Abstract
Functional dyspepsia (FD) is a widely prevalent gastrointestinal
disorder throughout the world, whereas the efficacy of current
treatment in the Western countries is limited. As the symptom is
equivalent to the traditional Chinese medicine (TCM) term “stuffiness
and fullness,” FD can be treated with Zhi-zhu Wan (ZZW) which is a kind
of Chinese patent medicine. However, the “multi-component” and
“multi-target” feature of Chinese patent medicine makes it challenge to
elucidate the potential therapeutic mechanisms of ZZW on FD. Presently,
a novel system pharmacology model including pharmacokinetic parameters,
pharmacological data, and component contribution score (CS) is
constructed to decipher the potential therapeutic mechanism of ZZW on
FD. Finally, 61 components with favorable pharmacokinetic profiles and
biological activities were obtained through ADME (absorption,
distribution, metabolism, and excretion) screening in silico. The
related targets of these components are identified by component
targeting process followed by GO analysis and pathway enrichment
analysis. And systematic analysis found that through acting on the
target related to inflammation, gastrointestinal peristalsis, and
mental disorder, ZZW plays a synergistic and complementary effect on FD
at the pathway level. Furthermore, the component CS showed that 29
components contributed 90.18% of the total CS values of ZZW for the FD
treatment, which suggested that the effective therapeutic effects of
ZZW for FD are derived from all active components, not a few
components. This study proposes the system pharmacology method and
discovers the potent combination therapeutic mechanisms of ZZW for FD.
This strategy will provide a reference method for other TCM mechanism
research.
Keywords: Zhi-zhu Wan, Zhishi, Baizhu, functional dyspepsia,
therapeutic mechanism, system pharmacology
Introduction
Functional dyspepsia (FD) is the pain or discomfort of the upper
digestive tract without organic pathology that readily explains
symptoms (Tack and Talley, [43]2013; Talley, [44]2016). The prevalence
of FD in the general population is as high as 12–15% (El-Serag and
Talley, [45]2004; Talley, [46]2016), and it significantly affects our
moods and reduces the quality of life (Brun and Kuo, [47]2010).
Treatments of FD involves eradication of Helicobacter pylori (Mokhtare
et al., [48]2017), acid inhibition with proton pump inhibitors,
tricyclic antidepressants (Ford et al., [49]2017), and prokinetic drugs
(Quigley, [50]2017). Unfortunately, meta-analyses emphasized that these
medications are still unsatisfactory for promoting the symptoms of FD,
and the efficacy of currently available treatments be limited (Vakil et
al., [51]2017). Clinical reports indicate that the safety and
effectiveness of the Zhi-zhu Wan (ZZW) in the treatment of FD are
remarkable.
ZZW is composed of two herbs, Zhishi (the immature fruit of Citrus
aurantium L. or Citrus sinensis Osbeck) and Baizhu (the roots of
Atractylodes macrocephala Koidz), which has prominence effect with FD
(Wang et al., [52]2012; Xia et al., [53]2012), and their promotion of
the gastrointestinal peristalsis activity has been confirmed in animal
experiments (Liu, [54]2007; Huang et al., [55]2012; Chen J. et al.,
[56]2016). Baizhu showed the bidirectional regulation effects on
gastrointestinal that might be related to the level of vasoactive
intestinal peptide (VIP) and p substance (SP) (Chen J. et al.,
[57]2016). The combination of Zhishi and Baizhu may exert its
therapeutic effects on FD by regulating the function of M and D
endocrine cell, increasing the expression of acetylcholine and nitrogen
monoxide, and regulating the gene expression of gut hormone receptor
(Liu, [58]2007).
In pharmacokinetic studies, the pharmacokinetics and pharmacodynamics
characteristics of ZZW after oral administration indicated that
hesperidin and naringenin might be destroyed in the intestinal tract,
metabolized by intestinal microflora, and excreted from bile or urine
(Sun et al., [59]2013). In pharmacologic studies, flavonoids in Zhishi
have a dose-dependent diastolic effect on pyloric circular smooth
muscle strips in rats. These studies confirmed that the Zhishi and
Baizhu could be beneficial in the treatment of patients with FD.
Nevertheless, there is no literature expounds the underlying
therapeutic mechanism of ZZW so far.
Considering the flaws of traditional experimental methods its
approaches are difficult to reveal the co-module association mechanism
of herb-component-gene-disease due to the “multi-component” and
“multi-target” features of the TCM systems. Systemic pharmacology is an
effective tool to elucidate the synergistic and potential mechanisms of
the networks between component-target and target-disease, it provides a
new perspective on the therapeutic mechanisms of TCM. Recently, several
system pharmacology models were used to decode the underlying mechanism
of herb pair (Cheng S. P. et al., [60]2016; Zhang et al., [61]2016; Yue
et al., [62]2017) and Chinese formulae (Zhang et al., [63]2015), but
most of them losts the synergistic information.
Currently, a novel system pharmacology model is developed to explore
the therapeutic mechanism of ZZW in the treatment of FD (Figure [64]1),
integrating pharmacokinetics synthesis screening, target identification
and network analysis. Specifically, four parameters are used for ADME
(absorption, distribution, metabolism, and excretion) screening to
ensure more comprehensive first. Subsequently, the target from docking
database and reference database are both retrieved to ensure the
accuracy and effectiveness of the component-target (C-T) network.
Ultimately, the network analysis combined with contribution score (CS)
are used to elucidate the synergistic molecular actions of
Zhishi-Baizhu. Hopefully, these results will provide a strategy for
illuminating the therapeutic mechanism of TCM at molecular level.
Figure 1.
[65]Figure 1
[66]Open in a new tab
The work scheme of system pharmacology approach.
Methods
Chemical components database
All components of ZZW were collected from five publicly available
natural product data sources: TCMSP database
([67]http://lsp.nwu.edu.cn/index.php), Shanghai Institute of Organic
Chemistry, Chinese Academy of Sciences. Chemistry Database [DB/OL]
([68]http://www.organchem.csdb.cn [1978-2018], Traditional Chinese
Medicine integrated database (TCMID,
[69]http://www.megabionet.org/tcmid/), Traditional Chinese Medicine
database@Taiwan (TCM@Taiwan, [70]http://tcm.cmu.edu.tw/zh-tw), and
TCM-MESH ([71]http://mesh.tcm.microbioinformatics.org/). For all
components, using Open Babel toolkit (version 2.4.1) to convert the
initial structure formats (e.g., mol2) to the unified SDF format.
Subsequently, the properties of components were retrieved from TCMSP,
including molecular weight (MW), oral bioavailability (OB), Caco-2
permeability (Caco-2), drug-likeness (DL), Moriguchi octanol-water
partition coefficient (LogP) (MLogP), number of acceptor atoms for
H-bonds (nHAcc), number of donor atoms for H-bonds (nHDon), and
topological polar surface area (TPSA), and GI absorption was retrieved
from SwissADME ([72]http://www.swissadme.ch/index.php).
