Genomics in food security:

100K Pathogen genome

Project

Bart Weimer, Ph.D. Professor

UC Davis - School of Veterinary Medicine

Director BGI@UCDavis

The Agricultural System

Biosecurity & Safety

Agriculture

Environment

Health

Changing world &

food safety challenges • World population predicted to

reach 9.2 billion by 2050

• Increased urbanization

• Developing countries increase ~1.5 to 2.0

• ~25 Mega-cites around world

• Increased density

• Increased distance from food supply

• Predicted increase in world-wide food-related disease outbreaks

• Intensive agriculture needed to feed the world

Industrialized = Aging

Developing = Baby boom 0

2

4

6

8

10

1990 2000 2010 2020 2030 2040 2050

Population (billions)

(modified from Z_punkt)

0

2

4

6

8

10

1990 2000 2010 2020 2030 2040 2050

Food Safety & Quality

• Example outbreaks associated with food:

• 2013 – Pet food, turtles, hedgehogs, tea, chicken, beef, pork, & Salmonella

• 2012 – Pet food, fruit, nuts, peanut butter, hamburger, tuna, chicken &

Salmonella

• 2011 – Veggies and Re-emergence of E. coli O104 (genomics required to solve)

• 2010 – Eggs and S. Enteritidis

• 2008 – Peanut butter and S. Typhimurium

• 2006 – Tomatoes, peppers and S. Typhimurium

• Estimated economic impact in the EU >€5 billion from Campylobacter &

Salmonella

(EFSA, 2012)

Food Safety News Jan 2012

Norovirus

61%

Salmonella

(non-typhoid)

12%

Clostridium

perfringens

11%

Campylobacter

spp.

9%

Staph aureus

3%

Toxoplasma

gondii

1%

E. coli O157:H7 2%

Listeria

monocytogenes

1%

http://www.cdc.gov/foodborneburden/PDFs/FACTSHEET_A_FINDINGS_updated4-13.pdf

Total FBI 2011

Norovirus

12%

Salmonella

(non-typhoid)

30%

Clostridium

perfringens

0%

Campylobacter

spp.

7%

Staphylococcus

aureus

0%

Toxoplasma

gondii

26%

E. coli

O157:H7

4%

Listeria

monocytogenes

21%

http://www.cdc.gov/foodborneburden/PDFs/FACTSHEET_A_FINDINGS_updated4-13.pdf

FBI deaths 2011

Foodborne Pathogens

• Salmonella particularly devastating:

• High serotype diversity

• High mobile element diversity

• Frequent horizontal gene transfer

• Emerging stable hypervirulence

• Heithoff et al.PLoS Pathog 8(4):e1002647

• Large genomic diversity within serotype

• E. coli O104 example of NGS & solutions

Salmonella Diversity

Salmonella species Number of

serovars

S. enterica 2,557

S. enterica subsp. enterica 1,531

S. enterica subsp. salamae 505

S. enterica subsp. arizonae 99

S. enterica subsp. diarizonae 336

S. enterica subsp. houtenae 73

S. enterica subsp. indica 13

S. bongori 22

Total (genus Salmonella) 2,579

1,630 serotypes

important in food animals

• Approximately 50-60

serotypes are most

common to cause FBI

in humans

• New foods becoming

associated with new

serotypes

Dynamic Microbial

Communities & Disease

FoodNet

Javiana

Newport

Typhimurium

Heidelberg

Enteritidis

Montevideo

Rela

tive r

ate

(L

og c

hange)

As one serotype declines others increase

Salmonella

Phylogenomics

16s Alignment

SNPs in 16s rDNA

Whole Genome Alignment

Entire genome reflect

all similarities

Active Outbreaks &

Complete Genomes • Salmonella enterica subsp.

enterica serovar Javiana

• Common in fresh cut

produce

• Only one previously

sequenced genome (JCVI,

2008), 19 contigs

• Isolate CFSAN001992_73:

• Clinical Arizona isolate from

produce-related 2012 outbreak

• Complete process from isolate to

finished genomic sequence <1

week

• 1 chromosome; 2 plasmids

containing never-seen sequence:

Collaboration with M. Allard, E. Brown, E. Strain, M. Hoffman, T. Muravanda,

S. Musser (FDA), B. Weimer (UC Davis), Jonas Kolach (PacBio)

Sequencing

discovers new genes

Pan-genome increases with each

isolate sequenced

Variable Salmonella genome

Micro. Ecol. 2011

New gene families

Core gene families

• We know <1% of the Earth’s microbiome

• Horizontal gene transfer is wide-spread and frequent

• High-quality, finished genomes are the starting point for:

