Is Whole Genome Sequencing Really Replacing Traditional Microbiology?

Transcription

1 Is Whole Genome Sequencing Really Replacing Traditional Microbiology? Peter Gerner-Smidt, MD, DSc Enteric Diseases Laboratory Branch InFORM II Phoenix, AZ, 18 November 2015 National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases

2 Characterization Of Foodborne Pathogens Today Biochemical panel GENUS/SPECIES: 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 O and H agglutination Min 2 PCRs + RFLP 7 PCRs + sequencing Disc diffusion OR broth micro dilution TAT: 1-2 weeks

3 Subtyping Of Foodborne Pathogens Today (PFGE) High-discriminatory but NOT phylogenetically relevant XbaI_BlnI PFGE-XbaI PFGE-BlnI PFGE-XbaI-pattern EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX EXAX PFGE-BlnI-pattern EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA EXAA Country Germany Germany Denmark France Georgia Georgia

4 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

5 AMD Initiative Advanced Molecular Detection 5 year budget initiative that started in fiscal year initial investment of $30 million; level funding requested for each of the remaining years wwwcdcgov/amd/indexhtml

6 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

7 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

8 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

9 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

10 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 Campylobacteraceae in its final stage Development of the PulseNet WGS Salmonella database has begun Training 40+ microbiologists in using the wgmlst tools at InFORM 2015

11 Partners In System Development International Partners: PulseNet International PHAC Statens Serum Institut DTU/CGE ECDC EFSA Institut Pasteur Public Health England GMI Academia US Partners: PulseNet OutbreakNet Academia FDA/CFSAN-CVM Genome Trakr USDA /FSIS-ARS NIH APHL 1 We neither have the capacity nor the knowledge to make the WGS transformation alone Foodborne Disease Branches and other CDC partners 2 What we do must be in sync with what others do to ensure national and international comparability of data

12 LIMS Combined Public Health WGS Workflow Database managers and end users Sequencer Raw sequences PulseNet databases Closed to the public Genus/species Serotype Pathotype Virulence Resistance 7-gene MLST emlst cgmlst wgmlst (SNPs) External storage NCBI, ENA, BaseSpace Allele Database Publical Domain (NCBI) Currently CDC Data pathway Analysis request Calculation engine Trimming, mapping, de novo assembly, SNP detection, allele detection Public Domain (NCBI) Currently CDC

13 Species Identification Using Average 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 >095 Gladney, Huang, Kucerova, Katz, Roache, Carleton, Tarr: Validation of Whole Genome Average Nucleotide Identity for Identification of Listeria monocytogenes and related species, SFAF 2015

14 ANI Of Listeria Box-plots of ANI-values for different within and between Listeria species combinations Data are preliminary and subject to change

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

16 Serotyping of Salmonella by WGS Zhang S et al J Clin Microbiol 2015 May;53(5):

17 Susceptibility Testing by WGS Not susceptibility testing but detection of resistance markers/genes Several tools to extract resistance markers available on the Web Eg, 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

18 wgmlst and PFGE in the 2014 Caramel Apples Listeria Outbreak Allele differences at node: median [min max] (>5,800 loci analyzed by BioNumerics PFGE software) 89 [89 89] 2014L [1 114] 2014L L L [1 6] 2014L L ,628 [0 1,694] 2014L L L L L L L L [0 10] 2014L L [0 44] 2014L-6577 Unrelated isolates (hot dog and patient) Highly-related patient isolate; different PFGE pattern Cluster 1 ( 6 allele differences) Cluster 2 ( 10 allele differences) Unrelated patient isolate (Sept 2014) Not closely related (minimum 1,628 allele differences) PFGE Pattern 1 PFGE Pattern 2 PFGE Pattern 3 Data are preliminary and subject to change

19 One Shot Characterization Of STEC by WGS ANI SerotypeFinder Virulence Finder 7-gene MLST ResFinder Explanation of Virulence and Resistance Markers:

20 Projected wgmlst Database Validation and Deployment Timeline Listeria monocytogenes 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 External validation Deployment Campylobacteraceae & Shiga toxin-producing E coli (STEC) Development and internal validation External validation Deployment Salmonella Development and internal validation External validation Deployment Vibrio, Shigella & other diarrheagenic E coli Cronobacter & Yersinia Development and internal validation Development and internal validation External validation Deployment External validation

21 Acknowledgements Public Health Agency of Canada Colleagues in EDLB & Office of Advanced Molecular Detection University of Georgia: X Deng Center for Genomic Epidemiology, DTU University of Oxford, M Maiden 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 US Department of Health and Human Services National Center for Emerging and Zoonotic Infectious Diseases Division of Foodborne, Waterborne, and Environmental Diseases