Quasi-metagenomics and realtime sequencing aided detection and subtyping of Salmonella enterica from food samples

Summary

We developed a system that combines detection and strain-level identification of Salmonella from food samples in a fraction of time taken by traditional methods. This is the first reported food safety application of genome sequencing on a portable device.

Situation

Quick and accurate pathogen detection and fingerprinting is critical and potentially lifesaving in foodborne outbreak responses. Currently, detecting and fingerprinting foodborne pathogens are separate processes that can take days or weeks to finish.

Response

Our study demonstrated substantial acceleration of the respective process through IMS-MDA and real-time nanopore sequencing. In one example, the combined use of the two methods delivered a less than 24 h turnaround time from a Salmonella-contaminated lettuce sample to strain-level identification of the pathogen, compared with that of ~2 weeks by traditional methods. Improved efficiency like this is important for further expanding the use of whole genome and metagenomics sequencing in microbial analysis of food. Our results suggest the potential of the quasi-metagenomics approach in areas where rapid detection and subtyping of foodborne pathogens is important, such as foodborne outbreak response and precision tracking and monitoring of foodborne pathogens in production environments and supply chains.

Impact

The study was published by Applied and Environmental Microbiology (AEM), one of the leading journals in the field. It was featured by AEM Spotlight. The work was reported by UGA Today and Food Safety News among other media and social media outlets. I was invited to talk about this study at a symposium at 2018 International Association for Food Protection Annual Meeting and an international symposium on food microbiome at Jiangnan University in China.

State Issue

Food Safety & Quality

Details

• Year: 2018
• Geographic Scope: National
• County: Spalding
• Program Areas:
  • Agriculture & Natural Resources