Abstract
Module‐based methods have made much progress in deconstructing
biological networks. However, it is a great challenge to quantitatively
compare the topological structural variations of modules (allosteric
modules [AMs]) under different situations. A total of 23, 42, and 15
coexpression modules were identified in baicalin (BA), jasminoidin
(JA), and ursodeoxycholic acid (UA) in a global anti‐ischemic mice
network, respectively. Then, we integrated the methods of module‐based
consensus ratio (MCR) and modified Z[summary] module statistic to
validate 12 BA, 22 JA, and 8 UA on‐modules based on comparing with
vehicle. The MCRs for pairwise comparisons were 1.55% (BA vs. JA),
1.45% (BA vs. UA), and 1.27% (JA vs. UA), respectively. Five conserved
allosteric modules (CAMs) and 17 unique allosteric modules (UAMs) were
identified among these groups. In conclusion, module‐centric analysis
may provide us a unique approach to understand multiple pharmacological
mechanisms associated with differential phenotypes in the era of
modular pharmacology.
__________________________________________________________________
Study Highlights.
WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?
☑ Module‐based methods, rather than independent genes or proteins, have
made much progress in deconstructing the complex networks and were
prospected in contributing the rational drug design paradigm.
WHAT QUESTION DID THIS STUDY ADDRESS?
☑ There is a great challenge to quantitatively compare topological
structural variations of modules in different situations. We used an
integrated method of module‐based consensus ratio and modified
Z[summary] statistics to validate compound‐dependent on‐modules based
on comparing the pharmacologic actions.
WHAT THIS STUDY ADDS TO OUR KNOWLEDGE
☑ Conserved AMs of BA, JA, and UA revealed their common mechanisms in
anticerebral ischemia, such as the MAPK and calcium‐signaling pathway,
and unique AMs found their divergent biological functions, such as the
BA Hedgehog signaling pathway.
HOW THIS MIGHT CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS
☑ The AM identification may help to explore the therapeutic target
modules rather than a single gene or protein in disease therapy. In
addition, this module‐centric analysis may provide a unique path to
reveal multiple pharmacologic mechanisms associated with differential
phenotypes.
There is increasing evidence that both pathogenesis of diseases and
mechanism of action of drugs have a module basis, as genes and proteins
should interact with each other in the network to execute certain
functions.[42]1, [43]2, [44]3 Module‐based methods have made much
progress in deconstructing complex networks and may contribute
substantially to rational drug design in the context of modular
pharmacology.[45]4, [46]5 Several studies have attempted to identify
module biomarkers or targets for cancers and many other diseases.[47]6,
[48]7, [49]8, [50]9, [51]10, [52]11 Causal coexpression methods with
module analysis have been applied to screen drugs with specific targets
and fewer side effects.[53]12 Such module‐targeting approaches rather
than targeting at independent genes or proteins may provide us an
intensive understanding of the underlying mechanisms of drug
actions.[54]13 In addition, actions of functionally similar drugs in
treating the same disease can be comparatively analyzed based on
modular functions. However, when different drugs are used to affect the
same disease network, their common and specific modular relationships
are often neglected.
In the gene coexpression network, the correlation patterns among genes
across microarray samples are described as the gene relationship
significance, the significant gene relationships have a coexpression
edge. These highly correlated genes often coordinate together as a
functional cluster, so the densely interconnected clusters are defined
as coexpression modules. Under different conditions, such as the
treatment with different drugs, the gene coexpression relationship can
be changed and manifest as an intramodular edge rewiring, which
reflects the different condition responses, so we defined the
significant topological structure‐changed modules under different
conditions as allosteric modules (AMs). In this study, coexpression and
AM‐based analysis was applied to elucidate and compare the
pharmacological mechanisms of three drugs in treating cerebral
ischemia.
Baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) are
bioactive ingredients extracted from Qingkailing, a traditional Chinese
medicine formula that is effective and widely used in treating patients
who undergo a stroke in China.[55]14 The pharmacological actions of BA
mainly include neuroprotection, anti‐inflammation, and
antioxidation,[56]15, [57]16, [58]17 and BA may act on TLR2/4 signaling
pathway, antioxidative, and antiapoptotic pathways, GABAergic
signaling, HSP70, and mitogen‐activated protein kinase (MAPK) cascades,
as well as the PI3K‐Akt‐PKB‐BAD‐CREB‐PCREB pathway.[59]14, [60]16,
[61]17, [62]18 The pharmacologic activities of JA include
neuroprotection, choleretic action, enzyme inhibition, and
anti‐inflammation,[63]19 and it may act on the NF‐γB pathway, PI3K
pathway, TLR4 pathway, and MAPK pathway.[64]20, [65]21, [66]22, [67]23
As for UA, it plays a unique role in modulating the apoptotic threshold
in both hepatic and nonhepatic cells, and it may inhibit apoptosis by
either stabilizing the mitochondrial membrane or modulating the
expression of specific upstream targets.[68]24, [69]25 It has been
shown that BA, JA, and UA all exert effects on multiple pathways in
animal models of cerebral ischemia.[70]26 Previous studies compared the
functions of these three drugs based on differentially expressed genes
as well as protein‐protein interaction networks, but the convergent or
divergent modules of different drugs were not identified.[71]27
In this study, we used the method of weighted gene coexpression network
analysis (WGCNA)[72]28 to identify coexpression modules of BA, JA, and
UA in a global anti‐ischemic mice network consisting of 374
stroke‐related cDNAs. The module‐based consensus ratio (MCR) and
Z[summary] module preservation statistic were used to validate
compound‐dependent on‐modules, and the conserved allosteric modules
(CAMs) and unique allosteric modules (UAMs) were also identified. Then,
modular analysis based on the drug‐induced gene coexpression and
functional changes was performed to illuminate and compare the
underlying pharmacological mechanisms of BA, JA, and UA in treating
cerebral ischemia.
MATERIALS AND METHODS
Gene expression datasets
Expression data, which originated from our previous studies,[73]29 were
obtained from ArrayExpress database
([74]http://www.ebi.ac.uk/arrayexpress/, E‐TABM‐662; see Supplementary
Text S1). Microarrays were constructed from a collection of 374 cDNAs
related to cerebral ischemia (Clontech Atlas 1.2 mouse brain
microarray, “Biostar40S” 4065, and 16,463 mouse oligo chips). The
specific 374 genes and their expression level are listed in
Supplementary Table S1. The procedures of RNA isolation, microarray
preparation, and gene collections were described in refs. [75]19 and
[76]30. Five groups of datasets were selected: sham group, vehicle
group (VE) (0.9% NaCl), BA‐treated group (5 mg/mL), JA‐treated group
(25 mg/mL), and UA‐treated group (7 mg/mL).
Coexpression module identification
The construction and module identification of the gene coexpression
networks were implemented following the protocols of the WGCNA R
package.[77]28 A matrix of pairwise correlations was constructed
between all pairs of probes across the measured samples by using
appropriate soft‐thresholding for each group (β = 4 for BA, 12 for JA,
and 8 for UA), the thresholds were selected when the network gets the
best scale‐free topology criterion.[78]31 To identify the coexpression
modules, topologic overlap measure was used to perform average linkage
hierarchic clustering, which got a dendrogram whose branches were
identified using the Dynamic Hybrid Tree Cut algorithm,[79]31 then, the
branches were defined as modules and each module was subsequently
assigned a color. We set the number three as the minimum module size in
all three groups.
Module‐based consensus ratio
We defined an MCR in Eq. 1 to compare the influence of different drugs
on the gene coexpression level in the context of whole networks. The
consensus module pairs were detected based on the overlapping genes
within the two modules, and the Fisher's exact test was used to select
significantly overlapped module pairs (P < 0.05). The MCR was defined
as the ratio of significantly overlapped module pairs to all the module
pairs between two drugs.
