Abstract
Traditional Chinese medicine (TCM) with the characteristics of
“multi-component-multi-target-multi-pathway” has obvious advantages in
the prevention and treatment of complex diseases, especially in the
aspects of “treating the same disease with different treatments”.
However, there are still some problems such as unclear substance basis
and molecular mechanism of the effectiveness of formula. Network
pharmacology is a new strategy based on system biology and
poly-pharmacology, which could observe the intervention of drugs on
disease networks at systematical and comprehensive level, and
especially suitable for study of complex TCM systems. Rheumatoid
arthritis (RA) is a chronic inflammatory autoimmune disease, causing
articular and extra articular dysfunctions among patients, it could
lead to irreversible joint damage or disability if left untreated. TCM
formulas, Danggui-Sini-decoction (DSD), Guizhi-Fuzi-decoction (GFD),
and Huangqi-Guizhi-Wuwu-Decoction (HGWD), et al., have been found
successful in controlling RA in clinical applications. Here, a network
pharmacology-based approach was established. With this model, key gene
network motif with significant (KNMS) of three formulas were predicted,
and the molecular mechanism of different formula in the treatment of
rheumatoid arthritis (RA) was inferred based on these KNMSs. The
results show that the KNMSs predicted by the model kept a high
consistency with the corresponding C-T network in coverage of RA
pathogenic genes, coverage of functional pathways and cumulative
contribution of key nodes, which confirmed the reliability and accuracy
of our proposed KNMS prediction strategy. All validated KNMSs of each
RA therapy-related formula were employed to decode the mechanisms of
different formulas treat the same disease. Finally, the key components
in KNMSs of each formula were evaluated by in vitro experiments. Our
proposed KNMS prediction and validation strategy provides
methodological reference for interpreting the optimization of core
components group and inference of molecular mechanism of formula in the
treatment of complex diseases in TCM.
Keywords: Traditional Chinese medicine (TCM), rheumatoid arthritis, key
gene network motif with significant (KNMS), mechanisms, network
pharmacology
Introduction
Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease with
symmetric inflammation of aggressive multiple joints ([41]Sodhi et al.,
2015). As the most common inflammatory rheumatic disease, the
prevalence of RA is about 0.5%-1.0% in the world ([42]Saraux et al.,
2006). The inflammatory cell infiltration of synovium, pannus
formation, and the progressive destruction of articular cartilage and
bone destruction are the main pathological properties of RA
([43]Brzustewicz and Bryl, 2015). The data from epidemiological
investigations shows that about 90% of RA patients developed bone
erosions within 2 years, eventually leading to joint deformities or
even disability ([44]Cecilia et al., 2013). Therefore, RA brings great
impact on the quality of life of patients and also imposes a heavy
burden on families and society.
Traditional Chinese medicine (TCM) has the advantages of definite
curative effect, safety and few side effects in the treatment of
rheumatoid arthritis and has attracted more and more attention in the
prevention and treatment of rheumatoid arthritis. TCM usually treats RA
and other complex diseases in the form of formulas, which has
theoretical advantages and rich clinical experience. In the study of RA
therapy-related formulas, increasing evidence confirmed that different
formulas can treat RA, which coincide with the theoretical concept of
“treating the same disease with different treatments” in TCM ([45]Fu
et al., 2014). Such as Danggui-Sini-decoction (DSD) ([46]Bang et al.,
2017), Guizhi-Fuzi-decoction (GFD) ([47]Peng et al., 2013), and
Huangqi-Guizhi Wuwu-Decoction (HGWD) ([48]Wang et al., 2010) etc., have
been found successful in controlling RA in TCM clinics. Previous
pharmacological studies have shown that DSD exert positive effects and
good anti-inflammatory function which might protect collagen-induced
arthritis rats from bone and cartilage destruction ([49]Cheng et al.,
2017). It has been reported that GFD could substantially inhibit the
activities of interleukin-6 and tumor necrosis factor-α in the serum of
adjuvant-induced arthritis rats, as well as inhibit the formation of
synovitis and pannus, and has obvious therapeutic effect on rheumatoid
arthritis ([50]He and Gu, 2008; [51]Xia and Song, 2011). In addition,
some pharmacological experimental studies have found that HGWD could
promote the apoptosis of synovial cells in rheumatoid arthritis rats
with abnormal hyperfunction ([52]Liu et al., 2017), and reduce the
degree of foot swelling in adjuvant arthritis rats, affect the
arthritis index of rats, and play a role in treating rheumatoid
arthritis ([53]Shi et al., 2006).
In these formulas, DSD consists of 7 herbs: Angelica sinensis (Oliv.)
Diels (Danggui, 12 g), Cinnamomum cassia (L.) J. Presl (Cinnamomi
ramulus, Guizhi, 9 g), Paeonia lactiflora Pall. (Baishao, 9 g), Asarum
sieboldii Miq. (Xixin, 3 g), Glycyrrhiza uralensis Fisch. ex DC.
(Gancao, 6 g), Tetrapanax papyrifer (Hook.) K. Koch (Medulla
tetrapanacis, Tongcao, 6 g), Ziziphus jujuba Mill. (Jujubae fructus,
Dazao, 8). GFD consists of 5 herbs: Cinnamomum cassia (L.) J. Presl
(Cinnamomi ramulus, Guizhi, 12 g), Aconitum carmichaeli Debeaux
(Aconiti lateralis radix praeparata, Fuzi, 15 g), Zingiber officinale
Roscoe (Shengjiang, 9 g), Glycyrrhiza uralensis Fisch. ex DC. (Gancao,
6 g), Ziziphus jujuba Mill. (Jujubae fructus, Dazao, 12). HGWD consists
of 5 herbs: Astragalus mongholicus Bunge (Huangqi, 15 g), Paeonia
lactiflora Pall. (Baishao, 12 g), Cinnamomum cassia (L.) J. Presl
(Cinnamomi ramulus, Gui zhi, 12 g), Zingiber officinale Roscoe
(Shengjiang, 25 g), Ziziphus jujuba Mill. (Jujubae fructus, Dazao, 4).
These traditional formulas are recorded in the Chinese pharmacopoeia
([54]National Pharmacopoeia Commission, 2015). However, the molecular
mechanism of these different formulas in treating rheumatoid arthritis
under the concept of “treating the same disease with different
treatments” is still unclear. How to develop new methods to detect the
key component groups of different formulas for treating rheumatoid
arthritis and speculate the possible mechanism not only provides the
benefit therapy strategy for the precise treatment of RA, but also
provides methodological reference for the analysis of the mechanism of
treating the same disease with different treatments in TCM.
