Abstract
Japanese encephalitis is a zoonotic disease caused by the Japanese
encephalitis virus (JEV). It is mainly epidemic in Asia with an
estimated 69,000 cases occurring per year. However, no approved agents
are available for the treatment of JEV infection, and existing vaccines
cannot control various types of JEV strains. Drug repurposing is a new
concept for finding new indication of existing drugs, and, recently,
the concept has been used to discover new antiviral agents. Identifying
host proteins involved in the progress of JEV infection and using these
proteins as targets are the center of drug repurposing for JEV
infection. In this study, based on the gene expression data of JEV
infection and the phenome-wide association study (PheWAS) data, we
identified 286 genes that participate in the progress of JEV infection
using systems biology methods. The enrichment analysis of these genes
suggested that the genes identified by our methods were predominantly
related to viral infection pathways and immune response-related
pathways. We found that bortezomib, which can target these genes, may
have an effect on the treatment of JEV infection. Subsequently, we
evaluated the antiviral activity of bortezomib using a JEV-infected
mouse model. The results showed that bortezomib can lower JEV-induced
lethality in mice, alleviate suffering in JEV-infected mice and reduce
the damage in brains caused by JEV infection. This work provides an
agent with new indication to treat JEV infection.
Keywords: Japanese encephalitis virus, drug repurposing, systems
biology, antiviral agents
1. Introduction
The Japanese encephalitis virus (JEV) is the main pathogen that causes
severe encephalitis in humans. JEV belongs to the genus of Flavivirus,
which also includes other arboviruses, such as the Dengue virus (DENV),
West Nile virus (WNV), and Zika virus (ZIKV) [[36]1]. JEV is a
positive-sense single-stranded RNA virus. The genome of JEV is
approximately 11 kb in length, containing a single open reading frame
(ORF) flanked by the 5′- and 3′-untranslated regions (UTRs). The ORF
encodes a long polyprotein that is cleaved into three structural
proteins (capsid [C], pre-membrane [prM], and envelope [E]) and seven
nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5)
[[37]2]. The structural proteins make up the infectious viral particle
and the nonstructural proteins participate in multiple steps of viral
life cycle including viral replication, virion assembly, and immune
evasion [[38]2].
Since the first record of the virus in the late 1800s, JEV has posed a
significant threat to global health [[39]3]. It is reported that there
are 69,000 cases of JEV infection per year [[40]4]. The average
mortality rate caused by JEV can be as high as 30% in the past 30
years, and the proportion of survival with permanent neurological or
psychiatric sequelae is approximately 44% [[41]1]. With its epidemic
area expansion, JEV affects approximately 25 countries in Asia, and
approximately 60% of the population lives with a risk of JEV infection
[[42]2]. At present, vaccination is the most effective way to prevent
JEV infection. The common vaccines include the inactivated mouse
brain-derived vaccine (JE-VAX), inactivated BHK-21 cell-derived
vaccine, live-attenuated vaccine (SA14-14-2), inactivated Vero
cell-derived vaccine, and the chimeric attenuated vaccine [[43]5].
However, approximately 80% cases of the JEV infection occur in areas
covered by the JEV vaccination program due to the failures of
immunization strategies or the limitation of vaccines themselves
[[44]1]. To date, no clinically approved antiviral agents have been
available for the treatment of JEV infection. Furthermore, few
randomized clinical trials have tested treatments for JEV. In the past
30 years, only six agents for the treatment of JEV infection have been
tested by clinical trials, but none of them have been found effective
[[45]1]. Therefore, it is essential and urgent to find a safe and
effective treatment.