[MATH: MCRa,b=
NMoverlap<
/mrow>NMa×NMb×1
00% :MATH]
(1)
where NM represents the number of modules, a and b represent two
different groups, and NM[overlap] represents the number of
significantly overlapped module pairs between the two groups.
On‐module and off‐module
In order to quantitatively assess whether modules in the drug groups
were changed in coexpression patterns independent of the vehicle, we
adopted a Z[summary] statistic implemented in the module preservation
function of WGCNA, which can assess whether the density and
connectivity patterns of modules defined in a reference dataset are
preserved in a test dataset.[80]32 A negative Z[summary] value
indicates the modules' disruption.[81]33 Compared to the vehicle, we
defined a module with a negative Z[summary] value as an on‐module,
which may be activated by a drug. On the other hand, a module with a
positive Z[summary] value is defined as an off‐module. The equation of
Z[summary] is as follows^32:
[MATH: Zsummary=median
(ZmeanCor,ZmeanAdj<
/msub>,ZpropvarExpl,ZmeamKME)+median
(Zcor.KIM,Zcor.KME,Zcor.cor)2
:MATH]
Conserved allosteric module and unique allosteric module
The Z[summary] value was also used to quantitatively assess whether a
specific AM is conserved or unique compared with each drug group. A
module with a Z[summary] value >2 indicates its preservation in a test
dataset.[82]32 So, compared to the test group, modules with a
Z[summary]value ≥2 are CAMs, which may be regarded as universal targets
of two or more drugs. If a module has a negative Z[summary] value
compared with any other groups, this module is defined as a UAM, which
reveals a specific target of this drug.
Functional annotation of modules
To characterize the function of modules, we performed Gene Ontology
(GO) and KEGG pathway enrichment analysis using the Database for
Annotation, Visualization, and Integrated Discovery.[83]34 For each
module, a ranked list of the enriched functionally relevant annotation
was provided. An overrepresentation of a term is defined as a modified
Fisher's exact P value with an adjustment for multiple tests using the
Benjamini method. In this analysis, all the genes on the array were set
as the background, and GO terms and pathways with a P < 0.05 were
considered as significant. To specify and simplify the enriched
biological functions of modules, we classified the GO terms and
pathways based on the GO slim[84]35 and KEGG functional hierarchies.
Western blotting analysis
The mitogen‐activated protein kinase 6 (MAP2K6) protein was selected to
validate the expression patterns in different groups. Standard Western
blotting analyses were performed, as described previously.[85]26 The
blots were probed with anti‐MEK6 antibody (1:1000 dilution, ab71938;
Abcam, UK), and β‐actin (1:1000 dilution, Tdybio, TDY041, China) was
used as an internal control. Western blot bands were quantified using
QuantityOne software by measuring the band intensity (area × outer
diameter) for each group. The results are expressed as fold changes by
normalizing the data to the control values.
RESULTS
Coexpression modules in the three drug groups
The gene coexpression network of BA, JA, and UA were constructed by
WGCNA, as described in the Methods section. Hierarchic clustering
procedures identified 23, 42, and 15 coexpression modules for BA, JA,
and UA, respectively. Each module corresponded to a branch of the
resulting clustering tree and labeled by a unique color (Figure [86]1
a–c). The detailed modules of each group labeled by colors and numbers
can be found in Supplementary Table S1. The average sizes (number of
genes) of BA, JA, and UA modules were 16 (range, 3–149), 9 (range,
3–46), and 25 (range, 3–95), respectively (Figure [87]1 d).
Figure 1.
Figure 1
[88]Open in a new tab
Hierarchic cluster tree and modules of the three drug groups. (a–c) The
hierarchic cluster tree (dendrogram) of baicalin (BA), jasminoidin
(JA), and ursodeoxycholic acid (UA), each major tree branch represents
a module, and each module is labeled with a color below the dendrogram.
