A parallel genome-wide RNAi screening strategy to identify host proteins important for entry of Marburg virus and H5N1 influenza virusReport as inadecuate




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Virology Journal

, 12:194

Emerging viruses

Abstract

BackgroundGenome-wide RNAi screening has been widely used to identify host proteins involved in replication and infection of different viruses, and numerous host factors are implicated in the replication cycles of these viruses, demonstrating the power of this approach. However, discrepancies on target identification of the same viruses by different groups suggest that high throughput RNAi screening strategies need to be carefully designed, developed and optimized prior to the large scale screening.

MethodsTwo genome-wide RNAi screens were performed in parallel against the entry of pseudotyped Marburg viruses and avian influenza virus H5N1 utilizing an HIV-1 based surrogate system, to identify host factors which are important for virus entry. A comparative analysis approach was employed in data analysis, which alleviated systematic positional effects and reduced the false positive number of virus-specific hits.

ResultsThe parallel nature of the strategy allows us to easily identify the host factors for a specific virus with a greatly reduced number of false positives in the initial screen, which is one of the major problems with high throughput screening. The power of this strategy is illustrated by a genome-wide RNAi screen for identifying the host factors important for Marburg virus and-or avian influenza virus H5N1 as described in this study.

ConclusionsThis strategy is particularly useful for highly pathogenic viruses since pseudotyping allows us to perform high throughput screens in the biosafety level 2 BSL-2 containment instead of the BSL-3 or BSL-4 for the infectious viruses, with alleviated safety concerns. The screening strategy together with the unique comparative analysis approach makes the data more suitable for hit selection and enables us to identify virus-specific hits with a much lower false positive rate.

Han Cheng, Katie Koning and Aileen O’Hearn contributed equally to this work.

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Author: Han Cheng - Katie Koning - Aileen O’Hearn - Minxiu Wang - Emily Rumschlag-Booms - Elizabeth Varhegyi - Lijun Rong

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







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