Abstract
Besides its anti-inflammatory, analgesic and anti-pyretic properties,
aspirin is used for the prevention of cardiovascular disease and
various types of cancer. The multiple activities of aspirin likely
involve several molecular targets and pathways rather than a single
target. Therefore, systematic identification of these targets of
aspirin can help us understand the underlying mechanisms of the
activities. In this study, we identified 23 putative targets of aspirin
in the human proteome by using binding pocket similarity detecting tool
combination with molecular docking, free energy calculation and pathway
analysis. These targets have diverse folds and are derived from
different protein family. However, they have similar aspirin-binding
pockets. The binding free energy with aspirin for newly identified
targets is comparable to that for the primary targets. Pathway analysis
revealed that the targets were enriched in several pathways such as
vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI
signaling and arachidonic acid metabolism, which are strongly involved
in inflammation, cardiovascular disease and cancer. Therefore, the
predicted target profile of aspirin suggests a new explanation for the
disease prevention ability of aspirin. Our findings provide a new
insight of aspirin and its efficacy of disease prevention in a
systematic and global view.
Keywords: Target, Cancer, Molecular docking, Aspirin, Cardiovascular
disease, Binding site
Introduction
Aspirin, also known as acetylsalicylic acid, is a nonsteroidal
anti-inflammatory drug (NSAID). The primary molecular mechanism of
aspirin is the selective acetylaton of Ser-530 of cyclooxygenase-1
(COX-1) ([30]Alfonso et al., 2014; [31]Dovizio et al., 2013; [32]Ghooi,
Thatte & Joshi, 1995; [33]Vane, 1971; [34]Vane & Botting, 2003),
thereby inhibiting prostaglandin synthesis. This was the basis for its
anti-inflammatory, antipyretic, and analgesic effects ([35]Vane &
Botting, 2003). In addition, recent studies revealed that
phospholipases A2 (PLA2) is functionally coupled with cyclooxygenase-1
and 2 pathway and part of its anti-inflammatory effects of aspirin may
be due to its binding with PLA2 ([36]Balsinde et al., 1999; [37]Singh
et al., 2005; [38]Touqui & Alaoui-El-Azher, 2001).
Besides its the anti-inflammatory, analgesic, and anti-pyretic
properties, aspirin is used for the prevention of cardiovascular
disease and various types of cancer ([39]Alfonso et al., 2014;
[40]Dovizio et al., 2013). Aspirin prevents myocardial infarction, and
ischemic stroke when used in the primary prevention of cardiovascular
disease ([41]Berger, Brown & Becker, 2008; [42]Nemerovski et al., 2012;
[43]Raju et al., 2011; [44]Schnell, Erbach & Hummel, 2012; [45]Younis
et al., 2010). Furthermore, aspirin is also highly effective in
preventing several common cancers ([46]Avivi et al., 2012; [47]Burn et
al., 2011; [48]Hassan et al., 2012; [49]Rothwell et al., 2010;
[50]Rothwell et al., 2012; [51]Thun, Jacobs & Patrono, 2012). Taking
aspirin daily reduced risk of distant metastasis by 30–40% and reduced
the risk of metastatic adenocarcinoma by almost a half ([52]Rothwell et
al., 2012). Although it has been convincingly shown that aspirin can
prevent cardiovascular disease and several cancer types, the molecular
mechanisms underlying these effects of aspirin are not entirely clear.
The multiple activities of aspirin cannot be attributed wholly to a
single target and most likely involve several molecular targets and
pathways ([53]Deng & Fang, 2012; [54]Din et al., 2012; [55]Sclabas et
al., 2005; [56]Singh et al., 2005). Therefore, systematic
identification of molecular targets of aspirin can help in
understanding the mechanisms underlying the activities and adverse
reactions of aspirin. Unfortunately, studies on the proteome-wide
target profile of aspirin are very limited.
In this study, we predicted the targets of aspirin whole proteome-wide
by combining structural bioinformatics and systems biology approaches.
Starting with the binding sites of aspirin (BSiteAs), the potential
targets of aspirin were discovered by using contact matrix based local
structural alignment algorithm (CMASA) which was developed in our lab
([57]Li & Huang, 2010). Then, molecular docking and free energy
calculation were applied to filter the improper targets to which the
aspirin can not bind. We also analyzed the diversity of the putative
targets and binding modes of aspirin. Finally, we performed the pathway
analysis for the putative targets. We found several new targets for
aspirin which are enriched in the pathways that are strongly involved
in inflammatory, cardiovascular disease and cancer, such as vascular
endothelial growth factor (VEGF), mitogen-activated protein
kinase(MAPK), Fc epsilon RI signaling and arachidonic acid metabolism
signaling pathways.
