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 “Complementary
Exposure” 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.
Subject terms: Bioinformatics, Comparative genomics, Proteomic analysis
Introduction
Coronaviruses (CoVs) typically affect the respiratory tract of mammals,
including humans, and lead to mild to severe respiratory tract
infections^[34]1. 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^[35]2. 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^[36]3,[37]4. 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^[38]2. 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)^[39]5, 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^[40]6. 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.
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^[41]7. 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^[42]8. 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^[43]9–[44]11. However, experimental approaches for drug
repurposing is costly and time-consuming^[45]12. Computational
approaches offer novel testable hypotheses for systematic drug
repositioning^[46]9–[47]11,[48]13,[49]14. 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^[50]1.
Viruses (including HCoV) require host cellular factors for successful
replication during infection^[51]1. Systematic identification of
virus–host protein–protein interactions (PPIs) offers an effective way
toward elucidating the mechanisms of viral infection^[52]15,[53]16.
Subsequently, targeting cellular antiviral targets, such as virus–host
interactome, may offer a novel strategy for the development of
effective treatments for viral infections^[54]1, including
SARS-CoV^[55]17, MERS-CoV^[56]17, Ebola virus^[57]18, and Zika
virus^[58]14,[59]19–[60]21. We recently presented an integrated
antiviral drug discovery pipeline that incorporated gene-trap
insertional mutagenesis, known functional drug–gene network, and
bioinformatics analyses^[61]14. This methodology allows to identify
several candidate repurposable drugs for Ebola virus^[62]11,[63]14. Our
work over the last decade has demonstrated how network strategies can,
for example, be used to identify effective repurposable
drugs^[64]13,[65]22–[66]27 and drug combinations^[67]28 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^[68]13,[69]23,[70]27,[71]28.
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. [72]1). 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. [73]1).
Fig. 1. Overall workflow of this study.
[74]Fig. 1
[75]Open in a new tab
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. a
Human coronavirus (HCoV)-associated host proteins were collected from
literatures and pooled to generate a pan-HCoV protein subnetwork. b
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. c, d Gene set enrichment analysis
was utilized to validate the network-based prediction. e Top candidates
were further prioritized for drug combinations using network-based
method 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. f 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.
Results
Phylogenetic analyses of 2019-nCoV/SARS-CoV-2
To date, seven pathogenic HCoVs (Fig. [76]2a, b) have been
found:^[77]1,[78]29 (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
[79]S1). 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. [80]2a).
Fig. 2. Phylogenetic analysis of coronaviruses.
[81]Fig. 2
[82]Open in a new tab
a 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. b 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). c–e The 3D structures of SARS-CoV nsp12 (PDB ID:
6NUR) (c), spike (PDB ID: 6ACK) (d), and nucleocapsid (PDB ID: 2CJR)
(e) shown were based on homology modeling. Genome information and
phylogenetic analysis results are provided in Supplementary Tables
[83]S1 and [84]S2.
HCoVs have five major protein regions for virus structure assembly and
viral replications^[85]29, including replicase complex (ORF1ab), spike
(S), envelope (E), membrane (M), and nucleocapsid (N) proteins (Fig.
[86]2b). The ORF1ab gene encodes the non-structural proteins (nsp) of
viral RNA synthesis complex through proteolytic processing^[87]30. 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.
[88]2c). The spike is a transmembrane glycoprotein that plays a pivotal
role in mediating viral infection through binding the host
receptor^[89]31,[90]32. Figure [91]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/SARS-CoV-2 is able to utilize ACE2 as an entry receptor in
ACE2-expressing cells^[92]33, 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^[93]34. In addition, the
nucleocapsid is also an important subunit for packaging the viral
genome through protein oligomerization^[94]35, and the single
nucleocapsid structure is shown in Fig. [95]2e.
Protein sequence alignment analyses indicated that the
2019-nCoV/SARS-CoV-2 was most evolutionarily conserved with SARS-CoV
(Supplementary Table [96]S2). 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 [97]S2).
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.
HCoV–host interactome network
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 [98]S3). 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. [99]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 (Fig. [100]3b). Gene
ontology (GO) biological process enrichment analysis 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 (Fig. [101]3c).
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. [102]3a) 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. 3. Drug-target network analysis of the HCoV–host interactome.
[103]Fig. 3
[104]Open in a new tab
a 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. b, c KEGG human
pathway (b) and gene ontology enrichment analyses (c) for the
HCoV-associated proteins.
