Abstract
Huang-Lian-Jie-Du Decoction (HLJDD) is a "Fangji" made up of
well-designed Chinese herb array and widely used to treat ischemic
stroke. Here we aimed to investigate pharmacological mechanism by
introducing an inter-module analysis to identify an overarching view of
target profile and action mode of HLJDD. Stroke-related genes were
obtained from OMIM (Online Mendelian Inheritance in Man). And the
potential target proteins of HLJDD were identified according to TCMsp
(Traditional Chinese Medicine Systems Pharmacology Database and
Analysis Platform). The two sets of molecules related to stroke and
HLJDD were respectively imported into STRING database to construct the
stroke network and HLJDD network, which were dissected into modules
through MCODE, respectively. We analyzed the inter-module connectivity
by quantify "coupling score" (CS) between HLJDD-modules (H-modules) and
stroke-modules (S-module) to explore the pharmacological acting pattern
of HLJDD on stroke. A total of 267 stroke-related proteins and 15
S-modules, 335 HLJDD putative targeting proteins, and 13 H-modules were
identified, respectively. HLJDD directly targeted 28 proteins in stroke
network, majority (16, 57.14%) of which were in S-modules 1 and 4.
According to the modular map based on inter-module CS analysis,
H-modules 1, 2, and 8 densely connected with S-modules 1, 3, and 4 to
constitute a module-to-module bridgeness, and the enriched pathways of
this bridgeness with top significance were TNF signaling pathway, HIF
signaling pathway, and PI3K-Akt signaling pathway. Furthermore, through
this bridgeness, H-modules 2 and 4 cooperatively work together to
regulate mitochondrial apoptosis against the ischemia injury. Finally,
the core protein in H-module 4 account for mitochondrial apoptosis was
validated by an in vivo experiment. This study has developed an
integrative approach by inter-modular analysis for elucidating the
"shotgun-like" pharmacological mechanism of HLJDD for stroke.
Keywords: Huang-Lian-Jie-Du decoction, stroke, inter-module analysis,
pharmacological mechanism, network pharmacology
Introduction
Stroke is a complex disease featured by various genetic variations and
dysfunction ([39]Matthew et al., 2012). The subsequent mess brought by
gene interactions and pathway crosstalk makes it difficult to obtain a
“magic bullet” acting on “single gene, single target” to achieve
therapeutic efficacy ([40]Roth et al., 2004; [41]Frantz, 2005;
[42]Hopkins, 2009; [43]Wang et al., 2018). These observations, coupled
with the increasing failure rate of drug discovery based on
reductionism ([44]Khanna, 2012) have led to calls for a new science of
“network medicine” to find a multi-target therapy modulating multiple
genes and their interactions ([45]Chandra and Padiadpu, 2013; [46]Wang
and Wang, 2013; [47]Harvey et al., 2015; [48]Greene and Loscalzo,
2017). Chinese herbal medicines, known as concoctions of numerous
chemical ingredients, have been suggested to act on multiple
pharmacological targets and therefore drew increasing attention in the
latest decades. “Fangji” was a well-designed Chinese herb array
according to principle of traditional Chinese medicine, in order to
improve therapeutic efficacy and/or reduce toxicity and adverse
reactions ([49]Wang et al., 2011; [50]Wang et al., 2013; [51]Duan et
al., 2015; [52]Liu and Wang, 2015). Systematic prediction of multiple
drug–target interactions from chemical, genomic, and pharmacological
data was expected to accelerate the drug discovery processes ([53]Yu et
al., 2012). This may provide a potential avenue to multi-target therapy
reversing the disease condition. As amount and sheer diversity of high
throughput data generated are enormous in the post-genomic age, it is
pertinent to explore underlying pathogenesis and pharmacological
mechanism by taking a more overarching view of Fangji multi-target
therapies on stroke ([54]Hasan et al., 2012; [55]Gu and Chen, 2014;
[56]Huang et al., 2014; [57]Li et al., 2017).
Huang-Lian-Jie-Du Decoction (HLJDD), also known as Hwangryun-Hae-Dok
Decoction or oren-gedoku-to in Japan, is an ancient traditional Chinese
formula first described in Wang Tao's "Wai Tai Mi Yao" 2,000 years ago.