Methods
Overview of pipeline for proteome-wide prediction of aspirin targets
The pipeline for proteome-wide prediction of aspirin targets is
outlined in [58]Fig. 1. Firstly, we constructed the structural database
of human proteins (17,425 non-redundant structures), and the binding
sites of aspirin (BSiteAs) were used to search against this database
using the program CMASA. Secondly, the binding abilities of aspirin to
these putative off-targets were estimated using molecular docking. If
aspirin docked unsuccessfully to the predicted binding pocket of a
particular protein, this protein was removed from the target list of
aspirin. Thirdly, the remaining putative targets were subject to
molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) free
energy calculation. Finally, we performed the pathway enrichment
analysis for these putative targets.
Figure 1. The pipeline of the structural proteome-wide prediction of aspirin
targets.
[59]Figure 1
[60]Open in a new tab
Starting with the binding sites of aspirin (BSiteAs), the pipeline
integrates local structure detecting, molecular docking, free energy
calculation, and pathway analysis.
The structure database of human proteins
The query database was built by integrating three available sources of
protein structure. The first source is the Protein Data Bank managed by
Research Collaboratory for Structural Bioinformatics (RCSB PDB)
([61]http://www.rcsb.org) ([62]Rose et al., 2011), which contains 6,983
structures of human proteins. The other two repositories are structure
models built by homology modeling techniques from Structure Atlas of
Human Genome ([63]Motono et al., 2011) ([64]http://bird.cbrc.jp/sahg)
and GPCR Research Database (GPCR-RD) ([65]Zhang & Zhang, 2010), which
have 16,529 and 1,028 structures, respectively. This procedure yielded
a total of 24,540 human protein structures. The three available sources
of protein structure were integrated and removed the redundancy using
CD-HIT Suite ([66]Huang et al., 2010). The identity cut-off was set to
0.95. There are totally 17,425 structures in our self-built
‘non-redundant’ structural database of human proteins. The structural
database represents a relatively complete and accurate library of human
proteins.
To detect and compare the aspirin binding sites in the structure database
We used all representative aspirin-protein complexes in our study.
There are a total of six aspirin-protein complex structures currently
available (1OXR, 1TGM, 2QQT, 3GCL, 4NSB and 3IAZ). Both 1OXR and 1TGM
are structures of phospholipase A2, we only used 1OXR in this study
([67]Fig. 2A). 2QQT and 3GCL are bovine lactoperoxidase, and we used
3GCL in this study. 4NSB and 3IAZ are structures of buffalo
chitinase-3-like protein 1 and bovine lactotransferrin, respectively.
The two structures were also used in our study. In addition, 1PTH, the
complex structure of salicylic acid and COX-1, was also used in this
study ([68]Fig. 2A). Therefore, a total of five complex structures
(1OXR, 3GCL, 4NSB, 3IAZ and 1PTH) were used in this study. The binding
sites of aspirin (BSiteAs) were defined as amino acid residues lying
within 5 Å from atoms of the aspirin. The BSiteAs were used to search
against 17,425 non-redundant structures using the program CMASA
([69]http://bsb.kiz.ac.cn/CMASA_flex) which was developed in our lab.
The accuracy and sensitivity of this tool were 0.96 and 0.86,
respectively. The workflow of CMASA was as following. First, the CMASA
parsed the query binding site and the database to be searched. Second,
for each structure in database, based on the query binding site, the
CMASA enumerated all possible combination of residues in the structure
using amino acid substitute. The blocks substitution matrix 62
(BLOSUM62, cutoff = 1) was used. These residue combinations are similar
to that in the query binding site. Each residue combination forms a
possible local structure. Third, the CMASA used contact matrix average
deviation (CMAD) between the query binding site and these local
structures to filter the candidate matches. Then, the CMASA calculated
the RMSD and the RMSD based P-value if the CMAD < cutoff. At last, the
CMASA ranked all of the hits and add their information. Hits are
considered significant if the CMASA E-value <0.01.