(d) The number and size of the modules in the three groups are shown.
The red, green, and blue bars represent the number of modules in the
BA, JA, and UA groups, respectively. The red dot, green square, and
blue triangle indicate the mean size of the modules in the BA, JA, and
UA groups, respectively. The vertical line indicates the range of
module size in each group.
Concordance of modules among the three groups
To investigate the influence of the three drugs on gene coexpression
levels in the context of whole networks, we compared the distribution
of all genes in the modules of the three groups. Figure [89]2 a–f shows
the concordance of gene composition and the number of module pairs with
a certain number of overlapping genes for each group's modules. The
number of module pairs with at least five overlapping genes was small,
and the MCRs for pairwise comparisons were only 1.55% (BA vs. JA),
1.45% (BA vs. UA), and 1.27% (JA vs. UA), respectively (Figure [90]2
g), indicating a low level of in‐module genes overlapping. Thus, there
was a big difference in the gene coexpression level and module
constitution among the three drug groups.
Figure 2.
Figure 2
[91]Open in a new tab
Concordance of modules among the three groups. (a) Concordance of
modules between the baicalin (BA) and jasminoidin (JA) groups; each row
of the table corresponds to the BA modules (labeled by color name and
module size), and each column corresponds to the JA modules. Numbers in
the table indicate gene counts in the intersection of the corresponding
modules of the BA and JA groups. Coloring of the table encodes ‐log
(P), with P being the Fisher's exact test P value for the overlap of
the two modules. Any P value < 0.05 is considered significant. The
darker the red color, the more significant the correlation. (b) The
number of modules with a certain amount of overlapping genes between
the BA and JA groups is shown. (c) Concordance of modules between the
BA and ursodeoxycholic acid (UA) groups; the table legend is the same
as panel a. (d) The number of modules with a certain amount of
overlapping genes between the BA and UA groups. (e) Concordance of
modules between the JA and UA groups; the table legend is the same as
a. (f) The number of modules with a certain amount of overlapping genes
between the JA and UA groups. (g) The module‐based consensus ratios
(MCRs) among the three drug groups.
CAMs in each group
In order to quantitatively assess whether a specific AM of one drug
group was conserved compared with the vehicle and other drug groups, we
used a Z[summary] statistic implemented in the module preservation
function of WGCNA.[92]32 A Z[summary] value >2 indicates that the
corresponding module is conserved. The CAMs and their Z[summary] values
with respect to different groups are listed in Table [93]1. Compared to
the vehicle group, only the JA_2 and JA_18 modules were conserved. When
pairwise comparisons were performed among the three drug groups, the
JA_2 module was also conserved in the UA group, the JA_22 module was
conserved in the BA group, the BA_5 module was conserved in both the JA
and UA groups, the UA_2 module was conserved in the JA group, and the
UA_11 module was conserved in the BA group. We named and visualized
these CAMs in Figure [94]3.
Table 1.