It is composed of four herbs: Coptidis Rhizoma (Coptis chinensis
Franch., rhizome), Radix Scutellariae (Scutellaria baicalensis Georgi.,
radix), Phellodendri Chinensis Cortex (Phellodendron chinense Schneid.,
cortex), and Gardeniae Fructus (Gardenia jasminoides Ellis., fructus)
with the ratio of 3:2:2:3. HLJDD was widely applied as a complementary
and alternative medicine to treat cerebral ischemia in Asian countries
([58]Kondo et al., 2000; [59]Xu et al., 2000; [60]Zhang et al., 2017;
[61]Fu et al., 2019). It is reported that HLJDD could reduce
ischemia-reperfusion brain injury ([62]Hwang et al., 2002) and promote
functional recovery in stroke ([63]Zou et al., 2016) by alleviating the
oxidative stress from reactive oxygen species (ROS), ameliorating
inflammatory damage, enhancing cortical neurogenesis, inducing
protective autophagy, and so on ([64]Wang et al., 2013; [65]Wang et
al., 2014; [66]Zou et al., 2016). The ingredients from HLJDD were also
studied for their anti-ischemia effect. For instance, baicalin, an
ingredient from Rhizoma Coptidis, was reported to reduce ischemic
infarct volume by regulating apoptotic and neurophysiological processes
([67]Liu et al., 2017), to protect brains against hypoxic-ischemic
injury via the PI3K/Akt signaling pathway ([68]Zhou et al., 2017);
jasminoidin from Fructus Gardeniae could attenuate inflammatory
response by suppressing ERK1/2 signaling pathway in brain microvascular
endothelial cells ([69]Li et al., 2016). Berberine, baicalin, and
jasminoidin are major ingredients responsible for the effectiveness of
HLJDD by amelioration of abnormal metabolism and regulation of
oxidative stress, neuron autophagy, and inflammatory response
([70]Zhang et al., 2017), and acted synergistically to exert protective
effects ([71]Zhang et al., 2016). It is reported that both of baicalin
and jasminoidin could attenuate ischemia/reperfusion injury by
suppressing mitochondrial apoptosis ([72]Jiang et al., 2016; [73]Zhao
et al., 2016; [74]Li, et al., 2017). And the process of mitochondrial
apoptosis is largely consequent on the translocation of Bax and Bak of
the Bcl-2 family to the mitochondrial outer membrane ([75]Love, 2003;
[76]Zhang et al., 2015). However, how the mitochondrial apoptosis is
attenuated by the ingredients of HLJDD remains unsettled.
All the above researches enriched the pharmacological targets of HLJDD.
However, the next challenge arising may be how to grab the specific
targeting community and action mode of HLJDD in the biological network.
As a complex adaptive system, biological network constitutes a set of
interacting units, "modules," which are suggested to be minimum
functional entities ([77]Sales-Pardo, 2017). The rewiring of these
interacting modules can bring out the nonlinear phenomena: chaotic
behaviors, synchronization, emergence, and subsequent phenotype
alteration ([78]Bandyopadhyay et al., 2010; [79]Tang et al., 2014).
Therefore, the interactions between these modules, not only the modules
themselves, should be investigated to elucidate process of biological
network response to perturbation ([80]Bandyopadhyay et al., 2010;
[81]Hsu et al., 2011), especially to drugs and multi-target therapies.
Accordingly exploring the target-on modules of HLJDD and how they work
together to execute sophisticated function causing phenotypic
alteration might be a promising opportunity to clarify the
pharmacological mechanisms of this multi-ingredient herb array.
In this paper, we introduced the modularity analysis integrating
inter-module connectivity calculation in protein to protein association
network to identify the target profile and action mode of HLJDD ([82]
Figure 1 ). Firstly, we constructed the stroke-related network and
HLJDD targeting protein network according to OMIM (Online Mendelian
Inheritance in Man) databases and TCMsp (Traditional Chinese Medicine
Systems Pharmacology Database and Analysis Platform), respectively.
Then we dissected the two networks into modules, respectively. The
stroke-modules (S-modules) and HLJDD-modules (H-module) were bridged by
integrating the two-dimensional network based on protein–protein
interaction background from STRING. Next, we analyzed the inter-module
connectivity between S-modules and H-modules to explore the
pharmacological acting pattern of HLJDD on stroke. Finally, we
validated the conclusions by an in vivo experiment.
Figure 1.
[83]Figure 1
[84]Open in a new tab
Flow chart for the inter-module coupling analysis strategy.
Materials and Methods
Stroke-Related Data Source and Network Construction
Genes related to stroke were derived from OMIM
([85]https://www.ncbi.nlm.nih.gov/omim/), a database of human genes and
genetic disorders. We searched "stroke" as a keyword in OMIM and
filtered the records for gene variations. As a result, all of genes
related to stroke were identified and mapped to the background in
STRING, which is an online database for functional protein association
networks ([86]https://string-db.org/cgi/input.pl), providing
associations between proteins based on curated databases,
experimentally determined, gene neighborhood, gene fusions, gene
co-occurrence, textmining, co-expression, or protein homology. Finally,
based on the proteins that correspond to imported stroke genes and
protein-to-protein interactions from STRING, proteins association
network related to stroke was constructed, in which proteins were
represented as vertex, and interaction confidence more than 0.4 (a
relatively low confidence to catch the broader scope of proteins
related to stroke) was set as the edge connecting corresponding
proteins.
HLJDD Potential Targets and Network Construction
The potential targets of herbs from HLJDD were obtained from TCMsp
([87]http://lsp.nwu.edu.cn/tcmsp.phpm), which is a systems pharmacology
platform of Chinese herbal medicines that captures the relationships
between drugs and targets ([88]Ru et al., 2014). For we aimed to
construct the holistic target landscape of HLJDD, we included all of
the ingredients in the HLJDD and all of the potential targets of these
ingredients. Then potential targets of the four herbs were merged as
the target protein of HLJDD. The union of the HLJDD potential targets
was also mapped to STRING background. Therefore, these targets were
regarded as vertex, and their interaction from STRING was used as the
edge, to construct the HLJDD target network. The weight of edges was
equal to the interaction confidence, which is a parameter to evaluate
the associations between protein in STRING. The cutoff of the edges was
set as 0.7, a mediate confidence to obtain herbs targets interactions
with high reliability.
Module Identification and Inter-Module Analysis
Both the stroke network and the HLJDD network were clustered to find
stroke-modules (abbreviated as S-module) and HLJDD-modules (abbreviated
as H-module), respectively, by MCODE (molecular complex detection).
MCODE was a cluster algorithmic-based software in cytoscape, which can
cluster a given network based on topology to find densely connected
regions. Pathways of these two groups of modules were enriched
according to KEGG database (Kyoto Encyclopedia of Genes and Genomes).