Network pharmacology has been widely used in the research of treating
the same diseases with different formulas. For example, Gao et al. used
network pharmacology to decode the mechanisms of Xiaoyao powder and
Kaixin powder in treating depression; Liu et al. clarified the
molecular mechanism of Sini San and Suanzaoren Tang in treating
insomnia based on network pharmacology, etc ([55]Yao et al., 2018;
[56]Liu et al., 2019). With the in-depth intersection of systems
biology, poly-pharmacology, bioinformatics and other technologies, and
the continuous improvement of the accuracy, reliability, and integrity
of data resources, the research ideas and technical means of network
pharmacology will be better applied to the mechanism research of
formulas in TCM and provide more innovation in methodology for the
molecular level research of TCM.
In this study, network pharmacology model was applied to analyze the
key gene network motif with significant (KNMS) of different formulas in
the treatment of RA. Coverage of RA pathogenic genes, coverage of
functional pathways and cumulative contribution of key nodes were
employed to evaluate the accuracy and reliability of KNMSs, and then
the validated KNMSs were used to infer the common potential mechanism
of different formulas in the treatment of RA. In summary, the proposed
network pharmacology strategy aims to identify major mechanism and
related pharmacological effects of different treatments in treating RA
through specific KNMSs, which may offer a new network-based method for
evaluating and selecting suitable treatment strategies of complex
diseases in TCM.
Materials and Methods
Flowchart
This phenomenon that different formulas treat the same diseases is
widely used in TCM clinical applications. However, there is lack of
systematic method to decode the mechanisms of treat the same disease
with different treatments. In this study, we designed a network
pharmacology model to decode the common and specific potential
mechanisms of 3 formulas in the treatment of RA, which may provide a
methodological reference for different formulas treat the same disease.
The workflow is illustrated in [57]Figure 1 and described as follows:
1) the components of DSD, GFD and HGWD were collected from TCMSP,
TCMID, and TCM@Taiwan; 2) ADME based methods were used to identify the
main active components; 3) the main active components from three
formulas and their predicted targets were used to construct the
component-target (C-T) networks; 4) The KNMSs were detected from
integrated C-T and target-target interaction networks; 5) the KNMSs
were validated by the coverage of RA pathogenic genes, coverage of
functional pathways and cumulative contribution of key nodes; 6)
Finally, all validated KNMSs were employed to decode the underlying
mechanism of different formulas treat the same disease.
Figure 1.
[58]Figure 1
[59]Open in a new tab
A schematic diagram of network pharmacology-based strategy to decode
the mechanisms of different formulas treat the same disease of TCM. DSD
represents Danggui-Sini-decoction, GFD represents
Guizhi-Fuzi-decoction, and HGWD represents Huangqi-Guizhi
Wuwu-Decoction, KNMSs represents key gene network motifs with
significant.
Component Identification
All chemical components of Danggui-Sini-decoction (DSD),
Guizhi-Fuzi-decoction (GFD), and Huangqi-Guizhi Wuwu-Decoction (HGWD)
were collected from Traditional Chinese Medicine Systems Pharmacology
(TCMSP) Database ([60]Ru et al., 2014)
([61]http://lsp.nwsuaf.edu.cn/tcmsp.php), Traditional Chinese Medicine
integrated database ([62]Xue et al., 2013) (TCMID,
[63]http://www.megabionet.org/tcmid/), and TCM@Taiwan ([64]Chen, 2012)
([65]http://tcm.cmu.edu.tw/zh-tw). The chemical identification and
concentration of the herbs in DSD, GFD, and HGWD were collected from
the previous reports. All chemical structures were prepared and
converted into canonical SMILES using Open Babel Toolkit (version
2.4.1). The targets of DSD, GFD, and HGWD were predicted by using
Similarity Ensemble Approach SEA ([66]Keiser et al., 2007)
([67]http://sea.bkslab.org/) and Swiss Target Prediction ([68]David
et al., 2014) ([69]http://www.swisstargetprediction.ch/).
ADME Screening
Components that meet the Lipinski’ rules of five usually have better
pharmacokinetic properties, higher bioavailability during metabolism in
the body, and are therefore more likely to be drug candidates
([70]Lipinski et al., 2012; [71]Damião et al., 2014). Oral
bioavailability (OB) refers to the extent and rate of active components
release from the herbs into the systemic circulation and is an
important indicator for evaluating the intrinsic quality of the
component ([72]Xu et al., 2012). Drug-like (DL) indicate the
characteristics that an ideal drug should have and was a comprehensive
reflection of the physical and chemical properties and structural
characteristics exhibited by successful drugs ([73]Tao et al., 2013).
In this study, active components from DSD, GFD, and HGWD were mainly
filtered by integrating Lipinski’s rules, oral bioavailability (OB),
and drug-likeness (DL). The detail of Lipinski’s rules includes
molecular weight lower than 500 Da, number of donor hydrogen bonds less
than 5, number of acceptor hydrogen bonds less than 10, the logP lower
than 5 and over -2, and meets only the criteria of 10 or fewer
rotatable bonds. Besides, oral bioavailability (OB), and drug-likeness
(DL) also were employed to screen the active components. The components
with OB values higher than 30% and DL values higher than 0.14 were
retained for further investigation ([74]Wang et al., 2018).
Networks Construction
The component-target (C-T) networks of three formulas were constructed
by using Cytoscape software (Version 3.7.0) ([75]Lopes et al., 2010).
The topological parameters of networks were analyzed using Cytoscape
plugin NetworkAnalyzer ([76]Jong et al., 2003).
Detection of Key Gene Network Motif With Significant (KNMS)
The exploration of motif structures in networks is an important issue
in many domains and disciplines. To find key gene network motifs with
significant (KNMS) of 3 formulas in the treatment of RA, a mathematical
algorithm was designed and described as follows:
To take advantage of the motif structure of the network, m motif
codebooks, and one index codebook are used to describe the random
walker’s movements within and between motifs, respectively. Motif
codebook i has one codeword for each node α∈i and one exit codeword.
The codeword lengths are derived from the frequencies at which the
random walker visits each of the nodes in the motif, p[α][∈][i], and
exits the motif, q [i↷]. We use p [i↻] to denote the sum of these
frequencies, the total use of codewords in motif i, and P^i to denote
the normalized probability distribution. Similarly, the index codebook
has codewords for motif entries. The codeword lengths are derived from
the set of frequencies at which the random walker enters each motif, q
[i↶]. We use q [↶] to denote the sum of these frequencies, the total
use of codewords to move into motifs, and Q to denote the normalized
probability distribution. We want to express average length of
codewords from the index codebook and the motif codebooks weighted by
their rates of use. Therefore, the map equation is
[MATH:
L(M)=<
/mo>q↶H(Q)+∑i=1<
/mn>mpi↻H(p<
mi>i) :MATH]
Below we explain the terms of the map equation in detail and we provide
examples with Huffman codes for illustration.