The Z[summary] value and the most significant functions of CAMs
The CAMs across all groups
Module names No. of genes Z[summary] value for each test group The most
significant GO term The most significant KEGG pathway
VE BA JA UA Terms P value Pathway P value
BA_5 CAM_BA/(JA, UA) 14 0.40 – 2.30 2.30 Calmodulin‐dependent protein
kinase activity 0.0198 Calcium signaling pathway 0.0064
JA_2 CAM_JA/(VE, UA) 31 3.90 −0.23 – 2.10 Positive regulation of
catalytic activity 8.89e‐05 MAPK signaling pathway 4.47e‐07
JA_18 CAM_JA/VE 8 2.60 0.69 – 0.02 Protein amino acid phosphorylation
1.82e‐04 Pathways in cancer 0.0077
JA_22 CAM_JA/BA 7 0.23 2.40 – −0.46 NA – NA –
UA_11 CAM_UA/BA 8 1.20 2.20 −0.81 – Regulation of mitochondrial
membrane permeability 0.0041 ALS 0.0392
UA_2 CAM_UA/JA 87 −0.55 0.33 3.70 – Intracellular signaling cascade
2.21e‐07 MAPK signaling pathway 8.31e‐11
[95]Open in a new tab
Modules are named based on the name of the corresponding reference and
test groups. For example, the green module in the BA group is conserved
in the JA and UA groups, and we name it as CAM_BA/(JA, UA). The
following columns include data on the number of genes in the module,
the Z[summary] value for different test groups (bold numbers indicate
the Z[summary] value ≥2), the most significant GO terms and KEGG
pathways with corresponding P values. ALS, amyotrophic lateral
sclerosis; BA, baicalin; CAM, conserved allosteric module; GO, Gene
Ontology; JA, jasminoidin; MAPK, mitogen‐activated protein kinase; NA,
not applicable; UA, ursodeoxycholic acid; VE, vehicle group.
Figure 3.
Figure 3
[96]Open in a new tab
The conserved allosteric modules (CAMs) and their significant
biological functions are shown. Modules in the orange dotted box are
identified from the baicalin (BA) group, modules in the light blue
dotted box are identified from the jasminoidin (JA) group, and modules
in the light green dotted box are identified from the ursodeoxycholic
acid (UA) group. The top five significantly enriched functions (black
font color represents Gene Ontology (GO) terms and the red font color
represents KEGG pathways) of each module are listed. The Venn diagram
in the middle indicates to which conserved module belongs to which
groups. MAPK, mitogen‐activated protein kinase; NA, not applicable; VE,
vehicle group.
Significant biological functions of CAMs
To characterize the biological function of the identified AMs, we
performed GO term and KEGG pathway enrichment analysis. The most
significant GO terms and pathways along with their P values of each CAM
are listed in Table [97]1. All of the significant GO terms and pathways
(P < 0.05) of the CAMs can be found in Supplementary Table S2. Among
the top five significant functions (Figure [98]3), the BA_5, JA_2, and
UA_2 from three groups were all enriched in MAPK signaling pathway; and
both the BA_5 and JA_18 were enriched in neurotrophin signaling
pathway. The UA_2 and JA_18 were both enriched in protein amino acid
phosphorylation, phosphorylation GO terms, and pathways in cancer.
Besides, amyotrophic lateral sclerosis was enriched by the JA_2 and
UA_11, and GnRH signaling pathway was enriched by the JA_2 and UA_2.
On‐modules and off‐modules in the three groups
Our prior studies reported that BA, UA, and JA were effective in
reducing the ischemic infarct volume compared to the vehicle group
(P < 0.05).[99]36 Based on the detection and statistical evaluation of
changes in coexpression patterns, we also observed whether the modules
in the three drug groups changed their gene coexpression levels
independent of the vehicle. Modules with a negative Z[summary] value
were considered as on‐modules, which might reflect the pharmacologic
actions of the three drugs. Compared with the vehicle, 12, 22, and 8
on‐modules were detected in the BA, JA, and UA groups, respectively. A
complete listing of these on‐modules is available in Supplementary
Table S3. On the other hand, 11, 20, and 7 off‐modules with a positive
Z[summary] value were detected in the BA, JA, and UA groups,
respectively.
