Whole-Genome Sequencing (WGS) for Food Safety

Transcription

1 Whole-Genome Sequencing (WGS) for Food Safety Errol Strain, Ph.D. Director, Biostatistics and Bioinformatics Staff Center for Food Safety and Applied Nutrition U.S. Food Drug Administration IFSH Meeting 5/22/2017

2 FDA Regulatory Use Cases 1. Do these new bacterial isolates from environmental/product testing match any clinical isolates in the DB? Is this product/facility causing illness? 2. Do these new clinical isolates match any environmental/food isolates in DB? Should we test product/swab a facility? 3. Are isolates collected at different points in time from the same facility a match? Is there a problem w/ a resident pathogen, harborage? 2

3 GenomeTrakr Data Flow Salmonella Listeria GenomeTrakr Labs & Collaborators

4 NGS-Based Surveillance (prior to NCBI Pathogen Detection) Initial Clustering: PFGE, K-mer, MASH, BLAST Goal: Find a group of Closely related isolates NCBI Missing SNP Pipeline: Find phylogenetically informative SNPs, FASTA alignment FDA Construct Phylogeny FDA 4

5 NCBI Pathogen Detection 5

6 CFSAN vs NCBI SNPs 6

7 Scientific Evidence Daubert Standard 1. Empirical testing: whether the theory or technique is falsifiable, refutable, and/or testable. 2. Whether it has been subjected to peer review and publication. Specific/Target Studies for pathogen have been published. Multiple software packages for mapping and calling SNPs. 3. The known or potential error rate. Well characterized at read level, less so for cluster analysis. 4. The existence and maintenance of standards and controls concerning its operation. Proficiency testing efforts through Global Microbial Identifier and also FDA GenomeTrakr network. 5. The degree to which the theory and technique is generally accepted by a relevant scientific community. Acceptance facilitated by open database (NCBI/SRA). 7

8 Why Build A Pipeline? 1. Regulatory Use and/or Accredited Labs NCBI methods not public and peer-reviewed Chain of custody local computation Results needed immediately 2. Pathogen and/or data not at NCBI Mycobacterium, Legionella* Food Industry private data 8

9 What Kind of Pipeline? SNPs wgmlst Unit of Measure Single Nucleotide Substitutions (other types of mutations are excluded) Allele - variant of a gene. Variation could arise form a number of sources, including SNPs, insertions, deletions, etc. Requirements Complete or high-quality reference genome for mapping Database of named alleles, must be actively maintained Pros Extremely High Resolution, Methods have been published and validated Relatively Fast, not directly dependent upon reference genome Cons Requires reference genome, computationally intense, requires local bioinformatics expertise Allele database must be centralized, cannot compute novel wgmlst types locally. wgmlst schemas not easy to publicly access 9 9

10 FDA Pipeline Requirements 1. Public, Peer-Reviewed Results may be subject to legal scrutiny Accessible to FDA-regulated industries 2. Reproducible 3. Documentation & Validation 4. Platform independent (fastq) 5. Run Locally 10 10

11 Background: CFSAN SNP Pipeline Mapping/Aligning (66+) SNP Detection (16+) Samtools SOAPsnp GATK Bowtie2 VarScan SNVer VarScan SHORE SMALT MaCH IMPUTE2 CLC Bio QualitySNPng DNABaser SNPdetector FreeBayes SolSNP DNAStar 11

12 CFSAN SNP Pipeline Documentation: Source Code: Pettengill JB, Luo Y, Davis S, Chen Y, Gonzalez-Escalona N, Ottesen A, Rand H, Allard MW, Strain E. (2014) An evaluation of alternative methods for constructing phylogenies from whole genome sequence data: a case study with Salmonella. PeerJ 2:e620 Davis S, Pettengill JB, Luo Y, Payne J, Shpuntoff A, Rand H, Strain E. (2015) CFSAN SNP Pipeline: an automated method for constructing SNP matrices from next-generation sequence data. PeerJ Computer Science 1:e

13 FDA\CFSAN Validation Efforts 1. Technical Performance Accuracy: Salmonella LT2 and Agona SL Intralaboratory variation, sequencing platform Salmonella Montevideo (180+ runs), PacBio vs short reads 3. Interlaboratory variation Salmonella Braenderup BAA-664 (PFGE control), ISO/CEN WG, GenomeTrakr PT set (Salmonella & Listeria), Global Microbial Identifier PT 4. Bioinformatics Pipeline Software Validation, Benchmark bioinformatic data sets Collaborations w/ Canada, CDC, NIH/NCBI 13

14 Proficiency Testing: GenomeTrakr 2014, 2015: Each lab in the GT network sequenced the same set of 8 strains. CFSAN PT analysis returned. Manuscript in preparation GMI (yearly since 2013) 2016 PT has wet and dry lab components 2016 PT includes K. pneumonia, L. mono, C. jejuni, E. coli PulseNet/GenomeTrakr harmonized PT Early

15 CFSAN Workflow 15 15

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17 Min-diff Minimum SNP distance to an isolate of a different sample type Food/Environmental vs Clinical (or Microbe) 17 17

18 8 SNPs Check SNP Cluster 18 18

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20 CFSAN Workflow CFSAN SNP Pipeline is run on NCBI SNP cluster Reference prefer complete genomes, drafts work almost as well High-Density SNP regions are filtered >3 SNPs in 1000 bases, phages/recombination/etc. Phylogenetic inference Maximum Likelihood Ambiguous sites are treated as missing data 20 20

21 Which Reference? 10 SNP Difference Strain Subtype PFGE 0% 0.1% 0.5% 1% 2.5% 5% % Divergence Serotype Subspecies Species 21

22 High-Quality Draft CFSAN SNP Pipeline: Listeria Draft vs PacBio Genome Complete (PacBio) 22

23 Interpretation SNP Distance How close are the isolates? No single threshold for all species/types, rough guides 1. <=20 SNPs match, virtually identical SNPs inconclusive 3. > 100 SNPs exclude Bootstrapping Do the isolates form a unique cluster w/ >= 95% support? Is the cluster distinct from other isolates in the tree? Results are critically evaluated and not used blindly 23 23

24 Forensic Needs WGS (SRA) Database: Random survey of bacteria not possible, need to continue to grow database and curate genotypes Thresholds for SNPs vs wgmlst: 1 SNP 1 INDEL 1 Recombination Well-Documented wgmlst databases 24

25 Example E. coli & Flour 25

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28 0-3 SNPs to clinical isolates 0-3 SNPs to other food/env isolates 28 28

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30 CFSAN SNP Pipeline 30 30

31 Future of GenomeTrakr & CFSAN SNP Pipeline 1. Local or web-based QA/QC and identification tools Detect sample mix-ups and low quality before data is submitted to NCBI/SRA, fix problems more quickly 2. Continue to build WGS databases Better thresholds for identity, increase odds of finding a match 3. Local SNP pipeline analysis Accredited labs don t have to send out data 31

32 Snapshot of Data 3/1 to 4/30 SNP/ERD Clusters # SNP Clusters % isolates in SNP clusters (3/2017) Total Campylobacter E.coli/Shigella (56%) 1132 Listeria (89%) 356 Salmonella (86%) 2100 * 2 or more isolates within 50 SNPs 32

33 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 33

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