Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Identifieur interne : 000088 ( Pmc/Corpus ); précédent : 000087; suivant : 000089Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2
Auteurs : Yadi Zhou ; Yuan Hou ; Jiayu Shen ; Yin Huang ; William Martin ; Feixiong ChengSource :
- Cell Discovery [ 2056-5968 ] ; 2020.
Abstract
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “
Url:
DOI: 10.1038/s41421-020-0153-3
PubMed: 32194980
PubMed Central: 7073332
Links to Exploration step
PMC:7073332Le document en format XML
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Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Shen, Jiayu" sort="Shen, Jiayu" uniqKey="Shen J" first="Jiayu" last="Shen">Jiayu Shen</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Huang, Yin" sort="Huang, Yin" uniqKey="Huang Y" first="Yin" last="Huang">Yin Huang</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id 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institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine,</institution><institution>Case Western Reserve University,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Case Comprehensive Cancer Center,</institution><institution>Case Western Reserve University School of Medicine,</institution></institution-wrap>Cleveland, OH 44106 USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">32194980</idno><idno type="pmc">7073332</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073332</idno><idno type="RBID">PMC:7073332</idno><idno type="doi">10.1038/s41421-020-0153-3</idno><date when="2020">2020</date><idno type="wicri:Area/Pmc/Corpus">000088</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000088</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2</title><author><name sortKey="Zhou, Yadi" sort="Zhou, Yadi" uniqKey="Zhou Y" first="Yadi" last="Zhou">Yadi Zhou</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Hou, Yuan" sort="Hou, Yuan" uniqKey="Hou Y" first="Yuan" last="Hou">Yuan Hou</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Shen, Jiayu" sort="Shen, Jiayu" uniqKey="Shen J" first="Jiayu" last="Shen">Jiayu Shen</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Huang, Yin" sort="Huang, Yin" uniqKey="Huang Y" first="Yin" last="Huang">Yin Huang</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Martin, William" sort="Martin, William" uniqKey="Martin W" first="William" last="Martin">William Martin</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation></author><author><name sortKey="Cheng, Feixiong" sort="Cheng, Feixiong" uniqKey="Cheng F" first="Feixiong" last="Cheng">Feixiong Cheng</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine,</institution><institution>Case Western Reserve University,</institution></institution-wrap>Cleveland, OH 44195 USA</nlm:aff></affiliation><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Case Comprehensive Cancer Center,</institution><institution>Case Western Reserve University School of Medicine,</institution></institution-wrap>Cleveland, OH 44106 USA</nlm:aff></affiliation></author></analytic><series><title level="j">Cell Discovery</title><idno type="eISSN">2056-5968</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="Par1">Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “<italic>Complementary Exposure</italic>” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Zumla, A" uniqKey="Zumla A">A Zumla</name></author><author><name sortKey="Chan, Jf" uniqKey="Chan J">JF Chan</name></author><author><name sortKey="Azhar, Ei" uniqKey="Azhar E">EI Azhar</name></author><author><name sortKey="Hui, Ds" uniqKey="Hui D">DS Hui</name></author><author><name sortKey="Yuen, Ky" uniqKey="Yuen K">KY Yuen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Paules, Ci" uniqKey="Paules C">CI Paules</name></author><author><name sortKey="Marston, Hd" uniqKey="Marston H">HD Marston</name></author><author><name sortKey="Fauci, As" uniqKey="Fauci A">AS Fauci</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="De Wit, E" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Cell Discov</journal-id><journal-id journal-id-type="iso-abbrev">Cell Discov</journal-id><journal-title-group><journal-title>Cell Discovery</journal-title></journal-title-group><issn pub-type="epub">2056-5968</issn><publisher><publisher-name>Springer Singapore</publisher-name><publisher-loc>Singapore</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">32194980</article-id><article-id pub-id-type="pmc">7073332</article-id><article-id pub-id-type="publisher-id">153</article-id><article-id pub-id-type="doi">10.1038/s41421-020-0153-3</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Zhou</surname><given-names>Yadi</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Hou</surname><given-names>Yuan</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Shen</surname><given-names>Jiayu</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Huang</surname><given-names>Yin</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0003-0616-0462</contrib-id><name><surname>Martin</surname><given-names>William</given-names></name><xref ref-type="aff" rid="Aff1">1</xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Cheng</surname><given-names>Feixiong</given-names></name><address><email>chengf@ccf.org</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref><xref ref-type="aff" rid="Aff3">3</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 0675 4725</institution-id><institution-id institution-id-type="GRID">grid.239578.2</institution-id><institution>Genomic Medicine Institute, Lerner Research Institute,</institution><institution>Cleveland Clinic,</institution></institution-wrap>Cleveland, OH 44195 USA</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine,</institution><institution>Case Western Reserve University,</institution></institution-wrap>Cleveland, OH 44195 USA</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2164 3847</institution-id><institution-id institution-id-type="GRID">grid.67105.35</institution-id><institution>Case Comprehensive Cancer Center,</institution><institution>Case Western Reserve University School of Medicine,</institution></institution-wrap>Cleveland, OH 44106 USA</aff></contrib-group><pub-date pub-type="epub"><day>16</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="pmc-release"><day>16</day><month>3</month><year>2020</year></pub-date><pub-date pub-type="collection"><year>2020</year></pub-date><volume>6</volume><elocation-id>14</elocation-id><history><date date-type="received"><day>5</day><month>2</month><year>2020</year></date><date date-type="accepted"><day>2</day><month>3</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id="Abs1"><p id="Par1">Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV–host interactome and drug targets in the human protein–protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV–host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the “<italic>Complementary Exposure</italic>” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.</p></abstract><kwd-group kwd-group-type="npg-subject"><title>Subject terms</title><kwd>Bioinformatics</kwd><kwd>Comparative genomics</kwd><kwd>Proteomic analysis</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">https://doi.org/10.13039/100000050</institution-id><institution>U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)</institution></institution-wrap></funding-source><award-id>K99HL138272</award-id><award-id>R00HL138272</award-id><principal-award-recipient><name><surname>Cheng</surname><given-names>Feixiong</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution>U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1" sec-type="introduction"><title>Introduction</title><p id="Par2">Coronaviruses (CoVs) typically affect the respiratory tract of mammals, including humans, and lead to mild to severe respiratory tract infections<sup><xref ref-type="bibr" rid="CR1">1</xref></sup>. In the past two decades, two highly pathogenic human CoVs (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), emerging from animal reservoirs, have led to global epidemics with high morbidity and mortality<sup><xref ref-type="bibr" rid="CR2">2</xref></sup>. For example, 8098 individuals were infected and 774 died in the SARS-CoV pandemic, which cost the global economy with an estimated $30 to $100 billion<sup><xref ref-type="bibr" rid="CR3">3</xref>,<xref ref-type="bibr" rid="CR4">4</xref></sup>. According to the World Health Organization (WHO), as of November 2019, MERS-CoV has had a total of 2494 diagnosed cases causing 858 deaths, the majority in Saudi Arabia<sup><xref ref-type="bibr" rid="CR2">2</xref></sup>. In December 2019, the third pathogenic HCoV, named 2019 novel coronavirus (2019-nCoV/SARS-CoV-2), as the cause of coronavirus disease 2019 (abbreviated as COVID-19)<sup><xref ref-type="bibr" rid="CR5">5</xref></sup>, was found in Wuhan, China. As of 24 February 2020, there have been over 79,000 cases with over 2600 deaths for the 2019-nCoV/SARS-CoV-2 outbreak worldwide; furthermore, human-to-human transmission has occurred among close contacts<sup><xref ref-type="bibr" rid="CR6">6</xref></sup>. However, there are currently no effective medications against 2019-nCoV/SARS-CoV-2. Several national and international research groups are working on the development of vaccines to prevent and treat the 2019-nCoV/SARS-CoV-2, but effective vaccines are not available yet. There is an urgent need for the development of effective prevention and treatment strategies for 2019-nCoV/SARS-CoV-2 outbreak.