key: cord-0734737-b4mdiont
authors: Zhou, Yadi; Hou, Yuan; Shen, Jiayu; Huang, Yin; Martin, William; Cheng, Feixiong
title: Network-based Drug Repurposing for Human Coronavirus
date: 2020-02-05
journal: nan
DOI: 10.1101/2020.02.03.20020263
sha: 21100aba41a4bfb48d7dc37f1bf5dbb38bf3867a
doc_id: 734737
cord_uid: b4mdiont

Human Coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle east respiratory syndrome coronavirus (MERS-CoV), and 2019 novel coronavirus (2019-nCoV), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV. Drug repurposing, represented 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 has the highest nucleotide sequence identity with SARS-CoV (79.7%) among the six other known pathogenic HCoVs. Specifically, the envelope and nucleocapsid proteins of 2019-nCoV 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 known HCoV-host interactions in the human protein-protein interactome, we computationally identified 135 putative repurposable drugs for the potential prevention and treatment of HCoVs. In addition, we prioritized 16 potential anti-HCoV repurposable drugs (including melatonin, mercaptopurine, and sirolimus) that were further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. Finally, we showcased three potential drug combinations (including sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the Complementary Exposure pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human protein-protein interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations toward future clinical trials for HCoVs.

Coronaviruses (CoVs) typically affect the respiratory tract of mammals, including humans, and lead to mild to severe respiratory tract infections [1] . In the past 2 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 [2] . For example, 8,098 individuals were infected and 774 died in the SARS-CoV pandemic, which cost the global economy with an estimated $30 to $100 billion [3, 4] . According to the World Health Organization (WHO), as of November 2019, MERS-CoV has had a total of 2,494 diagnosed cases causing 858 deaths, the majority in Saudi Arabia [2] . In December 2019, the third pathogenic HCoV, named 2019 novel coronavirus (2019-nCoV), was found in Wuhan, China. As of February 02, 2020, there have been over 14,000 cases with ~300 deaths for the 2019-nCoV pandemic (https://www.cdc.gov/coronavirus/2019-ncov/index.html); furthermore, human-to-human transmission has occurred among close contacts [5] . However, there are currently no effective medications against 2019-nCoV. Several national and international research . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint groups are working on the development of vaccines to prevent and treat the 2019-nCoV, but effective vaccines are not available yet. There is an urgent need for the development of effective prevention and treatment strategies for 2019-nCoV outbreak.

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 [6] . 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 [7] . 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 [8] [9] [10] . However, experimental approaches for drug repurposing is costly and timeconsuming. [11] Computational approaches offer novel testable hypotheses for systematic drug repositioning [8-10, 12, 13] . 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 have high risk of drug resistance by the rapid evolution of virus genomes [1] .

Viruses (including HCoV) require host cellular factors for successful replication during infections [1] . Systematic identification of virus-host protein-protein interactions (PPIs) offers an effective way toward elucidation of the mechanisms of viral infection [14, infections [1] , including SARS-CoV [16] , MERS-CoV [16] , Ebola virus [17] , and Zika virus [13, [18] [19] [20] . We recently presented an integrated antiviral drug discovery pipeline that incorporated gene-trap insertional mutagenesis, known functional drug-gene network, and bioinformatics analyses [13] . This methodology allows to identify several candidate repurposable drugs for Ebola virus [10, 13] . Our work over the last decade has demonstrated how network strategies can, for example, be used to identify effective repurposable drugs [12, [21] [22] [23] [24] and drug combinations [25] for multiple human diseases. For example, network-based drug-disease proximity that 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 [12, 22, 25] .

In this study, we present an integrative, antiviral drug repurposing methodology that 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 [26] ; 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 (Figure 1) . We follow this analysis with bioinformatics validation of drug-induced gene signatures and HCoV-induced . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint transcriptomics in human cell lines to inspect the postulated mechanism-of-action in a specific HCoV for which we propose repurposing (Figure 1 ).