L(M) represents the per-step description length for motif partition M.
That is, for motif partition M of n nodes into m motifs, the lower
bound of the average length of the code describing a step of the random
walker.
[MATH: q↶=∑i=1<
/mn>mqi↶ :MATH]
The rate at which the index codebook is used. The per-step use rate of
the index codebook is given by the total probability that the random
walker enters any of them motifs. This variable represents the
proportion of all codes representing motif names in the codes. Where q
[i↶] is probability of jumping out of Motif i.
[MATH:
H(Q)=−∑i=1<
/mn>m(qi↶/qi↶)log(qi↶/q↶) :MATH]
This variable represents the average byte length required to encode
motif names. The frequency-weighted average length of codewords in the
index codebook. The entropy of the relative rates to use the motif
codebooks measures the smallest average codeword length that is
theoretically possible. The heights of individual blocks under Index
codebook correspond to the relative rates and the codeword lengths
approximately correspond to the negative logarithm of the rates in base
2.
[MATH:
pi↻=∑α∈ipα+<
mi>qi↷ :MATH]
This variable represents the coding proportion of all nodes (including
jump-out nodes) belonging to motif i in the coding. The rate at which
the motif codebook i is used, which is given by the total probability
that any node in the motif is visited, plus the probability that the
random walker exits the motif and the exit codeword is used.
[MATH:
H(pi
)=−(qi↷/pi↻)log(qi↷/pi↻)−∑α∈i(pa/pi↻)log(pa/pi↻) :MATH]
This variable represents the average byte length required to encode all
nodes in motif i. The frequency-weighted average length of codewords in
motif codebook i. The entropy of the relative rates at which the random
walker exits motif i and visits each node in motif i measures the
smallest average codeword length that is theoretically possible. The
heights of individual blocks under motif codebooks correspond to the
relative rates and the codeword lengths approximately correspond to the
negative logarithm of the rates in base 2.
Contribution Coefficient Calculation
The contribution coefficient (CC) represents the network contribution
of KNMSs in 3 formulas. R value was used to determine the importance of
the components by the following mathematical model:
[MATH:
R=dc−dc(min)dcmax−dc(min) :MATH]
[MATH:
CC(i)=Σin<
msub>RiΣinRj
×100% :MATH]
where d[c] represents the degree of each component, which is calculated
by Cytoscape. R is an indicator to evaluate the importance of the
component.
Where n is the number of components from different KNMSs of DSD, GFD,
and HGWD, respectively; m is the number of components from C-T network
of DSD, GFD, and HGWD, respectively; Ri represents the indicator of
each component in KNMSs of DSD, GFD, and HGWD, and Rj represents the
indicator of each component in C-T network of DSD, GFD, and HGWD.
KEGG Pathway
To analyze the main function of the KNMSs, the pathway data were
obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG)
database ([77]Draghici et al., 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 ([78]Luo and Brouwer,
2013) in the R Bioconductor package
([79]https://www.bioconductor.org/).
Experimental Validation
Materials
Isoliquiritigenin, isorhamnetin and quercetin (≥98% purity by HPLC) was
obtained from Chengdu Pufei De Biotech Co., Ltd (Chengdu, China). Fetal
bovine serum (FBS) and Dulbecco’s modified Eagle’s medium (DMEM) were
purchased from Gibco (Grand Island, USA). Lipopolysaccharide (LPS) was
purchased from Sigma-Aldrich Co., Ltd (St Louis, USA).
Cell Culture and Treatment
RAW264.7 cells were obtained from the cell bank of the Chinese Academy
of Sciences (Shanghai, China). The cells were cultured in DMEM with 10%
FBS, and incubated at 37°C under 5% CO[2]. When RAW264.7 cells reached
80% confluency, the cells were treated with isoliquiritigenin,
isorhamnetin and quercetin for 2 h, then the cells were treated with
LPS (1 μg/ml) for 24 h.
Cell Viability Assay
MTT assay was utilized to measure cell viability. RAW264.7 cells
(6×10^4 per/well) were seeded in 96-well plates. After 24 h incubation,
RAW264.7 cells were treated with 1, 5, 10, 20, 40, and 80 μM
isoliquiritigenin, isorhamnetin and quercetin for 24 h. Ten μl of MTT
were added to reach a final concentration of 0.5 mg/ml, and incubated
for a further 4 h. The absorbance was measured at 570 nm with a
microplate reader (BioTek, USA).
Measurement of NO
Griess reagent was utilized to detected the level of NO in the culture
supernatant of RAW264.7 cells. After incubation with isoliquiritigenin,
isorhamnetin and quercetin for 2 h and LPS (1 μg/ml) for 24 h, the
culture supernatant was collected and mixed with Griess reagent for NO
assay. The absorbance was measured at 540 nm using a microplate reader.
Statistical Analysis
To compare the importance of motifs in three formulas, SPSS22.0 was
used for statistical analysis. One-way analysis of variance followed by
a Dunnett post-hoc test was used to compare more than two groups.
Obtained p-values were corrected by Benjamini-Hochberg false discovery
rate (FDR). Results were considered as statistically significant if the
p-value was <0.05.
Results
Chemical Analysis
Chemical analysis plays important roles in the study of substances
basis and mechanism of herbs in the formulas. By searching from the
literature, we collected the information on specific chemical
identification and concentration of the herbs in DSD, GFD and HGWD,
respectively. The detail information was shown in [80]Table 1 and
[81]Table S1 . The results suggest that chemical components of herbs
and the concentration of identified components provide an
experiment-aided chemical space for search of active components. This
will provide valuable reference for the further analysis.
Table 1.
The information on chemical analysis of the herbs from the literature
in DSD, GFD, and HGWD.
Herb Method Component Concentration Formula Ref.