Drug repurposing has recently become a very popular method for drug
discovery; drug repurposing provides old drugs (including approved
drugs, under research drugs, and withdrawn drugs) with new indications
by exploring new molecular pathways and targets [[46]6,[47]7]. With
this strategy, finding an alternative agent to treatment JEV infection
will be fast and safe. During the past decades, the traditional method
for drug repurposing depends on high-throughput screening of
small-molecule libraries consisting of approved and developing drugs
[[48]8]. However, the success rate of high-throughput screening for
effective repurposed drugs has dropped dramatically [[49]9]. With the
development of computational methods, the high-throughput omics data,
virtual screening, and text mining have been used for drug repurposing
[[50]9,[51]10]. One of the computational methods for antiviral drug
repurposing is to target pathogen to block its lifecycle. Using the
crystal structure of the E protein and the strategy of structural-based
virtual screening (SBVS), Leal et al. identified a compound exhibiting
marked antiviral activity against DENV with its EC50 being 3.1 µM
[[52]11]. The other methods for antiviral drug repurposing are
targeting host genes to inhibit pathogen infection. Identifying the
proteins participating in the pathogen infection process is the basis
of host-targeted drug repurposing approaches [[53]9]. Quan et al.
identified 170 Mycobacterium tuberculosis (Mtb) infection-associated
genes by theoretical genetic analysis, and obtained high potential
anti-Mtb drugs by targeting these genes [[54]12]. Therefore, it is
possible to rapidly identify effective therapeutics for JEV infection
using the method of drug repurposing through targeting JEV-susceptible
genes.
Systems biology has been used to identify the pathogenic mechanisms of
complex human diseases by integrating genetic variation, genomics,
pathways, and molecular networks [[55]13]. The advent of systems
biology provides a powerful method for facilitating drug development
and drug repurposing [[56]14]. The representative algorithms used in
the systems biology field include GeneRank and HotNet2 [[57]15,[58]16].
In this study, we applied the methods of HotNet2 and GeneRank to
identify the genes essential in JEV infection ([59]Figure 1).
Additionally, we analyzed Gene Ontology (GO) and Kyoto Encyclopedia of
Genes and Genomes (KEGG) athway enrichment of these genes to validate
our results. Using the information of the drug-target, we obtained the
agents that have a potential treatment effect on JEV infection. We
found that multiple targets of bortezomib play critical roles in the
progress of JEV infection based on the analysis of the PheWAS data of
encephalitis and of the gene expression data of human microglial cells
after JEV infection. Furthermore, we investigated the effect of
bortezomib using a JEV-infected mouse model. Overall, our research
provided a novel agent for the treatment of JEV infection.
Figure 1.
[60]Figure 1
[61]Open in a new tab
The pipeline for gene screening and drug repurposing. The dataset
[62]GSE57330 obtained from GEO database. The protein-protein
interaction (PPI) network used in the HotNet2 algorithm was obtained
from HINT, iRefIndex, and MultiNet. The protein-protein interaction
(PPI) network used in the GeneRank algorithm was derived from the
STRING database.
2. Results and Discussion
2.1. Screening of Genes Associated with JEV Infection by GeneRank Algorithm
The gene expression data could reveal the relationship between genes
and JEV infection. Therefore, we resorted to the Gene Expression
Omnibus (GEO)-contained gene expression datasets following JEV
infection to identify the JEV-susceptible genes. The dataset
[63]GSE57330 includes 12 samples that were detected at three time
points (6, 24, and 48 h) post JEV infection [[64]17]. Taking the gene
expression data detected at different time as a whole, we calculated
the value of fold change using the mean-gene expression. Thus, we
determined the genes that were upregulated and downregulated after JEV
infection of human microglial cells. Ordinarily, the genes whose fold
change values are at least two-fold above those of the uninfected group
and that have a p-value < 0.05 are defined as significantly associated
with JEV infection. However, this approach may ignore those genes
associated with JEV infection, for which the expression was not
significantly altered. Therefore, we used the GeneRank algorithm to
identify genes associated with JEV infection.