Figure 2. Structural diversity of the putative targets.
[70]Figure 2
[71]Open in a new tab
(A) The structural similarity network of the putative targets and the
structures of 1OXR and 1PTH are also shown. We analyzed the structural
similarity of the 23 putative targets by structural alignment. The RMSD
between two linked proteins in the network is smaller than 4 Å. The
primary targets of aspirin are colored with green, and the newly
identified targets are colored with red. (B) Structural alignment of
the putative binding sites of aspirin (BSiteAs) from two proteins
PLA2G1B (green) and CDK13 (red).
Molecular docking
The hit proteins have similar local structures with BSiteAs and
potential to bind to aspirin. However, it does not mean that aspirin
can certainly bind to these proteins. To assess whether aspirin can
bind to these proteins, flexible ligand-rigid protein docking was
performed using CHARMm-based DOCKER (CDOCKER) ([72]Wu et al., 2003) in
the Discovery Studio v3.1. The following steps are included in the
CDOCKER protocol. (1) A set of ligand conformations are generated using
high-temperature molecular dynamics with different random seeds. (2)
Random orientations of the conformations are produced by translating
the center of the ligand to a specified location within the receptor
active site, and performing a series of random rotations. A softened
energy is calculated and the orientation is kept if the energy is less
than a specified threshold. This process continues until either the
desired number of low-energy orientations is found, or the maximum
number of bad orientations have been tried. (3) Each orientation is
subjected to simulated annealing molecular dynamics. The temperature is
heated up to a high temperature then cooled to the target temperature.
(4) A final minimization of the ligand in the rigid receptor using
non-softened potential is performed. (5) For each final pose, the
CHARMm energy (interaction energy plus ligand strain) and the
interaction energy alone are calculated. The poses are sorted by CHARMm
energy and the top scoring (most negative, thus favorable to binding)
poses are retained. In this study, we generated 10 random conformations
for each ligand. The parameters of the dynamic steps, the dynamic
target temperature and include electrostatics are set to 1,000 steps,
1,000 K and True, respectively, which is the default setting in
CDOCKER. The binding sphere of CDOCKER is defined around the local
structure detected by CMASA. The center of binding sphere is set as the
center of the local structure. And the radius of binding sphere is set
as 10 Å, which allows the free rotation of aspirin. If aspirin docked
successfully to a particular protein, the binding poses of aspirin
showing the lowest energy were retained and used for MM-PBSA
([73]Kollman et al., 2000; [74]Kuhn et al., 2005) free energy
calculation. The 3-D structures of docked complexes were visualized
using PyMol v1.5.
MM-PBSA free energy calculation and entropy change estimation
The complex structures of aspirin and putative targets were further
used to obtain more accurate estimate of binding free energy. Binding
free energies (ΔG[bind]) of aspirin at the binding site on these target
proteins were calculated by using the MM-PBSA free energy calculation
protocol in Pipeline pilot v8.5
([75]http://accelrys.com/products/collaborative-science/biovia-pipeline
-pilot/) as follows:
[MATH: ΔGbindingPB=
G
complex−G
receptor−<
mi>Gligand :MATH]
(1)
where G[complex], G[receptor], G[ligand] are the free energies of the
complex, receptor and ligand respectively. The free energy of each
molecule on the right hand side can be considered as the sum of
molecular mechanics energy in gas phase(E[MM]) and solvation free
energy(Δsol).
As the entropy change (TΔS) is the most time-intensive part, it was not
included in the above calculation. We estimate the entropy change in
another way. In the study of [76]Chang & Gilson (2004), they computed
the entropy changes for different receptors upon same ligand binding.
The results showed the entropy changes for different receptors were
similar. Therefore, we can estimate an approximate entropy change. The
process of entropy change estimation is as following: (1) Based on the
formula:—RTlnK = ΔG = ΔH − TΔS (where R is the gas constant, T is the
absolute temperature), the entropy changes (TΔS) is equal to the sum of
ΔH and RTlnK; (2) The experiment study have shown that aspirin binds to
PLA2 enzyme (PDBID: 1OXR) specifically with a binding constant (K) of
1.56 × 10^5M^−1 ([77]Singh et al., 2005). For this complex, the
enthalpy change (ΔH) was calculated as −2.327 kcal/mol using the
MM-PBSA and RTln K was calculated as 7.055 kcal/mol. So the entropy
change (TΔS) is 4.728 kcal/mol (equal to −2.327 kcal/mol + 7.055
kcal/mol) for this complex. 3) Based on above assumption and
computation, we set the entropy change in the process of aspirin
binding to various putative targets as approximate value 4.728
kcal/mol.
