In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing
COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lun...
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| Veröffentlicht in: | PloS one 2024-07, Vol.19 (7), p.e0304425 |
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| creator | Alamin, Muhammad Habibulla Rahaman, Md Matiur Ferdousi, Farzana Sarker, Arnob Ali, Md Ahad Hossen, Md Bayazid Sarker, Bandhan Kumar, Nishith Mollah, Md Nurul Haque |
| description | COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases. |
| doi_str_mv | 10.1371/journal.pone.0304425 |
| format | Article |
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It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0304425</identifier><identifier>PMID: 39024368</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Antimitotic agents ; Antineoplastic agents ; Antiviral Agents - pharmacology ; Antiviral Agents - therapeutic use ; Asthma ; Bacterial pneumonia ; Biology and life sciences ; Bronchitis ; Chronic diseases ; Chronic obstructive pulmonary disease ; Computer Simulation ; COVID-19 ; COVID-19 - genetics ; COVID-19 - virology ; COVID-19 Drug Treatment ; CXCL10 protein ; Cystic fibrosis ; Datasets ; Density functional theory ; Density functionals ; Drug Repositioning - methods ; Drugs ; Emphysema ; Gene Regulatory Networks ; Genes ; Genetic transcription ; Global health ; Health aspects ; Humans ; Imatinib ; Intercellular adhesion molecule 1 ; Interferon regulatory factor 7 ; Lung diseases ; Lung Diseases - drug therapy ; Lung Diseases - virology ; Medical research ; Medicine and health sciences ; Medicine, Experimental ; Modules ; Molecular docking ; Molecular Docking Simulation ; Molecular dynamics ; Monocyte chemoattractant protein 1 ; Network analysis ; Nilotinib ; Physical Sciences ; Pneumonia ; Post-transcription ; Proteins ; Public health ; Rankings ; Risk factors ; SARS-CoV-2 - drug effects ; SARS-CoV-2 - genetics ; Severe acute respiratory syndrome coronavirus 2 ; Signatures ; Simulation methods ; Stability ; Stat1 protein ; Targeted cancer therapy ; TLR4 protein ; Toll-like receptors ; Transcription factors ; Tuberculosis ; World health</subject><ispartof>PloS one, 2024-07, Vol.19 (7), p.e0304425</ispartof><rights>Copyright: © 2024 Alamin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Alamin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 Alamin et al 2024 Alamin et al</rights><rights>2024 Alamin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c506t-5fed20bc8235c0b0fb3cae5782395a5f9b394b4ff8f9f2651b95136a87458a9d3</cites><orcidid>0000-0001-9799-9364 ; 0000-0002-3883-3396</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257407/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257407/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39024368$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Alamin, Muhammad Habibulla</creatorcontrib><creatorcontrib>Rahaman, Md Matiur</creatorcontrib><creatorcontrib>Ferdousi, Farzana</creatorcontrib><creatorcontrib>Sarker, Arnob</creatorcontrib><creatorcontrib>Ali, Md Ahad</creatorcontrib><creatorcontrib>Hossen, Md Bayazid</creatorcontrib><creatorcontrib>Sarker, Bandhan</creatorcontrib><creatorcontrib>Kumar, Nishith</creatorcontrib><creatorcontrib>Mollah, Md Nurul Haque</creatorcontrib><title>In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>COVID-19 caused by SARS-CoV-2 is a global health issue. 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The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. 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Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alamin, Muhammad Habibulla</au><au>Rahaman, Md Matiur</au><au>Ferdousi, Farzana</au><au>Sarker, Arnob</au><au>Ali, Md Ahad</au><au>Hossen, Md Bayazid</au><au>Sarker, Bandhan</au><au>Kumar, Nishith</au><au>Mollah, Md Nurul Haque</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-07-18</date><risdate>2024</risdate><volume>19</volume><issue>7</issue><spage>e0304425</spage><pages>e0304425-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>COVID-19 caused by SARS-CoV-2 is a global health issue. It is yet a severe risk factor to the patients, who are also suffering from one or more chronic diseases including different lung diseases. In this study, we explored common molecular signatures for which SARS-CoV-2 infections and different lung diseases stimulate each other, and associated candidate drug molecules. We identified both SARS-CoV-2 infections and different lung diseases (Asthma, Tuberculosis, Cystic Fibrosis, Pneumonia, Emphysema, Bronchitis, IPF, ILD, and COPD) causing top-ranked 11 shared genes (STAT1, TLR4, CXCL10, CCL2, JUN, DDX58, IRF7, ICAM1, MX2, IRF9 and ISG15) as the hub of the shared differentially expressed genes (hub-sDEGs). The gene ontology (GO) and pathway enrichment analyses of hub-sDEGs revealed some crucial common pathogenetic processes of SARS-CoV-2 infections and different lung diseases. The regulatory network analysis of hub-sDEGs detected top-ranked 6 TFs proteins and 6 micro RNAs as the key transcriptional and post-transcriptional regulatory factors of hub-sDEGs, respectively. Then we proposed hub-sDEGs guided top-ranked three repurposable drug molecules (Entrectinib, Imatinib, and Nilotinib), for the treatment against COVID-19 with different lung diseases. This recommendation is based on the results obtained from molecular docking analysis using the AutoDock Vina and GLIDE module of Schrödinger. The selected drug molecules were optimized through density functional theory (DFT) and observing their good chemical stability. Finally, we explored the binding stability of the highest-ranked receptor protein RELA with top-ordered three drugs (Entrectinib, Imatinib, and Nilotinib) through 100 ns molecular dynamic (MD) simulations with YASARA and Desmond module of Schrödinger and observed their consistent performance. Therefore, the findings of this study might be useful resources for the diagnosis and therapies of COVID-19 patients who are also suffering from one or more lung diseases.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39024368</pmid><doi>10.1371/journal.pone.0304425</doi><tpages>e0304425</tpages><orcidid>https://orcid.org/0000-0001-9799-9364</orcidid><orcidid>https://orcid.org/0000-0002-3883-3396</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2024-07, Vol.19 (7), p.e0304425 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_3082557940 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Antimitotic agents Antineoplastic agents Antiviral Agents - pharmacology Antiviral Agents - therapeutic use Asthma Bacterial pneumonia Biology and life sciences Bronchitis Chronic diseases Chronic obstructive pulmonary disease Computer Simulation COVID-19 COVID-19 - genetics COVID-19 - virology COVID-19 Drug Treatment CXCL10 protein Cystic fibrosis Datasets Density functional theory Density functionals Drug Repositioning - methods Drugs Emphysema Gene Regulatory Networks Genes Genetic transcription Global health Health aspects Humans Imatinib Intercellular adhesion molecule 1 Interferon regulatory factor 7 Lung diseases Lung Diseases - drug therapy Lung Diseases - virology Medical research Medicine and health sciences Medicine, Experimental Modules Molecular docking Molecular Docking Simulation Molecular dynamics Monocyte chemoattractant protein 1 Network analysis Nilotinib Physical Sciences Pneumonia Post-transcription Proteins Public health Rankings Risk factors SARS-CoV-2 - drug effects SARS-CoV-2 - genetics Severe acute respiratory syndrome coronavirus 2 Signatures Simulation methods Stability Stat1 protein Targeted cancer therapy TLR4 protein Toll-like receptors Transcription factors Tuberculosis World health |
| title | In-silico discovery of common molecular signatures for which SARS-CoV-2 infections and lung diseases stimulate each other, and drug repurposing |
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