The GeneRank algorithm was derived from the Google search engine
PageRank [[65]15]. It can take advantage of the biological network to
identify key genes associated with diseases, regardless of whether
their expression is altered significantly or not. To find the genes
associated with JEV infection, we ranked genes with the GeneRank
algorithm. Taking the absolute value of fold change as the initial
importance of a gene, we obtained the order of functional genes
participating in JEV infection. According to the result calculated by
GeneRank, we defined the top 1% genes as significant genes involved in
the JEV infection process ([66]Table S1). As indicated in [67]Table S1,
several genes have been reported to affect the process of JEV
infection. For example, the expression of the 2′,5′-oligoadenylate
synthetases (OAS) family (OAS1, OAS2 and OASL) inhibited the
replication of JEV in PK-15 cells in one previous study [[68]18]. The
members of the tripartite-motif containing (TRIM) protein were reported
to be a negative regulator of IFN-β during JEV infection and to inhibit
JEV replication by degrading the viral protein in some other studies
[[69]19,[70]20]. The results suggested that the genes identified by the
GeneRank algorithm may play critical roles in the lifecycle of JEV.
To understand the biological functional genes ranked by the GeneRank
algorithm, a Gene Ontology (GO) enrichment analysis was conducted using
the clusterProfiler package in R [[71]21]. A p-value < 0.05 was used as
the cutoff criterion. The results showed that these genes were involved
in different cellular functions, including immune response, response to
peptide, the regulation of DNA metabolic process, response to virus,
response to interferon-γ, and the regulation of innate immune response
([72]Figure 2). In addition, we investigated the involvement of these
genes in signal transduction pathways using clusterProfiler package. As
shown in [73]Figure 2, the most significant KEGG pathways in which the
downregulated genes were enriched included human cytomegalovirus
infection, Kaposi sarcoma-associated herpesvirus infection, and
proteoglycans in cancer. On the other hand, the upregulated genes were
enriched in viral infection pathways (including herpes simplex
infection, influenza A, Kaposi sarcoma-associated herpesvirus, and
human papillomavirus infection) and NOD-like receptor signaling
pathway. The results suggested that the genes ranked by the GeneRank
algorithm were involved in viral infection pathways and immune
response-related pathways.
Figure 2.
[74]Figure 2
[75]Figure 2
[76]Open in a new tab
Functional characterization of the genes ranked by the GeneRank
algorithm. Downregulated and upregulated genes that were ranked by the
GeneRank algorithm were subjected to a GO enrichment analysis
(biological processes) and a KEGG pathway enrichment analysis using the
clusterProfiler package in R. The top 20 of the GO and pathways in that
the up- and downregulated genes were significantly enriched,
respectively (p. adjust-value < 1 × 10^−8) are presented. (a) GO
enrichment analysis of upregulated genes; (b) GO enrichment analysis of
downregulated genes; (c) KEGG pathway enrichment analysis of
upregulated genes; (d) KEGG pathway enrichment analysis of
downregulated genes.
2.2. Drug Repurposing for JEV Infection by Targeting GeneRank-Derived Genes
To identify approved drugs for the treatment of JEV infection, we
collected the information about the association between chemical agents
and its targets from the Drug-Gene Interaction database (DGIdb,
[77]http://dgidb.genome.wustl.edu/), the Therapeutic Target Database
(TTD, [78]http://bidd.nus.edu.sg/group/cjttd/) and the DrugBank
([79]http://www.drugbank.ca/) [[80]22,[81]23,[82]24]. By targeting the
top 1% of genes derived from the GeneRank calculation, we obtained 91
agents that might have a potential effect on the treatment of JEV
infection ([83]Table S2). It should be noted that among these agents,
we found bortezomib, which was reported to have the ability to inhibit
DENV and ZIKV infection, with its chemical structure shown in
[84]Figure 3 [[85]25,[86]26]. Given that DENV, ZIKV, and JEV all belong
to the genus of flavivirus, we speculated that bortezomib may have the
potential ability to treat JEV infection. In addition to bortezomib,
other agents, such as aspirin, curcumin, etanercept, and minocycline,
were also found to have effects on the inhibition of JEV infection
([87]Table 1)
[[88]27,[89]28,[90]29,[91]30,[92]31,[93]32,[94]33,[95]34,[96]35].
Furthermore, according to the research of Chen et al., tumor necrosis
factor-α (TNF-α) plays a key role in JEV-induced neuronal death
[[97]36]. The inhibitors of TNF (such as lenalidomide and adalimumab)
may also have a potential effect on the treatment of JEV infection,
which is consistent with the mechanism underlying the treatment of
etanercept against JEV infection. Interestingly, these inhibitors were
also found in our study. The results suggested that the drugs
identified by targeting the top 1% of genes with the GeneRank
calculation may be effective in the treatment of JEV infection.