All of enriched pathways were classified according to KEGG pathway maps
([89]https://www.kegg.jp/kegg/pathway.html) to conduct the
heterogeneity analysis. The connectivity between S-modules and
H-modules was bridged by merging the stroke network and HLJDD network
to constitute a disease-drug bi-dimensional network. The proteins
related to disease or herbs and their interactions can be simplified as
a pair of networks D = (V, E) and H = (V, E), representing the
disease-related and herbs-related network. The two networks were merged
based on the background of STRING database into a union graph, as G =
(V, E), which contain the total of two sets of vertices and edges.
Therefore, the S-modules and H-modules were all in G. The
hypergeometric distribution was employed to calculate the significance
of the interaction of a pair of modules.
[MATH: p=∑k=xn(Mk)(N−Mn−k)
(Nn<
/mrow>) :MATH]
(1)
where x is observed inter-module connection; k and n represent the
numbers of inter-module connections and all possible interactions
between the pair of modules, respectively; M and N were the total
numbers of inter-module connections and all probably existing
inter-module interactions between any two modules in a network,
respectively. We set P ≤ 0.05 as significant. If the P-value of an
inter-module connection was less than 0.05, the inter-module
connectivity will be quantified by a novel parameter: coupling score
(CS), which were introduced to evaluate the inter-module connectivity
mediated by nodes and edges. The CS between any two modules was defined
as follows:
[MATH: CS=2t+∑i∈<
mi>Mx,j∈My
aij
:MATH]
(2)
where M[x] and M[y] denote a disease-module and herb-module connected
by at least one edge; t is the total number of overlapping nodes
between M[x] and M[y]. The symbols i and j represent a gene in M[x] and
M[y], respectively; a[ij] is the weight of edge between genes i and j.
Accordingly, based on this score, the modular map was constructed to
include all inter-module connectivity relationships.
In Vivo Experiment Validation
To further validate our conclusion, we employed the middle cerebral
artery obstruction (MCAO) animal model to examine the main ingredients'
effect on the protein related to new identified mechanism of HLJDD. All
the animal experiments were approved by the Ethics Committee of China
Academy of Chinese Medicine. The experimental procedures were in
accordance with the Prevention of Cruelty to Animals Act 1986 and NIH
Guidelines for the Care and Use of Laboratory Animals for Experimental
Procedures ([90]National Research Council (US) Institute for Laboratory
Animal Research, 1996).
A total of 24 Sprague-Dawley (SD) rats, weighing 200–220 g, were
subjected to MCAO in order to induce a focal cerebral
ischemia-reperfusion model. All the rats, except those in the
sham-operated group, were subjected to MCAO procedure, according to the
method described by [91]Longa et al. (1989). Briefly, after being
anesthetized with 2% pentobarbital (4 mg/kg, ip), the rats were exposed
and the external carotid artery (ECA) was prepared, and an intraluminal
filament was inserted from the ECA to ligate the left middle cerebral
artery for 1.5. Then the intraluminal filament was withdrawn for
reperfusion for 24 h. Rats in the sham group were also subjected to the
same surgical preparation for the insertion of the filament as other
groups, but no filament was inserted.
The standards of two major ingredients of HLJDD, baicalin (BA) and
jasminoidin (JA), were obtained from the National Institutes for Food
and Drug Control, and the purity was validated by fingerprint
chromatographic methodologies. All compounds were dissolved in 0.9%
saline just before the experiment. The rats were randomly divided into
four groups: sham-operated group (0.9% saline), vehicle group (0.9%
saline), BA (5 mg/ml)-treated group, and JA (25 mg/ml)-treated group in
this study. After the reperfusion, the rats received the responding
treatment by intraperitoneal injection at 2 ml/kg body weight. After 24
h reperfusion and treatment, the rats were sacrificed, and the
hippocampi of these rats were removed for western blotting.
The hippocampi were homogenized. After protein extraction and
concentration adjustment, proteins were separated by sodium dodecyl
sulfate (SDS)–polyacrylamide gel electrophoresis (PAGE) and transferred
to nitrocellulose membranes (Hybond-C, Amersham, Buckinghamshire, UK)
by electroblotting. Blots were stained with rabbit anti-Bak (Santa Cruz
Biotechnology, Santa Cruz, CA, USA) and anti-β-actin (Abcam, Cambridge,
UK) at a concentration of 1:1,000 and 1:5,000, respectively. After
cyclic membrane wash and staining by goat anti-rabbit IgG with
chemiluminescence (Amersham), the band density was determined with a
GS-700 densitometer (Bio-Rad). Each measurement was taken in three
replicates.
Results
Two S-Module Community With Diverse Functions
As a result, 303 genes related to stroke were identified. A total of
267 out of 303 genes were found corresponding to proteins. The official
symbols, domains, and annotations of the 267 proteins are shown in
[92]Supplementary Table 1.And 256 in 267 proteins were involved in the
stroke-related network (named as stroke network), and the other 11
proteins were distributed individually; 1,502 edges were included in
the network. According to MCODE, the stroke network was divided into 15
S-modules and many individual nodes ([93]Supplementary Figure 1). The
S-modules were interacting with each other to constitute a module map.
S-module 1 and S-module 2 were in the center of the module map and
possessed most neighbor modules: S-module 1 densely associated with
S-modules 3, 4, 5, and 7; S-module 2 was densely interacting with
S-module 10. The two module groups, led by S-module 1 and S-module 2,
constituted two communities of stroke network. The other S-modules were
sparsely connected ([94]Figure 2A).