To date, 7 pathogenic HCoVs (Figure 2A and 2B) have been found [1, 27] : (i) 2019-nCoV, SARS-CoV, MERS-CoV, HCoV-OC43, and HCoV-HKU1 are b genera, and (ii) HCoV-NL63 and HCoV-229E are a genera. We performed the phylogenetic analyses using the whole genome sequence data from 15 HCoVs to inspect the evolutionary relationship of 2019-nCoV with other HCoVs. We found that the whole genomes of 2019-nCoV had ~99.99% nucleotide sequence identity across three diagnosed patients (Supplementary Table S1 ). The 2019-nCoV shares the highest nucleotide sequence identity (79.7%) with SARS-CoV among the 6 other known pathogenic HCoVs, revealing conserved evolutionary relationship between 2019-nCoV and SARS-CoV (Figure 2A ).

HCoVs have five major protein regions for virus structure assembly and viral replications [27] , including replicase complex (ORF1ab), spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins ( Figure 2B) . The ORF1ab gene encodes the non-structural proteins (nsp) of viral RNA synthesis complex through proteolytic processing [28] . The nsp12 is a viral RNA-dependent RNA polymerase, together with cofactors nsp7 and nsp8 possessing high polymerase activity. From the protein threedimensional (3D) structure view of SARS-CoV nsp12, it contains a larger N-terminal extension (which binds to nsp7 and nsp8) and polymerase domain ( Figure 2C ). The . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint spike is a transmembrane glycoprotein that plays a pivotal role in mediating viral infection through binding the host receptor [29, 30] . Figure 2D 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 is able to utilize ACE2 as an entry receptor in ACE2-expressing cells [31] , suggesting potential drug targets for therapeutic development. In addition, the nucleocapsid is also an important subunit for packaging the viral genome through protein oligomerization [32] , and the single nucleocapsid structure was shown in Figure 2E .

Protein sequence alignment analyses indicated that the 2019-nCoV was most evolutionarily conserved with SARS-CoV (Supplementary Table S2 ). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV are two evolutionarily conserved regions, with sequence identities of 96% and 89.6%, respectively, compared to SARS- Table S2 ). However, the spike protein exhibited the lowest sequence conservation (sequence identity of 77%) between 2019-nCoV and SARS-CoV. Meanwhile, the spike protein of 2019-nCoV only has 31.9% sequence identity compared to MERS-CoV.

First, we assembled the CoV-associated host proteins from 4 known HCoVs (SARS-CoV, MERS-CoV, HCoV-229E, and HCoV-NL63), one mouse MHV, and one avian IBV (N protein) (Supplementary Table S3 ). In total, we obtained 119 host proteins associated with CoVs with various experimental evidences. Specifically, these host . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint 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 Figure   3A . 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 P value < 0.05), including measles, RNA transport, NF-kappa B signaling, Epstein-Barr virus infection, and influenza ( Figure 3B ). Gene ontology (GO) biological process enrichment analyses further confirmed multiple viral infection-related processes (adjusted P 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 ( Figure 3C ). We then mapped the known drug-target network (see Methods) into the HCoV-host interactome to search for druggable, cellular targets.

We found that 47 human proteins (39%, blue nodes in Figure 3A ) can be targeted by at least one approved drug or experimental drug under clinical trial. 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 therapeutic strategy by specifically targeting cellular proteins associated with HCoVs, such as drug repurposing.

The basis for the proposed network-based drug repurposing methodologies rests on the notions that the proteins that associate with and functionally govern a viral infection are localized in the corresponding subnetwork ( Figure 1A ) within the comprehensive human interactome network. For a drug with multiple targets to be effective against an . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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HCoV, its target proteins should be within or in the immediate vicinity of the corresponding subnetwork in the human interactome (Figure 1) , as we demonstrated in multiple diseases [12, 21, 22, 25] using this drug repurposing strategy. We used a state-ofthe-art network proximity measure to quantify the relationship between HCoV-specific subnetwork ( Figure 3A ) and drug targets in the human protein-protein interactome. We constructed a drug-target network by assembling target information for more than 2,000 FDA-approved or experimental drugs (see 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 evidences (see Methods). We used a Z-score (Z) measure and permutation test to reduce the study bias in network proximity analyses (including hub nodes in the human interactome network by literaturederived PPI data bias) as described in our recent studies [12, 25] .