Figure 3.
[98]Figure 3
[99]Open in a new tab
The chemical structure of bortezomib.
Table 1.
Agents reported to have an effect on the treatment of Japanese
encephalitis virus (JEV) infection. Among these agents, the effect of
minocycline and ribavirin on the treatment for JEV has been tested by
randomized clinical trials [[100]37,[101]38]. Etanercept and
minocycline inhibited JEV replication both in vitro and in vivo.
Agent Anti-JEV Potential Reference
Aspirin Aspirin suppressed JEV propagation in neuronal and nonneuronal
cells [[102]27]
Chlorpromazine Chlorpromazine reduced the positive rate of JEV
infection by 50% in vitro [[103]28]
Curcumin Curcumin inhibited the production of infective JEV particle in
vitro [[104]29]
Etanercept Etanercept significantly relieved clinical symptoms and
reduces mortality in JEV-infected mice [[105]30]
Genistein Genistein protected neurons from JEV-induced decrease in the
number of visible neurons [[106]31]
Minocycline Minocycline protected 70% of mice from JEV-induced death,
and inhibited JEV replication in vitro [[107]32]
Quercetin Quercetin inhibited JEV replication in vitro [[108]33]
Ribavirin Ribavirin inhibited JEV replication in vitro [[109]34]
Valproic acid Valproic acid reduced the cytopathic effects caused by
JEV [[110]35]
[111]Open in a new tab
2.3. Screening of Genes Associated with JEV Infection by the HotNet2
Algorithm
The HotNet2 (HotNet diffusion-oriented subnetworks) algorithm is based
on a heat diffusion kernel algorithm that considers the heats of
individual genes as well as the topology of gene-gene interactions.
Because the HotNet2 algorithm can reduce the false positive rate, can
identify subnetworks with high biological relevance, and can be
sensitive to both real and simulated data, it was used to find
significant subnetworks associated with various diseases [[112]16].
To further screen genes for JEV infection, we applied the HotNet2
algorithm to identify the genes that may contribute to JEV infection.
According to the SNP-to-gene mapping method, we mapped the single
nucleotide polymorphisms (SNPs) in the phenome-wide association study
(PheWAS) data to genes to identify potential genes associated with
encephalitis, which exhibits similar symptoms to those of JEV infection
[[113]39,[114]40]. To recognize the gene-interaction networks related
to encephalitis, we used the p-values derived from PheWAS data and the
HotNet2 algorithm to calculate the subnetwork. We obtained 16
subnetworks that involved 64 genes associated with encephalitis
([115]Table S3). It should be noted that four genes among the three
subnetworks belong to the ubiquitin proteasome system (UPS)
([116]Figure 4), which agrees with the results that
encephalitis-related viruses, including JEV, West Nile Virus (WNV), and
Venezuelan equine encephalitis virus (VEEV), could utilize the UPS to
promote viral entry, replication, and release
[[117]41,[118]42,[119]43]. In addition, the proteins (TAP1, TAP2,
TAPBP) interacting with PSMB8 and PSMB9 belong to antigen-loading
components that were important in the antiviral innate immune response
[[120]44]. The protein ADAR in the subnetwork was reported to inhibit
hepatitis C virus (HCV) replication through eliminating HCV RNA by
adenosine to inosine editing [[121]45]. These results confirmed that
the genes identified by the HotNet2 algorithm were important in JEV
infection.
Figure 4.
[122]Figure 4
[123]Open in a new tab
Significant subnetworks associated with encephalitis. (a–c) represent
different subnetworks related to encephalitis. The genes marked by
yellow belong to the ubiquitin proteasome system (UPS).
