third pa rty

Spe c ia tio n o f Campylo b ac te r

Co mme nsa l b a c te ria o n Chic ke n a nd

• the r fo wl

I nte rve ntio ns c a n kno c k do wn numb e rs, b ut ha rd to c o mple te ly e ra dic a te T hre e stra ins unde r sc rutiny

 Je juni  Co li  L

a ri

F

• o d Pa tho g e n I

D – Off the She lf

I so la te g e rms fro m o ff the she lf me a ts Assa y fo r pa tho g e ns Who le Ge no me Se q ue nc e fo r spe c ie s

 Co mmo n tre nd s?

 F

• o d type

 Ge o g ra phy  Inte rve ntio n me tho d  Pre se rva tio n me tho d

Wha t is 16S/ Sho tg un Me ta g e no mic s?

 16S Se q ue nc ing o n Ne xt-Ge n pla tfo rms fo llo ws a

simila r wo rkflo w, e xc e pt tha t it ta rg e ts a spe c ific g e ne (16S Rib o so ma l RNA) use d to ide ntify b a c te ria

 Me ta g e no mic s a pplie s this c o nc e pt to mixe d

c o nso rtia , re sulting in a pro file o f b a c te ria a b unda nc e (e .g ., yo ur mic ro b io me )

• 1. Se q ue nc e DNA
• 2. Alig nme nt to re fe re nc e da ta b a se s. Cla ssifying

unkno wn b a c te ria in to ta xo no mic g ro ups

• 3. Visua lize in phylo g e ne tic tre e s, pie c ha rts, o r o the r

a na lyse s b a se d o n q ue stio n to b e a ske d

POPUL AT IONS

I rrig a tio n Wa te r Mic ro b io me

L

• o king a t c ha ng e s in wa te r

mic ro b io me whe n E . c o li pre se nt

 Se a rc hing fo r ma rke rs o f c o nta mina tio n

Almo nd Ha rve st

 Sha king tre e s to re le a se fruit  Drying fo r a fe w d a ys  Ha rve ste r  Hulling  She lling

www.pinte re st.c o m

Ho spita l Mic ro b io me

L

• o king a t e nviro nme nt a nd pa tie nt

c o lo niza tio n

 Se a rc hing fo r c o rre la tio ns to und e rsta nd flo ra

Pro a c tive T re a tme nt?

 Our mic ro b io me pro te c ts us – ke e ps the b a d pla ye rs o ut  Antib io tic s kill o ur no rma l flo ra  Pro b io tic tre a tme nt c a n pre ve nt und e sira b le b ug s fro m

ta king ho ld

 Und e rsta nd ing wha t is o ut the re a llo ws d e c isio n ma king

with mo re c a rd s in yo ur d e c k

www.pinte re st.c o m

E pide mio lo g y

 T

ra c k a nd tra c e yo ur stra ins

 L

• o k fo r Sing le Nuc le o tide

Po lymo rphisms

 T

he se c ha ng e s ha ppe n in a n

• rg a nism o ve r time

 Diffe re ntia te s o ne b a c te ria fro m

a no the r

 Ge no me T

ra krApplic a tio n

 A Da ta b a se o f o rg a nisms tha t c a n

b e mine d to d e te rmine so urc e a nd tra c e b a c k

 Pub lic ly a va ila b le !

Authe ntic ity

Ne xt Ge ne ra tio n Se q ue nc ing – F rie nd!

Se que nc ing is the F utur e of F

• od Safe ty and

Mic r

• bial Sc ie nc e

Not Sc a ry! Just a wa y to g e t de ta ile d info rma tio n a b o ut the o rg a nism yo u a re

a na lyzing

 Ca n use g e no mic info to unde rsta nd

 Re sista nc e s  Phylo g e ny  Pa tho g e ne sis  E

pid e mio lo g y

 Authe ntic ity  Ma ke Da ta Drive n De c isio ns  Se nsitivity – Be tte r De c isio ns, F

a ste r. Sa ve s live s!