Network-based drug repurposing for HCoVs
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. [105]1a) 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. [106]1), as we demonstrated in multiple
diseases^[107]13,[108]22,[109]23,[110]28 using this network-based
strategy. We used a state-of-the-art network proximity measure to
quantify the relationship between HCoV-specific subnetwork (Fig.
[111]3a) 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 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 literature-derived PPI data bias) as described in our recent
studies^[112]13,[113]28.
In total, we computationally identified 135 drugs that were associated
(Z < −1.5 and P < 0.05, permutation test) with the HCoV–host
interactome (Fig. [114]4a, Supplementary Tables [115]S4 and [116]5). 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 Z-scores showed consistency
among the pooled 119 HCoV-associated proteins and other four individual
CoVs (Fig. [117]4b). The Pearson correlation coefficients of the
proximities of all the drugs for the pooled HCoV are 0.926 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.
Fig. 4. A discovered drug-HCoV network.
[118]Fig. 4
[119]Open in a new tab
a 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. b A heatmap highlighting network
proximity values for SARS-CoV, MERS-CoV, IBV, and MHV, respectively.
Color key denotes network proximity (Z-score) between drug targets and
the HCoV-associated proteins in the human interactome network. P value
was computed by permutation test.
Discovery of repurposable drugs for HCoV
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^[120]36 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 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. [121]5a and Table [122]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 [123]S4); (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. 5. A discovered drug-protein-HCoV network for 16 candidate repurposable
drugs.
[124]Fig. 5
[125]Open in a new tab
a 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. b–e Inferred mechanism-of-action
networks for four selected drugs: b toremifene (first-generation
nonsteroidal-selective estrogen receptor modulator), c irbesartan (an
angiotensin receptor blocker), d mercaptopurine (an antimetabolite
antineoplastic agent with immunosuppressant properties), and e
melatonin (a biogenic amine for treating circadian rhythm sleep
disorders).
Table 1.
Top 16 network-predicted repurposable drugs with literature-derived
antiviral evidence.
[126]graphic file with name 41421_2020_153_Tab1_HTML.jpg
[127]Open in a new tab
HBV hepatitis B virus, HCV hepatitis C virus, HDV hepatitis delta
virus, EBOV Ebola viruses, ZEBOV-GP Zaire Ebola virus glycoprotein, HIV
human immunodeficiency virus, EBV Epstein-Barr virus, ANDV Andes
orthohantavirus, EMCV encephalomyocarditis virus, FECV feline enteric
coronavirus, RSV respiratory syncytial virus, EV71 enterovirus 71,
HSV-1 and -2 herpes simplex viruses, CVB[4] Coxsackievirus B[4].
Selective estrogen receptor modulators
An overexpression of estrogen receptor has been shown to play a crucial
role in inhibiting viral replication^[128]37. 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^[129]37. 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^[130]18. Toremifene (Z = –3.23, Fig.
[131]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^[132]17,[133]38.
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^[134]39. As shown in Fig. [135]5b,
toremifene potentially affects several key host proteins associated
with HCoV, such as RPL19, HNRNPA1, NPM1, EIF3I, EIF3F, and
EIF3E^[136]40,[137]41. 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)^[138]18.
Altogether, network-predicted SERMs (such as toremifene and equilin)
offer candidate repurposable drugs for 2019-nCoV/SARS-CoV-2.
Angiotensin receptor blockers
Angiotensin receptor blockers (ARBs) have been reported to associate
with viral infection, including HCoVs^[139]42–[140]44. Irbesartan
(Z = –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. [141]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^[142]45,[143]46. SLC10A1 interacts with
C11orf74, a potential transcriptional repressor that interacts with
nsp-10 of SARS-CoV^[144]47. 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.
Immunosuppressant or antineoplastic agents
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^[145]48,[146]49. 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^[147]50. Indeed, the latest study revealed the clinical
application: sirolimus reduced MERS-CoV infection by over 60%^[148]51.
Moreover, sirolimus usage in managing patients with severe H1N1
pneumonia and acute respiratory failure can improve those patients’
prognosis significantly^[149]50. 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 auto-immune diseases, including rheumatoid
arthritis, systemic lupus erythematosus, and Crohn’s disease^[150]52.
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^[151]53,[152]54. Mechanistically, mercaptopurine
potentially target several host proteins in HCoVs, such as JUN, PABPC1,
NPM1, and NCL^[153]40,[154]55 (Fig. [155]5d).