2.4. Drug Repurposing for JEV Infection by Targeting HotNet2-Derived Genes
By targeting the genes identified by the HotNet2 algorithm, we obtained
20 agents that might have a potential effect on the treatment of JEV
infection ([124]Table 2). Interestingly, we found bortezomib among
these agents, which was consistent with the agents obtained by the
GeneRank calculation. Additionally, the targets of bortezomib belong to
the ubiquitin proteasome system, which reinforced our hypothesis that
bortezomib may have the ability to treat JEV infection.
Table 2.
Agents targeting the genes identified by the HotNet2 algorithm.
Serial Number Agents Indications Evidence in Antiviral
1 Amoxicillin bacterial infections N
2 AT-406 cancer N
3 Biotin dietary shortage or imbalance Y
4 Bortezomib multiple myeloma, lymphoma Y
5 Caffeine fatigue, neurasthenia Y
6 Carfilzomib multiple myeloma N
7 Clavulanate bacterial infections N
8 Doxorubicin various cancer Y
9 GDC-0152 cancer N
10 Glatiramer Acetate multiple sclerosis N
11 Insulin diabetes N
12 Interferon Beta-1A multiple sclerosis, condyloma acuminatum Y
13 Interferon Beta-1B multiple sclerosis Y
14 N-Acetylglucosamine osteoarthritis N
15 Niraparib ovarian cancer, fallopian tube cancer, breast cancer N
16 Olaparib ovarian cancer, breast cancer N
17 Pyruvic Acid dietary shortage or imbalance N
18 Rucaparib ovarian cancer N
19 Talazoparib breast cancer N
20 Veliparib breast cancer, non-small cell lung cancer N
[125]Open in a new tab
In addition to bortezomib, there were other agents that have been
reported to have antiviral activity ([126]Table 2). These agents may
also be used in the treatment of JEV infection. For example, interferon
beta-1A and interferon beta-1B belong to the interferon-I (IFN-I)
family, which has antiviral activity and has been reported to treat HCV
and Middle East respiratory syndrome coronavirus (MERS-CoV) infections
[[127]46,[128]47]. Caffeine has been reported to inhibit HCV
replication in vitro at nontoxic concentrations [[129]48]. However, the
level of HCV RNA showed no change in patients with long-term caffeine
consumption, and the value of IC50 for caffeine to inhibit HCV
replication is 0.7263 mM [[130]48,[131]49]. A higher dose of caffeine
may be needed to treat HCV infection compared with a regular dose.
Doxorubicin, an agent with a broad-spectrum anticancer activity, has
been reported to suppress Ebola virus (EBOV) replication in vitro, and
it can also inhibit other RNA virus by inducing IFN response [[132]50].
Thus, doxorubicin may also be used in the treatment of JEV infection.
Biotin, a B vitamin, can bind to the N protein of porcine epidemic
diarrhea virus (PEDV) and inhibit the replication of PEDV in vitro
[[133]51]. Since biotin is widely used to bind compounds or proteins to
trace them, it is feasible to tag antiviral agents with biotin to
improve the antiviral activity.
Furthermore, antibiotics, such as amoxicillin and clavulanate, were
also found in our results ([134]Table 2). Considering the fact that JEV
infection may also follow bacterial infection and that amoxicillin and
clavulanate can be used to relieve inflammation, it may be useful to
treat JEV-infected patients with amoxicillin or clavulanate.
Interestingly, although there is no evidence for carfilzomib having
antiviral activity, the targets and indications of carfilzomib are the
same as bortezomib [[135]52]. Therefore, it is possible that
carfilzomib has same effect as bortezomib on JEV infection treatment.
2.5. Therapeutic Effects of Bortezomib on JEV-Infected Mice
To further evaluate the above findings that bortezomib has the
potential ability to inhibit JEV infection, we established a mouse
model of JEV infection. Four-week-old BALB/c mice were randomly divided
into four groups: a PBS group; a JEV-infected group; a
bortezomib-treated group; and a JEV-infected and bortezomib-treated
group. The mice in the infected groups were intraperitoneally injected
with 10^6 PFU of the JEV P3 strain. We administered bortezomib
intravenously once every day for the first two days and then
administered it every two days ([136]Figure 5a). As anticipated, most
mice in the untreated infected group died of JEV infection with a
mortality rate of 90%. In contrast, the mortality rate of the
bortezomib-treated infected group was 40% ([137]Figure 5b). All of the
mice in the bortezomib and PBS groups survived until the end of the
experiment, indicating that bortezomib has the ability to protect mice
from death caused by JEV infection.
