Towards Next-generation Sequencing Analytics for Foodborne RNA Viruses: Examining the Effect of RNA Input Quantity and Viral RNA Purity

Detection and identification of viruses in food samples are technically challenging due largely to the low viral copy number in contaminated food items, and the lack of effective culture enrichment methods that are amenable to regulatory applications for many of the common foodborne viruses. Using an Illumina MiSeq platform and two hepatitis A virus (HAV) cell-culture adapted strains as a representative enteric virus species, this study examined the limits of single-stranded RNA (ssRNA) viral detection following next-generation sequencing without pre-amplification of the viral genome. Complete viral genome sequences were obtained from HAV samples of varying purities and with an input as low as 2ng total RNA containing 1.4×10(5) copies of viral RNA. In addition, single nucleotide variations were reproducibly detected over the range of concentrations examined, and their identity confirmed by alternate sequencing technology. In summary, next-generation sequencing technology has the potential for sensitive detection/identification of a viral genome at a low copy number. This study provides a benchmark for metagenomic sequencing application as is required for virus detection in complex food matrices using a culture-independent diagnostic approach.