Ne xt Ge ne r ation Se que nc ing – T he T e c hnology and its Applic ations – F r ie nd!

Je sse D. Mille r , Ph.D. JDMIL L E R@NSF .ORG 734.707.5413

Technology in the Food Safety World: Whole Genome Sequencing—friend or foe?

The Almond Conference Sacramento California December 5, 2017 Maria Hoffmann, Ph.D. Genomics Research Microbiologist

31

Finished Product Processing Facility Farm Ecologic Reservoirs Import Lines Global Point Source

Tracking contamination down…and FAST! SAVES LIVES

32

Some perspective on the food supply

• Tracking and Tracing of food pathogens
• Almost 200,000 registered food facilities (2/14)
• 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/year

• In the US there are more than 2 million farms

33

The Complex 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

34

Gold standard method for pathogen identification

PFGE: banding patterns determine discrimination within serovar.

PulseNet, est. 1996 http://www.cdc.gov/pulsenet/

35

• WGS is high resolution

∙ 3-5 million data points are collected for each isolate

• WGS analyses are statistically robust

Unlike PFGE patterns, WGS data can be analyzed in its evolutionary context. Accurate and stable genetic changes within pathogen genomes enable us to pin point specific common sources of outbreak strains (farms, processing plants, food types, and geographic regions)

PFGE v/s WGS

36

Pedigree vs Phylogeny

37

DNA based pathogen surviellance not new

• Flu: 1990s – flu vaccines

predicted from phylogenetic trees

• HIV: 1990s – early tracking of

HIV transmission using phylogenetics

http://evolution.berkeley.edu/evolibrary/news/081101_hivorigins

38

• CDC investigated a multistate (29 states) outbreak
• 410 confirmed cases between January 1st and July 7th, 2012
• Among the 326 case patient, 55 (17%) had been hospitalized
• Yellowfin tuna was implicated as source of this outbreak
• This product had been imported from an Indian corporation and was

used to make spicy tuna sushi for restaurants and grocery stores

• At this time no reference genome

was available at NCBI

Salmonella enterica serovar Bareilly

39

PFGE identical in red

NGS distinguishes geographical structure among closely related Salmonella Bareilly strains

40

11/17/10 Shell-on Shrimp Sri Lanka 01/14/10 Frozen Fish India 12/06/04 Crushed Chilis India 10/19/07 Coriander Powder India 03/12/01 Raw Shrimp Vietnam Environmental USA 09/18/08 Sand Goby Fish Vietnam 12/27/02 Frozen Shrimp India 11/13/09 Babgladeshi Fresh Water Fish (Bacha) Bangladesh Clinical MD 05/08/72 Feather Meal USA 02/08/07 Frozen Baila Bangladesh 07/12/02 Frozen Undeveined Shrimp India 03/17/08 Coriander Powder India 04/22/05 Sesame Seed India 05/09/07 Ginger Powder India 12/29/04 Frozen Shrimp India 06/01/09 Chili Powder India 04/06/10 Fish Stomach Vietnam 09/17/03 Coriander Powder India Clinical MD Environmental USA 1975-07- Frog Legs Unknown 02/05/08 Kheer Mix Pakistan 11/18/05 Cayenne Pepper India 08/08/05 Frozen Whole Tilapia Thailand 08/17/06 Lobster Tails Taiwan 05/02/72 Poultry Meal USA 1974-08- Nonfat Dry Milk Unknown 05/14/09 Irrigation Water USA 02/26/04 Frozen Raw Peeled Shrimp India ATCC 9115 12/05/05 Frozen Rock Lobster Tails United Arab Emirates 07/06/05 Fresh Canaloupe USA Environmental USA 07/09/01 Pabda Fish Bangladesh 03/14/05 Coriander Bangladesh 08/18/11 Coconut India 06/26/00 Scallops Indonesia 10/17/11 Punjabi Cheole Spice India 05/01/72 Poultry Feather Meal USA 02/17/11 Red Chili Powder Pakistan 04/17/08 Fennel Seeds United Arab Emirates 07/30/01 Whisker Fish Vietnam 11/16/05 Hilsa Fish Thailand 09/08/08 Chili Powder Thailand 1109/30/10 Sesame Seeds India 11/08/02 Cumin Powder India 03/27/02 Shrimp India 12/23/02 Frozen Raw Esomus Swaison Whole Vietnam 10/12/01 Frozen Rohu Rish India 10/17/00 Shrimp India 09/09/10 Chili Powder India 02/22/10 Ground Red Pepper USA 06/27/11 Organic Black Pepper India 05/13/03 Frozen Raw Shrimp India 03/01/06 Frozen Crab with Claws Sri Lanka Clinical NY Clinical NY Clinical MD Clinical NY Clinical MD Clinical NY Clinical NY Clinical MD Clinical NY Clinical NY Clinical MD Clinical NY Clinical NY Clinical NY Clinical NY Clinical NY Clinical NY Clinical NY Clinical MD Clinical MD Clinical NY Clinical MD Clinical NY Clinical NY Clinical NY Clinical NY Clinical NY 03/10/10 Coriander Mexico 08/01/06 Turmeric Powder India