</p><p id="Par3">Although investment in biomedical and pharmaceutical research and development has increased significantly over the past two decades, the annual number of new treatments approved by the U.S. Food and Drug Administration (FDA) has remained relatively constant and limited<sup><xref ref-type="bibr" rid="CR7">7</xref></sup>. A recent study estimated that pharmaceutical companies spent $2.6 billion in 2015, up from $802 million in 2003, in the development of an FDA-approved new chemical entity drug<sup><xref ref-type="bibr" rid="CR8">8</xref></sup>. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, could significantly shorten the time and reduce the cost compared to de novo drug discovery and randomized clinical trials<sup><xref ref-type="bibr" rid="CR9">9</xref>–<xref ref-type="bibr" rid="CR11">11</xref></sup>. However, experimental approaches for drug repurposing is costly and time-consuming<sup><xref ref-type="bibr" rid="CR12">12</xref></sup>. Computational approaches offer novel testable hypotheses for systematic drug repositioning<sup><xref ref-type="bibr" rid="CR9">9</xref>–<xref ref-type="bibr" rid="CR11">11</xref>,<xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR14">14</xref></sup>. However, traditional structure-based methods are limited when three-dimensional (3D) structures of proteins are unavailable, which, unfortunately, is the case for the majority of human and viral targets. In addition, targeting single virus proteins often has high risk of drug resistance by the rapid evolution of virus genomes<sup><xref ref-type="bibr" rid="CR1">1</xref></sup>.</p><p id="Par4">Viruses (including HCoV) require host cellular factors for successful replication during infection<sup><xref ref-type="bibr" rid="CR1">1</xref></sup>. Systematic identification of virus–host protein–protein interactions (PPIs) offers an effective way toward elucidating the mechanisms of viral infection<sup><xref ref-type="bibr" rid="CR15">15</xref>,<xref ref-type="bibr" rid="CR16">16</xref></sup>. Subsequently, targeting cellular antiviral targets, such as virus–host interactome, may offer a novel strategy for the development of effective treatments for viral infections<sup><xref ref-type="bibr" rid="CR1">1</xref></sup>, including SARS-CoV<sup><xref ref-type="bibr" rid="CR17">17</xref></sup>, MERS-CoV<sup><xref ref-type="bibr" rid="CR17">17</xref></sup>, Ebola virus<sup><xref ref-type="bibr" rid="CR18">18</xref></sup>, and Zika virus<sup><xref ref-type="bibr" rid="CR14">14</xref>,<xref ref-type="bibr" rid="CR19">19</xref>–<xref ref-type="bibr" rid="CR21">21</xref></sup>. We recently presented an integrated antiviral drug discovery pipeline that incorporated gene-trap insertional mutagenesis, known functional drug–gene network, and bioinformatics analyses<sup><xref ref-type="bibr" rid="CR14">14</xref></sup>. This methodology allows to identify several candidate repurposable drugs for Ebola virus<sup><xref ref-type="bibr" rid="CR11">11</xref>,<xref ref-type="bibr" rid="CR14">14</xref></sup>. Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR22">22</xref>–<xref ref-type="bibr" rid="CR27">27</xref></sup> and drug combinations<sup><xref ref-type="bibr" rid="CR28">28</xref></sup> for multiple human diseases. For example, network-based drug–disease proximity sheds light on the relationship between drugs (e.g., drug targets) and disease modules (molecular determinants in disease pathobiology modules within the PPIs), and can serve as a useful tool for efficient screening of potentially new indications for approved drugs, as well as drug combinations, as demonstrated in our recent studies<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR23">23</xref>,<xref ref-type="bibr" rid="CR27">27</xref>,<xref ref-type="bibr" rid="CR28">28</xref></sup>.</p><p id="Par5">In this study, we present an integrative antiviral drug repurposing methodology, which combines a systems pharmacology-based network medicine platform that quantifies the interplay between the virus–host interactome and drug targets in the human PPI network. The basis for these experiments rests on the notions that (i) the proteins that functionally associate with viral infection (including HCoV) are localized in the corresponding subnetwork within the comprehensive human PPI network and (ii) proteins that serve as drug targets for a specific disease may also be suitable drug targets for potential antiviral infection owing to common PPIs and functional pathways elucidated by the human interactome (Fig. <xref rid="Fig1" ref-type="fig">1</xref>). We follow this analysis with bioinformatics validation of drug-induced gene signatures and HCoV-induced transcriptomics in human cell lines to inspect the postulated mechanism-of-action in a specific HCoV for which we propose repurposing (Fig. <xref rid="Fig1" ref-type="fig">1</xref>).<fig id="Fig1"><label>Fig. 1</label><caption><title>Overall workflow of this study.</title><p>Our network-based methodology combines a systems pharmacology-based network medicine platform that quantifies the interplay between the virus–host interactome and drug targets in the human PPI network. <bold>a</bold> Human coronavirus (HCoV)-associated host proteins were collected from literatures and pooled to generate a pan-HCoV protein subnetwork. <bold>b</bold> Network proximity between drug targets and HCoV-associated proteins was calculated to screen for candidate repurposable drugs for HCoVs under the human protein interactome model. <bold>c</bold>, <bold>d</bold> Gene set enrichment analysis was utilized to validate the network-based prediction. <bold>e</bold> Top candidates were further prioritized for drug combinations using network-based method captured by the “<italic>Complementary Exposure</italic>” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. <bold>f</bold> Overall hypothesis of the network-based methodology: (i) the proteins that functionally associate with HCoVs are localized in the corresponding subnetwork within the comprehensive human interactome network; and (ii) proteins that serve as drug targets for a specific disease may also be suitable drug targets for potential antiviral infection owing to common protein–protein interactions elucidated by the human interactome.</p></caption><graphic xlink:href="41421_2020_153_Fig1_HTML" id="d29e468"></graphic></fig></p></sec><sec id="Sec2" sec-type="results"><title>Results</title><sec id="Sec3"><title>Phylogenetic analyses of 2019-nCoV/SARS-CoV-2</title><p id="Par6">To date, seven pathogenic HCoVs (Fig. <xref rid="Fig2" ref-type="fig">2a, b</xref>) have been found:<sup><xref ref-type="bibr" rid="CR1">1</xref>,<xref ref-type="bibr" rid="CR29">29</xref></sup> (i) 2019-nCoV/SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-OC43, and HCoV-HKU1 are β genera, and (ii) HCoV-NL63 and HCoV-229E are α genera. We performed the phylogenetic analyses using the whole-genome sequence data from 15 HCoVs to inspect the evolutionary relationship of 2019-nCoV/SARS-CoV-2 with other HCoVs. We found that the whole genomes of 2019-nCoV/SARS-CoV-2 had ~99.99% nucleotide sequence identity across three diagnosed patients (Supplementary Table <xref rid="MOESM1" ref-type="media">S1</xref>). The 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity (79.7%) with SARS-CoV among the six other known pathogenic HCoVs, revealing conserved evolutionary relationship between 2019-nCoV/SARS-CoV-2 and SARS-CoV (Fig. <xref rid="Fig2" ref-type="fig">2a</xref>).<fig id="Fig2"><label>Fig. 2</label><caption><title>Phylogenetic analysis of coronaviruses.</title><p><bold>a</bold> Phylogenetic tree of coronavirus (CoV). Phylogenetic algorithm analyzed evolutionary conservation among whole genomes of 15 coronaviruses. Red color highlights the recent emergent coronavirus, 2019-nCoV/SARS-CoV-2. Numbers on the branches indicate bootstrap support values. The scale shows the evolutionary distance computed using the p-distance method. <bold>b</bold> Schematic plot for HCoV genomes. The genus and host information of viruses was labeled on the left by different colors. Empty dark gray boxes represent accessory open reading frames (ORFs). <bold>c</bold>–<bold>e</bold> The 3D structures of SARS-CoV nsp12 (PDB ID: 6NUR) (<bold>c</bold>), spike (PDB ID: 6ACK) (<bold>d</bold>), and nucleocapsid (PDB ID: 2CJR) (<bold>e</bold>) shown were based on homology modeling. Genome information and phylogenetic analysis results are provided in Supplementary Tables <xref rid="MOESM1" ref-type="media">S1</xref> and <xref rid="MOESM1" ref-type="media">S2</xref>.