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Coronaviruses

Editor-in-Chief

ISSN (Print): 2666-7967
ISSN (Online): 2666-7975

Research Article

Impact of Prolonged Use of COVID-19 Drugs on the Human Neurological System using Insilco Drug-gene Interaction

Author(s): Wayez Naqvi, Prekshi Garg and Prachi Srivastava*

Volume 4, Issue 2, 2023

Published on: 22 August, 2023

Article ID: e260723219105 Pages: 9

DOI: 10.2174/2666796704666230726122536

Open Access Journals Promotions 2
Abstract

Background: Coronavirus disease (COVID-19) was an infectious illness brought on by the SARS-CoV-2 virus. The first known SARS-CoV-2 infection was detected in the Wuhan District of China. The diagnostic and therapeutic management of COVID-19 requires an immediate response, as an alternative, quicker in-silico techniques can be used, which can serve as a filter before wet lab validation.

Objective: A pharmaceutical drug, also known as a medication or medicine, is a chemical substance that is used to treat, cure, prevent, or diagnose a disease or to promote overall health. When a particular class of drugs is used to treat a diseased gene, it can also affect the various healthy non-diseased genes in the body, resulting in altered gene expression and gene function.

Methods: The adverse effects of medications prescribed to COVID-19 patients form the basis of this study, which genes were being targeted, and what disorders or traits were caused as a result of this activity.

Results: COVID-19 is said to cause inflammation of the brain's tissues; inflammation of brain tissue is also a risk factor for Alzheimer's disease. The SARS-CoV-2 infection activates the inflammasome pathway, which is seen in patients with neurodegenerative diseases such as Alzheimer's and Parkinson's.

Conclusion: SARS-CoV-2 can enter the brain via the olfactory system or can be transferred through infected immune cells. The virus could enter the body by infecting endothelial cells of the brain. The presence of ACE2 receptors, SARS-CoV-2 receptors, interleukin (IL)-6, IL-1b, tumour necrosis factor (TNF), and IL-17 disrupts the Blood Brain Barrier, allowing the virus to enter the brain.

Keywords: COVID-19, SARS-CoV-2, neurodegenerative disease, alzheimer’s, ACE2 receptors, blood brain barrier.