Different PFGE than the outbreak pattern Same PFGE but not part of the

• utbreak

Outbreak Isolates MD isolates – in green NY isolates – in purple

41

 Same PFGE cluster together (120 SNPs) 

• utbreak isolates cluster together with 100% bootstrap

 Closest neighbor differ by 20 SNPs 0-6 117 20

42

2-part paradigm shift

• 1. Whole genome sequencing

∙ High resolution data ∙ Harness established field of evolutionary theory for analyses

• 1. Open data

∙ Raw genome sequences made available to the public 1-2 days after collection ∙ Data made public *before* FDA analyses are preformed

43

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

45

FDA’s GenomeTrakr

• Distributed network of labs to use whole genome sequencing
• Contributing members:
• 13 FDA labs
• 11 PulseNet labs (state public health labs)
• 5 Dept. of Agriculture labs
• 7 University labs
• 1 U.S. hospital lab
• 2 international labs (Argentina, Mexico)
• 3 private contracting labs
• Data curation and bioinformatic support/analyses provided by National

Center for Biotechnology Information (NCBI) and FDA-CFSAN.

46

GenomeTrakr verses PulseNet?

clinicals -> PN food/env -> GT clinicals -> PN food/env -> GT

NCBI

47

Database growth:

Currently: over 150,000 genomes (all contributors)

48

Publicizing data

NCBI:

Sequences and metadata – fastq files in SRA DB, annotated assemblies in GenBank – metadata in BioSample DB (taxonomy, collected by, country and state, year, isolation source) – Private: city, county, zipcode, firm names, product names, patient data (age, sex, etc) Analyses – Phylogenetic trees for each pathogen published daily at NCBI: http://www.ncbi.nlm.nih.gov/projects/pathogens

GitHub:

– CFSAN SNP Pipeline: http://snp-pipeline.readthedocs.org/en/latest/index.html

49

http://www.ncbi.nlm.nih.gov/projects/pathogens

NOVEMBER 15, 2017

50

New isolate check - Salmonella

SNPs distance to same category SNPs distance to different category

51

Look at close matches within SNP cluster

52

Biosample: Isolate metadata

53

AMR genotype prediction

54

How do we use the GenomeTrakr information?

• Identify SNP cluster of interest from NCBI
• Download raw data AND run CFSAN SNP pipeline inhouse
• Run CFSAN SNP pipeline inhouse

55

What happens with a WGS link between a clinical and environmental sample?