Figure 2.
[95]Figure 2
[96]Open in a new tab
The S-module communities and enrichment KEGG pathways. (A) The S-module
communities. A total of 13 S-modules constituted two communities: the
first was centered by S-module 1; the other was centered by S-module 2.
The circles represented the proteins related to stroke; the circles
marked by red were S-module proteins targeted by HLJDD. Representative
enriched KEGG terms with minimum P-value are used for the annotation of
each S-module. (B) The categories of enriched KEGG pathways of the two
S-module communities. The categories of community 1 concentrated on
signal transduction, immune system, infectious diseases: parasitic, and
amino acid metabolism; community 2 focused on global and overview maps,
neurodegenerative diseases, and carbohydrate metabolism.
To investigate the biological function of S-modules, the KEGG pathway
enrichment was conducted ([97]Supplementary Table 2). As the center of
the two community, S-module 1 and S-module 2 were enriched for 21 and 6
signaling pathways, respectively. The representative enriched
annotation term with minimum P-value of S-module 1 was the calcium
signaling pathway, and the representative term of S-module 2 was
oxidative phosphorylation ([98]Figure 2A). Additionally, there were 1,
3, 2, 6, 5, and 5 signaling pathways enriched in S-modules 3, 4, 5, 7,
8, and 9, respectively. Furthermore, the enriched pathways of each
S-module were classified. According to the categories of S-modules, the
functions of two modular communities varied from each other. For the
modular community 1, the top 4 pathway categories were signal
transduction, immune system, and infectious diseases: parasitic and
amino acid metabolism, accounting for 19%, 11%, 11%, and 11% of the
total number of enriched pathways, respectively. These four sections
accounted for 52% of the total pathways. For the modular community 2,
categories showed more concentrated state: the top 3 pathway categories
were global and overview maps, neurodegenerative diseases, and
carbohydrate metabolism, accounting for 28%, 27%, and 18% of total
enriched pathways. These three categories accounted for 73% of total
pathways. Therefore, the pathological functions of the communities were
on different aspects ([99]Figures 2B and [100]3A).
Figure 3.
[101]Figure 3
[102]Open in a new tab
The number and distribution of enriched pathway of H-modules in each
category. (A) The number of enriched pathways of H-modules in
categories. The depth of the color was in proportion to the number of
pathways. The letters emphasized by red represented categories with
high enrichment frequency. (B) The radar chart of categories of
pathways. The length of the line in each point position showed the
number of the pathways in correspondent categories. The categories
circled by red rings were frequently enriched.
H-Modules Mainly Regulated Signal Transduction, Immune, Cancer, Infectious
Diseases, Nervous System
As collected from the TCMsp database, a total of 105, 35, 102, and 66
compounds and 234, 228, 288, and 310 target proteins of Rhizoma
Coptidis, Radix Scutellariae, Cortex Phellodendri, and Fructus
Gardeniae were identified respectively, and listed in
[103]Supplementary Table 3. After the target merging, there were 400
proteins that were regarded as the potential targets of HLJDD, and 335
out of 400 proteins were found as annotation in the STRING database
([104]Supplementary Table 4). As a result, the ultimate HLJDD target
network concluded with 300 proteins and 2,775 interactions. To
investigate the targeting position in the intra-structure of this
network, we also dissected the HLJDD network into modules. A total of
13 H-modules were identified by MCODE and 133 individual proteins not
belonging to any module ([105]Supplementary Figure 1).
According to KEGG pathway enrichment analysis of the H-modules
([106]Supplementary Table 5), the most signaling pathways (76 pathways
with significance) were enriched in H-module 1, among which the
representative pathway with minimum P-value was TNF signaling pathway.
In H-module 2, 72 pathways were enriched, and the minimum P-value
pathway was cell cycle. H-modules 1 and 2 were concentrated on signal
transduction, immune, cancer, and infectious diseases. The pathways
related to the above aspects accounted for 81.58% and 77.78% of the
total in H-modules 1 and 2, respectively. Additionally, 13, 33, 6, 7,
53, 2, 2, 8, and 4 pathways were enriched for H-modules 3, 4, 5, 6, 7,
9, 11, 12, and 13, respectively. These pathways were also categorized
based on the KEGG pathway maps ([107]Figures 3A, B). It is showed that
the function of H-modules mainly concentrated on five aspects: signal
transduction, immune, cancer, infectious diseases, and nervous system.
It is also remarkable that the frequently enriched category was signal
transduction, which may indicate the principal function of HLJDD.
Overlapping Proteins Between HLJDD and Stroke Network Were the Direct Targets
As it is aimed to provide the targeting basis of HLJDD in stroke, we
merged the HLJDD network and stroke network to construct the
disease-drug bi-dimensional network. This merged network contained 515
proteins and 4,416 interactions, in which S-modules and H-modules were
all involved. There were 28 overlapping proteins between stroke-related
proteins and HLJDD targeting proteins. This may indicate that the 28
proteins are the potential targets of HLJDD, and the action mode of
herbs was to directly regulate the disease gene. In the stroke network,
nearly a half (12, accounting for 42.86%) of the 28 overlapping were
located in S-module 1; 4 proteins were in S-module 4; and 1 protein was
distributed in S-modules 5 and 7, respectively; the other 10 proteins
were scattered around S-modules. This may indicate that S-module 1 and
its neighbor S-module 4 were the major direct targets of HLJDD
([108]Figure 2A), and the direct targets of HLJDD on stroke are mostly
involved in stroke community 1 rather than stroke community 2.
