Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections
The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus...
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| Veröffentlicht in: | PloS one 2023-03, Vol.18 (3), p.e0281981-e0281981 |
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| creator | Sarker, Bandhan Rahaman, Md Matiur Islam, Md Ariful Alamin, Muhammad Habibulla Husain, Md Maidul Ferdousi, Farzana Ahsan, Md Asif Mollah, Md Nurul Haque |
| description | The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections. |
| doi_str_mv | 10.1371/journal.pone.0281981 |
| format | Article |
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Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0281981</identifier><identifier>PMID: 36913345</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Aurora Kinase A - genetics ; Bioinformatics ; Biological activity ; Biological markers ; Biology and life sciences ; Biomarkers ; Coronaviruses ; COVID-19 ; COVID-19 - diagnosis ; COVID-19 - genetics ; COVID-19 Testing ; Datasets ; Diagnosis ; Digoxin ; DNA microarrays ; Drug development ; Drugs ; ESR1 protein ; Gelatinase A ; Gene expression ; Gene Regulatory Networks ; Genes ; Genomics ; Global economy ; Hesperidin ; Humans ; Infections ; Literature reviews ; Medical diagnosis ; Medicine and Health Sciences ; MicroRNAs - genetics ; Molecular docking ; Molecular Docking Simulation ; Mutation ; Myc protein ; Naltrindole ; Network analysis ; Nucleotide sequence ; Oleanolic acid ; Pandemics ; Physical Sciences ; Post-transcription ; Proscillaridin ; Proteins ; Receptors ; Ribonucleic acid ; RNA ; SARS-CoV-2 - genetics ; SARS-CoV-2 - metabolism ; Severe acute respiratory syndrome coronavirus 2 ; Stat4 protein ; Transcriptome ; Transcriptomics ; Vaccination ; Viral diseases ; Viruses</subject><ispartof>PloS one, 2023-03, Vol.18 (3), p.e0281981-e0281981</ispartof><rights>Copyright: © 2023 Sarker 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 2023 Public Library of Science</rights><rights>2023 Sarker 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>2023 Sarker et al 2023 Sarker et al</rights><rights>2023 Sarker 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><citedby>FETCH-LOGICAL-c693t-9853d108fb5c4303ce733c972aa9b63d880822577c1217797e03a798915bd1483</citedby><cites>FETCH-LOGICAL-c693t-9853d108fb5c4303ce733c972aa9b63d880822577c1217797e03a798915bd1483</cites><orcidid>0000-0002-3883-3396 ; 0000-0001-9799-9364</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/PMC10010564/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10010564/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36913345$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Selvaraj, Chandrabose</contributor><creatorcontrib>Sarker, Bandhan</creatorcontrib><creatorcontrib>Rahaman, Md Matiur</creatorcontrib><creatorcontrib>Islam, Md Ariful</creatorcontrib><creatorcontrib>Alamin, Muhammad Habibulla</creatorcontrib><creatorcontrib>Husain, Md Maidul</creatorcontrib><creatorcontrib>Ferdousi, Farzana</creatorcontrib><creatorcontrib>Ahsan, Md Asif</creatorcontrib><creatorcontrib>Mollah, Md Nurul Haque</creatorcontrib><title>Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.</description><subject>Analysis</subject><subject>Aurora Kinase A - genetics</subject><subject>Bioinformatics</subject><subject>Biological activity</subject><subject>Biological markers</subject><subject>Biology and life sciences</subject><subject>Biomarkers</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - diagnosis</subject><subject>COVID-19 - genetics</subject><subject>COVID-19 Testing</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Digoxin</subject><subject>DNA microarrays</subject><subject>Drug development</subject><subject>Drugs</subject><subject>ESR1 protein</subject><subject>Gelatinase A</subject><subject>Gene expression</subject><subject>Gene Regulatory Networks</subject><subject>Genes</subject><subject>Genomics</subject><subject>Global economy</subject><subject>Hesperidin</subject><subject>Humans</subject><subject>Infections</subject><subject>Literature reviews</subject><subject>Medical diagnosis</subject><subject>Medicine and Health Sciences</subject><subject>MicroRNAs - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sarker, Bandhan</au><au>Rahaman, Md Matiur</au><au>Islam, Md Ariful</au><au>Alamin, Muhammad Habibulla</au><au>Husain, Md Maidul</au><au>Ferdousi, Farzana</au><au>Ahsan, Md Asif</au><au>Mollah, Md Nurul Haque</au><au>Selvaraj, Chandrabose</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-03-13</date><risdate>2023</risdate><volume>18</volume><issue>3</issue><spage>e0281981</spage><epage>e0281981</epage><pages>e0281981-e0281981</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36913345</pmid><doi>10.1371/journal.pone.0281981</doi><tpages>e0281981</tpages><orcidid>https://orcid.org/0000-0002-3883-3396</orcidid><orcidid>https://orcid.org/0000-0001-9799-9364</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2023-03, Vol.18 (3), p.e0281981-e0281981 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2786466384 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Aurora Kinase A - genetics Bioinformatics Biological activity Biological markers Biology and life sciences Biomarkers Coronaviruses COVID-19 COVID-19 - diagnosis COVID-19 - genetics COVID-19 Testing Datasets Diagnosis Digoxin DNA microarrays Drug development Drugs ESR1 protein Gelatinase A Gene expression Gene Regulatory Networks Genes Genomics Global economy Hesperidin Humans Infections Literature reviews Medical diagnosis Medicine and Health Sciences MicroRNAs - genetics Molecular docking Molecular Docking Simulation Mutation Myc protein Naltrindole Network analysis Nucleotide sequence Oleanolic acid Pandemics Physical Sciences Post-transcription Proscillaridin Proteins Receptors Ribonucleic acid RNA SARS-CoV-2 - genetics SARS-CoV-2 - metabolism Severe acute respiratory syndrome coronavirus 2 Stat4 protein Transcriptome Transcriptomics Vaccination Viral diseases Viruses |
| title | Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections |
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