• Functional genomic studies

• Comparative genomics

• Forensics

• Metagenomics

The Value of

Microbial Genomes

Chain et al. (2009) Science 326: 236-237 Fraser et al. (2002) J Bacteriology 184: 6403-6405

Pathogen Evolution

• Vibrio evolution rapid

• Example for all enteric bacteria

• Also shown with environmental organisms

• Enterobacteria genome evolution

• HGT more common than appreciated

• Genome rearrangements influenced by biogeography & other bacterial community members

• Evidence for local pressure to induce population genome evolution

• Biogeography differences

• Likely to find footprints of geographical origin

• Requires large number of genomes to estimate

• Creates chimeric genomes

• Stress

• Induces SNPs

• Induces new virulence and drug resistance

• Mutations in DNA repair genes leads to SNPs

• Recombination events

• SNPs

• Large segments

• HGT

Shapiro et al., ‘12 Science; Denef & Banfield, ’12 Science

New Detection Paradigm

Specific gene

(PCR) Genome

(NGS, multiplex)

Microbial Communities

& Health

Community structure

Host association

Growth & metabolism

Who’s there?

What are they

doing?

Host changes Microbe changes

Kawamoto et al., ‘12 Science

Olszak et al., ‘12 Science

Desai & Weimer, ‘09

Bacteria Testing in

Food

Collect Sample

(25 or 325 g)

Detection

method

Pre-enrich

sample in 4 L

of broth

Protein

Immune

inhibitors

Fat

Non-

culturable

bacteria

Fermentation

product

inhibitors

Project aims to

combine 2 of 3 steps

Faster response: < 30 h

More informative results

Detection Genus, species

qPCR, etc. 8-30 h

Traditional: 3-5 days

Serotyping Serogroup, Serovar

Serology: 4-5 days

Strain Typing Genovar, Genome

PFGE, MLST, genotype

5-10 days

Aim: reduce time to result with low cost, sensitive, accuracy

Too little genomic information = difficult molecular methods

Sensitivity Specificity

Surveillance

Outbreak response

Outbreak investigation

Detection timing

Reduce time to result

Traditional - Enrichment

Collect

sample T0

T24 T36 T48 T60 T72+

Ship to lab

Pre-enrich

Log sample

Prep

Enrich

Plate

Selective enrich

Presumptive

Examine plate

Confirm

Bank

Examine plate

Confirm

Bank

ID

Characterize

Genomics

Next generation –

Culture independent

Collect

sample T0

T0.5 T1 T2 T3 T4+

Capture &

concentrate

Presumptive

Relative amt.

Directed plate

Directed enrich

DNA prep

Multiplex PCR

qPCR

Sequencing prep

Confirm PCR

Sequence

Bank DNA

ID

Characterize

In/out of event

NGS Costs are Falling

(DeWitt et al., 2011)

Genomics & Rapid

detection • German outbreak

• Chimeric genome – deleted genes = False negative

• Eae deletion – main PCR marker missing = False negative

• FSMA

• International reach with new regulations

• Open to investigate alternate detection methods - CID

• International trade now firmly in the FDA plan

• Public health effort

• Using whole genome sequencing (WGS) to investigate outbreaks

• Installing Illumina sequencers in field offices

• More robust biomarkers are needed for routine testing

2013 Poultry Sci 92:562–572

100K Pathogen

genome project

Increase food safety using microbe systems biology

Discover the genetic constituents that are robust to be predictive biomarkers for specific traits

Rapid ID and tracking

Understand evolution to build more robust detection systems

New isolate emergence and persistence

Integration into current practices http://100kgenome.vetmed.ucdavis.edu

2012 HHSInnovate Secretary’s Choice Awardee

integration of

100K Project • Produce a

database of

phylogenomic

diversity of

important FBI

• Industry

representative

genomes

important

• Background

organisms

M. Allard

100K Consortium

• Founding Members, Executive committee

• Agilent Technologies

• UC Davis (Weimer lab)

• FDA

• Additional Steering Committee members

• NIH (NCBI)

• CDC

• USDA

• Mars, Inc.

