Duplex RT-qPCR for the detection of hepatitis E virus in water, using a process control

Current methods for detecting human hepatitis E virus (HEV) in water samples face limitations due to low infectious doses and the absence of reliable cell culture techniques. Reverse transcription quantitative real-time PCR (RT-qPCR) is widely adopted for RNA virus detection, but requires a process control to ensure accurate results. While HEV genotype III is emerging as a public health concern in industrialized nations, routine monitoring remains challenging. The lack of a standardized, sensitive, and rapid detection method for HEV in water hinders effective surveillance. Prior research has shown that HEV contamination occurs through food and water, but no prior work had resolved the issue of false-negative results due to technical inefficiencies. This gap motivated the development of a method that integrates virus concentration and process control in a single workflow. The absence of a reliable process control in existing protocols limits diagnostic confidence. This study addresses the need for a robust and efficient detection system that can be used in routine diagnostic settings.

The study aimed to develop a rapid and sensitive method for detecting HEV in water samples. The focus was on addressing the limitations of current detection methods, particularly the lack of process control to monitor false-negative results. The method integrates virus concentration and RT-qPCR amplification in a streamlined workflow. The study sought to improve the reliability of HEV detection in routine laboratory settings. The specific problem addressed was the difficulty in detecting low HEV concentrations in water due to technical variability. The motivation stemmed from the increasing public health concerns related to HEV contamination in water. The study aimed to provide a solution that is both sensitive and practical for diagnostic use. The goal was to ensure accurate detection by incorporating a reliable process control into the detection protocol.

The method involved concentrating HEV from water samples by filtration on membrane filters. Adsorbed viruses were directly lysed from the filters before RNA extraction. RT-qPCR amplification followed the RNA extraction step. A one-step duplex RT-qPCR was developed to detect HEV RNA in parallel with a process control. Murine norovirus (MNV-1) was used as the process control to monitor false-negative results. The method was tested on bottled and tap water samples to determine its effectiveness. The filtration and lysis steps were optimized to maximize virus recovery. The RT-qPCR was designed to detect HEV and monitor the process control simultaneously.

The developed method achieved a detection limit of 700 to 3500 HEV genome copies per 0.5L of bottled water. In tap water samples, the detection limit was 3500 HEV genome copies per 0.5L. The use of MNV-1 as a process control improved the reliability of the results. The method successfully detected HEV in low-concentration water samples. The process control effectively monitored false-negative results in the detection workflow. The RT-qPCR amplification was efficient and consistent across all tested samples. The filtration and lysis steps provided sufficient virus recovery for accurate detection. The method proved to be a valuable tool for routine diagnostic laboratories.

The study demonstrated that the developed method is a reliable and sensitive approach for detecting HEV in water. The use of MNV-1 as a process control was effective in monitoring false-negative results. The method's detection limits were suitable for routine monitoring in diagnostic settings. The streamlined workflow of virus concentration and RT-qPCR amplification improved detection efficiency. The authors propose that this method can be used in routine diagnostic laboratories for HEV surveillance. The integration of a process control enhances the reliability of the detection process. The results suggest that the method is suitable for both bottled and tap water samples. The study supports the use of this method for improving HEV detection in environmental monitoring.