</p></caption><graphic xlink:href="41421_2020_153_Fig2_HTML" id="d29e530"></graphic></fig></p><p id="Par7">HCoVs have five major protein regions for virus structure assembly and viral replications<sup><xref ref-type="bibr" rid="CR29">29</xref></sup>, including replicase complex (ORF1ab), spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins (Fig. <xref rid="Fig2" ref-type="fig">2b</xref>). The ORF1ab gene encodes the non-structural proteins (nsp) of viral RNA synthesis complex through proteolytic processing<sup><xref ref-type="bibr" rid="CR30">30</xref></sup>. The nsp12 is a viral RNA-dependent RNA polymerase, together with co-factors nsp7 and nsp8 possessing high polymerase activity. From the protein 3D structure view of SARS-CoV nsp12, it contains a larger N-terminal extension (which binds to nsp7 and nsp8) and polymerase domain (Fig. <xref rid="Fig2" ref-type="fig">2c</xref>). The spike is a transmembrane glycoprotein that plays a pivotal role in mediating viral infection through binding the host receptor<sup><xref ref-type="bibr" rid="CR31">31</xref>,<xref ref-type="bibr" rid="CR32">32</xref></sup>. Figure <xref rid="Fig2" ref-type="fig">2d</xref> shows the 3D structure of the spike protein bound with the host receptor angiotensin converting enznyme2 (ACE2) in SARS-CoV (PDB ID: 6ACK). A recent study showed that 2019-nCoV/SARS-CoV-2 is able to utilize ACE2 as an entry receptor in ACE2-expressing cells<sup><xref ref-type="bibr" rid="CR33">33</xref></sup>, suggesting potential drug targets for therapeutic development. Furthermore, cryo-EM structure of the spike and biophysical assays reveal that the 2019-nCoV/SARS-CoV-2 spike binds ACE2 with higher affinity than SARS-CoV<sup><xref ref-type="bibr" rid="CR34">34</xref></sup>. In addition, the nucleocapsid is also an important subunit for packaging the viral genome through protein oligomerization<sup><xref ref-type="bibr" rid="CR35">35</xref></sup>, and the single nucleocapsid structure is shown in Fig. <xref rid="Fig2" ref-type="fig">2e</xref>.</p><p id="Par8">Protein sequence alignment analyses indicated that the 2019-nCoV/SARS-CoV-2 was most evolutionarily conserved with SARS-CoV (Supplementary Table <xref rid="MOESM1" ref-type="media">S2</xref>). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, with sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV (Supplementary Table <xref rid="MOESM1" ref-type="media">S2</xref>). However, the spike protein exhibited the lowest sequence conservation (sequence identity of 77%) between 2019-nCoV/SARS-CoV-2 and SARS-CoV. Meanwhile, the spike protein of 2019-nCoV/SARS-CoV-2 only has 31.9% sequence identity compared to MERS-CoV.</p></sec><sec id="Sec4"><title>HCoV–host interactome network</title><p id="Par9">To depict the HCoV–host interactome network, we assembled the CoV-associated host proteins from four known HCoVs (SARS-CoV, MERS-CoV, HCoV-229E, and HCoV-NL63), one mouse MHV, and one avian IBV (N protein) (Supplementary Table <xref rid="MOESM1" ref-type="media">S3</xref>). In total, we obtained 119 host proteins associated with CoVs with various experimental evidence. Specifically, these host proteins are either the direct targets of HCoV proteins or are involved in crucial pathways of HCoV infection. The HCoV–host interactome network is shown in Fig. <xref rid="Fig3" ref-type="fig">3a</xref>. We identified several hub proteins including JUN, XPO1, NPM1, and HNRNPA1, with the highest number of connections within the 119 proteins. KEGG pathway enrichment analysis revealed multiple significant biological pathways (adjusted <italic>P</italic> value < 0.05), including measles, RNA transport, NF-kappa B signaling, Epstein-Barr virus infection, and influenza (Fig. <xref rid="Fig3" ref-type="fig">3b</xref>). Gene ontology (GO) biological process enrichment analysis further confirmed multiple viral infection-related processes (adjusted <italic>P</italic> value < 0.001), including viral life cycle, modulation by virus of host morphology or physiology, viral process, positive regulation of viral life cycle, transport of virus, and virion attachment to host cell (Fig. <xref rid="Fig3" ref-type="fig">3c</xref>). We then mapped the known drug–target network (see Materials and methods) into the HCoV–host interactome to search for druggable, cellular targets. We found that 47 human proteins (39%, blue nodes in Fig. <xref rid="Fig3" ref-type="fig">3a</xref>) can be targeted by at least one approved drug or experimental drug under clinical trials. For example, GSK3B, DPP4, SMAD3, PARP1, and IKBKB are the most targetable proteins. The high druggability of HCoV–host interactome motivates us to develop a drug repurposing strategy by specifically targeting cellular proteins associated with HCoVs for potential treatment of 2019-nCoV/SARS-CoV-2.<fig id="Fig3"><label>Fig. 3</label><caption><title>Drug-target network analysis of the HCoV–host interactome.</title><p><bold>a</bold> A subnetwork highlighting the HCoV–host interactome. Nodes represent three types of HCoV-associated host proteins: targetgable (proteins can be targeted by approved drugs or drugs under clinical trials), non-targetable (proteins do not have any known ligands), neighbors (protein–protein interaction partners). Edge colors indicate five types of experimental evidence of the protein–protein interactions (see Materials and methods). 3D three-dimensional structure. <bold>b</bold>, <bold>c</bold> KEGG human pathway (<bold>b</bold>) and gene ontology enrichment analyses (<bold>c</bold>) for the HCoV-associated proteins.</p></caption><graphic xlink:href="41421_2020_153_Fig3_HTML" id="d29e632"></graphic></fig></p></sec><sec id="Sec5"><title>Network-based drug repurposing for HCoVs</title><p id="Par10">The basis for the proposed network-based drug repurposing methodologies rests on the notions that the proteins that associate with and functionally govern viral infection are localized in the corresponding subnetwork (Fig. <xref rid="Fig1" ref-type="fig">1a</xref>) within the comprehensive human interactome network. For a drug with multiple targets to be effective against an HCoV, its target proteins should be within or in the immediate vicinity of the corresponding subnetwork in the human protein–protein interactome (Fig. <xref rid="Fig1" ref-type="fig">1</xref>), as we demonstrated in multiple diseases<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR22">22</xref>,<xref ref-type="bibr" rid="CR23">23</xref>,<xref ref-type="bibr" rid="CR28">28</xref></sup> using this network-based strategy. We used a state-of-the-art network proximity measure to quantify the relationship between HCoV-specific subnetwork (Fig. <xref rid="Fig3" ref-type="fig">3a</xref>) and drug targets in the human interactome. We constructed a drug–target network by assembling target information for more than 2000 FDA-approved or experimental drugs (see Materials and methods). To improve the quality and completeness of the human protein interactome network, we integrated PPIs with five types of experimental data: (1) binary PPIs from 3D protein structures; (2) binary PPIs from unbiased high-throughput yeast-two-hybrid assays; (3) experimentally identified kinase-substrate interactions; (4) signaling networks derived from experimental data; and (5) literature-derived PPIs with various experimental evidence (see Materials and methods). We used a <italic>Z</italic>-score (<italic>Z</italic>) measure and permutation test to reduce the study bias in network proximity analyses (including hub nodes in the human interactome network by literature-derived PPI data bias) as described in our recent studies<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR28">28</xref></sup>.</p><p id="Par11">In total, we computationally identified 135 drugs that were associated (<italic>Z</italic> < −1.5 and <italic>P</italic> < 0.05, permutation test) with the HCoV–host interactome (Fig. <xref rid="Fig4" ref-type="fig">4a</xref>, Supplementary Tables <xref rid="MOESM1" ref-type="media">S4</xref> and <xref rid="MOESM1" ref-type="media">5</xref>). To validate bias of the pooled cellular proteins from six CoVs, we further calculated the network proximities of all the drugs for four CoVs with a large number of know host proteins, including SARS-CoV, MERS-CoV, IBV, and MHV, separately. We found that the <italic>Z</italic>-scores showed consistency among the pooled 119 HCoV-associated proteins and other four individual CoVs (Fig. <xref rid="Fig4" ref-type="fig">4b</xref>). The Pearson correlation coefficients of the proximities of all the drugs for the pooled HCoV are 0.926 vs. SARS-CoV (<italic>P</italic> < 0.001, <italic>t</italic> distribution), 0.503 vs. MERS-CoV (<italic>P</italic> < 0.001), 0.694 vs. IBV (<italic>P</italic> < 0.001), and 0.829 vs. MHV (<italic>P</italic> < 0.001). These network proximity analyses offer putative repurposable candidates for potential prevention and treatment of HCoVs.<fig id="Fig4"><label>Fig. 4</label><caption><title>A discovered drug-HCoV network.</title><p><bold>a</bold> A subnetwork highlighting network-predicted drug-HCoV associations connecting 135 drugs and HCoVs. From the 2938 drugs evaluated, 135 ones achieved significant proximities between drug targets and the HCoV-associated proteins in the human interactome network. Drugs are colored by their first-level of the Anatomical Therapeutic Chemical (ATC) classification system code. <bold>b</bold> A heatmap highlighting network proximity values for SARS-CoV, MERS-CoV, IBV, and MHV, respectively. Color key denotes network proximity (<italic>Z</italic>-score) between drug targets and the HCoV-associated proteins in the human interactome network. <italic>P</italic> value was computed by permutation test.