ADME screening
In modern drug discovery, early assessment of absorption, distribution,
metabolism, and excretion (ADME) screening has become an essential
process. The proper use of ADME results can give preference to those
drug candidates that are more likely to have good pharmacokinetic
properties and minimize potential drug-drug interactions (Wang J. H. et
al., [73]2017). In the present work, four ADME-related models,
including OB, Caco-2, DL, and GI absorption were employed to screen the
active components from ZZW (Figure [74]S1).
OB (%F) depicts the percentage of an orally administered dose of the
chemical components in herbs that reaches the systemic circulation,
which displays the convergence of the ADME process. A robust in silico
system OBioavail 1.1 (Xu et al., [75]2012) was performed to calculate
the OB values of all components in ZZW. Those components with suitable
OB ≥ 30% were selected as candidate components for further research.
Human intestinal cell line Caco-2 is generally employed to study the
passive diffusion of drugs across the intestinal epithelium, the
transport rates of components (nm/s) in Caco-2 monolayers represents
the intestinal epithelial permeability in TCMSP (Ru et al., [76]2014).
The Caco-2 value of the components in ZZW was obtained from TCMSP
([77]http://lsp.nwu.edu.cn/tcmsp.php). Compounds with Caco-2 > −0.4
were selected as candidate components, because components with Caco-2 <
−0.4 are not permeable.
DL is an established concept for drug design that is used to estimate
which compounds have the “drug-like” prospective. The DL values of
these components were calculated by the database-dependent DL
evaluation approach based on Tanimoto coefficient, which is expressed
as T (A, B) = (A × B)/(|A|^2 + |B|^2 − A × B). In this equation, A
represents the molecular descriptor of herbal components, and B is the
average molecular property of all components in Drugbank. The threshold
of DL was set to 0.18, which is used as a selection criterion for
“drug-like” compounds in the traditional Chinese herbs (Tao et al.,
[78]2013). During the screening process of Baizhu, we found that the DL
value of lactones was lower than 0.18 but higher than 0.14, Considering
lactones are the main active and characteristic compounds in BZ (China,
[79]2015), so the screening criterion of Baizhu was defined as DL ≥
0.14.
GI absorption is a pharmacokinetic behavior crucial to estimate at
various stages of the drug discovery processes, which can be calculated
by an accurate predictive model, IntestinaL EstimateD permeation method
(BOILED-Egg) (Daina and Zoete, [80]2016). The GI absorption value of
the components in ZZW was obtained from SwissADME
([81]http://www.swissadme.ch/index.php) (Daina et al., [82]2017). The
screening criterion of GI absorption was defined as high.
Targets identification
To obtain the target of active components in ZZW, the commonly used
databases, i.e., HitPick (Liu et al., [83]2013), Similarity Ensemble
Approach (SEA) (Keiser et al., [84]2007), STITCH (Szklarczyk et al.,
[85]2016), and Swiss Target Prediction (Gfeller et al., [86]2014), were
employed to identify the targets. All chemical structures were prepared
and converted into canonical SMILES using Open Babel Toolkit (version
2.4.1). In addition, the target results were confirmed by literature
reviews. Sequently, to anatomize the role of ZZW in the treatment of
FD, the relationship between the obtained targets and diseases was
calculated using the hypergeometric distribution algorithm:
[MATH:
P(ZZW, d)
=1−∑i=0k−1<
mrow>(Ki)(N−Kn−i)(Nn)
:MATH]
where N is the total number of targets in DisGeNET (Piñero et al.,
[87]2017), K is the number of targets associated with disease d, n is
the quantity about the targets of ZZW, k is the number of targets
shared by ZZW and disease d. P-value indicates the consequence of
relevance between ZZW and disease d (significant when P < 0.05).
Gene ontology and pathway analysis
To analyze the main function of the target genes, Gene Ontology (GO)
analysis was performed using the Diversity Visualization Integrated
Database (DAVID 6.8) (Huang et al., [88]2009). The false discovery rate
(FDR) (Dupuy et al., [89]2007) was calculated to correct the p-value.
The criterion for difference screening was FDR < 0.05.
The latest pathway data were obtained from the Kyoto Encyclopedia of
Genes and Genomes (KEGG) database (Draghici et al., [90]2007) for KEGG
pathway enrichment analyses. P-values were set at 0.05 as the cut-off
criterion. The results of analysis were annotated by Pathview (Luo and
Brouwer, [91]2013) in the R Bioconductor package
([92]https://www.bioconductor.org/).
Networks construction
The component-target network was established to find the key target.
Then, the target-pathway (T-P) network was constructed to find out the
relationship between the target and pathway. Cytoscape 3.5.1 (Shannon
et al., [93]2003), an open-source software platform for visualizing
complex networks, was employed to visualize the networks.
Contribution score calculation
To estimate the effect of each component of ZZW on FD treatment, we
established a mathematical formula:
[MATH:
Aij= ωei+|CAi+CBiCAi−CB
i|
:MATH]
(1)
[MATH:
ωei=Cedge
Tedge :MATH]
(2)
[MATH:
CS(i)=∑ijnCi×
[Aij×Pj] :MATH]
(3)
Where i is the number of components and j is the number of proteins.
The contribution score (CS) represents the network contribution of one
component and its effectiveness in FD. C represents the degree of each
component, P represents the degree of each protein, which is calculated
by Cytoscape 3.5.1. C[Ai] represents the degree of each component only
in Zhishi C-T network, and C[Bi] represents the degree of each
component only in Baizhu C-T network. A[ij] is the index of affinity
determined from the ω[ei] value.
Side effect prediction
Side effect information was obtained from SIDER, which accumulates
reported side effects from package inserts for marketed drugs (Kuhn et
al., [94]2010), To encode drug chemical structures, a fingerprint was
used, which consisted of 61 chemical substructures defined in the
PubChem database (Li et al., [95]2010). This resulted in a binary
profile referred to as chemical substructure profile. The side effect
prediction use the Ordinary canonical correlation analysis (OCCA)
framework (Mizutani et al., [96]2012).
Statistical analysis
To compare the molecular properties of all components in Zhishi and
Baizhu, SPSS22.0 was used for statistical analysis. Data were analyzed
using the student's t-test for comparison. When P < 0.05, the
differences were considered statistically significant.
Results
Based on a system pharmacology model, the therapeutic mechanisms of FD
by ZZW were elucidated. All ZZW compounds were collected from database
and literature. Next, the ADME method was used to screen for potential
active components. Then related targets, disease, and pathway were
identified from integrated predictive models. The obtained data were
used to construct C-T and T-P networks, respectively. Finally, the CS
of all compounds was calculated to illustrate the combination
mechanism.
Components comparisons in zhishi and baizhu
By a systematic search of the public databases, a total of 378
components were retrieved in Zhishi (150) and Baizhu (128).