Integrating with the pathway categories of stroke S-modules 1 and 4 in
the above sections ([109]Supplementary Table 2), we can infer that the
priority direct regulation of HLJDD relies on the effect on signal
transduction, immune, and infectious diseases, especially the signal
transduction. For example, 24% pathways of S-module 1 focus on signal
transduction, including calcium signaling pathway, TNF signaling
pathway, sphingolipid signaling pathway, cGMP-PKG signaling pathway,
and HIF-1 signaling pathway. This may be partly accounted for the
pharmacological mechanism of HLJDD on stroke ([110]Figures 4A, B).
Figure 4.
[111]Figure 4
[112]Open in a new tab
The inter-module coupling connectivity analysis and pathway comparison
between S-modules and H-modules. (A and B) were the pathway categories
of S-modules and H-modules, respectively. The categories marked by red
were major ones with large proportion. (C) The modular map constituted
by S-modules and H-modules based on inter-module analysis. H-modules 1,
2, and 8 densely connected with S-modules 1, 3, and 4 to constitute a
module-to-module coupling connectivity bridge. (D) The overlapping
situation of enriched pathways between S-modules 1, 3, and 4 and
H-modules 1, 2, and 8 in this bridgeness. (E) The P-value of the
overlapping pathways between S-modules 1, 3, and 4 and H-modules 1, 2,
and 8. X-axis and Y-axis were representative for the -log10 of p-value
in S-modules1, 3, and 4 and H-modules 1, 2, and 8, respectively.
Inter-Module Connectivity Between H-Modules and S-Modules Bridged More
Holistic Target Profile
HLJDD, which as a formula constituted of multiple herbs and numerous
ingredients, may act more like the "magic shotguns" mode. As the
cellular components were organized in a wide interaction pattern to
achieve mutual information propagation, these "shotguns" of HLJDD may
affect the stroke network not only by overlapping targets but also by
perturbing the fluctuation of this biological adaptive system.
Therefore, we employ the inter-module CS to explore a more holistic
landscape of the HLJDD action mode.
According to the modular map based on inter-module CS, we have also
noticed that several H-modules formed an inter-module coupling
connectivity with S-modules. In the modular map, H-modules 1, 2, 4, 7,
8, 9, 10, and 12 were densely connected with S-modules. H-modules 1, 2,
and 8 surrounded the major targeted S-modules 1, 3, and and 4 to
constitute a module-to-module coupling connectivity to bridge
formula-related and disease-related network as a potential targeting
pattern ([113]Figure 4C).
In this module-to-module bridgeness, by comparing the pathways of
S-modules 1, 3, and 4 with H-modules 1, 2, and 8, a total of 14
overlapping pathways were found. These 14 pathways mainly focused on
signal transduction (42.86%) and infectious diseases (28.57%), as shown
in [114]Table 1 and [115]Figures 4D, E. The other pathways belong to
the immune system, endocrine system, metabolic diseases, and cancers.
Among these pathways, the TNF signaling pathways were enriched with a
minimum p-value (1.52E-14). That also verified that these peripheral
H-modules included the same pathways with S-modules; that means, HLJDD
regulated these pathological pathways of stroke by forming the
module-to-module bridgeness in the biological system.
Table 1.
The enriched KEGG pathways and corresponding proteins of H-module 1, 2,
8 and S-module 1, 3, 4 in the bridgeness structure.
KEGG pathway Category P-value of H-module Genes of H-module H-module
S-module P-value of S-module Genes of S-module
hsa04668: TNF signaling pathway 3.2 Signal transduction 1.52E-14 ICAM1,
CSF2, IL6, TNF, CCL2, PTGS2, RELA, MMP9, CXCL2, EDN1, CXCL10, MAPK1,
FOS, JUN H-module 1, 2 S-module 1 0.001942 NOS1, NOS3, NOS2
hsa04066: HIF-1 signaling pathway 3.2 Signal transduction 3.85E-09
MAPK1, IL6, INS, RELA, BCL2, EDN1, VEGFA, IFNG, TLR4, STAT3 H-module 1,
2 S-module 1 0.04152 PIK3CA, NOS3, NOS2
hsa04151: PI3K-Akt signaling pathway 3.2 Signal transduction 4.75E-09
IL4, IL6, IL2RA, RELA, TP53, TLR4, BCL2L1, MAPK1, INS, CHRM2, BCL2,
VEGFA, FGF2, MYC, IL2 H-module 1, 2 S-module 4 0.044181 IL4, ITGA2, EPO
hsa04071: Sphingolipid signaling pathway 3.2 Signal transduction
6.60E-04 MAPK1, TNF, RELA, BCL2, TP53, OPRD1 H-module 1, 2 S-module 1
0.00702 TNF, GNAQ, PIK3CA, NOS3
hsa04022: cGMP-PKG signaling pathway 3.2 Signal transduction 0.002805
MAPK1, INS, ADRA2A, ADRA2C, ADRA2B, OPRD1 H-module 1, 2 S-module 1
0.016942 AGTR1, GNAQ, PIK3CA, NOS3
hsa04080: Neuroactive ligand-receptor interaction 3.3 Signaling
molecules and interaction 2.66E-05 OPRM1, PTGER3, CHRM4, C5AR1, DRD3,
CHRM2, ADRA2A, ADRA2C, ADRA2B, OPRD1 H-module 1 S-module 1 0.012249
AGTR1, F2, TBXA2R, PLG, HTR2A
