P3-75 In silico Adaptation of EDNA (E-probe Diagnostic Nucleic Acid Analysis) for Detection of Foodborne Pathogens

Wednesday, July 31, 2013
Exhibit Hall (Charlotte Convention Center)
Trenna Blagden, Oklahoma State University, Stillwater, OK
William Schneider, U.S. Department of Agriculture-ARS, Fort Detrick, MD
Ulrich Melcher, Oklahoma State University, Stillwater, OK
Jacqueline Fletcher, Oklahoma State University, Stillwater, OK
Introduction: Increasing consumption of fresh produce over the past decade has been accompanied by increasing numbers of foodborne illness outbreaks linked to contaminated product. A variety of pathogenic microbes can contaminate fresh produce at different points within the production process. The development of a pathogen detection method capable of providing a comprehensive microbial profile of a complex food sample would significantly enhance our capability to respond to outbreaks.

Purpose: Adapt a bioinformatics pipeline strategy, EDNA, to generate inclusive pathogen profiles using metagenomic data from complex food samples.

Methods: Fecal coliforms, strains of Escherichia coli O157:H7, pathogenic E. coli strains, and Shiga toxin sequence-specific electronic queries (e-probes) were developed and tested against sixteen mock sample databases (MSDs) containing 10,000 genome segments of both pathogen and a model plant host, Vitus vinifera (grapevine), using BLASTn parsed with an e-value of 1x10-3. Decoy e-probe sets, designed to determine background positive levels, were developed and queried against the MSDs for statistical analysis.  Precision (true positives/(false + true positives)) was calculated for all e-probe sets, and statistical confidence in positive calls was assessed via t-test.

Results: Optimum E-probe length was established by calculating precision. When microbe abundance in samples exceeded 0.5%, precision of E. coli e-probes of 20 and 40 nucleotides averaged 99% and 99.2%, respectively. The EDNA e-probes (20-40 nt) successfully detected E. coli at higher concentrations (> 0.5% abundance), and the Shiga toxin e-probes (80 nt) allowed detection when the toxin was > 1.0% abundance (precision = 100%). Longer E. colie-probes also identified the pathogen at concentrations above 5% abundance, but t-test statistical confidences were limited by the total number of e-probes available, despite the fact that precision was 100%.

Significance: This bioinformatics approach to microbial detection has the potential for simultaneous detection of all foodborne pathogens present in a food sample.