Interestingly, the species of components in Zhishi and Baizhu are
different, the major components of Zhishi are flavonoids and volatile
oil, whereas Baizhu is lactones and volatile oil. The detail
information of these components was provided in Table [97]S1.
To further describe the differences from the components of Zhishi and
Baizhu, nine properties of these components were compared, including
MW, MLogP, nHDon, nHAcc, OB, Caco-2 permeability, DL, TPSA, and GI
absorption. As shown in Figure [98]2, the eight value of the components
in Zhishi and Baizhu were significantly different (P < 0.01) but the
majority of the components did not violate Lipinski's rule of five
(Lipinski et al., [99]2001). (1) For MW, the average value of
components in Zhishi (393.39) is significantly higher than that in
Baizhu (252.67) (P = 7.20E-15). (2) For bioavailability, the average OB
value of Zhishi (28.94) is lower than that of Baizhu (37.76) (P =
9.72E-4). (3) For permeability, the average Caco-2 value of Zhishi
(−0.20) is significally lower than that of Baizhu (0.69) (P =
2.26E-08). (4) For DL, unlike OB and Caco-2, Zhishi possessed higher
average DL value (0.41), that is very different from that of Baizhu
(0.20) (P = 1.58E-11). (5) Compared with the components of Zhishi
(0.15), the MLogP value of Baizhu exhibited siginifically higher
average MLogP value (2.10) (P = 7.58E-08), which indicates the majority
components in Baizhu are hydrotropic, but that in Zhishi are
hydrophobic. (6) The values of nHAcc, nHDon, and TPSA in Zhishi (7.67,
3.50, 51.65, respectively) are all higher than those in Baizhu (2.95,
1.54, 120.45, respectively) (6.71E-19, 1.81E-08, 1.54E-14,
respectively).
Figure 2.
[100]Figure 2
[101]Open in a new tab
The molecular properties of all components in Zhishi and Baizhu.
Molecular properties including molecular weight (MW), oral
bioavailability (OB), Caco-2 permeability (Caco-2), drug-likeness (DL),
Moriguchi octanol-water partition coeff. Log P (MlogP), number of
acceptor atoms for H-bonds (nHAcc), number of donor atoms for H-bonds
(nHDon), and topological polar surface area (TPSA). *P < 0.01 by two
tailed t-test (vs. Zhishi).
All the results showed that there are differences between the
components of Zhishi and Baizhu, which may be due to the distinct
chemo-physical properties of the components from two herbs. Our results
also showed that the components from Baizhu have better pharmacokinetic
properties (OB and Caco-2), whereas the components in Zhishi have
better DL. Although there are obvious difference of main components
between Zhishi and Baizhu, the two herbs have the identical
spleen-fortifying and digestion-promoting, qi-promoting and
damp-dispelling effects, which may also elucidate why Zhishi-Baizhu can
produce synergistic effects.
Active components in zhishi-baizhu
Even though any TCM formulation contains multiple components, only a
few components possess satisfactory pharmacodynamic and pharmacokinetic
properties. In the current work, four ADME-related models, including
OB, Caco-2, DL, and GI, were employed to screen for active components.
After ADME screening, a few components that did not meet the four
screening criteria were also selected because of their high amount and
high bioactive. Therefore, 61 active components were filtered out of
the 378 components of ZZW. The detail information was shown in Table
[102]1. Additionally, we used Small Molecule Subgraph Detector (SMSD)
Toolkit (Rahman et al., [103]2009) to calculate the drug similarity
based on Tanimoto Coefficient, which was often used to predict
Drug-drug Interations (DDIs) (Takeda et al., [104]2017), and found that
in 1,891 pairs of similarity comparisons, the similarity of 1,018 pair
<=0.2, account for 54% (Figure [105]S3). In order to calculate the
potential side effect of all active compounds, we employ the OCCA
framework to predict the side effects and found the slight side effects
were mainly focused on agitation, weakness, and dizziness (Figure
[106]S4 and Table [107]S4).
Table 1.
The information of active components in ZZW.
ID Molecule_name MW OB Caco-2 DL MLOGP nHAcc nHDon TPSA GI absorption
BZ27 Atractylenolactam 229.32 56.48 1.23 0.15 2.85 1 1 29.1 High
BZ42 Anhydroatractylolide 234.34 52.24 1.24 0.15 3.44 2 0 26.3 High
BZ57 8β-methoxy-atractylenolide I 260.33 54.47 1.02 0.19 2.63 3 0 35.53
High
BZ59 14α-methyl butyryl-14-acetyl-2E,8E,10E-atractylentriol 316.40
64.50 0.20 0.23 2.71 4 2 66.76 High
BZ60 12α-methylbutyryl-14-acetyl-2E,8Z,10E-atractylentriol 358.43 62.69
0.41 0.29 3.07 5 1 72.83 High
BZ64 8β-ethoxyatractylenolide- II 276.38 56.48 1.08 0.21 3.37 3 0 35.53
High
BZ72 Isoasterolide A 232.32 52.65 1.27 0.15 3.35 2 0 26.3 High
BZ75 Atractylenolide VII 262.39 40.99 1.32 0.14 3.84 2 0 26.3 High
BZ83 Atractylodes macrocephala 462.68 45.96 0.85 0.81 2.47 3 1 46.53
High
BZ84 Biatractylolide 462.63 45.96 0.84 0.81 5.43 4 0 52.6 High
BZ100 8β-ethoxy atractylenolide-II 276.38 56.48 1.08 0.21 3.37 3 0
35.53 High
BZ102 Atractylenolide I 230.31 35.21 1.32 0.15 3.26 2 0 26.3 High
BZ107 Atractylone 216.324 25.99 1.74 0.13 3.42 1 0 13.14 High
BZ110 AtractylenolideII 232.32 43.54 1.31 0.15 3.35 2 0 26.3 High
BZ119 3β-acetoxyatractylone 274.36 34.74 1.19 0.22 2.83 3 0 39.44 High
BZ124 14-acetyl-12-senecioyl-2E,8Z,10E-atractylentriol 356.42 63.37
0.26 0.30 2.99 5 1 72.83 High
BZ125 Atractylenolide III 248.32 67.29 0.76 0.17 2.47 3 1 46.53 High
