Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis
| Autor: | Wenyan Chen, Gao-Yin Kong, Siyou Tan, Lai Wei, Hongxian Xiang, Lianhong Zou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
0301 basic medicine Drug Coronavirus disease 2019 (COVID-19) Bioinformatics analysis media_common.quotation_subject Gene regulatory network Druggability Drug Evaluation Preclinical Computational biology Biology 01 natural sciences Antiviral Agents Biochemistry 03 medical and health sciences Drug Development Genetics Humans Gene Regulatory Networks Protein Interaction Maps Molecular Biology media_common Network construction SARS-CoV-2 COVID-19 Computational Biology Human genetics COVID-19 Drug Treatment Toll-like receptors Clinical trial MicroRNAs 030104 developmental biology 010606 plant biology & botany Research Article |
| Zdroj: | Genes & Genomics |
| ISSN: | 2092-9293 1976-9571 |
| DOI: | 10.1007/s13258-020-01021-8 |
| Popis: | Background Since the outbreak of coronavirus disease 2019 (COVID-19) in China, numerous research institutions have invested in the development of anti-COVID-19 vaccines and screening for efficacious drugs to manage the virus. Objective To explore the potential targets and therapeutic drugs for the prevention and treatment of COVID-19 through data mining and bioinformatics. Methods We integrated and profoundly analyzed 10 drugs previously assessed to have promising therapeutic potential in COVID-19 management, and have been recommended for clinical trials. To explore the mechanisms by which these drugs may be involved in the treatment of COVID-19, gene-drug interactions were identified using the DGIdb database after which functional enrichment analysis, protein–protein interaction (PPI) network, and miRNA-gene network construction were performed. We adopted the DGIdb database to explore the candidate drugs for COVID-19. Results A total of 43 genes associated with the 10 potential COVID-19 drugs were identified. Function enrichment analysis revealed that these genes were mainly enriched in response to other invasions, toll-like receptor pathways, and they play positive roles in the production of cytokines such as IL-6, IL-8, and INF-β. TNF, TLR3, TLR7, TLR9, and CXCL10 were identified as crucial genes in COVID-19. Through the DGIdb database, we predicted 87 molecules as promising druggable molecules for managing COVID-19. Conclusions Findings from this work may provide new insights into COVID-19 mechanisms and treatments. Further, the already identified candidate drugs may improve the efficiency of pharmaceutical treatment in this rapidly evolving global situation. |
| Databáze: | OpenAIRE |
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