Figure 5.
[138]Figure 5
[139]Figure 5
[140]Open in a new tab
Therapeutic effects of bortezomib on JEV-infected mice. (a) Flow chart
of animal studies. Mice infected with JEV were treated with PBS or
bortezomib (0.5 mg/kg). Brain samples were analyzed on day 6 and day 23
of post-infection. ip: intraperitoneal injection. iv: intravenous
injection. (b) Survival of mice in each group during the 23 days after
JEV infection. Data are shown as Kaplan-Meier survival curves (n = 10
for each group). (c) Behavior score of mice in each group during 23
days after JEV infection. 0 = no restriction of movement; no blink
frequently; no body stiffening; no hind limb paralysis. 1 = no
restriction of movement; blink frequently; no body stiffening; no hind
limb paralysis. 2 = restriction of movement; blink frequently; no body
stiffening; no hind limb paralysis. 3 = restriction of movement; body
stiffening; no hind limb paralysis. 4 = restriction of movement; eyes
closed; body stiffening; hind limb paralysis, sometimes tremor. (d)
Bortezomib reduces the damage in brains caused by JEV infection.
Hematoxylin-eosin staining of brain coronal sections was performed to
observe the pathological changes.
To verify the effects of bortezomib on clinical symptoms of JEV, we
scored the clinical behavior of mice during the experiment [[141]32].
The JEV-infected mice showed different behavior than noninfected mice,
including movement limitations, frequent blinking, body stiffening, and
hind limb paralysis. The clinical behavior of the bortezomib-treated
infected group was alleviated compared with the untreated infected
group ([142]Figure 5c), indicating that bortezomib treatment prevented
the JEV-infected mice from pain. The mice in the bortezomib and PBS
groups did not show any alterations in behavior, suggesting that
bortezomib has the potential to alleviate the suffering caused by JEV
infection.
Moreover, to further explore the protection of bortezomib against JEV
infection in brains, we collected the brain tissues for
hematoxylin-eosin (H&E) staining on day 6 and day 23 post infection. As
is shown in [143]Figure 5d, the mice in the JEV-infection group
suffered from significant meningitis, vacuolar degeneration, and glial
nodules, while the symptoms of mice in the bortezomib-treated group
were remarkably alleviated. The mice without JEV infection did not show
any histological changes, regardless of whether the mice were treated
with bortezomib or not. The mice in all groups showed no evidence of
meningitis on day 23 post infection. This result indicated that
bortezomib could significantly reduce the damage in brains caused by
JEV infection. These results further suggested the ability of
bortezomib in the treatment of flavivirus infection and confirmed the
crucial role of UPS in the lifecycle of flaviviruses. However, as an
anticancer agent, bortezomib has many side effects, such as numbness,
erythematous plaques or nodules, purpuric eruptions, and folliculitis
[[144]53]. Therefore, it is necessary to control the dose in clinical
treatment and pay attention to the reaction of patients after taking
bortezomib.
3. Conclusions
At present, the treatment of JEV infection mainly depends on
symptomatic therapy and supportive therapy. Unfortunately, the effect
of the existing treatment is far from perfect. Approximately 30–50% of
survivors were reported to experience serious sequelae [[145]2].
Although many drugs have been found to have anti-JEV activity, the
evaluation of these drugs mainly focused on animal models and cellular
levels with few clinical trials reported. Therefore, it is it is
necessary to rapidly identify effective therapeutics for JEV infection
using the drug repurposing method. Furthermore, since JEV belongs to
the same genus as DENV and ZIKV, identifying the agents may provide
treatment strategies for those viruses as well.