ZS21 8-geranyloxypsoralen 338.4 41.92 1.178 0.418 3.23 4 0 52.58 High
ZS22 5-Geranyloxy-7-Methoxycoumarin 328.4 44.23 1.121 0.300 3.16 4 0
48.67 High
ZS23 Bergamottin 338.4 41.73 1.161 0.421 3.23 4 0 52.58 High
ZS24 Phellopterin 300.31 37.43 0.978 0.279 1.82 5 0 61.81 High
ZS25 Isoimperatorin 270.28 47.54 1.057 0.225 2.14 4 0 52.58 High
ZS26 6′-7′-dihydroxybergamottin 372.41 70.77 0.12 0.52 1.66 6 2 93.04
High
ZS27 Epoxybergamottin 354.4 57.25 0.922 0.523 2.48 5 0 65.11 High
ZS28 Cnidilin 300.31 42.42 0.948 0.280 1.82 5 0 61.81 High
ZS30 Epoxyaurapten 314.38 62.78 0.952 0.309 2.74 4 0 51.97 High
ZS34 Byakangelicin 334.32 34.89 −0.01 0.35 0.29 7 2 102.27 High
ZS35 Heraclenol 304.29 72.63 0.08 0.29 0.57 6 2 93.04 High
ZS36 Oxypeucedanin hydrate 304.29 33.07 −0.06 0.29 0.57 6 2 93.04 High
ZS39 Isoponcimarin 330.37 63.28 0.534 0.313 1.91 5 0 69.04 High
ZS40 Poncimarin 330.37 79.20 0.754 0.350 1.99 5 0 64.5 High
ZS41 Byakangelicol 316.31 45.21 0.760 0.356 1.08 6 0 74.34 High
ZS42 Oxypeucedanin 286.28 66.18 0.870 0.297 1.39 5 0 65.11 High
ZS71 Monohydryoxy-tetramethoxyflavone 358.34 45.38 1.19 0.37 0.4 7 1
87.36 High
ZS73 Diosmetin 300.26 42.87 0.46 0.27 0.22 6 3 100.13 High
ZS75 5-demethylnobiletin 388.37 89.03 1.01 0.48 0.11 8 1 96.59 High
ZS79 Chrysoeriol 300.26 41.60 0.45 0.27 0.22 6 3 100.13 High
ZS82 Sakuranetin 286.28 40.19 0.59 0.24 0.96 5 2 75.99 High
ZS85 Acacetin 284.26 37.69 0.65 0.24 0.77 5 2 79.9 High
ZS86 Isosakuranetin 286.28 37.59 0.58 0.24 0.96 5 2 75.99 High
ZS88 N-methyl tyramine-O-alpha-L-rhamnopyranoside 297.35 36.70 −0.04
0.19 −0.16 6 4 91.18 High
ZS104 Synephrine 167.21 75.25 0.63 0.04 0.65 3 3 52.49 High
ZS105
4-[(2S,3R)-5-[(E)-3-hydroxyprop-1-enyl]-7-methoxy-3-methylol-2,3-dihydr
obenzofuran-2-yl]-2-methoxy-phenol 358.39 50.76 0.03 0.39 1.09 6 3
88.38 High
ZS107 5,7,4′-Trimethylapigenin 312.32 39.83 1.01 0.3 1.25 5 0 57.9 High
ZS108 Hesperetin 302.28 47.74 0.28 0.27 0.41 6 3 96.22 High
ZS109 6-Methoxy aurapten 328.4 31.24 1.01 0.3 3.16 4 0 48.67 High
ZS110 Ammidin 270.28 34.55 1.13 0.22 2.14 4 0 52.58 High
ZS115 Naringenin 272.26 59.29 0.28 0.21 0.71 5 3 86.99 High
ZS117 Tetramethoxyluteolin 342.34 43.68 0.96 0.37 0.94 6 0 67.13 High
ZS123 Prangenin 286.28 43.60 0.8 0.29 1.39 5 0 65.11 High
ZS128 Eriodyctiol (flavanone) 288.25 41.35 0.05 0.24 0.16 6 4 107.22
High
ZS130 Hesperidin 610.56 13.33 −2.03 0.67 −3.04 15 8 234.29 Low
ZS131 Isolimonic acid 639.01 48.86 0.43 0.18 4.33 3 1 57.61 High
ZS134 Isosinensetin 372.37 51.15 1.16 0.44 0.63 7 0 76.36 High
ZS135 Sinensetin 372.37 50.56 1.12 0.45 0.63 7 0 76.36 High
ZS137 Luteolin 286.24 36.16 0.19 0.25 −0.03 6 4 111.13 High
ZS143 Naringin 580.53 6.92 −1.99 0.78 −2.77 14 8 225.06 Low
ZS144 Narirutin 580.53 8.15 −1.8 0.75 −2.77 14 8 225.06 Low
ZS145 Neohesperidin_qt 302.28 71.17 0.26 0.27 0.41 6 3 96.22 High
ZS146 Nobiletin 402.39 61.67 1.05 0.52 0.34 8 0 85.59 High
ZS149 Prangenin hydrate 304.29 72.63 0.14 0.29 0.57 6 2 93.04 High
ZS150 Neohesperidin 610.62 11.57 −2.05 0.69 −3.04 15 8 234.29 Low
[108]Open in a new tab
Active components from zhishi
Through ADME screening, 44 out of 150 components were selected from
Zhishi, and most of them have ideal pharmacokinetic profiles. For
example, hesperetin (ZS108, OB = 47.74%, Caco-2 = 0.28, DL = 0.27, GI =
high) exhibits antioxidants (de Souza et al., [109]2016),
anti-inflammatory(Choi and Lee, [110]2010), and vasoprotective (Kumar
et al., [111]2013) actions; Similarly, naringenin (ZS115, OB = 59.29%,
Caco-2 = 0.28, DL = 0.21, GI = high) has anti-inflammatory (Manchope et
al., [112]2017), antibacterial (Wang L. H. et al., [113]2017),
neuroprotective(Ramakrishnan et al., [114]2016) effects. It is worth
noting that the value of Caco-2 in dihydroflavonosides of Zhishi is
lower, such as narirutin (ZS144), naringin (ZS143), hesperidin (ZS130),
and neohesperidin (ZS150), however, the four flavonoids were the main
bioactive components in Zhishi and exhibited relatively high abundances
(Liu et al., [115]2012), so these components were also preserved.
Especially, the value of DL in synephrine (ZS104) is low, but it is the
marker components for quality control of Zhishi in Chinese Pharmacopeia
(China, [116]2015). For the above reasons, 44 components were
considered as potential active components of Zhishi (Table [117]1).
Active components from baizhu
Among 131 components in BZ, 17 components meet the screening criteria.
For instance, atractylenolide I, II, III (BZ102, OB = 35.21%, Caco-2 =
1.32, DL = 0.15, GI = high; BZ110, OB = 43.54%, Caco-2 = 1.31, DL =
0.15, GI = high; BZ125, OB = 67.29%, Caco-2 = 0.76, DL = 0.17, GI =
high) was the quality marker of BZ in Chinese Pharmacopeia (China,
[118]2015) and has anti-inflammatory (Ji et al., [119]2016),
anticoagulation effect (Tang et al., [120]2017) gastrointestinal repair
effects (Song et al., [121]2017); Atractylenolactam (BZ27, OB = 56.48%,
Caco-2 = 1.23, DL = 0.15, GI = high) exhibits anti-inflammatory
activity (Hoang et al., [122]2016); Biatractylolide (BZ84, OB = 45.96%,
Caco-2 = 0.84, DL = 0.81, GI = high) has a neuroprotective effect on
glutamate-induced injury in PC12 and SH-SY5Y cells (Zhu et al.,
[123]2017). Specially, atractylone has been showed to have
anti-microbial and anti-inflammatory activities (Sin et al.,
[124]1989), so it was also regarded to be active components. The detail
information of 17 components was showed in Table [125]1.
