Virtual screening of bioactive anti-SARS-CoV natural products and identification of 3ß,12-diacetoxyabieta-6,8,11,13-tetraene as a potential inhibitor of SARS-CoV-2 virus and its infection related pathways by MD simulation and network pharmacology.

Since the first prevalence of COVID-19 in 2019, it still remains the most devastating pandemic throughout the world. The current research aimed to find potential natural products to inhibit the novel coronavirus and associated infection by MD simulation and network pharmacology approach. Molecular docking was performed for 39 natural products having potent anti-SARS-CoV activity. Five natural products showed high binding interaction with the viral main protease for the SARS-CoV-2 virus, where 3ß,12-diacetoxyabieta-6,8,11,13 tetraene showed stable binding in MD simulation until 100 ns. Both 3ß,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A targeted 11 common genes that are related to COVID-19 and interact with each other. Gene ontology development analysis further showed that all these 11 genes are attached to various biological processes. The KEGG pathway analysis also showed that the proteins that are targeted by 3ß,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A are associated with multiple pathways related to COVID-19 infection. Furthermore, the ADMET and MDS studies reveals 3ß,12-diacetoxyabieta-6,8,11,13 as the best-suited compound for oral drug delivery.Communicated by Ramaswamy H. Sarma.

Virtual screening of functional foods and dissecting their roles in modulating gene functions to support post COVID-19 complications.

COVID-19 has become the focal point since 2019 after the outbreak of coronavirus disease. Many drugs are being tested and used to treat coronavirus infections; different kinds of vaccines are also introduced as preventive measure. Alternative therapeutics are as well incorporated into the health guidelines of some countries. This research aimed to look into the underlying mechanisms of functional foods and how they may improve the long-term post COVID-19 cardiovascular, diabetic, and respiratory complications through their bioactive compounds. The potentiality of nine functional foods for post COVID-19 complications was investigated through computational approaches. A total of 266 bioactive compounds of these foods were searched via extensive literature reviewing. Three highly associated targets namely troponin I interacting kinase (TNNI3K), dipeptidyl peptidase 4 (DPP-4), and transforming growth factor beta 1 (TGF-ß1) were selected for cardiovascular, diabetes, and respiratory disorders, respectively, after COVID-19 infections. Best docked compounds were further analyzed by network pharmacological tools to explore their interactions with complication-related genes (MAPK1 and HSP90AA1 for cardiovascular, PPARG and TNF-alpha for diabetes, and AKT-1 for respiratory disorders). Seventy-one suggested compounds out of one-hundred and thirty-nine (139) docked compounds in network pharmacology recommended 169 Gene Ontology (GO) items and 99 Kyoto Encyclopedia of Genes and Genomes signaling pathways preferably AKT signaling pathway, MAPK signaling pathway, ACE2 receptor signaling pathway, insulin signaling pathway, and PPAR signaling pathway. Among the chosen functional foods, black cumin, fenugreek, garlic, ginger, turmeric, bitter melon, and Indian pennywort were found to modulate the actions. Results demonstrate that aforesaid functional foods have attenuating roles to manage post COVID-19 complications. PRACTICAL APPLICATIONS: Functional foods have been approaching a greater interest due to their medicinal uses other than gastronomic pleasure. Nine functional food resources have been used in this research for their traditional and ethnopharmacological uses, but their directive-role in modulating the genes involved in the management of post COVID-19 complications is inadequately studied and reported. Therefore, the foods types used in this research may be prioritized to be used as functional foods for ameliorating the major post COVID-19 complications through appropriate science.

Expression of SARS-COV-2 cell receptor gene ACE2 is associated with immunosuppression and metabolic reprogramming in lung adenocarcinoma based on bioinformatics analyses of gene expression profiles.

The aberrant expression level of SARS-CoV-2 cell receptor gene ACE2 was reported in lung adenocarcinoma (LUAD) comorbidity of COVID-19. However, the association of ACE2 expression levels with immunosuppression and metabolic reprogramming in LUAD remains lacking. We investigated the expression level of ACE2, an association of ACE2 expression level with various types of immune signatures, immune ratios, and pathways. We employed a weighted gene co-expression network analysis (WGCNA) R package to identify the gene modules and investigated prognostic roles of hub genes in LUAD. Overexpression of ACE2 level was found in LUAD and ACE2 expression was negatively associated with various types of immune signatures including CD8+ T cells, CD4+ regulatory T cells, NK cells, and T cell activation. Besides, ACE2 upregulation was not only associated with CD8+ T cell/CD4+ regulatory T cell ratios but also linked with downregulation of immune-markers including CD8A, KLRC1, GZMA, GZMB, NKG7, CCL4, and IFNG. Moreover, the ACE2 expression level was found to be associated with the enrichment level of various metabolic pathways and it was also found that the metabolic pathways are directly positively correlated with the increased expression levels of ACE2, indicating that the overexpression of ACE2 is associated with metabolic reprogramming in LUAD. Furthermore, WGCNA based analysis revealed the gene modules in the high-ACE2-expression-level group of LUAD and identified GCLC and SLC7A11 hub genes which are not only highly expressed in lung adenocarcinoma but also correlated with the poor survival prognosis. Our analysis of ACE2 in LUAD tissues suggests that ACE2 is not only a receptor but is also associated with immunosuppression and metabolic reprogramming. This study underlines the clue for understanding the clinical significance of ACE2 in COVID-19 patients with LUAD comorbidity.

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach.

The COVID-19 virus has infected more than 38 million people and resulted in more than one million deaths worldwide as of October 14, 2020. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, economic inequality enhanced the risk of COVID-19 transmission. The per capita hospital beds correlated negatively with COVID-19 deaths. Blood types B and AB were protective factors for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV and influenza and pneumonia was associated with reduced COVID-19 risk. Increased intake of vegetables, edible oil, protein, vitamin D, and vitamin K was associated with reduced COVID-19 risk, while increased intake of alcohol was associated with increased COVID-19 risk. Other factors included age, sex, temperature, humidity, social distancing, smoking, health investment, urbanization level, and race. High temperature is a more compelling factor mitigating COVID-19 transmission than low temperature. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.