Gene signatures and potential therapeutic targets of Middle East respiratory syndrome coronavirus (MERS-CoV)-infected human lung adenocarcinoma epithelial cells.

BACKGROUND: Pathogenic coronaviruses include Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2. These viruses have induced outbreaks worldwide, and there are currently no effective medications against them. Therefore, there is an urgent need to develop potential drugs against coronaviruses. METHODS: High-throughput technology is widely used to explore differences in messenger (m)RNA and micro (mi)RNA expression profiles, especially to investigate protein-protein interactions and search for new therapeutic compounds. We integrated miRNA and mRNA expression profiles in MERS-CoV-infected cells and compared them to mock-infected controls from public databases. RESULTS: Through the bioinformatics analysis, there were 251 upregulated genes and eight highly differentiated miRNAs that overlapped in the two datasets. External validation verified that these genes had high expression in MERS-CoV-infected cells, including RC3H1, NF-κB, CD69, TNFAIP3, LEAP-2, DUSP10, CREB5, CXCL2, etc. We revealed that immune, olfactory or sensory system-related, and signal-transduction networks were discovered from upregulated mRNAs in MERS-CoV-infected cells. In total, 115 genes were predicted to be related to miRNAs, with the intersection of upregulated mRNAs and miRNA-targeting prediction genes such as TCF4, NR3C1, and POU2F2. Through the Connectivity Map (CMap) platform, we suggested potential compounds to use against MERS-CoV infection, including diethylcarbamazine, harpagoside, bumetanide, enalapril, and valproic acid. CONCLUSIONS: The present study illustrates the crucial roles of miRNA-mRNA interacting networks in MERS-CoV-infected cells. The genes we identified are potential targets for treating MERS-CoV infection; however, these could possibly be extended to other coronavirus infections.

Novel signaling pathways regulate SARS-CoV and SARS-CoV-2 infectious disease.

ABSTRACT: Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 induces severe infection, and it is responsible for a worldwide disease outbreak starting in late 2019. Currently, there are no effective medications against coronavirus. In the present study, we utilized a holistic bioinformatics approach to study gene signatures of SARS-CoV- and SARS-CoV-2-infected Calu-3 lung adenocarcinoma cells. Through the Gene Ontology platform, we determined that several cytokine genes were up-regulated after SARS-CoV-2 infection, including TNF, IL6, CSF2, IFNL1, IL-17C, CXCL10, and CXCL11. Differentially regulated pathways were detected by the Kyoto Encyclopedia of Genes and Genomes, gene ontology, and Hallmark platform, including chemokines, cytokines, cytokine receptors, cytokine metabolism, inflammation, immune responses, and cellular responses to the virus. A Venn diagram was utilized to illustrate common overlapping genes from SARS-CoV- and SARS-CoV-2-infected datasets. An Ingenuity pathway analysis discovered an enrichment of tumor necrosis factor- (TNF-) and interleukin (IL)-17-related signaling in a gene set enrichment analysis. Downstream networks were predicted by the Database for Annotation, Visualization, and Integrated Discovery platform also revealed that TNF and TNF receptor 2 signaling elicited leukocyte recruitment, activation, and survival of host cells after coronavirus infection. Our discovery provides essential evidence for transcript regulation and downstream signaling of SARS-CoV and SARS-CoV-2 infection.

Gene signatures of SARS-CoV/SARS-CoV-2-infected ferret lungs in short- and long-term models.

Coronaviruses (CoVs) consist of six strains, and the severe acute respiratory syndrome coronavirus (SARS-CoV), newly found coronavirus (SARS-CoV-2) has rapidly spread leading to a global outbreak. The ferret (Mustela putorius furo) serves as a useful animal model for studying SARS-CoV/SARS-CoV-2 infection and developing therapeutic strategies. A holistic approach for distinguishing differences in gene signatures during disease progression is lacking. The present study discovered gene expression profiles of short-term (3 days) and long-term (14 days) ferret models after SARS-CoV/SARS-CoV-2 infection using a bioinformatics approach. Through Gene Ontology (GO) and MetaCore analyses, we found that the development of stemness signaling was related to short-term SARS-CoV/SARS-CoV-2 infection. In contrast, pathways involving extracellular matrix and immune responses were associated with long-term SARS-CoV/SARS-CoV-2 infection. Some highly expressed genes in both short- and long-term models played a crucial role in the progression of SARS-CoV/SARS-CoV-2 infection, including DPP4, BMP2, NFIA, AXIN2, DAAM1, ZNF608, ME1, MGLL, LGR4, ABHD6, and ACADM. Meanwhile, we revealed that metabolic, glucocorticoid, and reactive oxygen species-associated networks were enriched in both short- and long-term infection models. The present study showed alterations in gene expressions from short-term to long-term SARS-CoV/SARS-CoV-2 infection. The current result provides an explanation of the pathophysiology for post-infectious sequelae and potential targets for treatment.