Long-read sequencing data from pure cultures of <i>Escherichia coli</i> O157:H7 and ground beef inoculated with <i>E. coli</i> O157:H7

<p dir="ltr">Foodborne pathogens are a significant cause of illness and infection with Shiga toxin-producing <i>Escherichia coli</i> (STEC) has the potential to produce life-threatening complications. The current methods to identify STEC in meat involve culture-based, molecular, and proteomic assays and take at least four days to complete. This time could be reduced by using long-read whole genome sequencing to identify foodborne pathogens. Therefore, the goal of this project was to evaluate using long-read sequencing to detect STEC in ground beef. The objectives of the project included: establishing optimal sequencing parameters, determining the limit of detection of all STEC virulence genes of interest in pure cultures and spiked ground beef, and evaluating selective sequencing to enhance STEC detection in ground beef. Sequencing libraries were run on Oxford Nanopore Technologies’ MinION sequencer. Optimal sequencing output was obtained using the default parameters in MinKNOW, except for setting the minimum read length to 1 kb. All genes of interest (<i>eae</i>, <i>stx1</i>, <i>stx2</i>, <i>fliC</i>, <i>wzx</i>, <i>wzy</i>, <i>rrsC</i>) were detected in DNA extracted from STEC pure cultures within 1 hour of sequencing, and 30X coverage was obtained within 2 hours. All virulence genes were confidently detected in STEC DNA quantities as low as 12.5 ng. In STEC inoculated ground beef, software-controlled selective sequencing improved virulence gene detection; however, several virulence genes were not detected due to high bovine DNA concentrations in the samples. Growth enrichment of inoculated meat samples in mTSB resulted in a 100-fold increase in virulence gene detection as compared to the unenriched samples. The results of this project suggest that further development of long-read sequencing protocols may result in a faster, less labor-intensive method to detect STEC in ground beef. The sequencing data from this project has been uploaded.</p>

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