In silico prediction and experimental evaluation of potential siRNAs against SARS-CoV-2 inhibition in Vero E6 cells.

Objective: The acute cases of pneumonia (COVID-19) were first reported from China in December 2019, and the pathogen was identified as SARS-CoV-2. Currently, many vaccines have been developed against this virus by using multiple genes, applying different platforms, and used for the vaccinations of the human population. Spike protein genes play an important role in host cell attachment and viral entry and have been extensively used for the development of vaccine and antiviral therapeutics. Short interfering RNA is also known as silencing RNA and contribute a significant role to regulate the expression of a specific gene. By using this technology, virus inhibition has been demonstrated against many viral diseases. Methods: In this work, we have reported the Insilico prediction, designing, and experimental validation of siRNAs antiviral potency against SARS-CoV-2-S-RBD. The siDirect 2.0 was selected for siRNAs prediction, and secondary structure was predicted by RNAfold while the HNADOCK was used for molecular docking analysis and specific binding of siRNAs to the selected target. We have used and evaluated four siRNAs for cellular toxicity and determination of antiviral efficiency based on the Ct value of q-real-time PCR in Vero E6 cells. Results: Based on the experimental evaluation and analysis of results from generated data, we observed that there is no cytotoxicity for any tested siRNAs in Vero E6 cells. Total four siRNA were filtered out from twenty-one siRNAs following the strict selection and scoring criteria. The better antiviral efficiency was observed in 3rd siRNAs based on the Ct value of q-real-time PCR. The results that emerged from this study encouraged us to validate the efficiency of these siRNAs in multiple cells by using alone and in a combination of two or more siRNAs to inhibit the SARS-CoV-2 proliferation. Conclusion: The Insilico prediction, molecular docking analysis provided the selection of better siRNAs. Based on the experimental evaluation only 3rd siRNA was found to be more effective than others and showed better antiviral efficiency. These siRNAs should also be evaluated in other cell lines either separately or in combination against SARS-CoV-2 to determine their antiviral efficiency.

Effect of insilico predicted and designed potential siRNAs on inhibition of SARS-CoV-2 in HEK-293 cells.

Objectives: The COVID-19 was identified for the first time from the sea food market, Wuhan city, China in 2019 and the pathogenic organism was identified as SARS-CoV-2. Currently, this virus has spread to 223 countries and territories and known as a serious issue for the global human community. Many vaccines have been developed and used for immunization. Methods: We have reported the insilico prediction, designing, secondary structure prediction, molecular docking analysis, and in vitro assessment of siRNAs against SARS-CoV-2. The online bioinformatic approach was used for siRNAs selection and designing. The selected siRNAs were evaluated for antiviral efficacy by using Lipofectamine 2000 as delivery agent to HEK-293 cells. The MTT assay was used for cytotoxicity determination. The antiviral efficacy of potential siRNAs was determined based on the Ct value of q-RT-PCR and the data analysis was done by Prism-GraphPad software. Results: The analyzed data resulted in the selection of only three siRNAs out of twenty-six siRNAs generated by online software. The secondary structure prediction and molecular docking analysis of siRNAs revealed the efficient binding to the target. There was no cellular toxicity observed in the HEK-293 cells at any tested concentrations of siRNAs. The purification of RNA was completed from inoculated cells and subjected to q-RT-PCR. The highest Ct value was observed in siRNA 3 than the others. The results offered valuable evidence and invigorated us to assess the potency of siRNAs by using alone or in combination in other human cells. Conclusion: The data generated from this study indicates the significance of in silico prediction and narrow down the potential siRNA' against SARS-CoV-2, and molecular docking investigation offered the effective siRNAs binding with the target. Finally, it is concluded that the online bioinformatics approach provided the prediction and selection of siRNAs with better antiviral efficacy. The siRNA-3 was observed to be the best for reduction of viral RNA in cells.

RNAi-mediated siRNA sequences to combat the COVID-19 pandemic with the inhibition of SARS-CoV2.

The outbreak of the COVID-19 pandemic has cost five million lives to date, and was caused by a positive-sense RNA virus named SARS-CoV2. The lack of drugs specific to SARS-CoV2, leads us to search for an effective and specific therapeutic approach. Small interfering RNA (siRNA) is able to activate the RNA interference (RNAi) pathway to silence the specific targeted gene and inhibit the viral replication, and it has not yet attracted enough attention as a SARS-CoV2 antiviral agent. It could be a potential weapon to combat this pandemic until the completion of full scale, effective mass vaccination. For this study, specific siRNAs were designed using a web-based bioinformatics tool (siDirect2.0) against 14 target sequences. These might have a high probability of silencing the essential proteins of SARS-CoV2. such as: 3CLpro/Mpro/nsp5, nsp7, Rd-Rp/nsp12, ZD, NTPase/HEL or nsp13, PLpro/nsp3, envelope protein (E), spike glycoprotein (S), nucleocapsid phosphoprotein (N), membrane glycoprotein (M), ORF8, ORF3a, nsp2, and its respective 5' and 3'-UTR. Among these potential drug targets, the majority of them contain highly conserved sequences; the rest are chosen on the basis of their role in viral replication and survival. The traditional vaccine development technology using SARS-CoV2 protein takes 6-8 months; meanwhile the virus undergoes several mutations in the candidate protein chosen for vaccine development. By the time the protein-based vaccine reaches the market, the virus would have undergone several mutations, such that the antibodies against the viral sequence may not be effective in restricting the newly mutated viruses. However, siRNA technology can make sequences based on real time viral mutation status. This has the potential for suppressing SARS-CoV2 viral replication, through RNAi technology.

Computational prediction of potential siRNA and human miRNA sequences to silence orf1ab associated genes for future therapeutics against SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) is an ongoing pandemic caused by an RNA virus termed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 possesses an almost 30kbp long genome. The genome contains open-reading frame 1ab (ORF1ab) gene, the largest one of SARS-CoV-2, encoding polyprotein PP1ab and PP1a responsible for viral transcription and replication. Several vaccines have already been approved by the respective authorities over the world to develop herd immunity among the population. In consonance with this effort, RNA interference (RNAi) technology holds the possibility to strengthen the fight against this virus. Here, we have implemented a computational approach to predict potential short interfering RNAs including small interfering RNAs (siRNAs) and microRNAs (miRNAs), which are presumed to be intrinsically active against SARS-CoV-2. In doing so, we have screened miRNA library and siRNA library targeting the ORF1ab gene. We predicted the potential miRNA and siRNA candidate molecules utilizing an array of bioinformatic tools. By extending the analysis, out of 24 potential pre-miRNA hairpins and 131 siRNAs, 12 human miRNA and 10 siRNA molecules were sorted as potential therapeutic agents against SARS-CoV-2 based on their GC content, melting temperature (Tm), heat capacity (Cp), hybridization and minimal free energy (MFE) of hybridization. This computational study is focused on lessening the extensive time and labor needed in conventional trial and error based wet lab methods and it has the potential to act as a decent base for future researchers to develop a successful RNAi therapeutic.