</p></caption><graphic xlink:href="41421_2020_153_Fig4_HTML" id="d29e735"></graphic></fig></p></sec><sec id="Sec6"><title>Discovery of repurposable drugs for HCoV</title><p id="Par12">To further validate the 135 repurposable drugs against HCoVs, we first performed gene set enrichment analysis (GSEA) using transcriptome data of MERS-CoV and SARS-CoV infected host cells (see Methods). These transcriptome data were used as gene signatures for HCoVs. Additionally, we downloaded the gene expression data of drug-treated human cell lines from the Connectivity Map (CMAP) database<sup><xref ref-type="bibr" rid="CR36">36</xref></sup> to obtain drug–gene signatures. We calculated a GSEA score (see Methods) for each drug and used this score as an indication of bioinformatics validation of the 135 drugs. Specifically, an enrichment score (ES) was calculated for each HCoV data set, and ES > 0 and <italic>P</italic> < 0.05 (permutation test) was used as cut-off for a significant association of gene signatures between a drug and a specific HCoV data set. The GSEA score, ranging from 0 to 3, is the number of data sets that met these criteria for a specific drug. Mesalazine (an approved drug for inflammatory bowel disease), sirolimus (an approved immunosuppressive drug), and equilin (an approved agonist of the estrogen receptor for menopausal symptoms) achieved the highest GSEA scores of 3, followed by paroxetine and melatonin with GSEA scores of 2. We next selected 16 high-confidence repurposable drugs (Fig. <xref rid="Fig5" ref-type="fig">5a</xref> and Table <xref rid="Tab1" ref-type="table">1</xref>) against HCoVs using subject matter expertise based on a combination of factors: (i) strength of the network-predicted associations (a smaller network proximity score in Supplementary Table <xref rid="MOESM1" ref-type="media">S4</xref>); (ii) validation by GSEA analyses; (iii) literature-reported antiviral evidence, and (iv) fewer clinically reported side effects. Specifically, we showcased several selected repurposable drugs with literature-reported antiviral evidence as below.<fig id="Fig5"><label>Fig. 5</label><caption><title>A discovered drug-protein-HCoV network for 16 candidate repurposable drugs.</title><p><bold>a</bold> Network-predicted evidence and gene set enrichment analysis (GSEA) scores for 16 potential repurposable drugs for HCoVs. The overall connectivity of the top drug candidates to the HCoV-associated proteins was examined. Most of these drugs indirectly target HCoV-associated proteins via the human protein–protein interaction networks. All the drug–target-HCoV-associated protein connections were examined, and those proteins with at least five connections are shown. The box heights for the proteins indicate the number of connections. GSEA scores for eight drugs were not available (NA) due to the lack of transcriptome profiles for the drugs. <bold>b</bold>–<bold>e</bold> Inferred mechanism-of-action networks for four selected drugs: <bold>b</bold> toremifene (first-generation nonsteroidal-selective estrogen receptor modulator), <bold>c</bold> irbesartan (an angiotensin receptor blocker), <bold>d</bold> mercaptopurine (an antimetabolite antineoplastic agent with immunosuppressant properties), and <bold>e</bold> melatonin (a biogenic amine for treating circadian rhythm sleep disorders).</p></caption><graphic xlink:href="41421_2020_153_Fig5_HTML" id="d29e789"></graphic></fig><table-wrap id="Tab1"><label>Table 1</label><caption><p>Top 16 network-predicted repurposable drugs with literature-derived antiviral evidence.</p></caption><graphic position="anchor" xlink:href="41421_2020_153_Tab1_HTML" id="d29e798"></graphic><table-wrap-foot><p><italic>HBV</italic> hepatitis B virus, <italic>HCV</italic> hepatitis C virus, <italic>HDV</italic> hepatitis delta virus, <italic>EBOV</italic> Ebola viruses, <italic>ZEBOV-GP</italic> Zaire Ebola virus glycoprotein, <italic>HIV</italic> human immunodeficiency virus, <italic>EBV</italic> Epstein-Barr virus, <italic>ANDV</italic> Andes orthohantavirus, <italic>EMCV</italic> encephalomyocarditis virus, <italic>FECV</italic> feline enteric coronavirus, <italic>RSV</italic> respiratory syncytial virus, <italic>EV71</italic> enterovirus 71, <italic>HSV-1 and -2</italic> herpes simplex viruses, <italic>CVB</italic><sub><italic>4</italic></sub> Coxsackievirus B<sub>4</sub>.</p></table-wrap-foot></table-wrap></p><sec id="Sec7"><title>Selective estrogen receptor modulators</title><p id="Par13">An overexpression of estrogen receptor has been shown to play a crucial role in inhibiting viral replication<sup><xref ref-type="bibr" rid="CR37">37</xref></sup>. Selective estrogen receptor modulators (SERMs) have been reported to play a broader role in inhibiting viral replication through the non-classical pathways associated with estrogen receptor<sup><xref ref-type="bibr" rid="CR37">37</xref></sup>. SERMs interfere at the post viral entry step and affect the triggering of fusion, as the SERMs’ antiviral activity still can be observed in the absence of detectable estrogen receptor expression<sup><xref ref-type="bibr" rid="CR18">18</xref></sup>. Toremifene (<italic>Z</italic> = –3.23, Fig. <xref rid="Fig5" ref-type="fig">5a</xref>), the first generation of nonsteroidal SERM, exhibits potential effects in blocking various viral infections, including MERS-CoV, SARS-CoV, and Ebola virus in established cell lines<sup><xref ref-type="bibr" rid="CR17">17</xref>,<xref ref-type="bibr" rid="CR38">38</xref></sup>. Compared to the classical ESR1-related antiviral pathway, toremifene prevents fusion between the viral and endosomal membrane by interacting with and destabilizing the virus membrane glycoprotein, and eventually inhibiting viral replication<sup><xref ref-type="bibr" rid="CR39">39</xref></sup>. As shown in Fig. <xref rid="Fig5" ref-type="fig">5b</xref>, toremifene potentially affects several key host proteins associated with HCoV, such as RPL19, HNRNPA1, NPM1, EIF3I, EIF3F, and EIF3E<sup><xref ref-type="bibr" rid="CR40">40</xref>,<xref ref-type="bibr" rid="CR41">41</xref></sup>. Equilin (<italic>Z</italic> = –2.52 and GSEA score = 3), an estrogenic steroid produced by horses, also has been proven to have moderate activity in inhibiting the entry of Zaire Ebola virus glycoprotein and human immunodeficiency virus (ZEBOV-GP/HIV)<sup><xref ref-type="bibr" rid="CR18">18</xref></sup>. Altogether, network-predicted SERMs (such as toremifene and equilin) offer candidate repurposable drugs for 2019-nCoV/SARS-CoV-2.</p></sec><sec id="Sec8"><title>Angiotensin receptor blockers</title><p id="Par14">Angiotensin receptor blockers (ARBs) have been reported to associate with viral infection, including HCoVs<sup><xref ref-type="bibr" rid="CR42">42</xref>–<xref ref-type="bibr" rid="CR44">44</xref></sup>. Irbesartan (<italic>Z</italic> = –5.98), a typical ARB, was approved by the FDA for treatment of hypertension and diabetic nephropathy. Here, network proximity analysis shows a significant association between irbesartan’s targets and HCoV-associated host proteins in the human interactome. As shown in Fig. <xref rid="Fig5" ref-type="fig">5c</xref>, irbesartan targets SLC10A1, encoding the sodium/bile acid cotransporter (NTCP) protein that has been identified as a functional preS1-specific receptor for the hepatitis B virus (HBV) and the hepatitis delta virus (HDV). Irbesartan can inhibit NTCP, thus inhibiting viral entry<sup><xref ref-type="bibr" rid="CR45">45</xref>,<xref ref-type="bibr" rid="CR46">46</xref></sup>. SLC10A1 interacts with C11orf74, a potential transcriptional repressor that interacts with nsp-10 of SARS-CoV<sup><xref ref-type="bibr" rid="CR47">47</xref></sup>. There are several other ARBs (such as eletriptan, frovatriptan, and zolmitriptan) in which their targets are potentially associated with HCoV-associated host proteins in the human interactome.</p></sec><sec id="Sec9"><title>Immunosuppressant or antineoplastic agents</title><p id="Par15">Previous studies have confirmed the mammalian target of rapamycin complex 1 (mTORC1) as the key factor in regulating various viruses’ replications, including Andes orthohantavirus and coronavirus<sup><xref ref-type="bibr" rid="CR48">48</xref>,<xref ref-type="bibr" rid="CR49">49</xref></sup>. Sirolimus (<italic>Z</italic> = –2.35 and GSEA score = 3), an inhibitor of mammalian target of rapamycin (mTOR), was reported to effectively block viral protein expression and virion release effectively<sup><xref ref-type="bibr" rid="CR50">50</xref></sup>. Indeed, the latest study revealed the clinical application: sirolimus reduced MERS-CoV infection by over 60%<sup><xref ref-type="bibr" rid="CR51">51</xref></sup>. Moreover, sirolimus usage in managing patients with severe H1N1 pneumonia and acute respiratory failure can improve those patients’ prognosis significantly<sup><xref ref-type="bibr" rid="CR50">50</xref></sup>. Mercaptopurine (<italic>Z</italic> = –2.44 and GSEA score = 1), an antineoplastic agent with immunosuppressant property, has been used to treat cancer since the 1950s and expanded its application to several auto-immune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and Crohn’s disease<sup><xref ref-type="bibr" rid="CR52">52</xref></sup>. Mercaptopurine has been reported as a selective inhibitor of both SARS-CoV and MERS-CoV by targeting papain-like protease which plays key roles in viral maturation and antagonism to interferon stimulation<sup><xref ref-type="bibr" rid="CR53">53</xref>,<xref ref-type="bibr" rid="CR54">54</xref></sup>. Mechanistically, mercaptopurine potentially target several host proteins in HCoVs, such as JUN, PABPC1, NPM1, and NCL<sup><xref ref-type="bibr" rid="CR40">40</xref>,<xref ref-type="bibr" rid="CR55">55</xref></sup> (Fig. <xref rid="Fig5" ref-type="fig">5d</xref>).