Angelica sinensis (Oliv.) Diels (Danggui) HPLC Ferulic acid 0.36 mg/g
DSD [82]Xie et al., 2007
Coniferylferulate 6.11 mg/g
Z-ligustilide 4.34 mg/g
E-ligustilide 0.23 mg/g
Z-3-butylidenephthalide 0.20 mg/g
E-3-butylidenephthalide 0.08 mg/g
Cinnamomum cassia (L.) J. Presl (Cinnamomi ramulus, Guizhi) UHPLC
Protocatechuic acid 0.11 mg/g DSD, GFD, HGWD [83]Liang et al., 2011
Coumarin 0.84 mg/g
Cinnamic alcohol 0.04 mg/g
Cinnamic acid 0.68 mg/g
Cinnamaldehyde 9.93 mg/g
Paeonia lactiflora Pall. (Baishao) HPLC Gallic acid 2.33 mg/g DSD, HGWD
[84]Li et al., 2011
Hydroxyl-paeoniflorin 1.89 mg/g
Catechin 0.03 mg/g
Albiflorin 4.44 mg/g
Paeoniflorin 4.81 mg/g
Benzoic acid 0.03 mg/g
1, 2, 3, 4, 6 -pentagalloylglucose 4.80 mg/g
Benzoyl -paeoniflorin 0.11 mg/g
Paeonol 0.07 mg/g
Asarum sieboldii Miq. (Xixin) HPLC Aristolochic acid A 0.009 mg/g DSD
[85]Gao et al., 2005
Glycyrrhiza uralensis Fisch. ex DC. (Gancao) HPLC Glycyrrhizin 97.49
mg/g DSD, GFD [86]Chen et al., 2009
Liquiritin 102.83 mg/g
Lsoliquritigenin 98.30 mg/g
Tetrapanax papyrifer (Hook.) K.Koch (Medulla tetrapanacis, Tongcao) RP-
HPLC Calceolar ioside B 0.86 mg/g DSD [87]Gao et al., 2007
Ziziphus jujuba Mill. (Jujubae fructus (Dazao) HPLC Rutin 0.21 mg/g
DSD, GFD, HGWD [88]Wang et al., 2013
Quercetin 0.008 mg/g
Isorhamnetin 0.17 mg/g
Aconitum carmichaeli Debeaux (Aconiti lateralis radix praeparata, Fuzi)
HPLC aconitine 0.28 mg/g GFD [89]Sun et al., 2009
hypaconitine 0.70 mg/g
mesaconitine 1.04 mg/g
benzoylaconine 0.009 mg/g
benzoylhypaconine 0.007 mg/g
benzoylmesaconin 0.07 mg/g
Zingiber officinale Roscoe (Shengjiang) HPLC 6-Gingerol 16.62 mg/g GFD,
HGWD [90]Zhang et al., 2009
6-Shogaol 4.92 mg/g
Astragalus mongholicus Bunge (Huangqi) HPLC Campanulin 0.42 mg/g HGWD
[91]Li et al., 2015
Formononetin 0.02 mg/g
[92]Open in a new tab
Active Components in DSD, GFD, and HGWD
By a comprehensive search of the TCMSP, TCMID, and TCM@Taiwan database,
812 components from seven herbs in DSD, 640 components from five herbs
in GFD, and 459 components from five herbs in HGWD were obtained. A TCM
formula usually contains large number of components, and ADME screening
approaches are always used to select active components. After ADME
screening, 124 active components in DSD, 120 active components in GFD,
and 48 active components in HGWD were passed the combined filtering
criteria which integrated by Lipinski’s rule, OB, and DL ([93] Table 2
). For further analysis of these active components, 31 common
components in three formulas and 93, 89, and 17 unique components in
DSD, GFD, and HGWD were found ([94] Figure 2 ). These results indicate
that three formulas might exert roles in treating RA by affecting the
common components and specific components.
Table 2.
Components in DSD, GFD, and HGWD for further analysis after ADME
screening.
ID Component MW Logp HDON HACC RBN OB DL Source
DSD1 (+)-catechin 290.29 1.02 5 6 1 54.83 0.24 Baishao
DSD2
(3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,
9-hexahydro-1H-cyclopenta [a]phenanthrene-15,16-dione 358.52 3.52 2 4 0
43.56 0.53 Baishao
DSD3
11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-ol
ide 470.71 3.82 2 5 1 64.77 0.38 Baishao
DSD4 albiflorin_qt 318.35 0.53 2 6 4 66.64 0.33 Baishao
DSD5 kaempferol 286.25 1.23 4 6 1 41.88 0.24 Baishao
DSD6 Lactiflorin 462.49 0.31 3 10 5 49.12 0.8 Baishao
DSD7 paeoniflorgenone 318.35 0.86 1 6 4 87.59 0.37 Baishao
DSD8 paeoniflorin_qt 318.35 0.69 2 6 4 68.18 0.4 Baishao
DSD9 (-)-catechin 290.29 1.02 5 6 1 49.68 0.24 Dazao
DSD10 (S)-Coclaurine 285.37 2.2 3 4 3 42.35 0.24 Dazao
DSD11 21302-79-4 486.76 4.53 3 5 3 73.52 0.77 Dazao
DSD12 berberine 336.39 3.75 0 4 2 36.86 0.78 Dazao
DSD13 coumestrol 268.23 2.43 2 5 0 32.49 0.34 Dazao
DSD14 Fumarine 353.4 1.95 0 6 0 59.26 0.83 Dazao
DSD15 Jujubasaponin V_qt 472.78 4.61 2 4 2 36.99 0.63 Dazao
DSD16 jujuboside A_qt 472.78 3.8 2 4 1 36.67 0.62 Dazao
DSD17 Jujuboside C_qt 472.78 3.8 2 4 1 40.26 0.62 Dazao
DSD18 malkangunin 432.56 2.72 2 7 6 57.71 0.63 Dazao
DSD19 Mauritine D 342.46 1.11 2 6 2 89.13 0.45 Dazao
DSD20 Moupinamide 313.38 2.46 3 5 6 86.71 0.26 Dazao
DSD21 Nuciferin 295.41 3.38 0 3 2 34.43 0.4 Dazao
DSD22 quercetin 302.25 1.07 5 7 1 46.43 0.28 Dazao
