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Published on: June 26, 2014
Assessment of Hepatitis E Virus RNA Detection in Meat Samples: Optimization of Pre-analytical Conditions
Guadalupe Di Cola1,2, Anabella C Fantilli3,4, Gonzalo Rodríguez-Lombardi5
1Facultad de Ciencias Médicas, Instituto de Virología "Dr. J. M. Vanella", Universidad Nacional de Córdoba, Enfermera Gordillo Gómez S/N, Ciudad Universitaria, 5016, Córdoba, Argentina. guadalupedicola@unc.edu.ar.
Abstract:
Hepatitis E virus (HEV) is primarily transmitted via the fecal-oral route and is considered an anthropozoonosis. Genotypes with zoonotic potential (mainly HEV-3 and HEV-4) can be transmitted through the consumption of raw or undercooked pork, wild boar, deer meat, or processed products. This study aims to explore methodologies for processing meat samples to establish a protocol for HEV detection in meat. The analysis of pre-analytical conditions involved comparing homogenization with PBS versus TRIzol, comparing tissue disruption methods (ultra-turrax versus mortar and pestle), and assessing nucleic acid extraction techniques (spin columns and magnetic beads) across three types of artificially contaminated meat matrices: pork, salmon (fish-meat), and salami. Each test included a process control virus (PP7) and an HEV transcript. Molecular detection was performed via RT-qPCR. Results indicated that TRIzol provided better recovery rates for homogenization, while spin columns were the most effective option for RNA extraction. Both the ultra-turrax homogenizer and the mortar-pestle methods were effective for pork and fish-meat homogenization, while the use of the UT yielded superior results for salami. HEV recovery rates were 36.7%, 26.3%, and 34.1% for salami, salmon, and pork meat, respectively. In conclusion, we reached a simple and reliable protocol for the detection of RNA-HEV from three meat matrices. This method, which includes homogenization with TRIzol, mechanical tissue disruption, and RNA extraction using spin columns followed by real-time PCR, can be applied in future studies to evaluate HEV prevalence in food sources and contribute to the discussion about HEV detection methodologies.