Graphical Abstract
[1]
Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Bernardini S. The COVID-19 pandemic. Crit Rev Clin Lab Sci 2020; 57(6): 365-88.
[http://dx.doi.org/10.1080/10408363.2020.1783198] [PMID: 32645276]
[2]
Ma L, Li H, Lan J, et al. Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic. Comput Biol Chem 2021; 95: 107599.
[http://dx.doi.org/10.1016/j.compbiolchem.2021.107599] [PMID: 34773807]
[3]
Sargsyan A, Kodamullil AT, Baksi S, et al. The COVID-19 ontology. Bioinformatics 2021; 36(24): 5703-5.
[http://dx.doi.org/10.1093/bioinformatics/btaa1057] [PMID: 33346828]
[4]
Wang W, Tian JH, Chen X, et al. Coronaviruses in wild animals sampled in and around Wuhan at the beginning of COVID-19 emergence. Virus Evol 2022; 8(1): veac046.
[http://dx.doi.org/10.1093/ve/veac046] [PMID: 35769892]
[5]
Alam I, Kamau AA, Kulmanov M. Functional pangenome analysis shows key features of E protein are preserved in SARS and SARS-CoV-2. Front Cell Infect Microbiol 2020; 10: 405.
[6]
Nisha Muralidharan RS. Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19. J Biomol Struct Dyn 2021; 39(7): 2673-8.
[7]
Justin Stebbing AP. COVID-19: Combining antiviral and anti-inflammatory treatments. Lancet Infect Dis 2021; 20(4): 400-2.
[8]
Ray M, Sable MN, Sarkar S, Hallur V. Essential interpretations of bioinformatics in COVID-19 pandemic. Meta Gene 2021; 27: 100844.
[http://dx.doi.org/10.1016/j.mgene.2020.100844] [PMID: 33349792]
[9]
Ishack S, Lipner SR. Bioinformatics and immunoinformatics to support COVID‐19 vaccine development. J Med Virol 2021; 93(9): 5209-11.
[http://dx.doi.org/10.1002/jmv.27017] [PMID: 33851735]
[10]
Cannataro M, Harrison A. Bioinformatics helping to mitigate the impact of COVID-19 – Editorial. Brief Bioinform 2021; 22(2): 613-5.
[http://dx.doi.org/10.1093/bib/bbab063]
[11]
Cotto KC, Wagner AH, Feng YY, et al. DGIdb 3.0: A redesign and expansion of the drug–gene interaction database. Nucleic Acids Res 2018; 46(D1): D1068-73.
[http://dx.doi.org/10.1093/nar/gkx1143] [PMID: 29156001]
[12]
Allan Peter Davis CJ. Comparative toxicogenomics database (CTD): Update 2021. Nucleic Acids Res 2021; 49(D1): D1138-43.
[13]
Cline MS, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007; 2(10): 2366-82.
[http://dx.doi.org/10.1038/nprot.2007.324] [PMID: 17947979]
[14]
Nadezhda T, Doncheva JH. Cytoscape stringApp: Network analysis and visualization of proteomics data. J Proteome Res 2018.
[PMID: 30450911]
[15]
Su G, Morris JH, Demchak B, Bader GD. Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics 2014; 47(1): 13.1-24.
[http://dx.doi.org/10.1002/0471250953.bi0813s47] [PMID: 25199793]
[16]
Beck T, Shorter T, Brookes AJ. GWAS Central: A comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies. Nucleic Acids Res 2020; 48(D1): D933-40.
[PMID: 31612961]
[17]
Mihir A, Kamat JA. PhenoScanner V2: An expanded tool for searching human genotype–phenotype associations. Bioinformatics 2019; 35(22): 4851-3.
[18]
Moore LJ. The genotype-tissue expression (GTEx) project. Biopreserv Biobank 2015; 13(5): 307-8.
[19]
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[20]
Staley JR, Blackshaw J, Kamat MA, et al. PhenoScanner: A database of human genotype–phenotype associations. Bioinformatics 2016; 32(20): 3207-9.
[http://dx.doi.org/10.1093/bioinformatics/btw373] [PMID: 27318201]
[21]
Lonsdale J, Thomas J, Salvatore M, et al. The genotype-tissue expression (GTEx) project. Nat Genet 2013; 45(6): 580-5.
[http://dx.doi.org/10.1038/ng.2653] [PMID: 23715323]
[22]
Huang DW, Sherman BT, Tan Q, et al. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007; 35(Web Server issue)(Suppl. 2): W169-75.
[http://dx.doi.org/10.1093/nar/gkm415] [PMID: 17576678]
[23]
Sinyor B, Mineo J, Ochner C. Alzheimer’s disease, inflammation, and the role of antioxidants. J Alzheimers Dis Rep 2020; 4(1): 175-83.
[http://dx.doi.org/10.3233/ADR-200171] [PMID: 32715278]
[24]
Marcello Ciaccio BL. COVID-19 and alzheimer’s disease. Brain Sci 2021; 11(3): 305.
[25]
Naughton SX, Raval U, Pasinetti GM. Potential novel role of COVID-19 in alzheimer’s disease and preventative mitigation strategies. J Alzheimers Dis 2020; 76(1): 21-5.
[http://dx.doi.org/10.3233/JAD-200537] [PMID: 32538855]
[26]
Louis Hardan DF-S-Y. COVID-19 and alzheimer’s disease: A literature. Rev Med 2021; 57(11)
[27]
Rahman MA, Islam K, Rahman S, Alamin M. Neurobiochemical cross-talk between COVID-19 and alzheimer’s disease. Mol Neurobiol 2021; 58(3): 1017-23.
[http://dx.doi.org/10.1007/s12035-020-02177-w] [PMID: 33078369]
[28]
Gharesouran J, Taheri M, Sayad A, Ghafouri-Fard S, Mazdeh M, Omrani MD. The growth arrest-specific transcript 5 (GAS5) and nuclear receptor subfamily 3 group C member 1 (NR3C1): Novel markers involved in multiple sclerosis. Int J Mol Cell Med 2018; 7(2): 102-10.
[PMID: 30276165]
[29]
Kassi E, Semaniakou A, Sertedaki A, et al. Sequencing analysis of the human glucocorticoid receptor (NR3C1) gene in multiple sclerosis patients. J Neurol Sci 2016; 363: 165-9.
[http://dx.doi.org/10.1016/j.jns.2016.02.054] [PMID: 27000245]

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