DSD23 Ruvoside_qt 390.57 1.42 3 5 2 36.12 0.76 Dazao
DSD24 Spiradine A 311.46 1.29 1 3 0 113.52 0.61 Dazao
DSD25 stepharine 297.38 1.76 1 4 2 31.55 0.33 Dazao
DSD26 Stepholidine 327.41 2.26 2 5 2 33.11 0.54 Dazao
DSD27 Ziziphin_qt 472.78 3.8 2 4 1 66.95 0.62 Dazao
DSD28 zizyphus saponin I_qt 472.78 3.8 2 4 1 32.69 0.62 Dazao
DSD29 2,6-di(phenyl)thiopyran-4-thione 280.43 4.39 0 0 2 69.13 0.15
Danggui
DSD30 (-)-Medicocarpin 432.46 1.26 4 9 4 40.99 0.95 Gancao
DSD31 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one 256.27 2.79 2 4 1
71.12 0.18 Gancao
DSD32
(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dih
ydrofuro[3,2-g]chromen-7-one 384.41 2.61 3 7 3 60.25 0.63 Gancao
DSD33
(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one
324.4 3.62 2 4 3 36.57 0.32 Gancao
DSD34
(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
322.38 4.46 2 4 3 39.62 0.35 Gancao
DSD35
(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphen
yl)prop-2-en-1-one 340.4 3.47 4 5 5 46.27 0.31 Gancao
DSD36 1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone 328.29
2.74 2 7 2 62.9 0.53 Gancao
DSD37 1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone 298.26
2.48 2 6 1 48.14 0.43 Gancao
DSD38 18α-hydroxyglycyrrhetic acid 486.76 4.54 3 5 1 41.16 0.71 Gancao
DSD39 1-Methoxyphaseollidin 354.43 3.66 2 5 3 69.98 0.64 Gancao
DSD40
2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone
354.38 2.99 4 6 3 44.15 0.41 Gancao
DSD41
2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methox
yphenol 338.43 4.35 1 4 2 36.21 0.52 Gancao
DSD42
3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy
-coumarin 368.41 3.92 3 6 4 59.62 0.43 Gancao
DSD43
3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone
354.38 3.02 4 6 3 66.37 0.41 Gancao
DSD44 3’-Hydroxy-4’-O-Methylglabridin 354.43 3.76 2 5 2 43.71 0.57
Gancao
DSD45 3’-Methoxyglabridin 354.43 3.76 2 5 2 46.16 0.57 Gancao
DSD46 5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone
352.41 3.29 2 5 4 30.49 0.41 Gancao
DSD47 6-prenylated eriodictyol 356.4 2.99 4 6 3 39.22 0.41 Gancao
DSD48 7,2’,4’-trihydroxy-5-methoxy-3-arylcoumarin 300.28 2.51 3 6 2
83.71 0.27 Gancao
DSD49 7-Acetoxy-2-methylisoflavone 294.32 3.41 0 4 3 38.92 0.26 Gancao
DSD50 7-Methoxy-2-methyl isoflavone 266.31 3.48 0 3 2 42.56 0.2 Gancao
DSD51 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol 308.35 4.27
2 4 1 58.44 0.38 Gancao
DSD52 8-prenylated eriodictyol 356.4 2.99 4 6 3 53.79 0.4 Gancao
DSD53 Calycosin 284.28 2.82 2 5 2 47.75 0.24 Gancao
DSD54 dehydroglyasperins C 340.4 3.11 4 5 3 53.82 0.37 Gancao
DSD55 DFV 256.27 2.79 2 4 1 32.76 0.18 Gancao
DSD56 echinatin 270.3 3.41 2 4 4 66.58 0.17 Gancao
DSD57 Eurycarpin A 338.38 3.29 3 5 3 43.28 0.37 Gancao
DSD58 formononetin 268.28 3.01 1 4 2 69.67 0.21 Gancao
DSD59 Gancaonin A 352.41 3.34 2 5 4 51.08 0.4 Gancao
DSD60 Gancaonin B 368.41 3.14 3 6 4 48.79 0.45 Gancao
DSD61 Gancaonin G 352.41 3.25 2 5 4 60.44 0.39 Gancao
DSD62 Gancaonin H 420.49 3.99 3 6 3 50.1 0.78 Gancao
DSD63 Glabranin 324.4 3.59 2 4 3 52.9 0.31 Gancao
DSD64 Glabrene 322.38 3.68 2 4 1 46.27 0.44 Gancao
DSD65 Glabridin 324.4 3.81 2 4 1 53.25 0.47 Gancao
DSD66 Glabrone 336.36 3.78 2 5 1 52.51 0.5 Gancao
DSD67 Glepidotin A 338.38 2.88 3 5 3 44.72 0.35 Gancao
DSD68 Glepidotin B 340.4 2.88 3 5 3 64.46 0.34 Gancao
DSD69 glyasperin B 370.43 3.14 3 6 4 65.22 0.44 Gancao
DSD70 Glyasperin C 356.45 3.53 3 5 4 45.56 0.4 Gancao
DSD71 glyasperin F 354.38 3.52 3 6 1 75.84 0.54 Gancao
DSD72 Glyasperins M 368.41 3.57 2 6 2 72.67 0.59 Gancao
DSD73 Glycyrin 382.44 3.78 2 6 5 52.61 0.47 Gancao
DSD74 Glycyrol 366.39 4.06 2 6 3 90.78 0.67 Gancao
DSD75 Glycyrrhiza flavonol A 370.38 2.18 4 7 1 41.28 0.6 Gancao
DSD76 Glypallichalcone 284.33 3.8 1 4 5 61.6 0.19 Gancao
DSD77 Glyzaglabrin 298.26 2.32 2 6 1 61.07 0.35 Gancao
DSD78 HMO 268.28 2.92 1 4 2 38.37 0.21 Gancao
DSD79 Inermine 284.28 2.19 1 5 0 75.18 0.54 Gancao
DSD80 Inflacoumarin A 322.38 4.36 2 4 3 39.71 0.33 Gancao
DSD81 Isoglycyrol 366.39 4.15 1 6 1 44.7 0.84 Gancao
DSD82 Isolicoflavonol 354.38 2.92 4 6 3 45.17 0.42 Gancao
DSD83 isoliquiritigenin 256.27 3.04 3 4 3 85.32 0.15 Gancao