</p></sec><sec id="Sec10"><title>Anti-inflammatory agents</title><p id="Par16">Inflammatory pathways play essential roles in viral infections<sup><xref ref-type="bibr" rid="CR56">56</xref>,<xref ref-type="bibr" rid="CR57">57</xref></sup>. As a biogenic amine, melatonin (<italic>N</italic>-acetyl-5-methoxytryptamine) (<italic>Z</italic> = –1.72 and GSEA score = 2) plays a key role in various biological processes, and offers a potential strategy in the management of viral infections<sup><xref ref-type="bibr" rid="CR58">58</xref>,<xref ref-type="bibr" rid="CR59">59</xref></sup>. Viral infections are often associated with immune-inflammatory injury, in which the level of oxidative stress increases significantly and leaves negative effects on the function of multiple organs<sup><xref ref-type="bibr" rid="CR60">60</xref></sup>. The antioxidant effect of melatonin makes it a putative candidate drug to relieve patients’ clinical symptoms in antiviral treatment, even though melatonin cannot eradicate or even curb the viral replication or transcription<sup><xref ref-type="bibr" rid="CR61">61</xref>,<xref ref-type="bibr" rid="CR62">62</xref></sup>. In addition, the application of melatonin may prolong patients’ survival time, which may provide a chance for patients’ immune systems to recover and eventually eradicate the virus. As shown in Fig. <xref rid="Fig5" ref-type="fig">5e</xref>, melatonin indirectly targets several HCoV cellular targets, including ACE2, BCL2L1, JUN, and IKBKB. Eplerenone (<italic>Z</italic> = –1.59), an aldosterone receptor antagonist, is reported to have a similar anti-inflammatory effect as melatonin. By inhibiting mast-cell-derived proteinases and suppressing fibrosis, eplerenone can improve survival of mice infected with encephalomyocarditis virus<sup><xref ref-type="bibr" rid="CR63">63</xref></sup>.</p><p id="Par17">In summary, our network proximity analyses offer multiple candidate repurposable drugs that target diverse cellular pathways for potential prevention and treatment of 2019-nCoV/SARS-CoV-2. However, further preclinical experiments<sup><xref ref-type="bibr" rid="CR64">64</xref></sup> and clinical trials are required to verify the clinical benefits of these network-predicted candidates before clinical use.</p></sec></sec><sec id="Sec11"><title>Network-based identification of potential drug combinations for 2019-nCoV/SARS-CoV-2</title><p id="Par18">Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating various viral infections<sup><xref ref-type="bibr" rid="CR65">65</xref></sup>. However, our ability to identify and validate effective combinations is limited by a combinatorial explosion, driven by both the large number of drug pairs and dosage combinations. In our recent study, we proposed a novel network-based methodology to identify clinically efficacious drug combinations<sup><xref ref-type="bibr" rid="CR28">28</xref></sup>. Relying on approved drug combinations for hypertension and cancer, we found that a drug combination was therapeutically effective only if it was captured by the “<italic>Complementary Exposure</italic>” pattern: the targets of the drugs both hit the disease module, but target separate neighborhoods (Fig. <xref rid="Fig6" ref-type="fig">6a</xref>). Here we sought to identify drug combinations that may provide a synergistic effect in potentially treating 2019-nCoV/SARS-CoV-2 with well-defined mechanism-of-action by network analysis. For the 16 potential repurposable drugs (Fig. <xref rid="Fig5" ref-type="fig">5a</xref>, Table <xref rid="Tab1" ref-type="table">1</xref>), we showcased three network-predicted candidate drug combinations for 2019-nCoV/SARS-CoV-2. All predicted possible combinations can be found in Supplementary Table <xref rid="MOESM1" ref-type="media">S6</xref>.<fig id="Fig6"><label>Fig. 6</label><caption><title>Network-based rational design of drug combinations for 2019-nCoV/SARS-CoV-2.</title><p><bold>a</bold> The possible exposure mode of the HCoV-associated protein module to the pairwise drug combinations. An effective drug combination will be captured by the “<italic>Complementary Exposure</italic>” pattern: the targets of the drugs both hit the HCoV–host subnetwork, but target separate neighborhoods in the human interactome network. <italic>Z</italic><sub>CA</sub> and <italic>Z</italic><sub>CB</sub> denote the network proximity (<italic>Z</italic>-score) between targets (Drugs A and B) and a specific HCoV. <italic>S</italic><sub>AB</sub> denotes separation score (see Materials and methods) of targets between Drug A and Drug B. <bold>b</bold>–<bold>d</bold> Inferred mechanism-of-action networks for three selected pairwise drug combinations: <bold>b</bold> sirolimus (a potent immunosuppressant with both antifungal and antineoplastic properties) plus dactinomycin (an RNA synthesis inhibitor for treatment of various tumors), <bold>c</bold> toremifene (first-generation nonsteroidal-selective estrogen receptor modulator) plus emodin (an experimental drug for the treatment of polycystic kidney), and <bold>d</bold> melatonin (a biogenic amine for treating circadian rhythm sleep disorders) plus mercaptopurine (an antimetabolite antineoplastic agent with immunosuppressant properties).</p></caption><graphic xlink:href="41421_2020_153_Fig6_HTML" id="d29e1116"></graphic></fig></p><sec id="Sec12"><title>Sirolimus plus Dactinomycin</title><p id="Par19">Sirolimus, an inhibitor of mTOR with both antifungal and antineoplastic properties, has demonstrated to improve outcomes in patients with severe H1N1 pneumonia and acute respiratory failure<sup><xref ref-type="bibr" rid="CR50">50</xref></sup>. The mTOR signaling plays an essential role for MERS-CoV infection<sup><xref ref-type="bibr" rid="CR66">66</xref></sup>. Dactinomycin, also known actinomycin D, is an approved RNA synthesis inhibitor for treatment of various cancer types. An early study showed that dactinomycin (1 μg/ml) inhibited the growth of feline enteric CoV<sup><xref ref-type="bibr" rid="CR67">67</xref></sup>. As shown in Fig. <xref rid="Fig6" ref-type="fig">6b</xref>, our network analysis shows that sirolimus and dactinomycin synergistically target HCoV-associated host protein subnetwork by “<italic>Complementary Exposure</italic>” pattern, offering potential combination regimens for treatment of HCoV. Specifically, sirolimus and dactinomycin may inhibit both mTOR signaling and RNA synthesis pathway (including DNA topoisomerase 2-alpha (TOP2A) and DNA topoisomerase 2-beta (TOP2B)) in HCoV-infected cells (Fig. <xref rid="Fig6" ref-type="fig">6b</xref>).</p></sec><sec id="Sec13"><title>Toremifene plus Emodin</title><p id="Par20">Toremifene is among the approved first-generation nonsteroidal SERMs for the treatment of metastatic breast cancer<sup><xref ref-type="bibr" rid="CR68">68</xref></sup>. SERMs (including toremifene) inhibited Ebola virus infection<sup><xref ref-type="bibr" rid="CR18">18</xref></sup> by interacting with and destabilizing the Ebola virus glycoprotein<sup><xref ref-type="bibr" rid="CR39">39</xref></sup>. In vitro assays have demonstrated that toremifene inhibited growth of MERS-CoV<sup><xref ref-type="bibr" rid="CR17">17</xref>,<xref ref-type="bibr" rid="CR69">69</xref></sup> and SARA-CoV<sup><xref ref-type="bibr" rid="CR38">38</xref></sup> (Table <xref rid="Tab1" ref-type="table">1</xref>). Emodin, an anthraquinone derivative extracted from the roots of rheum tanguticum, has been reported to have various anti-virus effects. Specifically, emdoin inhibited SARS-CoV-associated 3a protein<sup><xref ref-type="bibr" rid="CR70">70</xref></sup>, and blocked an interaction between the SARS-CoV spike protein and ACE2 (ref. <sup><xref ref-type="bibr" rid="CR71">71</xref></sup>). Altogether, network analyses and published experimental data suggested that combining toremifene and emdoin offered a potential therapeutic approach for 2019-nCoV/SARS-CoV-2 (Fig. <xref rid="Fig6" ref-type="fig">6c</xref>).