A catalytically and genetically optimized β-lactamase-matrix based assay for sensitive, specific, and higher throughput analysis of native henipavirus entry characteristicsReport as inadecuate




A catalytically and genetically optimized β-lactamase-matrix based assay for sensitive, specific, and higher throughput analysis of native henipavirus entry characteristics - Download this document for free, or read online. Document in PDF available to download.

Virology Journal

, 6:119

First Online: 31 July 2009Received: 03 July 2009Accepted: 31 July 2009

Abstract

Nipah virus NiV and Hendra virus HeV are the only paramyxoviruses requiring Biosafety Level 4 BSL-4 containment. Thus, study of henipavirus entry at less than BSL-4 conditions necessitates the use of cell-cell fusion or pseudotyped reporter virus assays. Yet, these surrogate assays may not fully emulate the biological properties unique to the virus being studied. Thus, we developed a henipaviral entry assay based on a β-lactamase-Nipah Matrix βla-M fusion protein. We first codon-optimized the bacterial βla and the NiV-M genes to ensure efficient expression in mammalian cells. The βla-M construct was able to bud and form virus-like particles VLPs that morphologically resembled paramyxoviruses. βla-M efficiently incorporated both NiV and HeV fusion and attachment glycoproteins. Entry of these VLPs was detected by cytosolic delivery of βla-M, resulting in enzymatic and fluorescent conversion of the pre-loaded CCF2-AM substrate. Soluble henipavirus receptors ephrinB2 or antibodies against the F and-or G proteins blocked VLP entry. Additionally, a Y105W mutation engineered into the catalytic site of βla increased the sensitivity of our βla-M based infection assays by 2-fold. In toto, these methods will provide a more biologically relevant assay for studying henipavirus entry at less than BSL-4 conditions.

Electronic supplementary materialThe online version of this article doi:10.1186-1743-422X-6-119 contains supplementary material, which is available to authorized users.

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Author: Mike C Wolf - Yao Wang - Alexander N Freiberg - Hector C Aguilar - Michael R Holbrook - Benhur Lee

Source: https://link.springer.com/







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