Anti-inflammatory agents
Inflammatory pathways play essential roles in viral
infections^[156]56,[157]57. 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^[158]58,[159]59. 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^[160]60. 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^[161]61,[162]62. 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. [163]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-cell-derived proteinases and suppressing fibrosis, eplerenone can
improve survival of mice infected with encephalomyocarditis
virus^[164]63.
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^[165]64 and clinical trials are required to
verify the clinical benefits of these network-predicted candidates
before clinical use.
Network-based identification of potential drug combinations for
2019-nCoV/SARS-CoV-2
Drug combinations, offering increased therapeutic efficacy and reduced
toxicity, play an important role in treating various viral
infections^[166]65. 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^[167]28. 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 (Fig. [168]6a).
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. [169]5a, Table [170]1), we showcased
three network-predicted candidate drug combinations for
2019-nCoV/SARS-CoV-2. All predicted possible combinations can be found
in Supplementary Table [171]S6.
Fig. 6. Network-based rational design of drug combinations for
2019-nCoV/SARS-CoV-2.
[172]Fig. 6
[173]Open in a new tab
a The possible exposure mode of the HCoV-associated protein module to
the pairwise drug 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. Z[CA] and Z[CB] denote
the network proximity (Z-score) between targets (Drugs A and B) and a
specific HCoV. S[AB] denotes separation score (see Materials and
methods) of targets between Drug A and Drug B. b–d Inferred
mechanism-of-action networks for three selected pairwise drug
combinations: b sirolimus (a potent immunosuppressant with both
antifungal and antineoplastic properties) plus dactinomycin (an RNA
synthesis inhibitor for treatment of various tumors), c toremifene
(first-generation nonsteroidal-selective estrogen receptor modulator)
plus emodin (an experimental drug for the treatment of polycystic
kidney), and d melatonin (a biogenic amine for treating circadian
rhythm sleep disorders) plus mercaptopurine (an antimetabolite
antineoplastic agent with immunosuppressant properties).
Sirolimus plus Dactinomycin
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^[174]50. The mTOR
signaling plays an essential role for MERS-CoV infection^[175]66.
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^[176]67. As shown in Fig. [177]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 (Fig. [178]6b).
Toremifene plus Emodin
Toremifene is among the approved first-generation nonsteroidal SERMs
for the treatment of metastatic breast cancer^[179]68. SERMs (including
toremifene) inhibited Ebola virus infection^[180]18 by interacting with
and destabilizing the Ebola virus glycoprotein^[181]39. In vitro assays
have demonstrated that toremifene inhibited growth of
MERS-CoV^[182]17,[183]69 and SARA-CoV^[184]38 (Table [185]1). 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^[186]70,
and blocked an interaction between the SARS-CoV spike protein and ACE2
(ref. ^[187]71). 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.
[188]6c).
Mercaptopurine plus Melatonin
As shown in Fig. [189]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^[190]53,[191]54. Melatonin
was reported in potential antiviral infection via its anti-inflammatory
and antioxidant effects^[192]58–[193]62. Melatonin indirectly regulates
ACE2 expression, a key entry receptor involved in viral infection of
HCoVs, including 2019-nCoV/SARS-CoV-2 (ref. ^[194]33). 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^[195]72,[196]73. JUN, also known as c-Jun, is a key
host protein involving in HCoV infectious bronchitis virus^[197]74. As
shown in Fig. [198]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/SARS-CoV-2 by
synergistically targeting papain-like protease, ACE2, c-Jun signaling,
and anti-inflammatory pathways (Fig. [199]6d). However, further
experimental observations on ACE2 pathways by melatonin in
2019-nCoV/SARS-CoV-2 are highly warranted.
Discussion
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. [200]5) and 3
potential drug combinations (Fig. [201]6) for targeting
2019-nCoV/SARS-CoV-2. However, although the majority of predictions
have been validated by various literature data (Table [202]1), all
network-predicted repurposable drugs and drug combinations must be
validated in various 2019-nCoV/SARS-CoV-2 experimental assays^[203]64
and randomized clinical trials before being used in patients.
We acknowledge several limitations in the current study. Although
2019-nCoV/SARS-CoV-2 shared high nucleotide sequence identity with
other HCoVs (Fig. [204]2), 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^[205]75
and Coronaviruses^[206]76. After adding its target, an RNA helicase
enzyme EIF4A^[207]76, silvestrol was predicted to be significantly
associated with HCoVs (Z = –1.24, P = 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^[208]25,[209]26. 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^[210]77. Drug targets representing nodes
within cellular networks are often intrinsically coupled with both
therapeutic and adverse profiles^[211]78, 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^[212]79,[213]80,
will significantly improve the accuracy of the proposed network-based
methodologies further.
