Objectives:
Objective 1: Adapt existing RT-qPCR HAV assays to multiplex RT-dPCR format and assess performance for quantification and detection of HAV RNA.
Objective 2: Assess the HAV RNA extraction efficiencies of two RNA extraction kits compatible with an automated workflow.
Objective 3: Compare Nanotrap concentration to azo-dye pretreatment for the detection and quantification of HAV RNA within intact virus capsids.
Objective 4: Assess the efficiency and performance characteristics of the complete workflow for the recovery and quantification of HAV RNA within intact virus capsids.
Abstract: Currently screening of soft fruits for hepatitis A virus (HAV) is performed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). While RT-qPCR can detect extremely low amounts of RNA, it cannot discriminate between residual HAV genetic material (RNA), which is harmless, and infectious HAV virions due to the long persistence of RNA compared to infectious virions. This exposes those in the produce supply chain to ongoing risks of large economic losses associated with product recalls premised on RT-qPCR screening. In alignment with the 2024 CPS Research Priority 13a “Screening assay for HAV”, herein, we propose the development of a novel workflow using functionalized hydrogel nanoparticles (Nanotrap Microbiome A particles), automated RNA isolation, and RT-digital PCR to concentrate intact virus capsids from fruit wash eluate, extract HAV RNA from the resulting concentrate, and detect and quantify the RNA template. The method will be developed and validated in a step-wise approach from the RT-dPCR analytical endpoint upstream to the Nanotrap concentration using Armored HAV RNA (RNA within capsid) and in vitro transcribed RNA (free RNA) certified quantitative control materials. Quantitative and qualitative performance of the whole process workflow will be assessed by determining the method’s 95% limit of detection, limit of quantification, and recovery efficiency for Armored HAV RNA and the exclusion efficiency for free HAV RNA. If successful, the developed method would be compatible with automated platforms for concentration and extraction greatly increasing screening throughput while simultaneously decreasing the times to results and analytical costs and increasing specificity for potentially infectious HAV virions. The workflow would deliver benefits throughout the soft fruit supply chain including decreased economic losses to growers, buyers, and sellers due to false positive screening results, improved risk management data and decision making for regulators, and improved product safety for consumers.