SARS-CoV-2-encoded small RNAs are able to repress the host expression of SERINC5 to facilitate viral replication.

Serine incorporator protein 5 (SERINC5) is a key innate immunity factor that operates in the cell to restrict the infectivity of certain viruses. Different viruses have developed strategies to antagonize SERINC5 function but, how SERINC5 is controlled during viral infection is poorly understood. Here, we report that SERINC5 levels are reduced in COVID-19 patients during the infection by SARS-CoV-2 and, since no viral protein capable of repressing the expression of SERINC5 has been identified, we hypothesized that SARS-CoV-2 non-coding small viral RNAs (svRNAs) could be responsible for this repression. Two newly identified svRNAs with predicted binding sites in the 3'-untranslated region (3'-UTR) of the SERINC5 gene were characterized and we found that the expression of both svRNAs during the infection was not dependent on the miRNA pathway proteins Dicer and Argonaute-2. By using svRNAs mimic oligonucleotides, we demonstrated that both viral svRNAs can bind the 3'UTR of SERINC5 mRNA, reducing SERINC5 expression in vitro. Moreover, we found that an anti-svRNA treatment to Vero E6 cells before SARS-CoV-2 infection recovered the levels of SERINC5 and reduced the levels of N and S viral proteins. Finally, we showed that SERINC5 positively controls the levels of Mitochondrial Antiviral Signalling (MAVS) protein in Vero E6. These results highlight the therapeutic potential of targeting svRNAs based on their action on key proteins of the innate immune response during SARS-CoV-2 viral infection.

A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework.

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.