Is Whole Genome Sequencing Really Replacing Traditional Microbiology?

National Center for Emerging and Zoonotic Infectious DiseasesDivision of Foodborne, Waterborne, and Environmental Diseases

Peter Gerner-Smidt, MD, DScEnteric Diseases Laboratory Branch

InFORM IIPhoenix, AZ, 18 November 2015

Characterization Of Foodborne Pathogens Today

PATHOTYPE: Shiga toxin producing and Enteroaggregative E. coli (STEC & Eagg EC)

VIRULENCE PROFILE: stx2a, aggR, aggA

SEQUENCE TYPE: ST678

ANTIMICROBIAL RESISTANCE: Ampicillin, Cefoxitin, Ceftriaxone, Streptomycin, Tetracycline, Sulfamethoxazole/Trimethoprim

GENUS/SPECIES:

Biochemical ‘panel’

O and H agglutinationMin. 2 PCRs + RFLP7 PCRs + sequencing

Disc diffusion ORbroth micro dilution

TAT: 1- 2 weeks

Subtyping Of Foodborne Pathogens Today (PFGE)

High-discriminatory but NOT phylogenetically relevantXbaI_BlnI

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90807060

PFGE-XbaI

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PFGE-BlnI

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EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0003EXAX01.0018EXAX01.0002EXAX01.0009EXAX01.0001EXAX01.0019EXAX01.0010EXAX01.0020EXAX01.0014EXAX01.0015EXAX01.0016EXAX01.0017EXAX01.0006EXAX01.0007EXAX01.0008EXAX01.0005

PFGE-BlnI-pattern

EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0003EXAA26.0004EXAA26.0019EXAA26.0002EXAA26.0002EXAA26.0001EXAA26.0018EXAA26.0010EXAA26.0020EXAA26.0011EXAA26.0015EXAA26.0016EXAA26.0017EXAA26.0009EXAA26.0008EXAA26.0007EXAA26.0006

Country

GermanyGermanyDenmarkFrance

Georgia

Georgia

Listeria Whole Genome Sequencing Works For Outbreak Surveillance

Possible to perform WGS in real-time Cost-efficient Superior discrimination and precision

Epidemiologically unrelated isolates with the same PFGE may often be differentiated

Linking case-patients with different PFGE patterns to the same single source outbreak

Refining outbreak case definitions Increasing confidence in links between clinical and food isolates Linking historic case-patients to current outbreaks

Now is the time to move beyond subtyping

AMD Initiative‘Advanced Molecular Detection’

5 year budget initiative that started in fiscal year 2014 - initial investment of $30 million; level funding requested for each of the remaining years

www.cdc.gov/amd/index.html

“Transforming Public Health Microbiology –PulseNet And Beyond”

Replacing traditional microbiology with WGS for characterization of foodborne pathogens:o Consolidation of multiple workflows into one: Identification –

serotyping – virulence profiling – antimicrobial resistance characterization – plasmid characterization- subtyping

Changing Role Of Public Health Laboratories In The World Of WGS -

State and Local Public Health Laboratories

All state and local public health laboratories will isolate and sequence foodborne pathogens, and perform routine analysis of WGS data from their own jurisdiction for the use in local and national laboratory surveillance

Changing Role Of Public Health Laboratories In The World Of WGS – CDC Laboratories

Data management & data analysis Training and quality assurance Protocol development Surge capacity for WGS WGS Troubleshooting National organism specific SME ‘Center for Classical Microbiology’

When WGS fails or new strains emerge Sentinel surveillance using classical methods

Better integration of laboratory and epidemiology Laboratory expertise is needed to use and interpret the data in

epidemiological contexts International activities Applied research

Preparing for a world without cultures

Path To WGS In Public Health Build capacity including training Harmonized between PulseNet & GenomeTrakr

Develop protocols and analytical platforms Validate WGS for CLIA (clinical testing) Validate protocols and platforms internally at CDC

and by external users in the public health laboratories

Establish quality assurance system Common to PulseNet and GenomeTrakr

Defining a quality standard for raw reads NCBI, FDA, USDA, CDC

Where Are We With Implementing WGS In Public Health Today?