• Pacific Biosciences

• Steering committee provides guidance for overall project direction and goals

• Affiliate Members

• UC Davis Food Science; Veterinary Diagnostic Lab

• Salisbury University (US)

• DoD - Walter Reed Hospital

• Mass General - Harvard hospital system

• RIVM (Netherlands)

• DTU (Denmark)

• MEFOSA (Lebanon)

• Sydney Technical University (Australia)

• Rajiv Gandhi Biotechnology Institute (India)

• Institute of Environmental Science & Research (NZ)

• Oak Ridge National Laboratory (ORNL)

• Additional negotiations in process with groups from Asia, Africa, Europe

• Corporate Affiliates

• Pacific Biosystems

• cBio

• OpGen

• Kapa Biosystems

• BGI@UCDavis

ADDITIONAL

PARTICIPANTS WELCOME

Process & outcome

Release

genomes to

public

Common

traits

Diagnostic

features Ecology

features

Genome

evolution

Infection

features

Traceability

features

Sequence genome

Collect

metadata

Analyze

genome for

actionable

features

Validate &

verify

information

Validate &

verify

sequence

Merge

genome

sequence &

metadata

Collect isolate

Submission Logistics

• Affiliates

• Many options

• In-kind

• Funding

• Isolates

• Analysis

• Data hosting

• Various levels of commitment available

• Groups providing funding & linking sequencing to important isolates

• Isolate submission

• Isolate agreement

• MTA

• Timing and specific isolates

• Submit isolates & metadata

• Authentication

• Bank isolates for DNA isolation & library construction

• Sequence –

• BGI@UCDavis

• Return data to submitter

• 12 months for review

• Deposit in NCBI for public access

• Data return & analysis

• Publication

Organisms of Interest

• Salmonella

• Listeria

• Campylobacter

• Vibrio

• E. coli

• Shigella

• Yersinia

• Clostridium

• Enterococcus

• Cronobacter

• Norovirus

• Hepatitis A

• Enteroviruses

• Short reads sequence followed by long read technologies for sub-set of isolates to complete genome

• Optical mapping will be used for a selected set to ensure genome quality

• Long read technology will be used to close 1,000 genomes

• Capture genomic diversity to represent pan-genome for the most important organisms

• World-wide representation

Initial focus

Isolate bank

Bacillus,3Brenneria,1 Campylobacter,100 Citrobacter,1

Cronobacter,3

Erwinia,1

Escherichia,203

Enterobacter,4

Klebsiella,2

Listeria,89

Moraxella,1

Proteus,1

Rummeliicillus,1

Salmonella,1350

Shigella,4

Streptococcus,1

Vibrio,149

Bacillus,3Brenneria,1 Campylobacter,100 Citrobacter,1

Cronobacter,3

Erwinia,1

Escherichia,203

Enterobacter,4

Klebsiella,2

Listeria,89

Moraxella,1

Proteus,1

Rummeliicillus,1

Salmonella,1350

Shigella,4

Streptococcus,1

Vibrio,149

Bacillus,8,0%

Brenneria,1,0%

Campylobacter,110,5%

Carnobacterium,1,0%

Citrobacter,1,0%

Cronobacter,3,0%

Erwinia,2,0%

Escherichia,203,9%

Enterococcus,210,9%

Enterobacter,4,0%

Exiguobacterium,1,0%

Klebsiella,2,0%

Lactococcus,1,0%Listeria,89,4%

Leuconostoc,3,0%

Moraxella,1,0%

Proteus,1,0%

Rummeliicillus,1,0%

Salmonella,1342,59%

Shigella,4,0%

Staphylococcus,1,0%

Streptococcus,2,0%

Vibrio,287,13%

Weisella,1,0%

Bacillus

Brenneria

Campylobacter

Carnobacterium

Citrobacter

Cronobacter

Erwinia

Escherichia

Enterococcus

Enterobacter

Exiguobacterium

Klebsiella

Lactococcus

Listeria

Leuconostoc

Moraxella

Proteus

Rummeliicillus

Salmonella

Shigella

Staphylococcus

Streptococcus

Vibrio

Authenticated & banked Isolates

(~3,500 isolates)

Bacillus,8,0%Brenneria,1,0%

Campylobacter,3295,55%

Carnobacterium,1,0%

Citrobacter,6,0%

Cronobacter,3,0%

Erwinia,2,0%Escherichia,203,3%Enterococcus,210,3%

Enterobacter,5,0%

Exiguobacterium,1,0%

Klebsiella,4,0%

Lactococcus,1,0%

Listeria,281,5%

Leuconostoc,3,0%

Moraxella,1,0%

Proteus,2,0%

Rummeliicillus,1,0%

Salmonella,1674,28%

Shigella,4,0%

Staphylococcus,1,0%

Streptococcus,2,0%

Vibrio,305,5%

Weisella,1,0%

Bacillus

Brenneria

Campylobacter

Carnobacterium

Citrobacter

Cronobacter

Erwinia

Escherichia

Enterococcus

Enterobacter

Exiguobacterium

Klebsiella

Lactococcus

Listeria

Leuconostoc

Moraxella

Proteus

Rummeliicillus

Salmonella

Shigella

Pending authentication & banking

(~15,000 isolates)