Significant biological functions of on‐modules
The GO function and KEEG pathway enrichment analysis revealed a wide
range of biological functions associated with the on‐modules in the
three drug groups. All of the significantly enriched GO terms and
pathways (P < 0.05) of the on‐modules are provided in Supplementary
Table S3. To specify and simplify the biological functions of the three
drugs, we classified the GO terms and pathways based on the GO
slim[100]35 and KEGG functional hierarchies (Figure [101]4). For the
on‐modules in the BA group, the top three GO function categories were
metabolism (16.8%), development (9.3%), and cell communication (7.5%);
and the top three pathway categories were signal transduction (29%),
cancer‐specific types (29%), and endocrine system (12%; Figure [102]4
a,b). As for the on‐modules in the JA group, the top three GO function
categories were metabolism (18.9%), binding (8.5%), and death (5.4%);
and the top three pathway categories were cancer‐specific types (17%),
replication and repair (14%), and signal transduction (14%; Figure
[103]4 c,d). As for the on‐modules in the UA group, the top three GO
function categories were metabolism (13.2%), development (9.6%), and
binding (9.2%); and the top three pathway categories were
cancer‐specific types (33.3%), signal transduction (15.2%), and
cellular community (12.1%; Figure [104]4 e,f). Among the top 10 GO
categories, catalytic activity, hydrolase activity, and phosphoprotein
phosphatase activity were unique in the BA group, death and cell death
were unique in the JA group, and cell differentiation was unique in the
UA group.
Figure 4.
Figure 4
[105]Open in a new tab
Classification of significant biological functions for the three drug
groups are shown. (a–f) The classification of significantly enriched
Gene Ontology (GO) terms and pathways (P < 0.05) for the baicalin (BA)
(a & b), the jasminoidin (JA) (c and d), and the ursodeoxycholic acid
(UA) (e and f) groups. (g) The overlapped circles show the
significantly enriched GO terms for the three groups. (h) The
overlapped circles show the significantly enriched KEGG pathways for
the three groups.
Moreover, we also compared the overlapping GO terms and pathways of the
on‐modules in the three drug groups (Figure [106]4 g,h). BA and JA
shared 19% GO terms and 24% pathways, BA and UA shared 18% GO terms and
32% pathways, whereas JA and UA shared 31% GO terms and 44% pathways.
This indicated that JA and UA had more overlapping GO terms and
pathways.
UAMs in the three groups
Furthermore, six modules of BA (i.e., the BA_8, BA_11, BA_12, BA_15,
BA_20, and BA_22 modules), nine modules of JA (i.e., the JA_5, JA_6,
JA_9, JA_13, JA_25, JA_28, JA_30, JA_32, and JA_41 modules), and two
modules of UA (i.e., the UA_13 and UA_14 modules) seemed to be unique
compared with both vehicle and the other two drug groups, which might
differentiate the mechanism of action of the three drugs. Among the
genes in these UAMs, DUSP4, FZD7, POU2F1, and MET in BA_8, GPX2 and
JUND in BA_11, LDB1 and vascular endothelial growth factor‐A in BA_12,
HTR2C in BA_20, CASP7 in JA_5, GPX2 and BAD in JA_6, RARA and E2F1 in
JA_25, NKD1 in JA_28, and DUSP10 in UA_13 were significantly
differentially expressed compared to vehicle‐based on the one‐way
analysis of variance. The UAMs of each group are listed and visualized
according to the module color in Figure [107]5.
Figure 5.
Figure 5
[108]Open in a new tab
The unique allosteric modules (UAMs) and their significant biological
functions. Modules in the orange dotted box are identified from the
baicalin (BA) group, the modules in the light blue dotted box are
identified from the jasminoidin (JA) group, and the modules in the
light green dotted box are identified from the ursodeoxycholic acid
(UA) group. The top five significantly enriched functions (black font
color represents the Gene Ontology (GO) terms and the red font color
represents the KEGG pathways) of each module are listed. These common
and divergent biological functions of each group are classified and
visualized in the middle of this figure (the black font color
represents GO terms' classification and the red font color represents
KEGG pathways' classification; the number of terms in a certain
category are also listed). NA, not applicable.
Divergent biological functions of the three drugs
To characterize the variant biological functions of the three drugs, we
compared the GO functions and pathways of the UAMs in the three groups.