Pathway enrichment analysis and interaction network construction
To analyze the significance of KEGG pathways associated with our
predicted targets, we collected UniProtKB accession number (AC) of
these targets and performed KEGG pathway annotation using the DAVID
tool ([78]http://david.abcc.ncifcrf.gov/). The significantly
over-represented KEGG pathways were identified based on the
Bonferroni-adjusted P value (P < 0.01) ([79]Huang, Sherman & Lempicki,
2009). In addition, based on these pathways, an integrated
targets-cellular effect interaction network was constructed.
Results
Identification of putative targets of aspirin in human proteome
We presented a proteome-wide prediction of aspirin targets using
structural bioinformatics and system biology approaches. We used
comparison of BSiteAs to recognize putative targets and further refined
by docking and MM-PBSA in structural bioinformatics part whereas
pathway enrichment analysis and interaction network construction were
performed in system biology section. The steps in our pipeline for
proteome-wide prediction of aspirin-binding proteins are shown in
[80]Fig. 1. Firstly, the binding sites of aspirin (BSiteAs) were used
as queries to search against 17,425 non-redundant structures of human
proteins in our self-build structure database using the program CMASA.
Totally, 79 proteins with putative BSiteAs were identified ([81]Table
S1). Of these proteins, the top 10 ranked proteins are members of the
phospholipase A2. cyclooxygenase, lactoperoxidase and Chitotriosidase
families, which are the primary targets of aspirin. The remaining 69
proteins have different structural folds from the primary targets.
The hit proteins have similar local structures with BSiteAs and
potential to bind to aspirin. However, it does not mean that aspirin
can certainly bind to these proteins. In the second step, molecular
docking was used to assess whether aspirin can bind to these proteins.
CDOCKER in the Discovery Studio v3.1 was used to dock aspirin to the
predicted binding site on these proteins. Proteins that failed to dock
aspirin were removed from the target list. Only 26 proteins were
considered for further analysis after filtering by molecular docking,
10 proteins of which are the primary targets of aspirin ([82]Table S1).
Finally, MM-PBSA free energy calculation was performed for the
lowest-energy protein-aspirin complex obtained in the docking step. In
total, 23 proteins bind to aspirin with binding free energies
(ΔG[bind]) < 0 listed in [83]Table 1 and selected as the putative
targets of aspirin. Next, we analyzed the binding modes and affinities
of aspirin to these targets, the structural similarity and pathway
enrichment of these targets and clinical outcomes of aspirin.
Table 1. The putative targets with binding free energies calculated by
MM-PBSA.
Gene name UniProtKB entry Family
[MATH: ΔGbindingPB :MATH]
(kcal/mol)
EXOSC3 [84]Q9NQT5 RRP40 family −33.0
MAPK12 [85]P53778 Kinase family −28.6
ITGAL [86]O43746 Integrin alpha chain family −28.0
PTGS2 [87]P35354 Prostaglandin G/H synthase family −20.2
PTGS1 [88]P23219 Prostaglandin G/H synthase family −27.6
PLA2G10 [89]O15496 Phospholipase A2 family −25.7
FBP1 [90]P09467 FBPase class 1 family −25.3
CUL4B [91]Q13620 Cullin family −23.0
MMP12 [92]P39900 Peptidase M10A family −18.6
CDK13 [93]Q14004 kinase family −18.4
TNFAIP6 [94]P98066 Hyaluronan-binding protein family −16.8
PLA2G3 [95]Q9NZ20 Phospholipase A2 family −14.7
HLA-A [96]O19619 MHC class I family −12.1
MOCS3 [97]O95396 HesA/MoeB/ThiF family −12.0
AIDA [98]Q96BJ3 AIDA family −11.1
RAC1 [99]P63000 Rho family −11.0
PLA2G5 [100]P39877 Phospholipase A2 family −10.8
PLA2G1B [101]P04054 Phospholipase A2 family −10.6
PLA2G2A [102]P14555 Phospholipase A2 family −10.6
TNFSF14 [103]O43557 Tumor necrosis factor family −10.3
CHIT1 [104]Q13231 Chitotriosidase family −10.0
EGFLAM [105]Q63HQ2 Pikachurin family −9.2
PLA2G2D [106]Q9UNK4 Phospholipase A2 family −6.0
[107]Open in a new tab
Structural diversity of the putative targets
In order to analyze the structural similarity of the 23 putative
targets, each pair of these targets were structurally aligned using the
program Combinatorial Extension (CE) ([108]Shindyalov & Bourne, 2001).