Target proteins of zhishi-baizhu
To determine the relationship between the target and FD, we collected
disease targets and used a hypergeometric distribution to describe the
relationship probability between targets and diseases. It's worth
noting that the target of active components is related to FD (p <
0.05). In addition, the active components-related targets were further
compared with all other disease in DisGeNET and the final relationship
was ranked by the P value. Among the top 20 diseases, 9 were mental
disorder (Figure [126]S2 and Table [127]S3) which is one of the
pathogenic factors of FD that confirmed by recent studies (Aro et al.,
[128]2015). Overall, most targets are related with FD, which indicated
that ZZW can be used to treat FD.
To explore the therapeutic mechanism of ZZW in the treatment of FD, 61
active components and 133 targets (Table [129]2) were used to construct
the C-T network (Figure [130]3). Several of these active components are
related multiple targets, resulting in 650 component-target
associations between 61 active components and 133 targets. The average
number of targets per component is 10.6, and the mean degree of
components per target is 4.9, it shows that ZZW handles multi-component
and multi-target characteristics of ZZW for treating FD. Acacetin
(ZS85, degree = 38) has the highest number of targets, followed by
luteolin (ZS137, degree = 36), chrysoeriol (ZS79, degree = 30), and
5,7,4′-Trimethylapigenin (ZS107, degree = 28), demonstrating the
crucial roles of these components in the treatment of FD.
Table 2.
The information of the related targets of ZZW.
Gene Protein name Uniprot ID
ABCB1 Multidrug resistance protein 1 [131]P08183
ABCB4 Multidrug resistance protein 3 [132]P21439
ABCC1 Multidrug resistance-associated protein 1 [133]P33527
ABCC2 Canalicular multispecific organic anion transporter 1 [134]Q92887
ABCC3 Canalicular multispecific organic anion transporter 2 [135]O15438
ABCG2 ATP-binding cassette sub-family G member 2 [136]Q9UNQ0
ABL1 Tyrosine-protein kinase ABL1 [137]P00519
ACE Angiotensin-converting enzyme [138]P12821
ACP1 Low molecular weight phosphotyrosine protein phosphatase
[139]P24666
ADIPOQ Adiponectin [140]Q15848
ADORA1 Adenosine receptor A1 [141]P30542
ADORA2A Adenosine receptor A2a [142]P29274
ADORA3 Adenosine receptor A3 [143]P33765
ADRA1A Alpha-1A adrenergic receptor [144]P35348
ADRA1B Alpha-1B adrenergic receptor [145]P35368
ADRA1D Alpha-1D adrenergic receptor [146]P25100
ADRB1 Beta-1 adrenergic receptor [147]P08588
ADRB2 Beta-2 adrenergic receptor [148]P07550
ADRB3 Beta-3 adrenergic receptor [149]P13945
ALDH2 Aldehyde dehydrogenase, mitochondrial [150]P05091
ALPI Intestinal-type alkaline phosphatase [151]P09923
AMY1A Alpha-amylase 1 [152]P04745
AMY2A Pancreatic alpha-amylase [153]P04746
AOC3 Membrane primary amine oxidase [154]Q16853
APP Amyloid-beta A4 protein [155]P05067
AR Androgen receptor [156]P10275
BCL6 B-cell lymphoma 6 protein [157]P41182
BDNF Brain-derived neurotrophic factor [158]P23560
BMP2 Bone morphogenetic protein 2 [159]P12643
CAMK2A Calcium/calmodulin-dependent protein kinase type II subunit
alpha [160]Q9UQM7
CAMK2B Calcium/calmodulin-dependent protein kinase type II subunit beta
[161]Q13554
CBR1 Carbonyl reductase [NADPH] 1 [162]P16152
CCK Cholecystokinin [163]P06307
CCL11 Eotaxin [164]P51671
CCL2 C-C motif chemokine 2 [165]P13500
CCR4 C-C chemokine receptor type 4 [166]P51679
CD80 T-lymphocyte activation antigen CD80 [167]P33681
CELA1 Chymotrypsin-like elastase family member 1 [168]Q9UNI1
CHRNA7 Neuronal acetylcholine receptor subunit alpha-7 [169]P36544
CNR1 Cannabinoid receptor 1 [170]P21554
CNR2 Cannabinoid receptor 2 [171]P34972
CREB1 Cyclic AMP-responsive element-binding protein 1 [172]P16220
CTSK Cathepsin K [173]P43235
CYP1A2 Cytochrome P450 1A2 [174]P05177
CYP2C19 Cytochrome P450 2C19 [175]P33261
CYP3A4 Cytochrome P450 3A4 [176]P08684
DPP4 Dipeptidyl peptidase 4 [177]P27487
DRD2 D(2) dopamine receptor [178]P14416
DRD3 D(3) dopamine receptor [179]P35462
EGFR Epidermal growth factor receptor [180]P00533
ESR1 Estrogen receptor [181]P03372
ESR2 Estrogen receptor beta [182]Q92731
FAAH Fatty-acid amide hydrolase 1 [183]O00519
FGF2 Fibroblast growth factor 2 [184]P09038
FOS Proto-oncogene c-Fos [185]P01100
FUT4 Alpha-(1,3)-fucosyltransferase 4 [186]P22083
GABRA3 Gamma-aminobutyric acid receptor subunit alpha-3 [187]P34903
GABRB3 Gamma-aminobutyric acid receptor subunit beta-3 [188]P28472
GABRG2 Gamma-aminobutyric acid receptor subunit gamma-2 [189]P18507
GHRL Appetite-regulating hormone [190]Q9UBU3
GLO1 Lactoylglutathione lyase [191]Q9UBU3
GSK3B Glycogen synthase kinase-3 beta [192]P49841
HDAC6 Histone deacetylase 6 [193]Q9UBN7
HIF1A Hypoxia-inducible factor 1-alpha [194]Q16665
HMOX1 Heme oxygenase 1 [195]P09601
HTR3A 5-hydroxytryptamine receptor 3A [196]P46098
IGF2R Cation-independent mannose-6-phosphate receptor [197]P11717
IL13 Interleukin-13 [198]P35225
IL2 Interleukin-2 [199]P60568
IL5 Interleukin-5 [200]P05113
IL8 Interleukin-8 [201]P10145
JUN Transcription factor AP-1 [202]P05412
KCNA3 Potassium voltage-gated channel subfamily A member 3 [203]P22001
MAOA Amine oxidase [flavin-containing] A [204]P21397
MAOB Amine oxidase [flavin-containing] B [205]P27338
MAP2K7 Dual specificity mitogen-activated protein kinase kinase 7
[206]O14733
MAP3K7 Mitogen-activated protein kinase kinase kinase 7 [207]O43318
MAPK8 Mitogen-activated protein kinase 8 [208]P45983
MAPT Microtubule-associated protein tau [209]P10636
MCL1 Induced myeloid leukemia cell differentiation protein Mcl-1
[210]Q07820
MMP1 22 kDa interstitial collagenase [211]P03956
MMP12 Macrophage metalloelastase [212]P39900
MMP9 67 kDa matrix metalloproteinase-9 [213]P14780