In total, we computationally identified 135 drugs that were associated (Z < -1.5 and P < 0.05, permutation test) with the HCoV-host interactome ( Figure 4A author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint vs. SARS-CoV (P < 0.001, t distribution), 0.503 vs. MERS-CoV (P < 0.001), 0.694 vs. IBV (P < 0.001), and 0.829 vs. MHV (P < 0.001). These network proximity analyses offer putative repurposable candidates for potential prevention and treatment of HCoVs.

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 expression data of drug-treated human cell lines from the Connectivity Map (CMAP) database [33] 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 P < 0.05 (permutation test) was used as cut-off for a significant association of gene signatures between a drug and a specific HCoV. 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 potential repurposable drugs ( Figure 5A and Table 1 ) 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 S4 ); (ii) validation by GSEA analyses;

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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint (iii) literature-reported antiviral evidences, and (iv) fewer clinically reported side effects.

Specifically, we showcased several selected repurposable drugs with literature-reported antiviral evidences as below.

Selective estrogen receptor modulators (SERMs). An overexpression of estrogen receptor has been shown to play a crucial role in inhibiting viral replication [34] .

SERMs have been reported to play a broader role in inhibiting viral replication through the non-classical pathways associated with estrogen receptor [34] . 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 [17] .

Toremifene (Z = -3.23, Figure 5A ), 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 [16, 35] . Interestingly, different from 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 [36] . As shown in Figure 5B , toremifene potentially affects several key host proteins associated with HCoV, such as RPL19, HNRNPA1, NPM1, EIF3I, EIF3F, and EIF3E [37, 38] . Equilin (Z = -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) [17] . Altogether, network-predicted SERMs (such as toremifene and equilin) offer potential repurposable candidates for HCoVs.

with viral infection, including HCoVs [39] [40] [41] . Irbesartan (Z = -5.98), a typical ARB, was . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint 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 Figure 5C , 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 [42, 43] . SLC10A1 interacts with C11orf74, a potential transcriptional repressor that interacts with nsp-10 of SARS-CoV [44] . 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.

confirmed the mammalian target of rapamycin complex 1 (mTORC1) as the key factor in regulating various viruses' replications, including Andes orthohantavirus and coronavirus [45, 46] . Sirolimus (Z = -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 [47] . Indeed, the latest study revealed the clinical application: sirolimus reduced MERS-CoV infection by over 60% [48] . Moreover, sirolimus usage in managing patients with severe H1N1 pneumonia and acute respiratory failure can improve those patients' prognosis significantly [47] . Mercaptopurine (Z = -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 autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, and Crohn's disease [49] . Mercaptopurine has been reported as a selective inhibitor of both . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint SARS-CoV and MERS-CoV by targeting papain-like protease which plays key roles in viral maturation and antagonism to interferon stimulation [50, 51] . Mechanistically, mercaptopurine potentially target several host proteins in HCoVs, such as JUN, PABPC1, NPM1, and NCL [37, 52] (Figure 5D ).

Anti-inflammatory agents. Inflammatory pathways play essential roles in viral infections [53, 54] . As a biogenic amine, melatonin (N-acetyl-5-methoxytryptamine) (Z = -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 [55, 56] . Viral infections are often associated with immune-inflammatory injury, in which the level of oxidative stress increases significantly and leaves negative effects on multiple organ functions [57] . 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 [58, 59] . 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 Figure 5E , melatonin indirectly targets several HCoV cellular targets, including ACE2, BCL2L1, JUN, and IKBKB. Eplerenone (Z = -1.59), an aldosterone receptor antagonist, is reported to have a similar anti-inflammatory effect as melatonin. By inhibiting mast-cellderived proteinases and suppressing fibrosis, eplerenone can improve survival of mice infected with encephalomyocarditis virus [60] .