</p></sec><sec id="Sec14"><title>Mercaptopurine plus Melatonin</title><p id="Par21">As shown in Fig. <xref rid="Fig5" ref-type="fig">5a</xref>, targets of both mercaptopurine and melatonin showed strong network proximity with HCoV-associated host proteins in the human interactome network. Recent in vitro and in vivo studies identified mercaptopurine as a selective inhibitor of both SARS-CoV and MERS-CoV by targeting papain-like protease<sup><xref ref-type="bibr" rid="CR53">53</xref>,<xref ref-type="bibr" rid="CR54">54</xref></sup>. Melatonin was reported in potential antiviral infection via its anti-inflammatory and antioxidant effects<sup><xref ref-type="bibr" rid="CR58">58</xref>–<xref ref-type="bibr" rid="CR62">62</xref></sup>. Melatonin indirectly regulates ACE2 expression, a key entry receptor involved in viral infection of HCoVs, including 2019-nCoV/SARS-CoV-2 (ref. <sup><xref ref-type="bibr" rid="CR33">33</xref></sup>). Specifically, melatonin was reported to inhibit calmodulin and calmodulin interacts with ACE2 by inhibiting shedding of its ectodomain, a key infectious process of SARS-CoV<sup><xref ref-type="bibr" rid="CR72">72</xref>,<xref ref-type="bibr" rid="CR73">73</xref></sup>. JUN, also known as c-Jun, is a key host protein involving in HCoV infectious bronchitis virus<sup><xref ref-type="bibr" rid="CR74">74</xref></sup>. As shown in Fig. <xref rid="Fig6" ref-type="fig">6d</xref>, mercaptopurine and melatonin may synergistically block c-Jun signaling by targeting multiple cellular targets. In summary, combination of mercaptopurine and melatonin may offer a potential combination therapy for 2019-nCoV/SARS-CoV-2 by synergistically targeting papain-like protease, ACE2, c-Jun signaling, and anti-inflammatory pathways (Fig. <xref rid="Fig6" ref-type="fig">6d</xref>). However, further experimental observations on ACE2 pathways by melatonin in 2019-nCoV/SARS-CoV-2 are highly warranted.</p></sec></sec></sec><sec id="Sec15" sec-type="discussion"><title>Discussion</title><p id="Par22">In this study, we presented a network-based methodology for systematic identification of putative repurposable drugs and drug combinations for potential treatment of 2019-nCoV/SARS-CoV-2. Integration of drug–target networks, HCoV–host interactions, HCoV-induced transcriptome in human cell lines, and human protein–protein interactome network are essential for such identification. Based on comprehensive evaluation, we prioritized 16 candidate repurposable drugs (Fig. <xref rid="Fig5" ref-type="fig">5</xref>) and 3 potential drug combinations (Fig. <xref rid="Fig6" ref-type="fig">6</xref>) for targeting 2019-nCoV/SARS-CoV-2. However, although the majority of predictions have been validated by various literature data (Table <xref rid="Tab1" ref-type="table">1</xref>), all network-predicted repurposable drugs and drug combinations must be validated in various 2019-nCoV/SARS-CoV-2 experimental assays<sup><xref ref-type="bibr" rid="CR64">64</xref></sup> and randomized clinical trials before being used in patients.</p><p id="Par23">We acknowledge several limitations in the current study. Although 2019-nCoV/SARS-CoV-2 shared high nucleotide sequence identity with other HCoVs (Fig. <xref rid="Fig2" ref-type="fig">2</xref>), our predictions are not 2019-nCoV/SARS-CoV-2 specific by lack of the known host proteins on 2019-nCoV/SARS-CoV-2. We used a low binding affinity value of 10 μM as a threshold to define a physical drug–target interaction. However, a stronger binding affinity threshold (e.g., 1 μM) may be a more suitable cut-off in drug discovery, although it will generate a smaller drug–target network. Although sizeable efforts were made for assembling large scale, experimentally reported drug–target networks from publicly available databases, the network data may be incomplete and some drug–target interactions may be functional associations, instead of physical bindings. For example, Silvestrol, a natural product from the flavagline, was found to have antiviral activity against Ebola<sup><xref ref-type="bibr" rid="CR75">75</xref></sup> and Coronaviruses<sup><xref ref-type="bibr" rid="CR76">76</xref></sup>. After adding its target, an RNA helicase enzyme EIF4A<sup><xref ref-type="bibr" rid="CR76">76</xref></sup>, silvestrol was predicted to be significantly associated with HCoVs (<italic>Z</italic> = –1.24, <italic>P</italic> = 0.041) by network proximity analysis. To increase coverage of drug–target networks, we may use computational approaches to systematically predict the drug-target interactions further<sup><xref ref-type="bibr" rid="CR25">25</xref>,<xref ref-type="bibr" rid="CR26">26</xref></sup>. In addition, the collected virus–host interactions are far from completeness and the quality can be influenced by multiple factors, including different experimental assays and human cell line models. We may computationally predict a new virus–host interactome for 2019-nCoV/SARS-CoV-2 using sequence-based and structure-based approaches<sup><xref ref-type="bibr" rid="CR77">77</xref></sup>. Drug targets representing nodes within cellular networks are often intrinsically coupled with both therapeutic and adverse profiles<sup><xref ref-type="bibr" rid="CR78">78</xref></sup>, as drugs can inhibit or activate protein functions (including antagonists vs. agonists). The current systems pharmacology model cannot separate therapeutic (antiviral) effects from those predictions due to lack of detailed pharmacological effects of drug targets and unknown functional consequences of virus–host interactions. Comprehensive identification of the virus–host interactome for 2019-nCoV/SARS-CoV-2, with specific biological effects using functional genomics assays<sup><xref ref-type="bibr" rid="CR79">79</xref>,<xref ref-type="bibr" rid="CR80">80</xref></sup>, will significantly improve the accuracy of the proposed network-based methodologies further.</p><p id="Par24">Owing to a lack of the complete drug-target information (such as the molecular “promiscuity” of drugs), the dose–response and dose–toxicity effects for both repurposable drugs and drug combinations cannot be identified in the current network models. For example, Mesalazine, an approved drug for inflammatory bowel disease, is a top network-predicted repurposable drug associated with HCoVs (Fig. <xref rid="Fig5" ref-type="fig">5a</xref>). Yet, several clinical studies showed the potential pulmonary toxicities (including pneumonia) associated with mesalazine usage<sup><xref ref-type="bibr" rid="CR81">81</xref>,<xref ref-type="bibr" rid="CR82">82</xref></sup>. Integration of lung-specific gene expression<sup><xref ref-type="bibr" rid="CR23">23</xref></sup> of 2019-nCoV/SARS-CoV-2 host proteins and physiologically based pharmacokinetic modeling<sup><xref ref-type="bibr" rid="CR83">83</xref></sup> may reduce side effects of repurposable drugs or drug combinations. Preclinical studies are warranted to evaluate in vivo efficiency and side effects before clinical trials. Furthermore, we only limited to predict pairwise drug combinations based on our previous network-based framework<sup><xref ref-type="bibr" rid="CR28">28</xref></sup>. However, we expect that our methodology remain to be a useful network-based tool for prediction of combining multiple drugs toward exploring network relationships of multiple drugs’ targets with the HCoV–host subnetwork in the human interactome. Finally, we aimed to systematically identify repurposable drugs by specifically targeting nCoV host proteins only. Thus, our current network models cannot predict repurposable drugs from the existing anti-virus drugs that target virus proteins only. Thus, combination of the existing anti-virus drugs (such as remdesivir<sup><xref ref-type="bibr" rid="CR64">64</xref></sup>) with the network-predicted repurposable drugs (Fig. <xref rid="Fig5" ref-type="fig">5</xref>) or drug combinations (Fig. <xref rid="Fig6" ref-type="fig">6</xref>) may improve coverage of current network-based methodologies by utilizing multi-layer network framework<sup><xref ref-type="bibr" rid="CR16">16</xref></sup>.</p><p id="Par25">In conclusion, this study offers a powerful, integrative network-based systems pharmacology methodology for rapid identification of repurposable drugs and drug combinations for the potential treatment of 2019-nCoV/SARS-CoV-2. Our approach can minimize the translational gap between preclinical testing results and clinical outcomes, which is a significant problem in the rapid development of efficient treatment strategies for the emerging 2019-nCoV/SARS-CoV-2 outbreak. From a translational perspective, if broadly applied, the network tools developed here could help develop effective treatment strategies for other emerging viral infections and other human complex diseases as well.</p></sec><sec id="Sec16" sec-type="materials|methods"><title>Methods and materials</title><sec id="Sec17"><title>Genome information and phylogenetic analysis</title><p id="Par26">In total, we collected DNA sequences and protein sequences for 15 HCoVs, including three most recent 2019-nCoV/SARS-CoV-2 genomes, from the NCBI GenBank database (28 January 2020, Supplementary Table <xref rid="MOESM1" ref-type="media">S1</xref>). Whole-genome alignment and protein sequence identity calculation were performed by Multiple Sequence Alignment in EMBL-EBI database (<ext-link ext-link-type="uri" xlink:href="https://www.ebi.ac.uk/">https://www.ebi.ac.uk/</ext-link>) with default parameters. The neighbor joining (NJ) tree was computed from the pairwise phylogenetic distance matrix using MEGA X<sup><xref ref-type="bibr" rid="CR84">84</xref></sup> with 1000 bootstrap replicates. The protein alignment and phylogenetic tree of HCoVs were constructed by MEGA X<sup><xref ref-type="bibr" rid="CR84">84</xref></sup>.</p></sec><sec id="Sec18"><title>Building the virus–host interactome</title><p id="Par27">We collected HCoV–host protein interactions from various literatures based on our sizeable efforts. The HCoV-associated host proteins of several HCoVs, including SARS-CoV, MERS-CoV, IBV, MHV, HCoV-229E, and HCoV-NL63 were pooled. These proteins were either the direct targets of HCoV proteins or were involved in critical pathways of HCoV infection identified by multiple experimental sources, including high-throughput yeast-two-hybrid (Y2H) systems, viral protein pull-down assay, in vitro co-immunoprecipitation and RNA knock down experiment. In total, the virus–host interaction network included 6 HCoVs with 119 host proteins (Supplementary Table <xref rid="MOESM1" ref-type="media">S3</xref>).