hsa04640: Hematopoietic cell lineage 5.1 Immune system 0.001527 IL4,
CSF2, IL6, TNF, IL2RA H-module 1 S-module 4 0.003017 IL4, ITGA2, EPO
hsa04915: Estrogen signaling pathway 5.2 Endocrine system 2.72E-04
OPRM1, MAPK1, FOS, JUN, MMP9, MMP2 H-module 1, 2 S-module 1 0.004103
GNAQ, MMP9, PIK3CA, NOS3
hsa05200: Pathways in cancer 6.1 Cancers: Overview 2.63E-09 IL6,
PTGER3, PTGS2, RELA, MMP9, TP53, BCL2L1, MMP2, STAT3, MAPK1, FOS, JUN,
BCL2, VEGFA, FGF2, MYC H-module 1, 2 S-module 1 0.00833 AGTR1, GNAQ,
MMP9, PIK3CA, GNB3, NOS2
hsa05142: Chagas disease (American trypanosomiasis) 6.10 Infectious
diseases: Parasitic 2.93E-10 MAPK1, FOS, IL6, TNF, CCL2, JUN, RELA,
IFNG, TLR4, IL10, IL2 H-module 1, 2 S-module 1 0.004712 TNF, GNAQ,
PIK3CA, NOS2
hsa05144: Malaria 6.10 Infectious diseases: Parasitic 3.46E-07 ICAM1,
IL6, TNF, CCL2, IFNG, TLR4, IL10 H-module 1, 2 S-module 4 0.048615
SELP, SELE
hsa05146: Amoebiasis 6.10 Infectious diseases: Parasitic 3.29E-05 CSF2,
IL6, TNF, RELA, IFNG, TLR4, IL10 H-module 1, 2 S-module 1 0.004971 TNF,
GNAQ, PIK3CA, NOS2
hsa05143: African trypanosomiasis 6.10 Infectious diseases: Parasitic
3.86E-05 ICAM1, IL6, TNF, IFNG, IL10 H-module 1, 2 S-module 1 1.65E-04
VCAM1, TNF, APOA1, GNAQ
hsa04931: Insulin resistance 6.7 Endocrine and metabolic diseases
0.003664 IL6, TNF, INS, RELA, STAT3 H-module 1, 2 S-module 1 0.005237
SREBF1, TNF, PIK3CA, NOS3
[116]Open in a new tab
Furthermore, through this bridgeness, more H-modules were connected to
more S-modules: H-modules 4, 7, 9, 10, and 12 could interact with
S-modules through this bridgeness to constitute a more complete target
profile on the disease network. For instance, viral carcinogenesis was
a pathway enriched in H-module 2, involving the protein BAX. And there
are also 12 proteins in H-module 4, one of which was Bak[1], enriched
in this pathway. Therefore, these H-modules could work together
cooperatively to constitute a more comprehensive target profile.
Western Blot Validation
To further validate the mechanism of HLJDD identified by inter-module
coupling analysis, we selected a protein (Bak[1]) in H-module 4, which
may act on S-modules by module-to-module bridgeness, and we employed
western blot assays to compare the untreated and treated groups.
According to western blot, as shown in [117]Figures 5A, B, the
expression of protein Bak in hippocampi significantly increased in the
vehicle group compared with the sham group (paired T-test, one-sided, P
< 0.05). Its expression significantly decreased in BA groups compared
with the vehicle group (paired T-test, one-sided, P < 0.01). There was
no statistical significance between JA and the vehicle group.
Figure 5.
Figure 5
[118]Open in a new tab
In vivo experiment validation of the protein involved in mechanism of
HLJDD. (A) is the blot of Bak and β-actin; (B) are the expression
levels of Bak/β-actin among different groups in Western blot; *P <
0.05, **P < 0.01 by one-side paired T-test.
Discussion
HLJDD, as a "fangji" formed by herb array, consists of numerous
multi-target ingredients. Therefore, the targets of HLJDD were neither
individual gene or protein, nor a single module, but "shotgun-like"
target profiles. In this paper, we explored the H-modules, as well as
drug- and disease-module inter-module coupling connectivity in stroke
to investigate multiple targeting pathways of HLJDD and how they work
together to cause phenotypic alteration.
S-Modules 1 and 4 Were the Core Pathological Module Targeted by HLJDD
In our stroke network, the S-module 1 was in the center of the modular
map, circled by S-modules 3, 4, 5, and 7, and so on. Majority of the
direct targets of HLJDD was distributed in S-modules 1 and 4. And the
modular map also showed that the S-modules 1 and 4 were surrounded by
H-modules ([119]Figure 6). Therefore, S-modules 1 and 4 were the core
targets regulated by HLJDD. Among the enriched pathways of S-module 1,
the most frequently enriched category signal transduction, including
calcium signaling pathway, TNF signaling pathway, sphingolipid
signaling pathway, cGMP-PKG signaling pathway, and HIF-1 signaling
pathway, exhibited a close relationship with stroke. All the above
pathways showed close relationship with the stroke process. For
example, the calcium signaling plays a critical role in the
inflammation of stroke, associated with immune- and injury-related
functions of astrocyte ([120]Hamby et al., 2012). Ca^2+ signaling
showed beneficial effects on neuronal and brain protection and
functional deficits after stroke ([121]Li et al., 2015). TNF signaling
is one of the key players in stroke inflammation progression:
inhibition of TNF signaling can rescue functional cortical plasticity
impaired in early post-stroke period ([122]Liguz-Lecznar et al., 2015).