NFKB1 Nuclear factor NF-kappa-B p105 subunit [214]P19838
NOS1 Nitric oxide synthase, brain [215]P29475
NOS2 Nitric oxide synthase, inducible [216]P35228
NOS3 Nitric oxide synthase, endothelial [217]P29474
NQO1 NAD(P)H dehydrogenase [quinone] 1 [218]P15559
NR1I2 Nuclear receptor subfamily 1 group I member 2 [219]O75469
NR4A2 Nuclear receptor subfamily 4 group A member 2 [220]P43354
ODC1 Ornithine decarboxylase [221]P11926
OPRD1 Delta-type opioid receptor [222]P41143
OPRK1 Kappa-type opioid receptor [223]P41145
OPRL1 Nociceptin receptor [224]P41146
OPRM1 Mu-type opioid receptor [225]P35372
PARP1 Poly [ADP-ribose] polymerase 1 [226]P09874
PDE11A Dual 3′,5′-cyclic-AMP and -GMP phosphodiesterase 11A [227]Q9HCR9
PDE4A cAMP-specific 3′,5′-cyclic phosphodiesterase 4A [228]P27815
PDE4D cAMP-specific 3′,5′-cyclic phosphodiesterase 4D [229]Q08499
PIK3CA Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit
alpha isoform [230]P42336
PIK3CG Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit
gamma isoform [231]P48736
PLA2G1B Phospholipase A2 [232]P04054
PLAA Phospholipase A-2-activating protein [233]Q9Y263
PLG Plasmin light chain B [234]P00747
PPARD Peroxisome proliferator-activated receptor delta [235]Q03181
PPARG Peroxisome proliferator-activated receptor gamma [236]P37231
PRKCB Protein kinase C beta type [237]P05771
PRKCG Protein kinase C gamma type [238]P05129
PTGS1 Prostaglandin G/H synthase 1 [239]P23219
PTGS2 Prostaglandin G/H synthase 2 [240]P35354
RARA Retinoic acid receptor alpha [241]P10276
REL Proto-oncogene c-Rel [242]Q04864
RELA Transcription factor p65 [243]Q04206
SHBG Sex hormone-binding globulin [244]P04278
SLC6A2 Sodium-dependent noradrenaline transporter [245]P23975
SLC6A3 Sodium-dependent dopamine transporter [246]Q01959
SLC6A4 Sodium-dependent serotonin transporter [247]P31645
SNCA Alpha-synuclein [248]P37840
SRD5A1 3-oxo-5-alpha-steroid 4-dehydrogenase 1 [249]P18405
STAT3 Signal transducer and activator of transcription 3 [250]P40763
SYK Tyrosine-protein kinase SYK [251]P43405
TACR1 Substance-P receptor [252]P25103
TACR2 Substance-K receptor [253]P21452
TACR3 Neuromedin-K receptor [254]P29371
TERT Telomerase reverse transcriptase [255]O14746
TLR4 Toll-like receptor 4 [256]O00206
TNFRSF1A Tumor necrosis factor receptor superfamily member 1A
[257]P19438
TNNI3 Troponin I, cardiac muscle [258]P19429
TP53 Cellular tumor antigen p53 [259]P04637
TTR Transthyretin [260]P02766
VDR Vitamin D3 receptor [261]P11473
VEGFA Vascular endothelial growth factor A [262]P15692
XDH Xanthine dehydrogenase/oxidase [263]P47989
[264]Open in a new tab
Figure 3.
[265]Figure 3
[266]Open in a new tab
Component-target network of ZZW. The orange and purple ellipse nodes
are active components of Baizhu and Zhishi, and the blue parallelogram
nodes are the related targets, while the red parallelogram nodes are
the same targets of Zhishi and Baizhu.
In Zhishi, 112 target proteins are identified for 44 active components
with 538 interactions. The causes of FD mainly include dyspepsia,
Helicobacter pylori infection, depression, etc. (Talley, [267]2016),
which can generate inflammation, gastrointestinal movement dysfunction,
and etc. Intriguingly, most of targets of the components in Zhishi are
related to inflammation and gastrointestinal peristalsis. For instance,
the three components of Zhishi, including ZS39, ZS108, and ZS143, may
interact with PPARA and PPARG, which are members of a subfamily of the
nuclear receptors and can modulate inflammatory responses (Varga et
al., [268]2011). The other six active components, ZS71, ZS85, ZS105,
ZS107, ZS128, and ZS134, were identified as interacting with PTGS1and
PTGS2, also known as COX-1 and COX-2, COX-1 is a constitute engine
expressed in most tissues including blood platelets and at any site of
inflammation and promotes the production of natural mucus lining that
protects the inner stomach, whereas COX-2 is involved in pain produced
by inflammation (Mandlik et al., [269]2015). Furthermore, we have found
that five components (ZS73, ZS79, ZS115, ZS117, and ZS145) are related
to ABCB1 and ABCC1-3, which may critically participate in the
protection of the intestinal barrier by excluding drugs, nutrients, or
bacterial compounds back into the gut lumen (Langmann et al.,
[270]2004).
In Baizhu, 39 target proteins are identified for 17 active components
with 112 interactions, including MAOA, MAOB, NOS1-3, TACR1, SLC6A4,
STAT3, etc. Interestingly, majority of them are related to mental
disorders and inflammation, which are confirmed associated with the
pathogenesis of FD and that may be a potential therapeutic mechanism of
Baizhu on FD. For example, MAOA and MAOB are the widely distributed
mitochondrial enzyme with high expression levels in gastro-intestinal
and hepatic as well as neuronal tissues, and are genetically associated
with the pathogenesis of mental disorders (Lin et al., [271]2000); In
addition, NOS1 and NOS3 can play a role in the pathogenesis and symptom
of depression, NOS2 is generally up-regulated in various tissues under
inflammatory conditions (Chakrabarti et al., [272]2012). Moreover,
SLC6A4 is significantly related with both increased depressive symptoms
and elevated IL-6 plasma levels suggesting that common
phathophysiological processes may be associated with depression and
inflammation (Su et al., [273]2009). It is worthy to mention that STAT3
rs2293152 polymorphism may be associated with the occurrence of
ulcerative colitis and might be used as a predictive factor for
ulcerative colitis (Wang et al., [274]2014). Overall, these results
suggested that Zhishi and Baizhu act synergistically to treat FD by
regulating inflammation, gastrointestinal peristalsis, and mental
disorders.
