11/28/2017 Whole Genome Sequencing for Cluster Detection Minnesota, - PDF document

11/28/2017 Whole Genome Sequencing for Cluster Detection Minnesota, 2017 Carlota Medus, PhD, MPH Epidemiologist Supervisor Sr. Foodborne Diseases Unit WGS for Cluster Detection in Minnesota • Whole genome sequencing (WGS) for S . Enteritidis (SE) cluster detection since 2014 • WGS for S . Typhimurium (STm) cluster detection since 2017 − SE and STm isolates sequenced at MDH − hqSNP analysis at Wadsworth Center, New York State Department of Health • All other Salmonella and STEC are sequenced, but only analyzed on request. All Listeria isolates are also sequenced at MDH and analyzed at CDC • Subtyping using pulsed ‐ field gel electrophoresis (PFGE) continues SE and STm WGS Data Flow Wadsworth Illumina Sequenced Center, at MDH Basespace NYDOH MDH Lab hqSNP analysis Notification evaluates the to epi data, identifies (trees, heat maps, clusters raw data files) 1

11/28/2017 SE hqSNP Tree, April 2017 • Includes closely related (within 10 SNPs) 2014 ‐ 2017 isolates 0.02 2

11/28/2017 10 9 8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 9 10 WGS for Cluster Detection • Define “cluster” − Specificity of case definition is important for finding the epi link − Flexibility to later expand the case definition if warranted by the epi data Salmonella Enteritidis Retrospective Study, Minnesota, 2000 ‐ 2014 MDH00215 ‐ Sporadic 4/19/01 MDH00247 ‐‐ Sporadic 8/6/12 MDH00204 ‐ Sporadic 5/14/01 MDH00221 ‐ Sporadic 5/14/01 MDH00203 ‐ Sporadic 7/11/00 MDH00214 ‐ Sporadic 3/12/01 MDH00206 ‐ Sporadic 8/23/00 MDH00217 ‐ Sporadic 6/10/13 MDH00248 ‐ Sporadic 6/10/13 MDH00237 Sporadic 6/22/11 MDH00236 ‐ Sporadic 5/7/11 MDH00207 ‐ Sporadic 8/31/2000 MDH00233 ‐ Sporadic 12/7/2001 MDH00205 ‐ Sporadic 8/22/2000 MDH00216 ‐ Sporadic 4/30/2001 MDH00224 ‐ Sporadic 6/11/2001 MDH00254 0-2 SNPs MDH00252 MDH00253 MDH00234 MDH00226 ‐ Sporadic 6/21/2001 MDH00231 ‐ Sporadic 7/16/2001 MDH00202 ‐ Sporadic 7/7/2000 Defined Outbreak Samples MDH00208 ‐ Sporadic ‐ Same time, PFGE, and MLVA as Outbreak 1 MDH00209 MDH00210 0-2 SNPs Outbreak 1 ‐ Sept 2000 MDH00211 MDH00222 ‐ In ‐ vivo, same as E2001001070 MDH00223 Outbreak 2 ‐ May 2001 MDH00219 0-2 SNPs MDH00225 ‐ In ‐ vivo, same as E2001001070 MDH00228 ‐ In ‐ vivo, same as E2001001070 MDH00220 MDH00218 Outbreak 3 ‐ Aug 2001 MDH00213 ‐ Sporadic ‐ Same PFGE and time as Outbreak 1 MDH00232 ‐ Sporadic 10/17/01 MDH00227 Outbreak 4 ‐ Nov 2003 0-1 SNPs MDH00230 MDH00251 MDH00229 MDH00235 ‐ Sporadic 10/3/05 Outbreak 5 ‐ Aug 2008 MDH00243 ‐ Sporadic, same PFGE and time as Outbreak 5 MDH00245 ‐ Sporadic 6/26/12 MDH00249 0 SNPs MDH00250 Outbreak 6 ‐ Spring 2014 MDH00246 ‐ Sporadic 7/30/12 MDH00255 ‐ OH Sample 1 1 SNP MDH00256 ‐ OH Sample 2 MDH00241 ‐ Sporadic, same PFGE and time as Outbreak 5 Outbreak 7 ‐ Spring 2014 MDH00239 MDH00242 0-3 SNPs MDH00244 ‐ Environmental sample from Outbreak 5 MDH00238 MDH00240 Taylor et al. J Clin Micro Oct 2015 3

11/28/2017 WGS Results Spreadsheet Lab ID PFGE pattern Lab ID for closely related isolates Number of SNPs Cluster ID WGS Results Spreadsheet Lab ID for closely related isolates Number of SNPs 4

11/28/2017 Shared Network Folder Communication of Results, Summary • Lab − Interprets the analysis − Identifies clusters − Enters data in a spreadsheet − Sends email to epis • Epi − Rely on email to start cluster investigations − Have access to  Spreadsheets  hqSNP Trees  Heat map (SNP matrix) SE Cluster Comparison, January ‐ October, 2017 PFGE WGS Number of clusters 14 22 Median number of cases per cluster 3.5 3 Range of number of cases per cluster 2 ‐ 62 2 ‐ 13 Number of clusters with ≥ 10 cases 5 2 Number of clusters with pattern .0004 isolates 1 8 Number of clusters with pattern .0002 isolates 1 7 5