The top five significantly enriched GO terms and pathways of the UAMs
are listed in Figure [109]5. There were no overlapping GO terms among
the three groups; two pathways were shared by BA and JA (i.e., MAPK
signaling pathway and colorectal cancer pathway). Based on GO slim
classification, BA had more effects on cell communication (4 terms) and
signal transduction (3 terms); JA exerted more impacts on cell
proliferation (3 terms), nucleobase, nucleoside, nucleotide, and
nucleic acid metabolism (3 terms), and binding (3 terms); whereas UA
might act more on cell organization and biogenesis (2 terms; Figure
[110]5). With respect to pathways, 3, 14, and 0 pathways were enriched
by the UAMs in BA, JA, and UA groups, respectively. Except for two
overlapping pathways, BA acted on Hedgehog signaling pathway, and JA
impacted progesterone‐mediated oocyte maturation, melanoma, prostate
cancer, mismatch repair, etc. (Figure [111]6 a). Therefore, these three
drugs had divergent pharmacologic actions in treating cerebral
ischemia.
Figure 6.
Figure 6
[112]Open in a new tab
(a) Schematic diagram of the contributing pathways of the three drug
groups. The top five enriched pathways of the unique allosteric modules
(UAMs) are listed. The orange color lines represent pathways enriched
by the UAMs in the baicalin (BA) group, and the light blue lines
represent the UAMs in the jasminoidin (JA) group; no pathway is
enriched by the UAMs in the ursodeoxycholic acid (UA) group. The length
of these lines indicates its approximate enriched ‐log(P value). (b)
Western blotting analysis indicates the active patterns of
mitogen‐activated protein kinase 6 (MAP2K6) under different conditions.
*P < 0.05 vs. vehicle. MAPK, mitogen‐activated protein kinase.
Western blotting validation
MAP2K6 is an MAPK, which is involved in many pathological processes,
such as cerebral ischemia.[113]37 In this study, MAP2K6 was clustered
into modules of all the three drug groups. Western blotting analysis
showed that the expression level of MAP2K6 increased significantly in
all the three groups compared with the vehicle (Figure [114]6 b).
DISCUSSION
Modularity has been deemed as a fundamental concept of disease and
drug‐target networks.[115]38 Studies with a modular design may help to
deconstruct complex networks and reveal the relationships between drug
actions and disease outcomes.[116]5 In this study, a low MCR was
obtained among the three drug groups, indicating a difference in their
pharmacologic actions globally. From the modular perspective, variant
drug‐induced coexpression patterns were also noted. BA, JA, and UA
modules were all associated with extensive biological functions,
including GO functional categories of metabolism, development, and
binding, as well as pathway categories of signal transduction,
cancer‐specific types, etc. These module‐enriched functions may provide
useful implications on the overall difference in the pharmacologic
effects of the three drugs at a systems level.
Both on‐modules of three drugs enriched extensive functions and
pathways, involving their known mechanisms, such as the MAPK pathway
for BA and JA,[117]16, [118]23 and anti‐apoptosis for BA and
UA.[119]17, [120]25 Not surprisingly, there were both common and unique
functions of different drugs on the same disease. In order to analyze
the pharmacologic mechanisms in depth and in detail, the CAMs and
on‐modules should be considered. In this study, two modules (JA_22 and
JA_18) were conserved in the vehicle group, which were not affected by
these drugs. The activated modules with different drug‐induced
coexpression patterns may reflect disease‐related pharmacologic
mechanisms. Similarly, the activated modules may also be conserved in
different drug groups; for example, BA_5 module was conserved in both
the JA and UA groups, and this module significantly enriched the
transcription activator activity, MAPK signaling pathway, and
neurotrophin signaling pathway, which have been shown to be closely
related to cerebral ischemia.[121]39 These commonly presented modules
may be universal therapeutic targets of the three drugs in the
treatment of cerebral ischemia.