The targets were considered to be similar if the root-mean-square
deviation (RMSD) between two structures is less than 4 Å. A network of
targets was generated by linking structurally similar targets
([109]Fig. 2A). The primary targets of aspirin are colored with green,
and the newly identified targets are colored with red. Only two
proteins HLA class I histocompatibility antigen, A-2 alpha chain
(HLA-A) and axin interactor, dorsalization-associated protein (AIDA)
clustered with the phospholipase A2 family. Proteins cyclooxygenase-1
and 2 are linked together but not similar with the other proteins.
Therefore, the overall structures of the newly identified targets and
the primary targets were not similar. It indicates the structural
diversity of the 23 putative targets. However, the 23 targets have
similar local structures. For example, proteins group IB phospholipase
A2 (PLA2G1B) and cyclin-dependent kinase 13 (CDK13) which belong to
different protein families have very similar binding sites of aspirin
(BSiteAs) ([110]Fig. 2B).
Diverse binding modes of aspirin to the putative targets
In our study, aspirin was docked to the predicted binding sites on
putative targets. Results of these docking experiments reveal diverse
binding modes of aspirin to these targets ([111]Fig. 3). Some examples
are given follows. As a first example, aspirin binds to protein CDK13
with a novel mode compared to ATP-analog inhibitor of the kinase
([112]Fig. 3A). ATP-analog inhibitors exhibit inhibitory activity of
kinase by competitive binding to its ATP binding site. In contrast,
aspirin binds to CDK13 in the vicinity of the ATP binding site and
interact with loops (L1 and L2) which are important for ATP binding.
Another example is that aspirin binds to protein ras-related C3
botulinum toxin substrate 1 (RAC1), which is very different from GTP
binding ([113]Fig. 3B). Aspirin binds to the other side of RAC1 and
interact with the N-terminal part of switch II (sequence ^56 WDTAG),
which is crucial for interaction between RAC1 and protein Arfaptin
([114]Tarricone et al., 2001). The Arfaptin mediates cross-talk between
Rac and Arf signaling pathways. The last example is that aspirin binds
to protein integrin alpha-L (ITGAL) in the binding site of its
inhibitor BQM ([115]Guckian et al., 2008) ([116]Fig. 3C). The analysis
of the binding mode of aspirin to the other 19 putative targets is
shown in the [117]Figs. S1–[118]S23. The statistic of 23 putative
binding pockets of aspirin is shown in the [119]Fig. S24. The
coordinates of 23 putative binding pockets are shown in [120]Table S2.
The size of the 23 binding pockets is ranging from 10 to 26 residues,
if we define the residues lying within 5 Å from aspirin as binding
pockets. The pocket size of aspirin binding to phospholipase A2 (PDBID:
1OXR) and cyclooxygenase-1 (PDBID: 1PTH) is fall in this range. The
most used amino acids in pockets are leucine (L), cysteine (C), valine
(V), glycine (G), tyrosine (Y), isoleucine (I), alanine (A) and
phenylalanine (F). The eight amino acids appeared in the pockets more
than 20 times. All the eight amino acids are with hydrophobic side
chain except cysteine (C). It suggests that hydrophobic interaction is
important for aspirin binding. Additionally, the H-bond is also
important for aspirin binding because 78% (18/23) binding pockets
formed H-bonds with aspirin. The most used amino acids involved in
H-bond formation are aspartic acid (D), lysine (K) and histidine (H).
Therefore, the combinations of these charged amino acids (D, K and H)
and hydrophobic amino acids (L, V, G, Y, I, A and F) may form the
pocket that aspirin prefers to bind to.
Figure 3. Diverse binding modes of aspirin to the putative targets.
