Design of antiviral AGO2-dependent short hairpin RNAs

The increasing emergence and re-emergence of RNA virus outbreaks underlines the urgent need to develop effective antivirals. RNA interference (RNAi) is a sequence-specific gene silencing mechanism that is triggered by small interference RNAs (siRNAs) or short hairpin RNAs (shRNAs), which exhibit significant promise for antiviral therapy. AGO2-dependent shRNA (agshRNA) generates a single-stranded guide RNA effector and presents significant advantages over traditional siRNA and shRNA. In this study, we applied a logistic regression algorithm to a previously published chemically siRNA efficacy dataset and built a machine learning-based algorithm with high predictive power. Using this algorithm, we designed siRNA sequences targeting diverse RNA viruses, including human enterovirus A71 (EV71), Zika virus (ZIKV), dengue virus 2 (DENV2), mouse hepatitis virus (MHV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and developed them into agshRNAs according to the rule of agshRNA design. These agshRNAs inhibited viral replication in infected cells and their efficiencies of antiviral effects displayed a consistent trend with the score ranking of siRNA sequences predicted by the algorithm. Using the agshRNA targeting EV71 as an example, we showed that the anti-EV71 effect of agshRNA was more potent compared with the corresponding siRNA and shRNA. Moreover, the antiviral effect of agshRNA is dependent on AGO2-processed guide RNA, which can load into the RISC. We also confirmed the antiviral effect of agshRNA in vivo. Together, this work develop a novel approach that combines machine learning-based algorithm and agshRNA design to custom design antiviral agshRNAs with high efficiency.