In summary, our network proximity analyses offer multiple putative repurposable drugs that target diverse cellular pathways for potential prevention and treatment of . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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HCoVs. However, further preclinical experiments and clinical trials are required to verify the clinical benefits of these network-predicted candidates before clinical use.

Drug combinations, offering increased therapeutic efficacy and reduced toxicity, play an important role in treating various viral infections [61] . 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 [25] . 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 'Complementary Exposure' pattern: the targets of the drugs both hit the disease module, but target separate neighborhoods ( Figure 6A ). Here we sought to identify drug combinations that may provide a synergistic effect in treating HCoVs with welldefined mechanism-of-action by network analysis. For the 16 potential repurposable drugs ( Figure 5A) , we showcased three network-predicted candidate drug combinations in the potential treatment of HCoVs.

Sirolimus, an inhibitor of mTOR with both antifungal and antineoplastic properties, has demonstrated to improves outcomes in patients with severe H1N1 pneumonia and acute respiratory failure [47] . The mTOR signaling plays an essential role for MERS-CoV infection [62] . Dactinomycin, also known actinomycin D, is an approved RNA synthesis inhibitor for treatment of various cancer . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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types. An early study showed that dactinomycin (1 microgram/ml) inhibited the growth of feline enteric CoV [63] . As shown in Figure 6B , our network analysis shows that sirolimus and dactinomycin synergistically target HCoV-associated host protein subnetwork by 'Complementary Exposure' 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 ( Figure 6B ).

Toremifene is the approved first generation nonsteroidal SERMs for the treatment of metastatic breast cancer [64] . SERMs (including toremifene) inhibited Ebola virus infection [17] by interacting with and destabilizing the Ebola virus glycoprotein [36] . In vitro assays have demonstrated that toremifene inhibited growth of MERS-CoV [16, 65] and SARA-CoV [35] (Table 1) . Emodin, an anthraquinone derivative extracted from the roots of rheum tanguticum, have been reported to have various anti-virus effects. Specifically, emdoin inhibited SARS-CoV associated 3a protein [66] , and blocked an interaction between the SARS-CoV spike protein and ACE2 [67] . Altogether, network analyses and published experimental data suggested that combining toremifene and emdoin offered a potential therapeutic approach for HCoVs ( Figure 6C ). Figure 5A , 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 [50, 51] . Melatonin was reported in potential treatment of . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint viral infection via its anti-inflammatory and antioxidant effects [55] [56] [57] [58] [59] . Melatonin indirectly regulates ACE2 expression, a key entry receptor involved in viral infection of HCoVs, including 2019-nCoV [31] . JUN, also known as c-Jun, is a key host protein involving in HCoV infectious bronchitis virus [68] . As shown in Figure 6D , 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 by synergistically targeting papain-like protease, ACE2, c-Jun signaling, and anti-inflammatory pathways ( Figure 6D) . However, further experimental and clinical validations are highly warranted.

In this study, we presented a network-based methodology for systematic identification of putative repurposable drugs and drug combinations for potential treatment of HCoV.

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 putative repurposable drugs ( Figure 5 ) and 3 putative drug combinations (Figure 6 ) for the potential treatment of HCoVs, including 2019-nCoV. However, all network-predicted repurposable drugs and drug combinations must be validated in preclinical models and randomized clinical trials before being used in patients.

We acknowledge several limitations in our current study. In this study, we used a low binding affinity value of 10 µM as a threshold to define a physical drug-target . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint interaction. However, a stronger binding affinity threshold (e.g., 1µM) 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-protein interactions may be functional associations, instead of physical bindings. We may use computational approaches to systematically predict the drug-target interactions further [24, 69] . In addition, the collected virus-host interactions are far from complete 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 HCoVs using sequence-based and structure-based approaches [70] . 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 virushost interactions. Drug targets representing nodes within cellular networks are often intrinsically coupled with both therapeutic and adverse profiles [71] , as drugs can inhibit or activate protein functions (including antagonists versus agonists). Comprehensive identification of the virus-host interactome for 2019-nCoV, with specific biological effects using functional genomics assays [72, 73] , will significantly improve the accuracy of current network-based methodologies.