11/28/2017 Domestic WGS SE Clusters, January ‐ October, 2017 Cluster ID Corresponding PFGE Number of Cases Epidemiological Link 2 .0004 13 Kitfo from Ethiopian store subcluster; other 4 .0021 12 Restaurant 9 .0049 9 Likely lettuce (multi ‐ state) 16 .0004 8 Live poultry (multi ‐ state) 17 .0005 6 Live poultry (multi ‐ state) 7 .0004 4 Unsolved 19 .0004 & .0034 3 Live poultry (multi ‐ state) 21 .0004 3 Live poultry suspected 20 .0004 2 Live poultry suspected Travel ‐ Associated WGS SE Clusters, January ‐ October, 2017 Cluster ID Corresponding Number of Cases Epidemiological Link PFGE 1 .0002 7 Dominican Rep. 6 .0004 6 Mexico ‐ some same resort 10 .0002 4 Dominican Rep. ‐ some same resort 5 .0004 3 Jamaica ‐ same resort 3 .0008 2 Mexico ‐ same resort 11 .0116 & .1076 2 Dominican Rep. 12 .0002 2 Dominican Rep. ‐ same resort 13 .0054 2 Dominican Rep. 14 .0042 2 Mexico ‐ same resort 15 .0004 2 Mexico 18 .0002 2 Dominican Rep. No ID .0002 2 Dominican Rep. No ID .0002 2 Dominican Rep. WGS for SE Cluster Detection • Real ‐ life example of using WGS in surveillance − January ‐ April, 2017 6

11/28/2017 2017 SE hqSNP Tree, April, 2017 • Isolates in a 90 ‐ day time period • Includes closely related (within 10 SNPs) 2014 ‐ 0.02 2017 isolates Cluster 3 Travel to Mexico Cluster 6 Cluster 11 Cluster 13 Travel to Cluster 10 Dominican Cluster 12 Republic Cluster 1 0.02 SE hqSNP Tree, April, 2017 • Isolates in a 90 ‐ day time period • Includes closely related (within 10 SNPs) 2014 ‐ 0.02 2017 isolates 7

11/28/2017 Cluster 9 (lettuce) Cluster 7 (unsolved) Travel to PFGE pattern Cluster 5 Jamaica .0004 Cluster 2 (kitfo) Cluster 4 (restaurant) Cases by week of specimen collection, PFGE alone (n=27) No. of Cases Week of Specimen Collection WGS Cluster cases by week of specimen collection No. of Cases Week of Specimen Collection 8

11/28/2017 Cluster 4 April, 2017 SE hqSNP 60 ‐ day tree Cases by week of specimen collection, PFGE alone (n=17) No. of Cases Week of Specimen Collection Cases by week of specimen collection, PFGE alone (n=17) No. of Cases Week of Specimen Collection Cases by week of specimen collection by WGS No. of Cases Week of Specimen Collection 9

11/28/2017 June 2017 Cluster 4 SE hqSNP 60 ‐ day tree 13 ‐ 16 SNP from the outbreak 4 ‐ 6 SNP from the outbreak Cluster 4 ‐ Restaurant outbreak, 0 ‐ 2 SNPs hqSNP, June 2017 Part of the Outbreak or Not? Outbreak isolates • Specimen collection 4/8/17, onset of illness 3/21 • Lives 35 miles from the outbreak restaurant 10

11/28/2017 Part of the Outbreak or Not? Outbreak isolates • Specimen collection 5/15 and 5/22 • (>2 months after last patron specimen collection date) • 167 miles from the outbreak restaurant • Both 17 years of age • One not interviewed • One did not eat at the restaurant and… 11

11/28/2017 Hypothetical Example, Salmonella Baconitidis 60 ‐ day hqSNP Tree, September, 20XX 0.02 Hypothetical Example, Salmonella Baconitidis Cluster 2 ‐ Vet Diagnostic Lab isolates; porcine Previous year ‐ pork ‐ assoc. outbreak Cluster 3 ‐ Restaurant outbreak; pork suspected Cluster 4 ‐ Wedding reception outbreak Cluster 5 ‐ Interviews pending Cluster 1 ‐ Outbreak associated with a pig roast at an event 0.02 Hypothetical Example, Salmonella Baconitidis Cluster 2 ‐ Vet Diagnostic Lab isolates; porcine Previous year ‐ pork ‐ assoc. outbreak Cluster 3 ‐ Restaurant outbreak; pork suspected Cluster 4 ‐ Wedding reception outbreak Cluster 5 ‐ Interviews pending Cluster 1 ‐ Outbreak associated with a pig roast at an event 0.02 12

11/28/2017 Ongoing Issues • Cluster approach generalizable to other pathogens, to other states − Conceptually, yes:  Specificity of the case definition; expand if necessary − Some of the details, not:  The precise number of SNPs will vary and the tree could look different depending on how the analysis is done, the pipeline, the reference genome  The number of SNPs we should consider closely related may be different for different pathogens, and maybe even affected by the reservoir Final Thoughts • The power of WGS in epidemiology is readily apparent − Better defines which cases are part of an outbreak • Using WGS for cluster identification is not simple and one ‐ directional − i.e., “Related” sequences may or may not represent a common source outbreak; may differ by serotype − Epidemiology is extremely important in informing what we should consider a cluster Final Thoughts (cont.) • wgMLST − New strategies for communication will have to be developed − Standardization of reports from lab to epi 13

11/28/2017 Final Thoughts (cont.) • Epi ‐ lab collaboration and on ‐ going communication are key Acknowledgements Minnesota Dept. of Health, Minnesota Dept. of Health, Foodborne Diseases Unit Public Health Laboratory Angie Jones Taylor Kirk Smith Dave Boxrud Dana Eikmeier Xiong (Sean) Wang Marijke Decuir Megan Nichols Josh Rounds Victoria Lappi Amy Saupe Enterics lab, PFGE lab, & Sequencing Stephanie Meyer Core Team Diarrhea New York Dept. of Health Federal Partners Wadsworth Center CDC Bill Wolfgang FDA Pascal Lapierre NCBI Kim Musser Samantha Wirth Bioinformatics Core 14