DSD84 isorhamnetin 316.28 1.31 4 7 2 49.6 0.31 Gancao
DSD85 Isotrifoliol 298.26 2.54 2 6 1 31.94 0.42 Gancao
DSD86 Jaranol 314.31 2.8 2 6 3 50.83 0.29 Gancao
DSD87 kanzonols W 336.36 3.97 2 5 1 50.48 0.52 Gancao
DSD88 Licoagrocarpin 338.43 3.94 1 4 3 58.81 0.58 Gancao
DSD89 Licoagroisoflavone 336.36 2.95 2 5 2 57.28 0.49 Gancao
DSD90 licochalcone a 338.43 4.74 2 4 6 40.79 0.29 Gancao
DSD91 Licochalcone B 286.3 3.17 3 5 4 76.76 0.19 Gancao
DSD92 licochalcone G 354.43 4.21 3 5 6 49.25 0.32 Gancao
DSD93 Licocoumarone 340.4 4.1 3 5 4 33.21 0.36 Gancao
DSD94 licoisoflavanone 354.38 3.54 3 6 1 52.47 0.54 Gancao
DSD95 Licoisoflavone 354.38 2.99 4 6 3 41.61 0.42 Gancao
DSD96 Licoisoflavone B 352.36 3.54 3 6 1 38.93 0.55 Gancao
DSD97 licopyranocoumarin 384.41 2.47 3 7 3 80.36 0.65 Gancao
DSD98 Licoricone 382.44 3.08 2 6 5 63.58 0.47 Gancao
DSD99 liquiritin 418.43 0.43 5 9 4 65.69 0.74 Gancao
DSD100 Lupiwighteone 338.38 3.23 3 5 3 51.64 0.37 Gancao
DSD101 Medicarpin 270.3 3.07 1 4 1 49.22 0.34 Gancao
DSD102 naringenin 272.27 2.47 3 5 1 59.29 0.21 Gancao
DSD103 Odoratin 314.31 2.81 2 6 3 49.95 0.3 Gancao
DSD104 Phaseol 336.36 4.59 2 5 2 78.77 0.58 Gancao
DSD105 Phaseolinisoflavan 324.4 3.77 2 4 1 32.01 0.45 Gancao
DSD106 Pinocembrin 256.27 2.85 2 4 1 64.72 0.18 Gancao
DSD107 Quercetin der. 330.31 2.55 3 7 3 46.45 0.33 Gancao
DSD108 Semilicoisoflavone B 352.36 3.55 3 6 1 48.78 0.55 Gancao
DSD109 shinpterocarpin 322.38 4.13 1 4 0 80.3 0.73 Gancao
DSD110 Sigmoidin-B 356.4 3.02 4 6 3 34.88 0.41 Gancao
DSD111 Vestitol 272.32 2.89 2 4 2 74.66 0.21 Gancao
DSD112 ent-Epicatechin 290.29 2.83 5 6 1 48.96 0.24 Guizhi
DSD113 beta-sitosterol 414.79 3.2 1 1 6 36.91 0.75 Guizhi
DSD114 sitosterol 414.79 2.71 1 1 6 36.91 0.75 Guizhi
DSD115 (-)-taxifolin 304.27 1.66 5 7 1 60.51 0.27 Guizhi
DSD116 DMEP 282.32 1.93 0 6 10 55.66 0.15 Guizhi
DSD117 paryriogenin A 466.72 4.62 1 4 1 41.41 0.76 Tongcao
DSD118 (3S)-7-hydroxy-3-(2,3,4-trimethoxyphenyl)chroman-4-one 330.36
1.59 1 6 4 48.23 0.33 Xixin
DSD119 [(1S)-3-[(E)-but-2-enyl]-2-methyl-4-oxo-1-cyclopent-2-enyl]
(1R,3R)-3-[(E)-3-methoxy-2-methyl-3-oxoprop-1-enyl]-2,2-dimethylcyclopr
opane-1-carboxylate 360.49 4.18 0 5 8 62.52 0.31 Xixin
DSD120 4,9-dimethoxy-1-vinyl-$b-carboline 254.31 2.96 0 3 3 65.3 0.19
Xixin
DSD121 Caribine 326.43 0.53 2 5 0 37.06 0.83 Xixin
DSD122 Cryptopin 369.45 2.38 0 6 2 78.74 0.72 Xixin
DSD123 sesamin 354.38 2.25 0 6 2 56.55 0.83 Xixin
DSD124 ZINC05223929 354.38 2.25 0 6 2 31.57 0.83 Xixin
GFD1 (-)-catechin 290.29 1.02 5 6 1 49.68 0.24 Dazao
GFD2 (+)-catechin 290.29 1.02 5 6 1 54.83 0.24 Dazao
GFD3 (S)-Coclaurine 285.37 2.2 3 4 3 42.35 0.24 Dazao
GFD4 21302-79-4 486.76 4.53 3 5 3 73.52 0.77 Dazao
GFD5 berberine 336.39 3.75 0 4 2 36.86 0.78 Dazao
GFD6 coumestrol 268.23 2.43 2 5 0 32.49 0.34 Dazao
GFD7 Fumarine 353.4 1.95 0 6 0 59.26 0.83 Dazao
GFD8 Jujubasaponin V_qt 472.78 4.61 2 4 2 36.99 0.63 Dazao
GFD9 jujuboside A_qt 472.78 3.8 2 4 1 36.67 0.62 Dazao
GFD10 Jujuboside C_qt 472.78 3.8 2 4 1 40.26 0.62 Dazao
GFD11 malkangunin 432.56 2.72 2 7 6 57.71 0.63 Dazao
GFD12 Mauritine D 342.46 1.11 2 6 2 89.13 0.45 Dazao
GFD13 Moupinamide 313.38 2.46 3 5 6 86.71 0.26 Dazao
GFD14 Nuciferin 295.41 3.38 0 3 2 34.43 0.4 Dazao
GFD15 quercetin 302.25 1.07 5 7 1 46.43 0.28 Dazao
GFD16 Ruvoside_qt 390.57 1.42 3 5 2 36.12 0.76 Dazao
GFD17 Spiradine A 311.46 1.29 1 3 0 113.52 0.61 Dazao
GFD18 stepharine 297.38 1.76 1 4 2 31.55 0.33 Dazao
GFD19 Stepholidine 327.41 2.26 2 5 2 33.11 0.54 Dazao
GFD20 Ziziphin_qt 472.78 3.8 2 4 1 66.95 0.62 Dazao
GFD21 zizyphus saponin I_qt 472.78 3.8 2 4 1 32.69 0.62 Dazao
GFD22 (-)-Medicocarpin 432.46 1.26 4 9 4 40.99 0.95 Gancao
GFD23 (2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one 256.27 2.79 2 4 1
71.12 0.18 Gancao
GFD24
(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dih
ydrofuro[3,2-g]chromen-7-one 384.41 2.61 3 7 3 60.25 0.63 Gancao
GFD25
(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one
324.4 3.62 2 4 3 36.57 0.32 Gancao
GFD26
(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one
322.38 4.46 2 4 3 39.62 0.35 Gancao
GFD27
(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphen
yl)prop-2-en-1-one 340.4 3.47 4 5 5 46.27 0.31 Gancao
