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The impact of HIV-1 genetic diversity on the efficacy of a combinatorial RNAi-based gene therapy

Abstract

A hurdle for human immunodeficiency virus (HIV-1) therapy is the genomic diversity of circulating viruses and the possibility that drug-resistant virus variants are selected. Although RNA interference (RNAi) is a powerful tool to stably inhibit HIV-1 replication by the expression of antiviral short hairpin RNAs (shRNAs) in transduced T cells, this approach is also vulnerable to pre-existing genetic variation and the development of viral resistance through mutation. To prevent viral escape, we proposed to combine multiple shRNAs against important regions of the HIV-1 RNA genome, which should ideally be conserved in all HIV-1 subtypes. The vulnerability of RNAi therapy to viral escape has been studied for a single subtype B strain, but it is unclear whether the antiviral shRNAs can inhibit diverse virus isolates and subtypes, including drug-resistant variants that could be present in treated patients. To determine the breadth of the RNAi gene therapy approach, we studied the susceptibility of HIV-1 subtypes A–E and drug-resistant variants. In addition, we monitored the evolution of HIV-1 escape variants. We demonstrate that the combinatorial RNAi therapy is highly effective against most isolates, supporting the future testing of this gene therapy in appropriate in vivo models.

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Acknowledgements

We thank the NIH AIDS Research and Reference Program (Division of AIDS, NIAID, NIH) for providing HIV-1 isolates. We are grateful to the participants of the Amsterdam Cohort Studies, collaboration between the Amsterdam Health Service, the Academic Medical Center of the University of Amsterdam and Sanquin Blood Supply Foundation. We thank Berend Hooibrink (AMC Cell Biology) for live-cell sorting. RNAi research in the Berkhout lab is sponsored by NWO-CW (Top Grant) and ZonMw (Translational Gene Therapy Grant).

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Herrera-Carrillo, E., Berkhout, B. The impact of HIV-1 genetic diversity on the efficacy of a combinatorial RNAi-based gene therapy. Gene Ther 22, 485–495 (2015). https://doi.org/10.1038/gt.2015.11

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