RNA interference (RNAi) is a regulatory mechanism of gene expression mediated by small RNAs. By using the RNAi technique, exogenous double-stranded RNA (dsRNA) designed to target mRNA, suppresses target gene expression levels in plants. In this study, we adopted the RNAi mechanism as a tool to protect plants from viruses. We designed and synthesized several dsRNAs targeting the pepper mottle virus (PepMoV) genes HC-Pro and NIb. When used on Nicotiana benthamiana plants, these dsRNAs protected the plant against viral infection over a specific period. By optimizing dsRNA and virus injection time, the protection efficiency of dsRNA by targeting virus genes could be maximized. It seems that exogenous dsRNA-derived RNA-induced silencing complex was able to defend the host against viral infection instantly. Furthermore, each dsRNA designed to target different regions within a transcript had varying levels of effects on virus survival in the host plants. When targeting the middle part of both the NIb and HC-Pro genes using the dsRNAs, the highest viral growth inhibitory effect was observed. An RLM-5′ RACE was performed using plant leaves infected with PepMoV after dsRNA treatment and it was observed that most of the mRNA cleavages occurred close to the 3′ part within the dsRNA target position on the mRNA. These results suggest that the dsRNA tool can be used as a plant vaccine platform for crop protection.
African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.
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