GFD28 1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone 328.29
2.74 2 7 2 62.9 0.53 Gancao
GFD29 1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone 298.26
2.48 2 6 1 48.14 0.43 Gancao
GFD30 18α-hydroxyglycyrrhetic acid 486.76 4.54 3 5 1 41.16 0.71 Gancao
GFD31 1-Methoxyphaseollidin 354.43 3.66 2 5 3 69.98 0.64 Gancao
GFD32
2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone
354.38 2.99 4 6 3 44.15 0.41 Gancao
GFD33
2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methox
yphenol 338.43 4.35 1 4 2 36.21 0.52 Gancao
GFD34
3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy
-coumarin 368.41 3.92 3 6 4 59.62 0.43 Gancao
GFD35
3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone
354.38 3.02 4 6 3 66.37 0.41 Gancao
GFD36 3’-Hydroxy-4’-O-Methylglabridin 354.43 3.76 2 5 2 43.71 0.57
Gancao
GFD37 3’-Methoxyglabridin 354.43 3.76 2 5 2 46.16 0.57 Gancao
GFD38 5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone
352.41 3.29 2 5 4 30.49 0.41 Gancao
GFD39 6-prenylated eriodictyol 356.4 2.99 4 6 3 39.22 0.41 Gancao
GFD40 7,2’,4’-trihydroxy-5-methoxy-3-arylcoumarin 300.28 2.51 3 6 2
83.71 0.27 Gancao
GFD41 7-Acetoxy-2-methylisoflavone 294.32 3.41 0 4 3 38.92 0.26 Gancao
GFD42 7-Methoxy-2-methyl isoflavone 266.31 3.48 0 3 2 42.56 0.2 Gancao
GFD43 8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol 308.35 4.27
2 4 1 58.44 0.38 Gancao
GFD44 8-prenylated eriodictyol 356.4 2.99 4 6 3 53.79 0.4 Gancao
GFD45 Calycosin 284.28 2.82 2 5 2 47.75 0.24 Gancao
GFD46 dehydroglyasperins C 340.4 3.11 4 5 3 53.82 0.37 Gancao
GFD47 DFV 256.27 2.79 2 4 1 32.76 0.18 Gancao
GFD48 echinatin 270.3 3.41 2 4 4 66.58 0.17 Gancao
GFD49 Eurycarpin A 338.38 3.29 3 5 3 43.28 0.37 Gancao
GFD50 formononetin 268.28 3.01 1 4 2 69.67 0.21 Gancao
GFD51 Gancaonin A 352.41 3.34 2 5 4 51.08 0.4 Gancao
GFD52 Gancaonin B 368.41 3.14 3 6 4 48.79 0.45 Gancao
GFD53 Gancaonin G 352.41 3.25 2 5 4 60.44 0.39 Gancao
GFD54 Gancaonin H 420.49 3.99 3 6 3 50.1 0.78 Gancao
GFD55 Glabranin 324.4 3.59 2 4 3 52.9 0.31 Gancao
GFD56 Glabrene 322.38 3.68 2 4 1 46.27 0.44 Gancao
GFD57 Glabridin 324.4 3.81 2 4 1 53.25 0.47 Gancao
GFD58 Glabrone 336.36 3.78 2 5 1 52.51 0.5 Gancao
GFD59 Glepidotin A 338.38 2.88 3 5 3 44.72 0.35 Gancao
GFD60 Glepidotin B 340.4 2.88 3 5 3 64.46 0.34 Gancao
GFD61 glyasperin B 370.43 3.14 3 6 4 65.22 0.44 Gancao
GFD62 Glyasperin C 356.45 3.53 3 5 4 45.56 0.4 Gancao
GFD63 glyasperin F 354.38 3.52 3 6 1 75.84 0.54 Gancao
GFD64 Glyasperins M 368.41 3.57 2 6 2 72.67 0.59 Gancao
GFD65 Glycyrin 382.44 3.78 2 6 5 52.61 0.47 Gancao
GFD66 Glycyrol 366.39 4.06 2 6 3 90.78 0.67 Gancao
GFD67 Glycyrrhiza flavonol A 370.38 2.18 4 7 1 41.28 0.6 Gancao
GFD68 Glypallichalcone 284.33 3.8 1 4 5 61.6 0.19 Gancao
GFD69 Glyzaglabrin 298.26 2.32 2 6 1 61.07 0.35 Gancao
GFD70 HMO 268.28 2.92 1 4 2 38.37 0.21 Gancao
GFD71 Inermine 284.28 2.19 1 5 0 75.18 0.54 Gancao
GFD72 Inflacoumarin A 322.38 4.36 2 4 3 39.71 0.33 Gancao
GFD73 Isoglycyrol 366.39 4.15 1 6 1 44.7 0.84 Gancao
GFD74 Isolicoflavonol 354.38 2.92 4 6 3 45.17 0.42 Gancao
GFD75 isoliquiritigenin 256.27 3.04 3 4 3 85.32 0.15 Gancao
GFD76 isorhamnetin 316.28 1.31 4 7 2 49.6 0.31 Gancao
GFD77 Isotrifoliol 298.26 2.54 2 6 1 31.94 0.42 Gancao
GFD78 Jaranol 314.31 2.8 2 6 3 50.83 0.29 Gancao
GFD79 kaempferol 286.25 1.23 4 6 1 41.88 0.24 Gancao
GFD80 kanzonols W 336.36 3.97 2 5 1 50.48 0.52 Gancao
GFD81 Licoagrocarpin 338.43 3.94 1 4 3 58.81 0.58 Gancao
GFD82 Licoagroisoflavone 336.36 2.95 2 5 2 57.28 0.49 Gancao
GFD83 licochalcone a 338.43 4.74 2 4 6 40.79 0.29 Gancao
GFD84 Licochalcone B 286.3 3.17 3 5 4 76.76 0.19 Gancao
GFD85 licochalcone G 354.43 4.21 3 5 6 49.25 0.32 Gancao
GFD86 Licocoumarone 340.4 4.1 3 5 4 33.21 0.36 Gancao
GFD87 licoisoflavanone 354.38 3.54 3 6 1 52.47 0.54 Gancao
GFD88 Licoisoflavone 354.38 2.99 4 6 3 41.61 0.42 Gancao
GFD89 Licoisoflavone B 352.36 3.54 3 6 1 38.93 0.55 Gancao
GFD90 licopyranocoumarin 384.41 2.47 3 7 3 80.36 0.65 Gancao
GFD91 Licoricone 382.44 3.08 2 6 5 63.58 0.47 Gancao
GFD92 liquiritin 418.43 0.43 5 9 4 65.69 0.74 Gancao
GFD93 Lupiwighteone 338.38 3.23 3 5 3 51.64 0.37 Gancao
GFD94 Medicarpin 270.3 3.07 1 4 1 49.22 0.34 Gancao