Owing to 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 current network models. For example, Mesalazine, an approved drug for inflammatory bowel disease, is . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint a top network-predicted candidate drug ( Figure 5A ) associated with HCoVs. Yet, several clinical studies showed the potential pulmonary toxicities (including pneumonia) associated with mesalazine usage [74, 75] . 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 [25] .

However, we expect that our methodology reminds to be a useful network-based tools 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 [76] ) with the networkpredicted repurposable drugs (Figure 5 ) or drug combinations (Figure 6 ) may improve coverage of current network-based methodologies by utilizing multi-layer network framework.

In conclusion, this study offers a powerful, integrated network-based systems pharmacology methodology for rapid identification of repurposable drugs and drug combinations for the potential treatment of HCoV. 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 outbreak. From a translational perspective, if broadly applied, the network tools developed here could help develop effective treatment strategies for other types of virus and human diseases as well.

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In total, we collected DNA sequences and protein sequences for 15 HCoVs, including three most recent 2019-nCoV genomes, from the NCBI GenBank database (January 28, 2019, Supplementary Table S1). Whole genome alignment and protein sequence identity calculation were performed by Multiple Sequence Alignment in EMBL-EBI database with default parameters. The neighbor joining (NJ) tree was computed from the pairwise phylogenetic distance matrix using MEGA X [77] with 1000 bootstrap replicates. The protein alignment and phylogenetic tree of HCoVs were constructed by MEGA X.

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 coimmunoprecipitation and RNA knock down experiment. In total, the virus-host interaction network included 6 HCoVs with 119 host proteins (Supplementary Table   S3 ).

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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint

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 [78] .

Here, we collected drug-target interaction information from the DrugBank database (v4.3) [79] , Therapeutic Target Database (TTD) [80] , PharmGKB database, ChEMBL (v20) [81] , BindingDB [82] , and IUPHAR/BPS Guide to PHARMACOLOGY [83] . The chemical structure of each drug with SMILES format was extracted from DrugBank [79] . Here, only drug-target interactions meeting the following three criteria were used: (i) binding affinities, including Ki, Kd, IC50 or EC50 each ≤ 10 μM; (ii) the target was marked as 'reviewed' in the UniProt database [84] ; 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 study [12] .

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 . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint evidences: (i) Binary PPIs tested by high-throughput yeast-two-hybrid (Y2H) systems;

(ii) Binary, physical PPIs from protein three-dimensional (3D) structures; (iii) Kinasesubstrate 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 [85] as well as their official gene symbols based on GeneCards (https://www.genecards.org/). 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 [12, 86, 87] .

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 , the set of host genes associated with a specific HCoV, and , the set of drug targets, we computed the network proximity of with the target set of each drug using the "closest" method: 

. CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint where ( , ) is the shortest distance between gene and in the human protein interactome. The network proximity was converted to Z-score based on permutation tests:

where E CCC and E were the mean and standard deviation of the permutation test repeated 1,000 times, each time with two randomly selected gene lists with similar degree distributions to those of and . The corresponding P value was calculated based on the permutation test results. Z-score < -1.5 and P < 0.05 were considered significantly proximal drug-HCoV associations. All networks were visualized using Gephi 0.9.2 (https://gephi.org/).

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 HI was calculated for each pair of drugs and using the following method:

. CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint where 〈 • 〉 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 Complementary Exposure patterns in targets' modules of both drugs without overlapping toxic mechanisms. [25] 

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 [88] (GSE1739) and Calu-3 cells [89] (GSE33267) respectively. One transcriptome data set was MERS-CoV infected Calu-3 cells [90] (GSE122876). Adjusted P 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 [33] , 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 [91] as follows:

. CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint combinations. An effective drug combination will be captured by the 'Complementary Exposure' pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. ZCA and ZCB denote the network proximity (Z-score) between targets (Drugs A and B) and a specific HCoV. . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.03.20020263 doi: medRxiv preprint 

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