WGS capacity in 27 public health laboratories External validation of PulseNet Listeria WGS database including

identification and subtyping happening in 10 public health laboratories

CLIA validation of WGS for identification and reference characterization of Listeria at CDC

Development and internal validation of PulseNet WGS databases for Shiga toxin-producing E. coli (STEC) and Campylobacteraceaein its final stage

Development of the PulseNet WGS Salmonella database has begun Training 40+ microbiologists in using the wgMLST tools at InFORM

2015

Partners In System Development

International Partners:

PulseNet International PHACStatens Serum Institut DTU/CGE

ECDC EFSA Institut Pasteur Public Health England

GMI Academia

U.S. Partners:

PulseNet OutbreakNet AcademiaFDA/CFSAN-CVM Genome Trakr

USDA /FSIS-ARS NIH APHL

Foodborne Disease Branches

and other CDC partners

1. We neither have the capacity nor the knowledge to make the WGS transformation alone

2. What we do must be in sync with what others do to ensure national and international comparability of data

Combined Public Health WGS Workflow

Allele Database

Calculation engineTrimming, mapping, de novo assembly, SNP detection, allele detectionPublic Domain (NCBI)

Currently CDC

‘PulseNet’ databasesClosed to the public

Database managers and end users

Publical Domain (NCBI) Currently CDC

External storageNCBI, ENA, BaseSpace

Sequencer

Raw sequences

LIMS

Data pathwayAnalysis request

Genus/speciesSerotypePathotypeVirulenceResistance

7-gene MLSTeMLSTcgMLSTwgMLST(SNPs)

Species Identification UsingAverage Nucleotide Identity (ANI)

Similarity measure of the nucleotide content in homologous regions between two isolates Similar to old fashioned DNA-DNA hybridization

A robust way to identify the species of an isolate with well characterized reference strains by WGS

Species identity if ANI >0.95

Gladney, Huang, Kucerova, Katz, Roache, Carleton, Tarr: Validation of Whole Genome Average Nucleotide Identity for Identification of Listeria monocytogenes and related species, SFAF 2015

ANI Of ListeriaBox-plots of ANI-values for different within and between Listeria species combinations

Data are preliminary and subject to change

Tools To Perform Specific Tasks One At A Time Is Available On The Web

e.g., http://www.genomicepidemiology.org/

Serotyping of Salmonella by WGShttp://www.denglab.info/SeqSero

Zhang S et al. J Clin Microbiol. 2015 May;53(5):1685-92

‘Susceptibility Testing’ by WGS

Not susceptibility testing but detection of resistance markers/genes Several tools to extract resistance markers available on the Web

• E.g., ResFinder from the CGE Resistance markers are not always expressed Must be validated against phenotypic data In production, the correlation between phenotypic and genotypic

data must be monitored Only known resistance markers will be detected

Need for sentinel phenotypic surveillance to detect new resistance

wgMLST and PFGE in the 2014 Caramel Apples Listeria Outbreak

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)

PFGE Pattern 1

PFGE Pattern 2 PFGE Pattern 3

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.2014L-6572

2014L-67162014L-67042014L-6707

2014L-6684

2014L-67102014L-66562014L-67242014L-66812014L-66952014L-66772014L-66792014L-67142014L-67232014L-66602014L-67132014L-6577 .

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Data are preliminary and subject to change

‘One Shot’ Characterization Of STEC by WGS

ANISerotypeFinderVirulence Finder7-gene MLSTResFinder

Explanation of Virulence and Resistance Markers:

Projected wgMLST Database Validation and Deployment TimelineApr 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 →

Listeria monocytogenes

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

Salmonella

Vibrio, Shigella &other diarrheagenicE. coli

Cronobacter &Yersinia

Acknowledgements

National Center for Emerging and Zoonotic Infectious DiseasesDivision of Foodborne, Waterborne, and Environmental Diseases

Disclaimers: “The findings and conclusions in this presentation are those of the author and do not necessarily

represent the official position of the Centers for Disease Control and Prevention”

“Use of trade names is for identification only and does not imply endorsement by the Centers for Disease Control and Prevention or by the U.S. Department of Health and Human Services.”

Public Health Agency of Canada

Colleagues in EDLB & Office of Advanced Molecular DetectionUniversity of Georgia: X. Deng

Center for Genomic Epidemiology, DTUUniversity of Oxford, M. Maiden