[121]Figure 3
[122]Open in a new tab
The docking experiments reveal diverse binding modes of aspirin to
these targets (A) Aspirin binding to protein CDK13 (3LQ5.pdb). (B)
Aspirin binding to protein RAC1 (1RYH.pdb). © Aspirin binding to
protein ITGAL (3BQM.pdb). The overview and close-up view of the binding
mode of aspirin to their putative targets are shown in A-C and D-F,
respectively. The close-up view (D-F) show all amino acids in the
vicinity of aspirin. Aspirin and the known ligands of the three
proteins are colored with green and red, respectively (A-C). The
residues involved in binding to both aspirin and the ligands are shown
as sticks and colored with blue (A-C). SLQ (A), GNP (B) and BQM © are
ATP-analog inhibitor of the kinase CDK13, the substrate of the protein
RAC1 and the inhibitor of protein ITGAL, respectively.
The binding affinities of aspirin to putative targets
To obtain accurate estimate of the binding energy of different putative
targets with aspirin, MM-PBSA free energy calculation protocol was
used. In the combination of the experimental study ([123]Singh et al.,
2005) and MM-PBSA free energy calculation, we obtained the estimated
entropy change (TΔS = 4.728 kcal/mol) upon aspirin binding. The entropy
changes do not have large fluctuations when the same ligand binds to a
different acceptor based on the study of [124]Chang & Gilson (2004).
Therefore, the entropy changes when aspirin binds to various putative
targets was assumed as 4.728 kcal/mol to compare free energies
associated with different aspirin binding putative targets. The binding
free energies including entropy change for the 23 proteins binding to
aspirin were calculated and listed in [125]Table 1. The binding free
energies of the 23 proteins with aspirin are varied from −6.0 (group
IID secretory phospholipase A2, PLA2G2D) to −33.0 (exosome component 3,
EXOSC3) kcal/mol. Overall, the binding free energies for newly
identified targets (the average −18.4 kcal/mol) are comparable to that
for the primary targets (the average −15.3 kcal/mol).
Pathway enrichment and interaction network of putative targets
Using the DAVID tool, we find that our predicted targets are
significantly overrepresented for several pathways (p < 0.01)
([126]Table 2). Some of these pathways are strongly involved in
inflammation, cardiovascular disease and cancer, such as VEGF
signaling, Fc epsilon RI signaling, arachidonic acid metabolism,
gonadotropin-releasing hormone (GnRH) signaling and MAPK signaling. To
illustrate the relationship between the putative targets and their
cellular effect, an integrated interaction network of targets-cellular
effect based on their associated pathways was constructed ([127]Fig.
4). The interactions between predicted targets and the major effects
involved in cancer development, inflammation and cardiovascular disease
were present in this network. Represented by green circles in the
network, the predicted targets regulate VEGF, epsilon RI signaling,
arachidonic acid metabolism, and MAPK pathways through interactions
with other proteins (gray circles) connecting the pathways. Inhibition
of predicted targets is expected to down-regulate these pathways, and
then prevent inflammation and decrease the risk of cardiovascular
disease and cancer.
Table 2. The pathways significantly overrepresented by our predicted targets
(p < 0.01).
Pathway Count Percentage Adjust P-value[128]^a Reference[129]^b
VEGF signaling pathway 9 40.9 4.60E−10 [130]Cross et al. (2003)
Arachidonic acid metabolism 8 36.4 2.00E−09 [131]Spector et al. (2004)
Fc epsilon RI signaling pathway 8 36.4 1.50E−08 [132]Kawakami & Galli
(2002)
Alpha-Linolenic acid metabolism 6 27.3 1.10E−08 [133]Hamberg et al.
(2003)
Linoleic acid metabolism 6 27.3 1.00E−07 [134]Shureiqi et al. (2003)
Ether lipid metabolism 6 27.3 2.80E−07 [135]Nagan & Zoeller (2001)
GnRH signaling pathway 7 31.8 1.40E−06 [136]Ruf & Sealfon (2004)
Glycerophospholipid metabolism 6 27.3 6.40E−06 [137]Racenis et al.
(1992)
Long-term depression 6 27.3 6.10E−06 [138]Ito (2002)
MAPK signaling pathway 8 36.4 2.30E−05 [139]Dent et al. (2003)
Vascular smooth muscle contraction 6 27.3 5.50E−05 [140]Kim et al.
(2008)
[141]Open in a new tab
Notes.
^a
P-value was adjusted using Benjamini & Hochberg method.
^b
The literature references link the refined putative targets with