New York State s experience with analyzing, interpreting, and sharing whole genome sequence data for surveillance of enteric organisms.

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1 New York State s experience with analyzing, interpreting, and sharing whole genome sequence data for surveillance of enteric organisms. InForm 11/18/15 William Wolfgang, PhD Wadsworth Center, NYSDOH william.wolfgang@health.ny.gov

2 We are engaged in 3 Major projects May 2013 FDA GenomeTrakr - building a genomic database 1190 genomes - Mostly Salmonella from food and environment November 2014 CDC-AMD - real-time surveillance 333 genomes -L. monocytogenes, E. coli, and Salmonella October 2013 Wadsworth Center real-time surveillance of Salmonella Enteritidis 733 genomes - All Salmonella Enteritidis we receive from patients

3 Salmonella Enteritidis clusters are poorly resolved by PFGE Our most commonly received strain of Salmonella each year. a. 1/2 have the same PFGE DNA fingerprint. b. And 2/3 have a very common endemic PFGE DNA fingerprint. c. These endemic patterns are of limited use to our epidemiologists. PFGE types detected over a two year period PFGE types

4 Two years ago we started sequencing of all SE in real-time Goals 1. Evaluate WGS efficacy compared to PFGE. i. Is cluster resolution better? ii. Is it faster and cheaper? 2. Develop analytical and interpretive tools. 3. Develop communication pipeline to epidemiologists. 4. Acquire a real data set to evaluate evolving informatic methods.

5 Analysis of WGS data using Pascal s pipeline

6 We use a Reference based SNP analysis to compare isolates in a phylogenetic tree Pluses Widely used method. In house pipeline was feasible. No large database is required. Very high resolution. Computationally fast. Minuses Requires high quality reference genomes. Lack of standards. Bacteria X SNP Bacteria Y

7 Every week Pascal s Pipeline returns some useful data GC-69

8 Every week Pascal s Pipeline returns some useful data

9 And every week we share these data with: Ourselves through our LIMS system NCBI - Sequence and metadata i. Upload two spreadsheets and the sequence files Epidemiologists - we identify i. New clusters of 2 isolates - 0 to 7 SNP different ii. Additions to existing clusters no time limit iii. Indicate if the event occurred within the past 60 days Our report: Salmonella serotype Enteritidis GC-96: A new cluster of two isolates has been detected, containing: IDR (JEGX ) and IDR (JEGX ). These isolates have 1 single nucleotide differences (snps) out of 6918 snps and are considered closely related.

10 Using our SE data to inform the road ahead 502 isolates sequenced in real time Collected over 598 days (8/27/2013 to 4/16/2015) 32 PFGE types i. 438/502 (87%) isolates were part of endemic patterns Identified 86 Genomic Clusters i. About 60% of isolates reside in clusters ii. Most clusters contain 4 or fewer isolates; mean 3.4 iii. 5 non-endemic PFGE clusters identified # of Genomic Clusters Frequency Distribution of Isolates in Genomic Clusters (GC) Isolates

11 WGS detects many clusters PFGE JEGX contains 45 genomic clusters PFGE JEGX contains 8 genomic clusters PFGE JEGX contains 5 genomic clusters PFGE JEGX contains 13 genomic clusters PFGE JEGX contains 4 genomic clusters PFGE JEGX contains 12 genomic clusters

12 PFGE types are not monphyletic and Genomic clusters can contain multiple PFGE types GC-41 GC-20 GC-35

13 NYC Red Rooster restaurant outbreak NYCDOHMH requested WGS. 6 isolates were JEGX isolates was JEGX Q: Was the outlier related? A: Yes; There was 0 SNPs difference Enteritidis Enteritidis JEGX JEGX

14 17% of Genomic clusters harbor two PFGE types Commons pairs have all lost the same plasmid SLA5 JEGX JEGX JEGX JEGX JEGX JEGX

15 WGS is great!!! It works really well to confirm or refute epi. findings. It provides the ultimate resolution. Subdivide Endemic PFGE types. It is more stable than PFGE. And data can be readily shared. In house through our LIMS With partners through BaseSpace and FTP And with NCBI through their submission portal And NOW with CDC through BioNumerics 7.5!!!!

16 But challenges still exist. Standardization and QA/QC Making the data useful to our epidemiologists. i. Prioritization of clusters ii. Better visualization

17 Prioritization and visualization of clusters in time Cluster = 0 to 4 SNPS Indicates appearance of 4 isolates in a 60 day window Date sample collected

18 Visualization of clusters in time 12 instead of 86 clusters of interest. Date

19 Summary WGS Vastly improved resolution i. many more clusters are detected ii. clusters are more stable It is great for supporting epidemiological investigations i. particularly for excluding samples It is not as useful at informing epidemiological investigations i. we need to develop prioritization schemes

20 Next steps

21 Stop doing both PFGE and WGS!!! We may be ready soon for Listeria!!! All isolates are being sequenced and analyzed. Bionumerics 7.5 will let pilot labs analyze data locally. How could WGS labs share data with PFGE labs? Infer PFGE types from genome data?

22 Wadsworth Center Bacteriology Kim Musser Michelle Dickinson Samantha Wirth Kara Levinson Kara Mitchell Sequencing Core Matt Shudt Zhen Zhang Charles MacGowan Melissa Leisner Danielle Loranger Bioinformatics Core Mike Palumbo Pascal LaPierre PFGE Lab Dianna Bopp Deb Baker Lisa Thompson Acknowledgments Minnesota DOH David Boxrud Angie Taylor Victoria Lappi BCDC NYSDOH Madhu Anand Andie Newman NCBI Bill Klimke Martin Shumway Yuriy Skripchenko FDA Eric Brown Peter Evans Marc Allard Errol Strain Ruth Timme CDC Eija Trees Heather Carleton Steven Stroika