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.
[214]5a). Yet, several clinical studies showed the potential pulmonary
toxicities (including pneumonia) associated with mesalazine
usage^[215]81,[216]82. Integration of lung-specific gene
expression^[217]23 of 2019-nCoV/SARS-CoV-2 host proteins and
physiologically based pharmacokinetic modeling^[218]83 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^[219]28.
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^[220]64) with the
network-predicted repurposable drugs (Fig. [221]5) or drug combinations
(Fig. [222]6) may improve coverage of current network-based
methodologies by utilizing multi-layer network framework^[223]16.
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.
Methods and materials
Genome information and phylogenetic analysis
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
[224]S1). Whole-genome alignment and protein sequence identity
calculation were performed by Multiple Sequence Alignment in EMBL-EBI
database ([225]https://www.ebi.ac.uk/) with default parameters. The
neighbor joining (NJ) tree was computed from the pairwise phylogenetic
distance matrix using MEGA X^[226]84 with 1000 bootstrap replicates.
The protein alignment and phylogenetic tree of HCoVs were constructed
by MEGA X^[227]84.
Building the virus–host interactome
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 [228]S3).
Functional enrichment analysis
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^[229]85.
Building the drug–target network
Here, we collected drug–target interaction information from the
DrugBank database (v4.3)^[230]86, Therapeutic Target Database
(TTD)^[231]87, PharmGKB database, ChEMBL (v20)^[232]88,
BindingDB^[233]89, and IUPHAR/BPS Guide to PHARMACOLOGY^[234]90. The
chemical structure of each drug with SMILES format was extracted from
DrugBank^[235]86. Here, drug–target interactions meeting the following
three criteria were used: (i) binding affinities, including K[i], K[d],
IC[50], or EC[50] each ≤10 μM; (ii) the target was marked as “reviewed”
in the UniProt database^[236]91; 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^[237]13,[238]23,[239]28.
Building the human protein–protein interactome
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^[240]92 as well as their official gene symbols based on
GeneCards ([241]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^[242]13,[243]23,[244]28,[245]93.
Network proximity measure
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 C, the set of host genes associated with
a specific HCoV, and T, the set of drug targets, we computed the
network proximity of C with the target set T of each drug using the
“closest” method:
[MATH: dCT<
/mi>=1∣∣C∣∣+<
mo>∣∣T∣∣∑c∈C
mint∈T
dc,t
+∑t∈T
minc∈C
dc,t, :MATH]
1
where d(c, t) is the shortest distance between gene c and t in the
human protein interactome. The network proximity was converted to
Z-score based on permutation tests:
[MATH:
Zd<
mrow>CT=<
mrow>dCT−dr¯σ<
mrow>r, :MATH]
2
where
[MATH: dr¯ :MATH]
and σ[r] 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 C and T. 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 ([246]https://gephi.org/).
Network-based rational prediction of drug combinations
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 S[AB] was calculated for
each pair of drugs A and B using the following method:
[MATH:
SAB=dAB<
/mi>−dAA<
/mi>+dBB<
/mi>2, :MATH]
3
where
[MATH: d⋅ :MATH]
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^[247]28.
Gene set enrichment analysis
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^[248]94
([249]GSE1739) and Calu-3 cells^[250]95 ([251]GSE33267), respectively.
One transcriptome data set was MERS-CoV-infected Calu-3 cells^[252]96
([253]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^[254]36, 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^[255]97 as follows:
[MATH: ES=ESup−ESdown,sgnESup≠sgnESdown0,else :MATH]
4
ES[up] and ES[down] were calculated separately for the up- and
down-regulated genes from the HCoV signature data set using the same
method. We first computed a[up/down] and b[up/down] as
[MATH:
a=max1<
/mn>≤j≤sjs−Vjr<
/mrow>, :MATH]
5
[MATH:
b=max1<
/mn>≤j≤sVjr<
/mrow>−j−1s,
:MATH]
6
where j = 1, 2, …, s 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 j is denoted by V(j), where 1 ≤ V(j) ≤ r,
with r being the number of genes (12,849) from the drug profile. Then,
ES[up/down] was set to a[up/down] if a[up/down] > b[up/down], and was
set to −b[up/down] if b[up/down] > a[up/down]. 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 P < 0.05,
and the number of such HCoV signature data sets were used as the final
GSEA score that ranges from 0 to 3.
Supplementary information
[256]Supplementary Table S1-S6^ (520.8KB, pdf)
Acknowledgements