HIF-1α signaling, which was involved in necroptosis, modulated
blood–brain barrier integrity after focal ischemia ([123]Geng et al.,
2017). Sphingosine-1-phosphate, a key signaling molecule in the
sphingolipid signaling pathway, is critical for sequelae after
stressful stimulations: regulating glial cell activation,
vasoconstriction, endothelial barrier function, and neuronal death
pathways, which act as important components in many neurological
conditions. Activation of sphingosine-1-phosphate receptor-1 by FTY720,
a known sphingosine 1-phosphate receptor agonist, is neuroprotective
after ischemic stroke in rats ([124]Rosen et al., 2007; [125]Hasegawa
et al., 2010; [126]Pfeilschifter et al., 2010; [127]Maceyka and
Spiegel, 2014; [128]Prager et al., 2015; [129]Sun et al., 2016). For
pathways of stroke, S-module 4 also exhibited a close relationship to
stroke. Also setting the signal transduction category as an example,
the PI3K-Akt signaling pathway was enriched in S-module 4. It is
reported that regulating PI3K/Akt signaling may induce ischemic damage
attenuation in cerebral artery occlusion. In summary, the core
pathological modules targeted by HLJDD were S-modules 1 and 4, which
mainly include signal transductions related to neuroprotective effect.
Figure 6.
[130]Figure 6
[131]Open in a new tab
The inter-module connectivity between S-modules 1 and 4 and H-modules.
(A) The inter-module coupling connectivity between S-module 1 and
H-modules. The red circle and blue triangle represented the proteins
related to stroke and HLJDD, respectively. The triangles fulfilled with
red represented the proteins related to stroke and regulated by HLJDD.
(B) The coupling connectivity between S-module 4 and H-modules.
Modular Connectivity Revealed the “Shotgun-Like” Action Pattern of HLJDD
S-modules 1, 3, and 4 and surrounding H-modules 1, 2, and 8 constitute
a bridging relationship between disease network to herb network. Among
the overlapping pathways of this bridge structure, TNF signaling
pathway was the top pathway with the most statistical significance. The
“shotgun-like” action was exhibited in the TNF signaling pathway: four
proteins (VCAM1, TNF, MMP9, and PIK3CA) in the S-module 1 were found in
the TNF signaling pathway downstream; and 14 and 9 proteins of
H-modules 1 and 2 were also found not only in the downstream, but also
in the upstream of this pathway. The H-modules 1 and 2 constitute a
comprehensive targeting set to regulate this pathway. This pathway was
suggested to play a role in cerebral ischemia and impaired functional
cortical plasticity and to be a primary process of releasing of
inflammatory cytokines ([132]Liguz-Lecznar et al., 2015; [133]McCoy and
Tansey, 2015; [134]Liu, et al., 2016; [135]Hollander et al., 2017;
[136]Wang et al., 2017).
Another pathway with top statistical significance was HIF-1 signaling
pathway, which was enriched in S-module 1 and H-modules 1 and 2. A
total of 17 target proteins in H-modules 1 and 2 belong to the HIF-1
signaling pathway, including MAPK1, IL6, INS, RELA, BCL2, EDN1, VEGFA,
IFNG, TLR4, STAT3, AKT1, EGFR, ERBB2, SERPINE1, NOS3, EGF, and TIMP,
which were overlapping with three proteins in S-module 1 in this
pathway, PIK3CA, NOS3, and NOS2. And the targets of H-modules 1 and 2
contained the downstream and upstream of the stroke-related proteins in
this pathway. It has been reported that HIF-1 played an important role
in the antioxidant's neuroprotection in ischemic stroke ([137]Zhang et
al., 2014). HIF-1α can be served as an upstream regulator of cerebral
glycerol concentrations and brain edema ([138]Higashida et al., 2011).
That means HLJDD regulated the pathological proteins and neighboring
proteins closely related to stroke process to constitute a targeting
network in the HIF signaling pathway.
Other significant pathways were also regulated by HLJDD by the similar
action mode. Activation of the PI3K-Akt pathway was reported to promote
neuroprotection against cerebral ischemia-reperfusion injury by
decreasing nerve cell apoptosis ([139]Lv et al., 2017; [140]Li et al.,
2019). The regulation mode on these overlapping pathways may provide a
characteristic action pattern of multiple target ingredients, like many
"shotguns" to form a regulating pathway profile, mainly concentrating
on inflammatory response, antioxidant, and apoptosis.
Besides these overlapping pathways, the specific pathway of S-module 1
also has crosstalk with H-module enriched pathways. For example,
inflammatory mediator regulation of TRP channels was a specific pathway
in S-module 1. And this pathway can be regulated by MAPK signaling and
calcium signaling, which were pathways in H-module 1. Therefore, the
HLJDD regulated the upstream pathways of the stroke pathological
pathway through the pathway crosstalk. Above all, HLJDD regulated the
stroke-related core pathological pathways as well as their upstream
and/or downstream pathways to constitute the waterfall of the pathway
and to contribute a therapeutic effect.
