Detection of HEV in naturally infected swine from central Argentina by an optimized HEV/MS2 duplex RT‐qPCR. (1st July 2019)

• Record Type: Journal Article
• Authors: Marziali, Federico; Acosta, Julian; Bolatti, Elisa; Mirazo, Santiago; Skejich, Patricia; Silva, Patricia; Brassard, Julie; Costaguta, Alejandro; Gardiol, Daniela; Cavatorta, Ana Laura
• Is Part Of: Zoonoses and public health. Volume 66:Number 7(2019)
• Page Start: 729
• Page End: 738
• Publication Date: 2019-07-01
• Subjects: Hepatitis E virus -- internal control -- RT‐qPCR -- swine; Zoonoses -- Periodicals; Public health -- Periodicals; 636.0896959
• DOI: 10.1111/zph.12620
• Languages: English
• ISSNs: 1863-1959

Abstract

Abstract: Hepatitis E virus (HEV) is currently considered as a global health concern due to the recognition of its zoonotic transmission to humans, mainly from swine, and its association with the development of severe cases of hepatitis in human risk populations. The lack of updated data on HEV state of infection in swineherds of Argentina, and the necessity of robust technologies for its detection in complex biological samples, positions HEV as an emerging issue in public health. Here, we have optimized a RT‐qPCR with internal control for a more precise and accurate HEV RNA detection in swine stool samples. We implemented this optimized molecular tool to analyse the current epidemiological scenario of HEV infection in swine from the core region of commercial activity of Argentina. A total of 135 stool samples were collected from 16 different farms and tested for HEV presence, resulting in 11 positive cases (8.1%). Phylogenetic analysis demonstrated that all of them correspond to HEV genotype 3 and that different subtypes circulate in the region. Moreover, two of the detected strains presented a high nucleotide similarity with a previously identified isolate from human sewage discharges, suggesting the zoonotic transmission of HEV to humans. Collectively, this work provides a better understanding of HEV epidemiology in Argentina while contributes to the improvement of HEV detection technologies.