</p></sec><sec id="Sec19"><title>Functional enrichment analysis</title><p id="Par28">Next, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses to evaluate the biological relevance and functional pathways of the HCoV-associated proteins. All functional analyses were performed using Enrichr<sup><xref ref-type="bibr" rid="CR85">85</xref></sup>.</p></sec><sec id="Sec20"><title>Building the drug–target network</title><p id="Par29">Here, we collected drug–target interaction information from the DrugBank database (v4.3)<sup><xref ref-type="bibr" rid="CR86">86</xref></sup>, Therapeutic Target Database (TTD)<sup><xref ref-type="bibr" rid="CR87">87</xref></sup>, PharmGKB database, ChEMBL (v20)<sup><xref ref-type="bibr" rid="CR88">88</xref></sup>, BindingDB<sup><xref ref-type="bibr" rid="CR89">89</xref></sup>, and IUPHAR/BPS Guide to PHARMACOLOGY<sup><xref ref-type="bibr" rid="CR90">90</xref></sup>. The chemical structure of each drug with SMILES format was extracted from DrugBank<sup><xref ref-type="bibr" rid="CR86">86</xref></sup>. Here, drug–target interactions meeting the following three criteria were used: (i) binding affinities, including <italic>K</italic><sub>i</sub>, <italic>K</italic><sub>d</sub>, IC<sub>50</sub>, or EC<sub>50</sub> each ≤10 μM; (ii) the target was marked as “reviewed” in the UniProt database<sup><xref ref-type="bibr" rid="CR91">91</xref></sup>; and (iii) the human target was represented by a unique UniProt accession number. The details for building the experimentally validated drug–target network are provided in our recent studies<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR23">23</xref>,<xref ref-type="bibr" rid="CR28">28</xref></sup>.</p></sec><sec id="Sec21"><title>Building the human protein–protein interactome</title><p id="Par30">To build a comprehensive list of human PPIs, we assembled data from a total of 18 bioinformatics and systems biology databases with five types of experimental evidence: (i) binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems; (ii) binary, physical PPIs from protein 3D structures; (iii) kinase-substrate interactions by literature-derived low-throughput or high-throughput experiments; (iv) signaling network by literature-derived low-throughput experiments; and (v) literature-curated PPIs identified by affinity purification followed by mass spectrometry (AP-MS), Y2H, or by literature-derived low-throughput experiments. All inferred data, including evolutionary analysis, gene expression data, and metabolic associations, were excluded. The genes were mapped to their Entrez ID based on the NCBI database<sup><xref ref-type="bibr" rid="CR92">92</xref></sup> as well as their official gene symbols based on GeneCards (<ext-link ext-link-type="uri" xlink:href="https://www.genecards.org/">https://www.genecards.org/</ext-link>). In total, the resulting human protein–protein interactome used in this study includes 351,444 unique PPIs (edges or links) connecting 17,706 proteins (nodes), representing a 50% increase in the number of the PPIs we have used previously. Detailed descriptions for building the human protein–protein interactome are provided in our previous studies<sup><xref ref-type="bibr" rid="CR13">13</xref>,<xref ref-type="bibr" rid="CR23">23</xref>,<xref ref-type="bibr" rid="CR28">28</xref>,<xref ref-type="bibr" rid="CR93">93</xref></sup>.</p></sec><sec id="Sec22"><title>Network proximity measure</title><p id="Par31">We posit that the human PPIs provide an unbiased, rational roadmap for repurposing drugs for potential treatment of HCoVs in which they were not originally approved. Given <italic>C</italic>, the set of host genes associated with a specific HCoV, and <italic>T</italic>, the set of drug targets, we computed the network proximity of <italic>C</italic> with the target set <italic>T</italic> of each drug using the “closest” method:<disp-formula id="Equ1"><label>1</label><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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\begin{document}$$\left\langle d_{CT} \right\rangle = \frac{1}{\vert\vert C \vert\vert +\vert\vert T \vert\vert}\left({\sum\limits_{c \in C}} {\min}_{t \in T}\,d\left( {c,t} \right) + {\sum\limits_{t \in T}} {\min}_{c \in C}\,d\left(c,t\right) \right),$$\end{document}</tex-math><mml:math id="M2"><mml:mrow><mml:mfenced close="⟩" open="⟨" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>∣</mml:mo><mml:mo>∣</mml:mo><mml:mi>C</mml:mi><mml:mo>∣</mml:mo><mml:mo>∣</mml:mo><mml:mo>+</mml:mo><mml:mo>∣</mml:mo><mml:mo>∣</mml:mo><mml:mi>T</mml:mi><mml:mo>∣</mml:mo><mml:mo>∣</mml:mo></mml:mrow></mml:mfrac><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:munder><mml:mrow><mml:mo mathsize="big">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo>∈</mml:mo><mml:mi>C</mml:mi></mml:mrow></mml:munder><mml:msub><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>∈</mml:mo><mml:mi>T</mml:mi></mml:mrow></mml:msub><mml:mspace width="0.25em"></mml:mspace><mml:mi>d</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mi>c</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:munder><mml:mrow><mml:mo mathsize="big">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>∈</mml:mo><mml:mi>T</mml:mi></mml:mrow></mml:munder><mml:msub><mml:mrow><mml:mi>min</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo>∈</mml:mo><mml:mi>C</mml:mi></mml:mrow></mml:msub><mml:mspace width="0.25em"></mml:mspace><mml:mi>d</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mi>c</mml:mi><mml:mo>,</mml:mo><mml:mi>t</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ1.gif" position="anchor"></graphic></alternatives></disp-formula>where <italic>d</italic>(<italic>c</italic>, <italic>t</italic>) is the shortest distance between gene <italic>c</italic> and <italic>t</italic> in the human protein interactome. The network proximity was converted to <italic>Z</italic>-score based on permutation tests:<disp-formula id="Equ2"><label>2</label><alternatives><tex-math id="M3">\documentclass[12pt]{minimal}
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\begin{document}$$Z_{d_{CT}} = \frac{{d_{CT} - \overline {d_r} }}{{\sigma _r}},$$\end{document}</tex-math><mml:math id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:mover accent="true"><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo accent="true">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ2.gif" position="anchor"></graphic></alternatives></disp-formula>where <inline-formula id="IEq1"><alternatives><tex-math id="M5">\documentclass[12pt]{minimal}
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\begin{document}$$\overline {d_r}$$\end{document}</tex-math><mml:math id="M6"><mml:mover accent="true"><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo accent="true">¯</mml:mo></mml:mover></mml:math><inline-graphic xlink:href="41421_2020_153_Article_IEq1.gif"></inline-graphic></alternatives></inline-formula> and <italic>σ</italic><sub><italic>r</italic></sub> were the mean and standard deviation of the permutation test repeated 1000 times, each time with two randomly selected gene lists with similar degree distributions to those of <italic>C</italic> and <italic>T</italic>. The corresponding <italic>P</italic> value was calculated based on the permutation test results. <italic>Z</italic>-score < −1.5 and <italic>P</italic> < 0.05 were considered significantly proximal drug–HCoV associations. All networks were visualized using Gephi 0.9.2 (<ext-link ext-link-type="uri" xlink:href="https://gephi.org/">https://gephi.org/</ext-link>).</p></sec><sec id="Sec23"><title>Network-based rational prediction of drug combinations</title><p id="Par32">For this network-based approach for drug combinations to be effective, we need to establish if the topological relationship between two drug–target modules reflects biological and pharmacological relationships, while also quantifying their network-based relationship between drug targets and HCoV-associated host proteins (drug–drug–HCoV combinations). To identify potential drug combinations, we combined the top lists of drugs. Then, “separation” measure <italic>S</italic><sub><italic>AB</italic></sub> was calculated for each pair of drugs <italic>A</italic> and <italic>B</italic> using the following method:<disp-formula id="Equ3"><label>3</label><alternatives><tex-math id="M7">\documentclass[12pt]{minimal}
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\begin{document}$$S_{AB} = \left\langle {d_{AB}} \right\rangle - \frac{{\left\langle {d_{AA}} \right\rangle + \left\langle {d_{BB}} \right\rangle }}{2},$$\end{document}</tex-math><mml:math id="M8"><mml:mrow><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfenced close="⟩" open="⟨" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mfenced close="⟩" open="⟨" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close="⟩" open="⟨" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ3.gif" position="anchor"></graphic></alternatives></disp-formula>where <inline-formula id="IEq2"><alternatives><tex-math id="M9">\documentclass[12pt]{minimal}