Furthermore, through the bridgeness structure constituted by S-modules
1, 3, and 4 and H-modules 1, 2, and 8, more H-modules worked
cooperatively on S-module. For instance, both the protein Bak[1] in
H-module 4 and BAX in H-module 2 were enriched in the viral
carcinogenesis pathway. It is demonstrated that BAX and BAK are
required for the initiation of apoptosis at the mitochondria ([141]Ren
et al., 2010). It is reported that the oligomerization of the Bax and
Bak is an irreversible step leading to the execution of apoptosis, and
inhibition of Bax/Bak oligomerization allowed cells to evade apoptotic
stimuli and rescued neurons from death after excitotoxicity ([142]Niu
et al., 2017). Previous studies have demonstrated that both the
ingredients baicalin and jasminoidin extracted from HLJDD could
suppress mitochondrial apoptosis induced by ischemia/reperfusion
injury, but the exact mechanism involved in these core proteins was far
from clear. In our in vivo experiment, BA inhibited the protein
expression Bak. This suggested that regulating mitochondrial apoptosis
by inhibiting the Bak expression is an important mechanism of HLJDD in
protecting against the neuronal injury. Therefore, through this
module-to-module bridgeness, H-modules 2 and 4 cooperatively work
together to regulate more comprehensive aspect of this pathway related
to mitochondrial apoptosis against the ischemia injury. This also
supported that it is the alteration of interaction between proteins
from different H-modules that contributed to the phenotype reversion
rather than a single target or a single module.
Accordingly, the core pathological modules were S-modules 1 and 4. We
can infer that it is formatting an inter-module coupling connectivity
between H-modules and stroke pathological modules, which contributed to
pharmacological mechanism of HLJDD in stroke, mainly involving the TNF
signaling pathway, the HIF signaling pathway, and the PI3K-Akt
signaling pathway. Furthermore, through this bridgeness, H-modules 2
and 4 cooperatively work together to regulate mitochondrial apoptosis
against the ischemia injury. These regulation targets were not a simple
protein or a single module but constitute targeting pathway profiles,
by pathway crosstalk, upstream and downstream and vertically converges
to integral regulation.
Conclusions
Our integrative approach is a step toward elucidating the
"shotgun-like" pharmacological mechanism of multi-target and
multi-ingredient "Fangji" by inter-modular analysis in complex diseases
like stroke. Our methodology identified a subset of modules that can
serve as potential targets of response mechanism to drug activity. Our
findings offer a glimpse of the “tuning” from target entities to the
relationship between these entities.
The targets in our study were based on the open database. It seems to
be likely to produce some false positives that prevent us from a
flawless work. And the validation experiment is still focusing on
single targets. Therefore, one of our future works will be designing an
integrative strategy using the effect-based multi-omics data from
experiments to detect more mechanism with more accuracy and precision.
In addition, we will try to design the further validation experiments
for the inter-module connectivity.
Ethics Statement
All of the animal experiments were approved by Ethics Committee of
China Academy of Chinese Medicine. The experimental procedures were in
accordance with the Prevention of Cruelty to Animals Act 1986 and NIH
Guidelines for the Care and Use of Laboratory Animals for Experimental
Procedures [[143]National Research Council (US) Institute for
Laboratory Animal Research, 1996].
Author Contributions
PW and FS designed the research. PW performed the research and wrote
the paper. LD, MZ, and YW performed the experiment validation. WZ, JM,
HH and XX contributed to data and statistical analysis. PW and XX
revised the manuscript.
Funding
This work was supported by grants from the National Natural Science
Foundation of China (81873024; 81773923; 81473372; 81373986) and
Inheritance Program from Institute of Chinese Materia Media of China
Academy of Chinese Medical Sciences (ZXKT18002).
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Abbreviations
HLJDD, Huang-Lian-Jie-Du Decotion; OMIM, Online Mendelian Inheritance
in Man; TCMsp, Traditional Chinese Medicine Systems Pharmacology
Database and Analysis Platform; CS, coupling score; MCODE, molecular
complex detection; KEGG, Kyoto Encyclopedia of Genes and Genomes;
stroke module, S-module; HLJDD module, H-module.
Supplementary Material
The Supplementary Material for this article can be found online at:
[144]https://www.frontiersin.org/articles/10.3389/fphar.2019.01288/full
#supplementary-material
Figure S1
Stroke-related protein-protein interaction network and modules; HLJDD
targeting protein-protein interaction network and modules. (A)
Stroke-related protein-protein interaction network. (B) HLJDD targeting
protein-protein interaction network. (C) Modules detected by MCODE in
stroke-related protein-protein interaction network, abbreviated as
S-modules. (D) Modules detected by MCODE in HLJDD targeting
protein-protein interaction network, abbreviated as H-modules. Each
vertex represents a protein, circles and triangles are the
stroke-related proteins and HLJDD targeting proteins, respectively. The
vertexes fulfilled by red represent the overlapping proteins between
the stroke-related proteins and HLJDD targeting proteins.
[145]Click here for additional data file.^ (3.6MB, png)
Table S1
The stroke-related proteins and annotation.
[146]Click here for additional data file.^ (65.7KB, docx)
Table S2
The ingredients and target of herbs from HLJDD.
[147]Click here for additional data file.^ (279.2KB, docx)
Table S3
Target proteins and annotation of HLJDD.
[148]Click here for additional data file.^ (92KB, docx)
Table S4
The enriched KEGG pathways and their categories of stroke-modules.
[149]Click here for additional data file.^ (21.9KB, docx)
Table S5
The enriched KEGG pathways and their categories of HLJDD-modules.
[150]Click here for additional data file.^ (55.4KB, docx)
References