• Likely result in the following steps:

(1) facility/farm inspection and sampling (2) Pathogen positive samples are sequenced and submitted to the database (3) traceback/trace forward of raw materials and finished product (4) WGS is powerful tool that supports investigation

56

Salmonella Braenderup 2014 pre-outbreak

• In 2014, FDA conducted baseline environmental sampling in

nut butter processing facilities

• A few of the samples tested positive for S. Braenderup and a

PFGE pattern matched several cases of recent salmonellosis without a common link

• WGS was performed on both environmental and clinical

isolates and found to be extremely close (2 SNP differences)

57

Salmonella Braenderup

• env. swab

clinical

58

Comparing Traditional and Retrospective Outbreaks in Nut Butters

Salmonella Tennessee (Company A, Brand A Peanut Butter,

2006/2007): 715 cases, 129 hospitalizations, no deaths

Salmonella Typhimurium (Company B, Brand B Peanut butter,

2008/2009): 714 cases, 166 hospitalizations, 9 deaths

Salmonella Bredeney (Company C/Brand C Peanut butter, 2012):

42 cases, 10 hospitalizations, 0 deaths

Retrospective Outbreak Investigation

Salmonella Braenderup (Company D/Brand D nut butter, 2014):

6 cases, 1 hospitalization, no deaths

Traditional Outbreak Investigations

59

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 Traditional Approach to Foodborne Illness Investigation

Contaminated food enters commerce

Identify contaminated food and confirm that product or environmental sample PFGE pattern matches the clinical sample pattern Identify illnesses and get PFGE pattern from clinical samples Source of contamination identified too late to prevent most illnesses

CDC FDA/FSI S

Number of Cases Days

60

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

Number of Cases Days

61

Immediate benefits of WGS to industry, growers, and distributers

• Earlier intervention means:

1) Reduced amount of recalled product; 2) fewer sick patients 3) less impact overall and minimal damage to brand recognition.

62

The Fresh-cut Tomato Supply Chain is complex

63

WGS-based monitoring can pinpoint root causes

64

Field 1 Field 2 Processing facility

Example 1

65

Field 1 Field 2 Processing facility

Example 2

66

• 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?

Benefits to industry, growers, and distributers (continued)

67

One Data Record - Many Possibilities

…..AAGCTTGGAGATCTACGTGTACCTAGTCGAAGACTGAGGCTCTA….

SNP Serotype wgMLST Markers Virulence

Resistance (Disinfectant, Heat, Heavy metal…)

Ecological Fitness Biofilm persistence Unknown Adaptation

68

Improving Food Safety

1. Identify source of foodborne outbreaks more quickly

~ WGS provides an integrated food safety surveillance system ~ permits international capacity building through integration of foreign food safety entities into the GT network

2. Transparency of open data gives industry full access

~ Genome data made public in real-time ~ Public software and analysis tools readily available to industry for viewing

• f results

3. Food Safety Modernization Act (2011) – preventive Controls, Improve Industry Practices

~ WGS compliments rapid testing methods with environmental monitoring for repeat positives and problems w/ resident pathogens.

69

70

Acknowledgements

• FDA
• Center for Food Safety and Applied Nutrition
• Center for Veterinary Medicine
• Office of Regulatory Affairs
• State Health and University Labs
• Alaska
• Arizona
• California
• Florida
• Hawaii
• Maryland
• Michigan
• Minnesota
• New Mexico
• New York
• North Carolina
• Ohio
• Penn State
• South Dakota
• Texas
• Virginia
• Washington
• National Institutes of Health
• National Center for Biotechnology

Information

• USDA/FSIS
• Eastern Laboratory
• CDC
• Enteric Diseases Laboratory

CFSAN contributors: Eric Brown Ruth Timme Errol Strain Marc Allard James Pettengill Yan Yao Maria Sanchez-Leon

Thank you!

Use #AlmondConf to be part of the conversation on Facebook and Twitter

What’s Next

Tuesday, December 5 at 4:15 p.m.

• State of the Industry – Hall C

Be sure to join us at 5:30 p.m. in Hall A+B for Dedicate Trade Show Time and Opening Reception, sponsored by The Bank of Stockton