Contribution score analysis
A mathematical formula was established to simulate the effect of each
component of ZZW on the treatment of FD. The CS value of each active
component in ZZW is calculated and showed in Figure [275]4 and Table
[276]S4. According to the calculation results, the top 6 components
with a sum of CS of 49.49% are acacetin (ZS85), luteolin (ZS137),
chrysoeriol (ZS79), 5,7,4′-Trimethylapigenin (ZS107), diosmetin (ZS73),
Tetramethoxyluteolin (ZS117), and 29 components can contribute the
effects of ZZW on FD with a sum of CS of 90.18%. It has been proved
that the effective therapeutic effect of ZZW on FD is derived from all
active components, rather than a few components. These results may
fully clarify why the herbs in ZZW could generate synergistic and
combination effects on FD.
Figure 4.
[277]Figure 4
[278]Open in a new tab
The CS and accumulative CS of active components in ZZW.
Potential synergistic mechanisms analysis of zhishi and baizhu
GO enrichment analysis for targets
GO enrichment analysis based on DAVID Functional Annotation Clustering
Tool was performed to identify the biological significance of the
primary target with FDR > 0.01 and the gene count above the mean value.
In the C-T network (Figure [279]3), 37 (84%) components in Zhishi and
17 (94%) components in Baizhu have 18 same targets, including MAPT,
OPRD1, OPRK1, OPRL1, OPRM1, AR, PTGS1, PTGS2, DRD2, DRD3, NOS1, NOS2,
NOS3, MAOA, MAOB, ACE, SRD5A1, and SLC6A2. Surprisingly, these targets
are mainly distributed in GO:0042755 eating behavior (OPRD1, OPRK1,
OPRL1, OPRM1), GO:0006809 nitric oxide biosynthetic process, GO:0045909
positive regulation of vasodilation (NOS1, NOS2, NOS3), GO:0019229
regulation of vasoconstriction (ACE), GO:0042420 dopamine catabolic
process (MAOA, MAOB), GO:0042417 dopamine metabolic process (DRD2,
DRD3), GO:0007611 learning or memory (MAPT, DRD3, PTGS2), GO:0006954
inflammatory response (PTGS1, PTGS2), GO:0042493 response to drug
(SRD5A1, SLC6A2, MAOB, DRD2, DRD3, PTGS2). Ninety percent of these GO
terms are located on the related GO terms of FD. These results suggest
that targets are related to FD at different levels, indicating that ZZW
could produce a combination effect on FD.
In order to further dissect the combination effects of Zhishi and
Baizhu, all the target interacting with the active components of Zhishi
and Baizhu were enriched by GO enrichment analysis, respectively. As
shown in Figure [280]5, there are six shared GO biological process (BP)
terms between Zhishi and Baizhu, including oxidation-reduction process,
inflammatory response, protein phosphorylation, and so on are all
closely associated with FD. For instance, the oxidation-reduction
process has previously been shown to correlate with the pathogenesis of
depression (Grases et al., [281]2014) and inflammatory diseases of the
gastrointestinal tract (such as H. pylori infection and IBD) (Van Hecke
et al., [282]2017), and the role of inflammatory response in FD is
extensive, such as anti-depression (Miller and Raison, [283]2016),
eradicating H. pylori infection and improving dyspepsia (White et al.,
[284]2015), etc. To our surprise, 18 common gene GO terms matched only
one-third of the 6 shared GO terms, this results prove once again that
the treatment of ZZW for FD is a synergistic effect form.
Figure 5.
[285]Figure 5
[286]Open in a new tab
Go enrichment analysis of the targets of ZZW. The green part represents
the shared GO terms of Zhishi and Baizhu.
In addition, the other 12 groups of Zhishi are also related to the
treatment of FD. For instance, many investigations suggest that the
regulation of cytosolic calcium ion concentration has an important role
in anti-depression treatment (Yamawaki et al., [287]2001), and the
abnormalities of ERK1/2 signaling may be crucial for the vulnerability
of depression (Dwivedi and Zhang, [288]2016), moreover, the ERK
activity constitutively or transiently may serve as a negative
regulator of vascular inflammation by suppressing endothelial NF-κB
activation, and play an anti-inflammatory role (Maeng et al.,
[289]2006). The other eight groups of Baizhu are also related to FD.
For instance, patients with functional dyspepsia have a lower threshold
both to the initial symptomatic recognition and to the perception of
pain during gastric distension (Bradette et al., [290]1991), and
depression is associated with increased platelet activation (Morel-Kopp
et al., [291]2009).
Collectively, these results suggest that Zhishi and Baizhu may play
synergistic and complementary effects on FD from the perspective of GO
enrichment analysis.
Pathway analysis to explore the therapeutic mechanisms of ZZW
To elaborate on the significant pathways involved in ZZW for FD
therapy, all target proteins were mapped onto KEGG pathways with degree
≥ 12 (the median valve) resulting in a target-pathway (T-P) network
(Figure [292]6). The T-P network contains 108 nodes (24 pathways and 84
targets and 353 edges). NFKB1, PIK3CA, RELA, MAPK8, and JUN were in the
top-ranking degrees in the T-P network and linked by 19, 18, 18, 16,
and 13 pathways (Figure [293]6). NFKB1 encoding pro-inflammatory
cytokines, chemokines, and molecules involved in carcinogenesis was
markedly up-regulated in H. pylori GC026-challenged cells
(Castaño-Rodríguez et al., [294]2015); PIK3CA can active the PI3K
signaling pathway in gastric cancer through up-regulation or mutation
(Li et al., [295]2005); RELA, the principal effector of canonical NF-κB
signaling (Parker et al., [296]2014); MAPK8 was mediators of signal
transduction from the cell surface to the nucleus, and can regulate
AP-1 transcriptional activity by multiple mechanisms (Whitmarsh and
Davis, [297]1996); JUN were phosphorylated through
homeodomain-interacting protein kinase 3 after cAMP stimulation (Lan et
al., [298]2007). Noticeably, the target in the top-ranking degrees were
almost related to FD inducing factors, such as inflammation and
organisms infection, indicating that anti-inflammation and
anti-microbial play a crucial role in the treatment of FD.
Figure 6.
[299]Figure 6
[300]Open in a new tab
Target-pathway network of ZZW. The red nodes are the common targets of
Zhishi and Baizhu, and the green represents the different targets, the
yellow represents the pathways. The orange area represents the
depression-related pathway, the green area represents the inflammation
and infection-related pathway.