Isolates by region

NorthAmerica74%

SouthAmerica

1%

Europe7%

Asia4%

Australia0%

Africa3%

MiddleEast1%

Unknown10%

NumberofIsolatesbyRegion

NorthAmerica

SouthAmerica

Europe

Asia

Australia

Africa

MiddleEast

Unknown

100K Sequencing

Process Bank culture

Isolate DNA

Library automation

Make library

Short read technologies

Long read technologies

Optical mapping

Sequence library Genome DB

Project progress

• Year 1:

• Focus on the top 50 Salmonella outbreak serotypes

• Banked ~3500 isolates

• Developing world-wide partnerships

• Automation of sequence library construction

• Sequence 1800 isolates

• Year 2-5

• Bank additional isolates

• Automated, routine library construction

• Sequence ~25K genomes/year

• Finish 1000 genomes to a single closed genome

• Generate epigenomic data

• Define high resolution map assemblies for small set

• Define need for additional bioinformatics

100K project Web site

http://100kgenome.vetmed.ucdavis.edu

Public health

Outcomes • Federal agencies embracing NGS for outbreak investigation, trace

backs, and monitoring

• PFGE vs NGS

• Implementation is not a simple path

• Pan-genome value

• Discover new sets of genes that are >50% of the genome that we ignore today

• New robust testing methods that allows routine testing in plant

• Human and animal public health

• 100K database enables a new era of diagnostics tools

• Definition of virulence, antibiotic resistance, source, insights for mitigation, and window into emerging strain differences as a sentinels

• Host adaptation, zoonotic movement, supply chain changes

Innovation in

Methods

• Culture independent methods (CIM) to capture & concentrate

• Enrich

• Detect

• ID

• Coupling genomics & biomarkers with existing methods and CIM

• Increase speed

• Increase information diversity

• Serotypes

• Pathogens

Microbial

Isolation & detection • Classical approach –

grow what we can…

• BAM/ISO & growth

• Enrichment and ELISA

• Limited by those we know how to grow

• We know how to grow ~1% of bacteria

• Non-culturable bacteria common

• Viruses often don’t grow in vitro – limits detection approaches

• Strain diversity and continued outbreaks creating demand for new, next generation methods

• Rapid methods – looking for bacterial signatures…

• Finding new organisms without growth

• Enables customized approaches for screening

• Molecular methods

• Provides bacterial community structure information

• Individual genome sequencing

• Provides bacterial metabolism capability

• Can be linked to food characteristics

Examples of

molecular tools • Data analysis post sequencing

• Comparison for SNPs

• Gene content = annotation

• Content comparison = forensics

• Gene

• Protein

• COGs/GO use in statistical enrichment

• Sea mammal outbreak (SNP)

• Food outbreak (SNP)

• Going beyond SNPs and beyond

• Biomarker hunting

• Genomics – limited based on sequences available

Rapid Detection

Technologies to enhance food safety & security

Eliminate enrichment

Fast

Sensitive

Reliable

Food, water & environment

E. coli (B. Findley)

Existing Limitations

• Molecular assays are limited by too few genomes to

develop robust biomarker genes quickly

• Genome evolution more complex that previously

appreciated

• Lack of information to create robust assays for improved

detection using PCR

• Genome sequencing will enable technological advances

and increase reliability of PCR assays

Rapid detection

Capture

with fluidized bed

(15 mL to 40 L)

Presumptive ID (ELISA, PCR)

0 min

30–35 min

5 min

Detailed DNA/RNA analysis

Confirm ID, molecular serotype,

sequence, toxin production,

community analysis

40 min

120-135 min

& beyond…

microbe

bead

Blake & Weimer, ‘97 AEM

Weimer et al., ’00 JBBM

Weimer et al., ’01 AEM

Walsh et al., ’01 JBBM

Desai et al., ‘08 AEM

Maga et al., ‘12 AEM

Detection in Food &

Enrichment Broth

Organism

ImmunoFlow Commercial

Immunoppt. Food

E. coli

O157

Apple juice 0 0

Hamburger 80 40 30

Beer 0 0

Sprouts 100 100

Halibut 40 30 40

Chicken 60 0 20

In Food Enrich Broth

LM

SE

% positive

GlycoBind Detection

• Replaces Ab–based tests

• Capture ligands are cell

receptors

• Broad organism capture

surface

• Used in static & flow

setting

• Detection with RT-PCR

• WGS has arrived...

