Lab Matters Summer 2018 - Page 56

APHL 2018 Annual Meeting Poster Abstracts Food Safety Increased Use of ‘Council to Improve Foodborne Outbreak Response’ Products through Branding and Messaging S. Shea 1 , L. Granen 1 , R. Atkinson-Dunn 2 ; 1 Association of Public Health Laboratories, Silver Spring, MD, 2 Utah State Public Health Laboratory, Taylorsville, UT The Council to Improve Foodborne Outbreak Response (CIFOR) was founded in 2006. The Council is a multidisciplinary collaboration of national associations and federal agencies working together to improve methods for detecting, investigating, controlling and preventing foodborne disease outbreaks. APHL is a founding member of CIFOR and our representatives have contributed to CIFOR guidelines, processes and products that facilitate improved foodborne outbreak response. CIFOR is one of the only entities with representation across the entire spectrum of organizations, jurisdictions, geographies and professions that focuses on foodborne outbreak response. With other member organizations, we collectively represent epidemiology programs, environmental health programs, public health laboratories and regulatory agencies at the local, state and federal levels. The food industry is represented on the Industry Workgroup. All of these audiences are considered when creating CIFOR promotional materials. Although CIFOR guidelines, processes and products are widely available, the member organizations recognize a need to increase the use of these tools nationwide. The Promote Development Team, one of 4 core development teams within CIFOR, is tasked with promoting the use of CIFOR products and promoting CIFOR as a credible source of information for use by decision-makers. In 2017, The Promote Development Team created a CIFOR Marketing Plan to ensure consistency in our branding and messaging, coordinate marketing communications, be proactive in building awareness of the overall CIFOR mission/vision and innovatively market CIFOR. Activities include, building a new CIFOR website, designing branded templates for CIFOR documents and presentations, providing suggested web content for member organizations, disseminating end-user testimonials on the impact of CIFOR, distributing canned social media, blog, newsletter and email content on CIFOR and its products, presenting about CIFOR at annual meetings and conferences and launching a CIFOR app. Examples of these activities are highlighted in this poster. Presenters: Shari Shea, MHS, MT (ASCP), Association of Public Health Laboratories, Silver Spring, MD, Phone: 240.485.2777, Email: sharon.shea@aphl.org and Robyn Atkinson-Dunn, PhD, HCLD/PHLD, Utah Public Health Laboratory, Taylorsville, UT, Phone: 801.965.2424, Email: rmatkinson@utah.gov Using Whole Genome Sequencing Data for Salmonella Serotype Prediction A. Stewart, Texas Department of State Health Services, Austin, TX Texas Department of State Health Services (DSHS) Laboratory Service Section typically receives 3,000–3,500 Salmonella isolates each year. Currently, the laboratory performs serotyping on all Salmonella isolates received using molecular or conventional serotyping techniques, which are time consuming and costly. Since 2015, DSHS has been performing whole genome sequencing 54 LAB MATTERS Summer 2018 (WGS) on foodborne bacteria and thus generating an enormous amount of sequencing data waiting to be fully utilized. The free SeqSero software allows identification of Salmonella serotypes from raw sequencing reads. To determine the utility of using SeqSero to predict Salmonella serotype, we are performing a retrospective study comparing SeqSero analysis with our standard serotyping methods. SeqSero will be used to infer the serotypes from a pool of 2,300 Salmonella isolates sequenced in the lab between 2015 and 2017. The isolates we selected for this study are from PFGE clusters/potential outbreaks. These isolates were collected from routine Salmonella surveillance activities of food/food manufacture swabs. We predict a high concordance between SeqSero prediction and our standard methods. The raw sequence reads will be analyzed using the FDA/CFSAN GalaxyTrakr software. GalaxyTrakr utilizes the open-source Galaxy platform as a packaging tool, GUI and integrates SeqSero and other analysis tools to host a runtime environment for bioinformatics projects. The analysis is performed on the GalaxtTrakr. org website. This project is on-going. Our preliminary results indicated a good concordance between SeqSero and our standard methods. We will expand our study to include up to 800 isolates and will present a final report with complete information at the APHL Annual Conference. For the isolates which produce discrepancies between SeqSero and standard methods, we will examine the results to understand the limitations of SeqSero based serotype determination. The DSHS laboratory hopes to replace our current serotyping methods and implement routine sequencing of Salmonella isolates with WGS. This study will confirm the practicality of using SeqSero in serotype prediction and help establish criteria for when Seqsero should not be utilized for prediction. Using SeqSero for serotyping will shorten the total test time, reduce labor and cost and make outbreak investigation more efficient. Presenter: Alesha Stewart, PhD, APHL/CDC ARLN Fellow, Texas Department of State Health Services, Austin, TX, Phone: 512.776.7591, Email: alesha.stewart@dshs.texas.gov Laboratory Confirmation of Enterotoxigenic Escherichia coli Detected by Culture-Independent Diagnostic Tests — Minnesota, 2015–2017 R. Fowler 1 , D. Boxrud 2 , E. Cebelinski 2 , S. Vetter 2 ; 1 Centers for Disease Control and Prevention, Atlanta, GA, 2 Minnesota Public Health Laboratory, St. Paul, MN Enterotoxigenic Escherichia coli (ETEC) is a common cause of travelers’ diarrhea and can cause foodborne disease outbreaks. Historically, clinical laboratories have not tested for ETEC because diagnostic methods were not available. Increasingly, diagnostic testing is shifting from culture-based methods to culture- in \[[XYۛX\ QKX[]XUP˂]\[X[\[[\Hۙ\X][ۈ\[\\]]H܈XXšX[\Z[[H[Y[YX][ۈوUP]XZUP\˜H\ܝXH][[Z[\K\]Z\[[X[Xܘ]ܚY\XZ]\]\܈[X[[\\ˈ[\ݙHUPXܘ]ܞB\Z[[KHۙ\YYUP[Q \]]H[\\[YX\\YH\X][ۈ]Y[UPۙ\X][ۈ[\][ۂو[\H[ܝ \[ Mx$̌ MZ[\HXXX[Xܘ]ܞH ST HXZ]Y X[[\\]]H܈UP˜HHQ [P\^H0H[[ [ٚ\HXYۛXK ^\Y\X[ۋ[[ۙHM[\\[XY܈UP\][ۋ JH\H]]KUPԈ[[\Hۙ\X][ۈ]\\B܈UP\]]HQ[\\[\H\[[ۈBXXX[XTT ܙ