GSK3326595 is a promising drug to prevent SARS-CoV-2 Omicron and other variants infection by inhibiting ACE2-R671 di-methylation.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused COVID-19 epidemic is worsening. Binding of the Spike1 protein of SARS-CoV-2 with the angiotensin-converting enzyme 2 (ACE2) receptor mediates entry of the virus into host cells. Many reports show that protein arginine methylation by protein arginine methyltransferases (PRMTs) is important for the functions of these proteins, but it remains unclear whether ACE2 is methylated by PRMTs. Here, we show that PRMT5 catalyses ACE2 symmetric dimethylation at residue R671 (meR671-ACE2). We indicate that PRMT5-mediated meR671-ACE2 promotes SARS-CoV-2 receptor-binding domain (RBD) binding with ACE2 probably by enhancing ACE2 N-glycosylation modification. We also reveal that the PRMT5-specific inhibitor GSK3326595 is able to dramatically reduce ACE2 binding with RBD. Moreover, we discovered that meR671-ACE2 plays an important role in ACE2 binding with Spike1 of the SARS-CoV-2 Omicron, Delta, and Beta variants; and we found that GSK3326595 strongly attenuates ACE2 interaction with Spike1 of the SARS-CoV-2 Omicron, Delta, and Beta variants. Finally, SARS-CoV-2 pseudovirus infection assays uncovered that PRMT5-mediated meR671-ACE2 is essential for SARS-CoV-2 infection in human cells, and pseudovirus infection experiments confirmed that GSK3326595 can strongly suppress SARS-CoV-2 infection of host cells. Our findings suggest that as a clinical phase II drug for several kinds of cancers, GSK3326595 is a promising candidate to decrease SARS-CoV-2 infection by inhibiting ACE2 methylation and ACE2-Spike1 interaction.

Correlation between severe COVID-19 and type 2 diabetes mellitus

Objective: To investigate the changes and significance of various indicators in patients with severe coronavirus disease 2019(COVID-19)pneumonia combined with type 2 diabetes mellitus(T2DM), and provide a theoretical basis for early clinical disease prediction, diagnosis and treatment. Method: A retrospective analysis of 80 patients with severe COVID-19 pneumonia in Wuhan Ninth Hospital from January to April 2020, among them, 42 cases were combined with type 2diabetes mellitus(COVID-19 combined with T2DM group), and 38 cases were not combined with type 2 diabetes mellitus(COVID-19 without T2DM group), including age,gender, medical history, laboratory examinations, and disease outcome were analyzed.

Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis.

COVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein-protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids.

BACKGROUND: In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people's health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein-protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. RESULTS: In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. CONCLUSIONS: In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.