Myxobolus cerebralis

Food Type

Aerobic Plate Count before

E. coli O157:H7 spike

(cfu/gm)

Lower

Detection Limit

(cfu/gm)

Water 0 4

Apple juice 8 4

Spinach 577 4

Hamburger 24856 400

Salami 9350000 40000

Milk 0 40000

1

No enrichment

3 hour total time

4 cell sensitivity

<5% variation

Desai et al., 2008.

AEM 74:2254-2258

Molecular salmonella

testing in 2 hours

Report

Salmonella,

serogroup &

serotype

Nano

electrophoresis

(30 mins)

Lyse cells Add template PCR

(45 mins)

• Colony

• Enrichment broth

• Lab medium

• Bead capture

Action based on

1. Salmonella detection

2. serotype determination

Molecular

detection validation • Validation Approach

• ~1750 isolates tested

• Designed to detect ~40 most common serotypes

• Multiple matrices done

• Verification & Validation

• Used by 3 independent labs

• 100% accurate in 6 independent blinded panels for

Salmonella identification

• 98% accurate in correct serotype determination

Complex implementation

• Total DNA/RNA extraction from food

• Possible and being done for PCR and qRT-PCR

• Metagenomics – HARD & UNCLEAR reliability

• Rapid detection strategies

• Genomics and systems biology

• Reduction in time to result

• Robust & accurate genomic tests

• Requires access to genomes/genotypes

• The longest step is enrichment

• Eliminate pre-enrichment step for direct detection?

• PulseNet & genomics

• NGS is fast and direct

• Data rich

• Work flow remains unclear for best implementation

Workflow innovation

NGS of

entire genome

Library

construction

NGS

Sequencing

Bioinformatics &

statistics

1.5 hours Total time 6 hours 48 hours

~24-130 hours*

Enrichment

& colony isolation

Result

Nano Electrophoresis

Colony

multiplex

PCR

Broth

multiplex

PCR

Culture independent

capture/concentration

Presumptive ID

with

Solid phase ELISA

Nano

Electrophoresis

Critical Needs

• Robust biomarkers

• Fast, actionable answers

• Novel sequencing

strategies

SE SNP Analysis

Multiple SE snp-types

‘93 sea lion & otter

in 3 tissues

‘93, ’02, ‘08, ’11,

rodent, equine, sea

mammal

‘98, ‘05, ’08, sea

mammal

Yearly SNP evolution

elephant seal, lung

sea lion, liver

elephant seal, kidney

equine feces

‘08, sea lion, liver

’98 otter, abdomen

’05 sea lion, uterus

1993

‘02 rodent feces ’11 elephant seal urine ’11 elephant seal brain

Open questions

• Can individual food components modify the microbiome to enrich or exclude zoonotic pathogens?

• Genomic variation & outbreaks

• Host adaptation

• Transmission

• Virulence

• Antibiotic resistance

• Robust biomarkers & detection reliability

• Serotype and genotype

• Detection methods

• BAM –

• Sample prep –

• capture concentration coupled to trusted methods (ELISA)

• Molecular assays – PCR, robust biomarkers, mass spec

• Genomics – sequencing, metagenomics, directed sequencing

• Routine use vs outbreak investigation vs traceback

Acknowledgements

Weimer Lab

• Dr. Yi Xie

• Dr. Richard Jeannotte

• Dr. Holly Ganz

• Dr. Marie Forquin

• Dr. Prerak Desai

• Dr. Jigna Shah

• Ms. Nugget Dao

• Ms. Mai Lee Yang

• Ms. Kao Thao

• Ms. Winnie Ng

Thanks to the sponsors:

FDA

USDA

DARPA

US Air Force

Agilent Technologies

CA Dairy industry

Pacific BioSciences

Mars, Inc.

UCD Wildlife Health Center

cBio

• Dr. Kumar Hari

• Dr. Ravi Jane

UCD/CAHFS/SVM

• Dr. Kris Clothier

• Dr. Barb Byrne

• Dr. Woutrina Miller

• Dr. Linda Harris

• Dr. Maria Marco

Agilent Technologies

• Dr. Rudi Grimm

• Dr. Lenore Kelly

• Dr. Steffan Müeller

• Dr. Steve Royce

• Dr. Paul Zavitsanos

PacBio

• Jonas Korlach

• Luke Hickey

Thank You…

Bart Weimer Professor, UC Davis

Director, BGI@UC Davis

Director, 100K Genome Project

Director, Integration Core, NIH-West Coast Metabolomics Center

bcweimer@ucdavis.edu

530.754.0109

Questions?