Identifying the functional genes in JEV infection is essential, not
only for finding new antiviral agents but also for understanding the
virus replication and pathogenesis. This study utilized the HotNet2 and
GeneRank algorithms to identify host genes participating in the
progress of JEV infection. We combined the gene expression data with
the protein-protein interaction (PPI) database to rank JEV
infection-related genes that could be used as the targets to find new
antiviral agents. The results showed that host proteins involved in JEV
infection include viral infection pathways and immune response-related
pathways, which was consistent with the infection mechanism of JEV.
Afterwards, we found that bortezomib might be a potential agent for the
treatment of JEV infection by targeting these genes. In addition, we
identified genetic interaction networks related to encephalitis by the
HotNet2 algorithm. Using these genes as the targets to screen drugs, we
also found that bortezomib could be used for JEV treatment.
Based on the above results, we confirmed the effect of bortezomib on
the treatment of JEV infection in mouse model. Mice treated with
bortezomib showed a significant alleviation in histopathological
symptoms and clinical symptoms, and a 30% reduction in mortality caused
by JEV was observed, compared with the mortality of untreated
JEV-infected mice ([146]Figure 5). These results further support the
application of host-targeted approaches for new antiviral agents.
Above all, our results provided new insights into the molecular
mechanism of JEV infection and offered a novel agent for the treatment
of JEV infection.
4. Materials and Methods
4.1. Data Resources
In this study, the PheWAS data were derived from the work by Denny et
al., which included 3144 phenotype-associated single nucleotide
polymorphisms (SNPs) [[147]40]. The JEV infection datasets (GEO
accession No. [148]GSE57330) came from GEO
([149]www.ncbi.nlm.nih.gov/geo/) [[150]17]. The protein-protein
interaction (PPI) network used in the HotNet2 algorithm was obtained
from HINT database ([151]http://hint.yulab.org), iRefIndex database
([152]http://irefindex.org), and MultiNet, which included approximately
390,000 interactions [[153]16,[154]54,[155]55,[156]56]. The
protein-protein interaction (PPI) network used in the GeneRank
algorithm was derived from the STRING database (Version: 10.5,
[157]http://string-db.org) [[158]57].
Information about the association between chemical agents and its
targets was obtained from the Drug-Gene Interaction database (DGIdb,
[159]http://dgidb.genome.wustl.edu/), the Therapeutic Target Database
(TTD, [160]http://bidd.nus.edu.sg/group/cjttd/), and the DrugBank
([161]http://www.drugbank.ca/).
4.2. GeneRank Algorithm
Genes can be ranked by the GeneRank method, based on their expression
values and interaction information. The GeneRank algorithm was derived
from PageRank [[162]15]. The algorithm is described as follows:
[MATH:
rjn=
(1−d)exj+
d∑i=1Nwijrin−1degi
:MATH]
(1)
where the importance of gene j and i after n or n − 1 iterations is
represented by
[MATH:
rjn :MATH]
and
[MATH:
ri<
mi>n−1 :MATH]
, respectively; the initial importance of gene j is represented by
[MATH:
exj
:MATH]
,
[MATH:
exj
:MATH]
is defined as the fold change value in this work;
[MATH:
wij :MATH]
represents the relationship between gene j and gene i in the PPI
network, if gene i interacts with gene j, then
[MATH:
wij :MATH]
= 1, otherwise
[MATH:
wij :MATH]
= 0;
[MATH:
degi :MATH]
is the out-degree of gene i, which means the number of genes
interacting with gene i; the total number of genes in the PPI network
is represented by N; and the parameter d (0 ≤ d < 1) is a constant
representing the proportion of PPI network in calculation. The greater
d is, the more important PPI network is. In this study, we set the
value of d to 0.5.
4.3. HotNet2 Algorithm
The HotNet diffusion-oriented subnetworks (HotNet2) algorithm is a
topology-based method for finding significant subnetworks associated
with disease. Originally, the HotNet2 algorithm was used to analyze
somatic mutation data from cancer datasets [[163]16].