The pathways associated with these targets showed more significant
features (Figure [301]5), Neuroactive ligand-receptor interaction
(hsa04080) pathway exhibits the highest number of target connections
(degree = 25), followed by Calcium signaling pathway (hsa04020, degree
= 19), Kaposi's sarcoma-associated herpesvirus infection (hsa05167, n =
19), cAMP signaling pathway (hsa04024, degree = 19), Fluid shear stress
and atherosclerosis (hsa05418, degree = 16). Based on the results of
pathways analysis, it was found that these high-degree pathways were
closely related to neuroprotection, anti-inflammation, and
anti-microbial. Specially, the crucial neuroactive ligand-receptor
interaction pathway has been applied into the analysis of mental
disorders (Adkins et al., [302]2012; Kong et al., [303]2015), which is
regulated by 25 potential targets (ADORA1, ADORA2A, ADORA3, etc.). In
addition, calcium signaling pathway is a major signal transduction, and
can affect the development of some of the major psychiatric diseases
such as bipolar disorder and schizophrenia by regulating neuronal
excitability, information processing and cognition (Berridge,
[304]2014). Nevertheless, cAMP is one of the most common and universal
second messengers, and was proven that its abnormalities would be
linked with psychotic depression (Perez et al., [305]2002).
In order to further explore the synergetic mechanism of Zhishi and
Baizhu in the treatment of FD in ZZW, we have constructed a
comprehensive pathway. As shown in Figure [306]7, in the calcium
regulation center, Zhishi can act on the genes of the upstream pathway,
such as ADRA1A, ADRA1B, and ADRA1D, ADORA2A, DRD2, while Baizhu can act
the genes in downstream, such as PRKCB, CAMK2A, and NOS1, these results
can indicate Zhishi and Baizhu play synergistic and complementary
effects on learning and memory, vasodilatory, anti-inflammatory, and
anti-thrombotic.
Figure 7.
[307]Figure 7
[308]Open in a new tab
Distribution of target proteins of ZZW on the compressed FD pathway.
Additionally, in the inflammation regulation center, Zhishi can act the
genes of the upstream pathway, such as FGF2, BDNF, TLR4, and TNFRSF1A,
while Baizhu can act the gene in the downstream pathway, such as
PIK3CA, CCL2, and PTGS2, which are associated with the pathway of
inflammation and synthesis of inflammatory mediators.
As the pathogenic factors of FD are related to inflammation, mental
disorder, and organisms infection, so the above results suggest that
Zhishi and Baizhu can exert a synergistic effect on FD at the pathway
level.
Discussion
FD is a common digestive disease associated with many pathogenic
factors, such as gastric and duodenal perturbations (Tack and Talley,
[309]2013), organisms infection (Futagami et al., [310]2015), mental
disorders (Aro et al., [311]2015), etc. The related genes of FD include
NFKB1 (Castaño-Rodríguez et al., [312]2015), PIK3CA (Li et al.,
[313]2005), RELA (Parker et al., [314]2014), MAPK8 (Whitmarsh and
Davis, [315]1996), JUN (Lan et al., [316]2007), and etc; and the
involved pathway include neuroactive ligand-receptor interaction
pathway (Kong et al., [317]2015), calcium signaling pathway (Berridge,
[318]2014), cAMP signaling pathway (Perez et al., [319]2002), MAPK
signaling pathway (Allison et al., [320]2009), NF-κB pathway (Marengo
et al., [321]2018), and etc. Our study found that ZZW can treat FD by
adjusting the related genes and pathways of dyspepsia, Helicobacter
pylori infection, and depression. Thus, it is indirectly confirmed the
relationship between FD and the above-mentioned pathogenic factors.
In this manuscript, we illuminate the synergistic effect of ZZW on FD
from four aspects. Firstly, the C-T network showed 80 percent of the
components in Zhishi and Baizhu have 18 same targets, involving
GO:0042755 eating behavior, GO:0006809 nitric oxide biosynthetic
process, GO:0045909 positive regulation of vasodilation, GO:0019229
regulation of vasoconstriction, GO:0042420 dopamine catabolic process,
GO:0042417 dopamine metabolic process, GO:0007611 learning or memory,
GO:0006954 inflammatory response, and GO:0042493 response to drug. This
indicates that the herbs in ZZW have the cooperation effects on FD.
Secondly, the CS of each component in ZZW are calculated and showed
that 29 components can contribute the effects of ZZW for FD with a sum
of 90.18% of CS. It is proved that the effective therapeutic effect of
ZZW on FD is derived from all active components, not a few components.
Thirdly, GO enrichment analysis indicated that all the target
interacting with the active components of Zhishi and Baizhu have six
shared GO BP terms, which are all closely associated with FD, whereas
the 18 same targets GO terms cannot cover the shared GO terms of the
target interacting with the all components, and the other components
also have action, namely the components work together to play a
synergistic effect. Finally, the pathway analysis proves again that
Zhishi and Baizhu can exert a synergistic effect on the treatment of FD
through acting the upstream and downstream gene in the calcium
signaling pathway, cAMP signaling pathway, MAPK signaling pathway, and
NF-κB pathway. Recent studies also established that the compatibility
of Zhishi and Baizhu can promote the function of modulation of
gastroinfestinal motility via regulating the levels of MTL and VIP (Li
et al., [322]2007). All these results suggest that ZZW could produce a
combination effect on FD.
In this study, system pharmacology and network pharmacology were used
to construct a strategy for decoding the TCM pharmacologic molecular
mechanism. This strategy combined physicochemical properties, network
topological features, function analysis, and pathway analysis, and
provided a reference for the new methods.
Currently, system pharmacology provides a powerful tool for exploring
the compatibility and mechanism of TCM formulae (Yue et al.,
[323]2017), but its findings mainly rely on theoretical analyses, thus
additional experiments are needed to validate our findings as well as
potential clinical significance. It is noteworthy that the OB values of
four flavanone glycoside which are the high content in Zhishi (Zeng et
al., [324]2016), were <30%. Therefore, the metabolites of these
flavanone glycosides by gut microbiota may be a critical step in the
emergence of their bioactivities in vivo, especially under the disease
state (Chen F. et al., [325]2016).
Author contributions
A-PL, Z-LL, and D-GG provided the concept and designed the study. CW,
QR, and X-TC conducted the analyses and wrote the manuscript. CW, QR,
X-TC, Z-QS, Z-CN, J-HG, X-LM, and D-RL participated in data analysis.
A-PL, Z-LL, and D-GG contributed to revising and proof-reading the
manuscript. All authors read and approved the final manuscript.
Conflict of interest statement
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. The reviewer CF and handling Editor
declared their shared affiliation.
Footnotes
Funding. This study is financially supported by the Fundamental
Research Funds for the Central public welfare research institutes
(grant No. YZ-1811 and YZ-1655), Hong Kong Baptist University Strategic
Development Fund [grant No. SDF13-1209-P01 and SDF15-0324-P02(b)], the
Faculty Research Grant of Hong Kong Baptist University (grant No.
FRG1/14-15/070 and FRG2/15-16/038), the Natural Science Foundation
Council of China (grant No. 31501080).
Supplementary material
The Supplementary Material for this article can be found online at:
[326]https://www.frontiersin.org/articles/10.3389/fphar.2018.00841/full
#supplementary-material
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References