UAMs can be found by contrastive analysis among different groups based
on the drug‐induced specific coexpression patterns, which may
discriminate the precise details about the actions of different drugs.
No overlapping enriched GO terms were found among the UAMs of the BA,
JA, and UA groups, demonstrating the unique characteristics of these
modules. Except for some basic regulations of molecular functions and
cell biological processes, cerebral ischemia‐related functions were
enriched by these UAMs; for example, BA enriched phosphate metabolic
process and Wnt receptor signaling pathway,[122]40 whereas JA enriched
angiogenesis[123]41 and calcium ion binding.[124]39 In terms of
pathways, MAPK signaling pathway and colorectal cancer were enriched by
the UAMs of both the BA and JA groups. Besides, BA also acted on
Hedgehog signaling pathway, and JA exerted an impact on Huntington
disease, melanoma, mismatch repair, etc. Thus, these unique modules may
be used to differentiate the distinct actions of different drugs in
treating the same disease.
Significant differential expressed genes in the UAMs of BA, JA, and UA
were found to be important for cerebral ischemia therapy. Vascular
endothelial growth factor‐A in BA_12 were shown to be a target for
regulates angiogenesis after ischemic stroke.[125]42 BAD in JA_6 was
known to play an important role in Bad and Bcl‐X(L)
interaction‐affected neuroprotection.[126]43, [127]44 E2F1 in JA_25
plays an important role in modulating neuronal death in response to
excitotoxicity and cerebral ischemia.[128]45 These important genes in
UAMs may provide new clues in cerebral ischemia therapy.
To identify the similar or disparate functions of different drugs, gene
expression profiles have been widely used when comparing drug
responses, but most previous studies merely focused on the expression
difference or chemical structure similarity of a single gene.[129]46,
[130]47, [131]48 However, it has been demonstrated that a complex
disease is rarely caused by a single gene, but a cluster of
functionally related genes.[132]49 Modules are considered to be stable
groups in biological networks, and the module biomarkers may be robust,
which are not likely to be affected by individual gene expression
changes.[133]50 Thus, a coexpressed module may provide more
implications to infer drug actions.[134]51 The CAMs and UAMs of
different drugs may serve as universal or specific targets in disease
treatment. Based on these responsive modules, we may identify both
similar and diverse actions of different drugs in treating the same
disease, which cannot be easily obtained by analyzing a single gene.
Our module‐based analysis may provide a framework to compare the
actions of multiple drugs in treating the same disease, but some
limitations also exist. For example, quantitative analysis was absent
in modular function comparisons that were mainly based on functional
annotations, which might restrict the precise assessment of
similarities between drugs. In addition, the dynamic variations of
modules among different groups were not evaluated, which should be
taken into account in future studies.
In conclusion, both CAMs and UAMs of BA, JA, and UA were identified in
mice anti‐ischemic networks, which may serve as universal and specific
therapeutic targets of the three drugs. It is demonstrated that the
modules of each drug are related with several divergent biological
functions. Our module‐centric analysis may provide unique insights into
the comparison of pharmacologic mechanisms associated with multiple
drugs.
Supporting information
Supporting Information
[135]Click here for additional data file.^ (103.8KB, docx)
Supporting Information
[136]Click here for additional data file.^ (106.5KB, doc)
Supporting Information
[137]Click here for additional data file.^ (236KB, doc)
Supporting Information
[138]Click here for additional data file.^ (5.4KB, xml)
Source of Funding
The authors' work was funded by the National Natural Science Foundation
of China (90209015) and National Eleventh Five‐year Key Technologies
R&D Program of China (2006BAI08B04‐06).
Conflict of Interest
The authors declared no conflict of interest.
Author Contributions
B.L. and Y.Y. wrote the manuscript. Z.W. and Y.W. designed the
research. B.L., J.L., and P.W. performed the research. B.L., Y.Z., and
X.Z. analyzed the data. R.K. and H.W. contributed new
reagents/analytical tools.
References