The initial input in the HotNet2 algorithm is a heat vector containing
the fraction of each gene and a network of protein interactions. At
each step, the nodes passed their heat and received heat from adjacent
nodes, but also a fraction β (0 ≤ β ≤ 1) of heat was retained. This
process runs until equilibrium. Therefore, the heat of each node at
equilibrium depends on its initial heat, the local topology of the
network around the nodes, and the value β. The process is described as
follows:
F = β (I − (1 − β) × W) − 1 (2)
where
[MATH:
Wij={1deg(
j) if noede i interacts with
j,0 otherwise. :MATH]
where deg(i) is the number of neighbors (i.e., the degree) of protein
in the interaction network.
In this study, we used the p-values of encephalitis derived from PheWAS
data as heat scores in the HotNet2 algorithm.
4.4. Agents and Virus
Bortezomib (PS-341, powder) was purchased from Selleck Chemicals
(Houston, TX, USA). DMSO and PEG300 were purchased from Sigma-Aldrich
(St. Louis, MO, USA). JEV P3 strains were kindly provided by Yun-Feng
Song, State Key Laboratory of Agricultural Microbiology, Huazhong
Agricultural University, China.
4.5. Animal Studies
All female BALB/c mice (4-week-old) were purchased from the Hubei
Provincial Center for Disease Control and Prevention (Wuhan, China).
The mice were randomly divided into four groups: a PBS group (PBS, n =
15); a JEV-infected group (JEV, n = 15); a bortezomib-treated group
(bortezomib, n = 15); a JEV-infected and bortezomib-treated group
(JEV-bortezomib, n = 15). For the JEV-infected group, the mice were
intraperitoneally injected with 10^6 PFU of JEV P3 strain in 100 μL
PBS. For the PBS group, mice were intraperitoneally injected with 100
μL PBS. For the bortezomib-treated and vehicle-treated group, mice were
intravenously injected with 0.5 mg/kg bortezomib or with PBS with 2%
DMSO and 30% PEG 300.
After JEV infection, the mice were treated with bortezomib once every
day for the first two days and were then treated once every two days.
On day 6 and day 23 post infection, five mice from each group were
euthanized, and the brains were used for subsequent H&E staining. Ten
remaining mice were monitored daily to assess behavior and mortality.
Behavioral scoring was performed basing on the presence of symptoms
[[164]32]. This experiment was approved by the Scientific Ethic
Committee of Huazhong Agricultural University (HZAUMO-2017-032).
4.6. H&E Staining
For the histology analysis, brain tissues were fixed in 4%
paraformaldehyde and were embedded in paraffin. Paraffin sections were
stained with hematoxylin-eosin for pathological analysis.
4.7. Data Analysis
All statistical analyses were conducted using GraphPad Prism v5.0
(GraphPad Software Inc., San Diego, CA, USA). Cytoscape 3.6.1 was used
to visualize the subnetworks. The clusterProfiler, an R package, was
used to perform the enrichment analysis of genes.
Supplementary Materials
The following are available online. Table S1: The functional genes
participating in JEV infection; Table S2: The potential anti-JEV agents
discovered by GeneRank algorithm; Table S3: The significant subnetworks
associated with encephalitis.
[165]Click here for additional data file.^ (232.7KB, zip)
Author Contributions
H.-Y.Z. conceived and designed the project; B.-M.L., X.-Y.T., Y.Q. and
M.-Y.L. performed and analyzed the data; Q.-Y.Z. and Y.-F.S. designed
the experiments; B.-M.L. and X.-Y.T. performed the experiments; B.-M.L.
wrote the manuscript; and H.-Y.Z. revised the manuscript.
Funding
This research was supported by the Special Projects for Technological
Innovation in Hubei (grant number: 2018ABA107).
Conflicts of Interest
We have received research/grant support from Wuhan Bio-Links Technology
Co., Ltd. Huazhong Agricultural University and the developers of the
methods for drug discovery and drug repositioning may benefit
financially pursuant to the University’s Policy on Inventions, Patents
and Technology Transfer, even if these methods are not used in the
commercialized therapy.
Abbreviations
DMSO dimethyl sulfoxide
PBS phosphate buffer saline
PEG300 polyethylene glycol 300
PFU plaque forming unit
[166]Open in a new tab
Footnotes
Sample Availability: Samples of the compounds are not available from
the authors.
References