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\begin{document}$$\left\langle {d_ \cdot } \right\rangle$$\end{document}</tex-math><mml:math id="M10"><mml:mfenced close="⟩" open="⟨" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mo>⋅</mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math><inline-graphic xlink:href="41421_2020_153_Article_IEq2.gif"></inline-graphic></alternatives></inline-formula> was calculated based on the “closest” method. Our key methodology is that a drug combination is therapeutically effective only if it follows a specific relationship to the disease module, as captured by <italic>Complementary Exposure</italic> patterns in targets’ modules of both drugs without overlapping toxic mechanisms<sup><xref ref-type="bibr" rid="CR28">28</xref></sup>.</p></sec><sec id="Sec24"><title>Gene set enrichment analysis</title><p id="Par33">We performed the gene set enrichment analysis as an additional prioritization method. We first collected three differential gene expression data sets of hosts infected by HCoVs from the NCBI Gene Expression Omnibus (GEO). Among them, two transcriptome data sets were SARS-CoV-infected samples from patient’s peripheral blood<sup><xref ref-type="bibr" rid="CR94">94</xref></sup> (GSE1739) and Calu-3 cells<sup><xref ref-type="bibr" rid="CR95">95</xref></sup> (GSE33267), respectively. One transcriptome data set was MERS-CoV-infected Calu-3 cells<sup><xref ref-type="bibr" rid="CR96">96</xref></sup> (GSE122876). Adjusted <italic>P</italic> value less than 0.01 was defined as differentially expressed genes. These data sets were used as HCoV–host signatures to evaluate the treatment effects of drugs. Differential gene expression in cells treated with various drugs were retrieved from the Connectivity Map (CMAP) database<sup><xref ref-type="bibr" rid="CR36">36</xref></sup>, and were used as gene profiles for the drugs. For each drug that was in both the CMAP data set and our drug–target network, we calculated an enrichment score (ES) for each HCoV signature data set based on previously described methods<sup><xref ref-type="bibr" rid="CR97">97</xref></sup> as follows:<disp-formula id="Equ4"><label>4</label><alternatives><tex-math id="M11">\documentclass[12pt]{minimal}
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\begin{document}$${\mathrm {ES}} = \left\{ {\begin{array}{*{20}{c}} {{\mathrm{ES}_{\mathrm{up}}} - {\mathrm {ES}_{\mathrm{down}}},{\mathrm {sgn}}\left( {{\mathrm {ES}_{\mathrm{up}}}} \right)\, \ne \,{\mathrm {sgn}}\left( {{\mathrm {ES}_{\mathrm{down}}}} \right)} \\ {0,\mathrm {else}} \end{array}} \right.$$\end{document}</tex-math><mml:math id="M12"><mml:mrow><mml:mi mathvariant="normal">ES</mml:mi><mml:mo>=</mml:mo><mml:mfenced close="" open="{" separators=""><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign="center"><mml:msub><mml:mrow><mml:mi mathvariant="normal">ES</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">up</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant="normal">ES</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">down</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mi mathvariant="normal">sgn</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">ES</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">up</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width="0.25em"></mml:mspace><mml:mo>≠</mml:mo><mml:mspace width="0.25em"></mml:mspace><mml:mi mathvariant="normal">sgn</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">ES</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">down</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign="center"><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi mathvariant="normal">else</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ4.gif" position="anchor"></graphic></alternatives></disp-formula>ES<sub>up</sub> and ES<sub>down</sub> were calculated separately for the up- and down-regulated genes from the HCoV signature data set using the same method. We first computed <italic>a</italic><sub>up/down</sub> and <italic>b</italic><sub>up/down</sub> as<disp-formula id="Equ5"><label>5</label><alternatives><tex-math id="M13">\documentclass[12pt]{minimal}
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\begin{document}$$a = \mathop{\max}\limits_{1 \le j \le s}\left( {\frac{j}{s} - \frac{{V\left( j \right)}}{r}} \right),$$\end{document}</tex-math><mml:math id="M14"><mml:mrow><mml:mi>a</mml:mi><mml:mo>=</mml:mo><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>j</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:munder><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mfrac><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>V</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ5.gif" position="anchor"></graphic></alternatives></disp-formula><disp-formula id="Equ6"><label>6</label><alternatives><tex-math id="M15">\documentclass[12pt]{minimal}
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\begin{document}$$b = \mathop{\max}\limits_{1 \le j \le s}\left( {\frac{{V\left( j \right)}}{r} - \frac{{j - 1}}{s}} \right),$$\end{document}</tex-math><mml:math id="M16"><mml:mrow><mml:mi>b</mml:mi><mml:mo>=</mml:mo><mml:munder><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>j</mml:mi><mml:mo>≤</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:munder><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mfrac><mml:mrow><mml:mi>V</mml:mi><mml:mfenced close=")" open="(" separators=""><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:mfrac><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>j</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href="41421_2020_153_Article_Equ6.gif" position="anchor"></graphic></alternatives></disp-formula>where <italic>j</italic> = 1, 2, …, <italic>s</italic> were the genes of HCoV signature data set sorted in ascending order by their rank in the gene profiles of the drug being evaluated. The rank of gene <italic>j</italic> is denoted by <italic>V</italic>(<italic>j</italic>), where 1 ≤ <italic>V</italic>(<italic>j</italic>) ≤ <italic>r</italic>, with <italic>r</italic> being the number of genes (12,849) from the drug profile. Then, ES<sub>up/down</sub> was set to <italic>a</italic><sub>up/down</sub> if <italic>a</italic><sub>up/down</sub> > <italic>b</italic><sub>up/down</sub>, and was set to −<italic>b</italic><sub>up/down</sub> if <italic>b</italic><sub>up/down</sub> > <italic>a</italic><sub>up/down</sub>. Permutation tests repeated 100 times using randomly generated gene lists with the same number of up- and down-regulated genes as the HCoV signature data set were performed to measure the significance of the ES scores. Drugs were considered to have potential treatment effect if ES > 0 and <italic>P</italic> < 0.05, and the number of such HCoV signature data sets were used as the final GSEA score that ranges from 0 to 3.</p></sec></sec><sec sec-type="supplementary-material"><title>Supplementary information</title><sec id="Sec25"><p><supplementary-material content-type="local-data" id="MOESM1"><media xlink:href="41421_2020_153_MOESM1_ESM.pdf"><caption><p>Supplementary Table S1-S6</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Yadi Zhou, Yuan Hou</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary Information</bold> accompanies the paper at (10.1038/s41421-020-0153-3).</p></sec><ack><title>Acknowledgements</title><p>This work was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) under Award Number K99 HL138272 and R00 HL138272 to F.C. The content of this publication does not necessarily reflect the views of the Cleveland Clinic.</p></ack><notes notes-type="author-contribution"><title>Author contributions</title><p>F.C. conceived the study. Y.Z. and Y.H. developed the network methodology and performed all computational experiments. J.S., Y.Z., Y.H., and W.M. performed data analysis. F.C., Y.Z., and Y.H. wrote and critically revised the manuscript with contributions from other co-authors.</p></notes><notes notes-type="data-availability"><title>Data availability</title><p>All predicted repurposable drugs and network-predicted drug combinations can be freely accessed at <ext-link ext-link-type="uri" xlink:href="https://github.com/ChengF-Lab/2019-nCoV">https://github.com/ChengF-Lab/2019-nCoV</ext-link>.</p></notes><notes notes-type="data-availability"><title>Code availability</title><p>All codes can be freely accessed at <ext-link ext-link-type="uri" xlink:href="https://github.com/ChengF-Lab/2019-nCoV">https://github.com/ChengF-Lab/2019-nCoV</ext-link>.</p></notes><notes notes-type="COI-statement"><title>Conflict of interest</title><p id="Par34">The authors declare that they have no conflict of interest.</p></notes><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zumla</surname><given-names>A</given-names></name><name><surname>Chan</surname><given-names>JF</given-names></name><name><surname>Azhar</surname><given-names>EI</given-names></name><name><surname>Hui</surname><given-names>DS</given-names></name><name><surname>Yuen</surname><given-names>KY</given-names></name></person-group><article-title>Coronaviruses—drug discovery and therapeutic options</article-title><source>Nat. 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