GFD95 naringenin 272.27 2.47 3 5 1 59.29 0.21 Gancao
GFD96 Odoratin 314.31 2.81 2 6 3 49.95 0.3 Gancao
GFD97 Phaseol 336.36 4.59 2 5 2 78.77 0.58 Gancao
GFD98 Phaseolinisoflavan 324.4 3.77 2 4 1 32.01 0.45 Gancao
GFD99 Pinocembrin 256.27 2.85 2 4 1 64.72 0.18 Gancao
GFD100 Quercetin der. 330.31 2.55 3 7 3 46.45 0.33 Gancao
GFD101 Semilicoisoflavone B 352.36 3.55 3 6 1 48.78 0.55 Gancao
GFD102 shinpterocarpin 322.38 4.13 1 4 0 80.3 0.73 Gancao
GFD103 Sigmoidin-B 356.4 3.02 4 6 3 34.88 0.41 Gancao
GFD104 Vestitol 272.32 2.89 2 4 2 74.66 0.21 Gancao
GFD105 ent-Epicatechin 290.29 2.83 5 6 1 48.96 0.24 Guizhi
GFD106 beta-sitosterol 414.79 3.2 1 1 6 36.91 0.75 Guizhi
GFD107 sitosterol 414.79 2.71 1 1 6 36.91 0.75 Guizhi
GFD108 (-)-taxifolin 304.27 1.66 5 7 1 60.51 0.27 Guizhi
GFD109 DMEP 282.32 1.93 0 6 10 55.66 0.15 Guizhi
GFD110 6-gingerol 294.43 3.45 2 4 10 35.64 0.16 Shengjiang
GFD111 6-shogaol 276.41 4.95 1 3 9 31 0.14 Shengjiang
GFD112 (R)-Norcoclaurine 271.34 1.73 4 4 2 82.54 0.21 Fuzi
GFD113 6-Demethyldesoline 453.64 -0.53 4 8 5 51.87 0.66 Fuzi
GFD114 benzoylnapelline 463.67 2.9 2 5 4 34.06 0.53 Fuzi
GFD115 Deltoin 328.39 2.95 0 5 4 46.69 0.37 Fuzi
GFD116 Deoxyandrographolide 334.5 2.71 2 4 4 56.3 0.31 Fuzi
GFD117 ignavine 449.59 0.72 3 6 3 84.08 0.25 Fuzi
GFD118 isotalatizidine 407.61 0.41 3 6 4 50.82 0.73 Fuzi
GFD119 karakoline 377.58 0.8 3 5 2 51.73 0.73 Fuzi
GFD120 Karanjin 292.3 3.42 0 4 2 69.56 0.34 Fuzi
HGWD1 (+)-catechin 290.29 1.02 5 6 1 54.83 0.24 Baishao
HGWD2
(3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,
9-hexahydro-1H-cyclopenta[a]phenanthrene-15,16-dione 358.52 3.52 2 4 0
43.56 0.53 Baishao
HGWD3
11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-ol
ide 470.71 3.82 2 5 1 64.77 0.38 Baishao
HGWD4 albiflorin_qt 318.35 0.53 2 6 4 66.64 0.33 Baishao
HGWD5 kaempferol 286.25 1.23 4 6 1 41.88 0.24 Baishao
HGWD6 Lactiflorin 462.49 0.31 3 10 5 49.12 0.8 Baishao
HGWD7 paeoniflorgenone 318.35 0.86 1 6 4 87.59 0.37 Baishao
HGWD8 paeoniflorin_qt 318.35 0.69 2 6 4 68.18 0.4 Baishao
HGWD9 (-)-catechin 290.29 1.02 5 6 1 49.68 0.24 Dazao
HGWD10 (S)-Coclaurine 285.37 2.2 3 4 3 42.35 0.24 Dazao
HGWD11 21302-79-4 486.76 4.53 3 5 3 73.52 0.77 Dazao
HGWD12 berberine 336.39 3.75 0 4 2 36.86 0.78 Dazao
HGWD13 coumestrol 268.23 2.43 2 5 0 32.49 0.34 Dazao
HGWD14 Fumarine 353.4 1.95 0 6 0 59.26 0.83 Dazao
HGWD15 Jujubasaponin V_qt 472.78 4.61 2 4 2 36.99 0.63 Dazao
HGWD16 jujuboside A_qt 472.78 3.8 2 4 1 36.67 0.62 Dazao
HGWD17 Jujuboside C_qt 472.78 3.8 2 4 1 40.26 0.62 Dazao
HGWD18 malkangunin 432.56 2.72 2 7 6 57.71 0.63 Dazao
HGWD19 Mauritine D 342.46 1.11 2 6 2 89.13 0.45 Dazao
HGWD20 Moupinamide 313.38 2.46 3 5 6 86.71 0.26 Dazao
HGWD21 Nuciferin 295.41 3.38 0 3 2 34.43 0.4 Dazao
HGWD22 quercetin 302.25 1.07 5 7 1 46.43 0.28 Dazao
HGWD23 Ruvoside_qt 390.57 1.42 3 5 2 36.12 0.76 Dazao
HGWD24 Spiradine A 311.46 1.29 1 3 0 113.52 0.61 Dazao
HGWD25 stepharine 297.38 1.76 1 4 2 31.55 0.33 Dazao
HGWD26 Stepholidine 327.41 2.26 2 5 2 33.11 0.54 Dazao
HGWD27 Ziziphin_qt 472.78 3.8 2 4 1 66.95 0.62 Dazao
HGWD28 zizyphus saponin I_qt 472.78 3.8 2 4 1 32.69 0.62 Dazao
HGWD29 ent-Epicatechin 290.29 2.83 5 6 1 48.96 0.24 Guizhi
HGWD30 beta-sitosterol 414.79 3.2 1 1 6 36.91 0.75 Guizhi
HGWD31 sitosterol 414.79 2.71 1 1 6 36.91 0.75 Guizhi
HGWD32 (-)-taxifolin 304.27 1.66 5 7 1 60.51 0.27 Guizhi
HGWD33 DMEP 282.32 1.93 0 6 10 55.66 0.15 Guizhi
HGWD34 6-gingerol 294.43 3.45 2 4 10 35.64 0.16 Shengjiang
HGWD35 6-shogaol 276.41 4.95 1 3 9 31 0.14 Shengjiang
HGWD36
(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3
-ol 300.33 2.88 1 5 2 64.26 0.42 Huangqi
HGWD37 1,7-Dihydroxy-3,9-dimethoxy pterocarpene 314.31 2.85 2 6 2 39.05
0.48 Huangqi
HGWD38 3,9-di-O-methylnissolin 314.36 3.28 0 5 3 53.74 0.48 Huangqi
HGWD39 7-O-methylisomucronulatol 316.38 2.85 1 5 4 74.69 0.3 Huangqi
HGWD40 Bifendate 418.38 1.75 0 10 7 31.1 0.67 Huangqi
HGWD41 Calycosin 284.28 2.82 2 5 2 47.75 0.24 Huangqi
HGWD42 formononetin 268.28 3.01 1 4 2 69.67 0.21 Huangqi
HGWD43 isoflavanone 316.33 2.76 2 6 3 109.99 0.3 Huangqi
HGWD44 isorhamnetin 316.28 1.31 4 7 2 49.6 0.31 Huangqi
HGWD45 Jaranol 314.31 2.8 2 6 3 50.83 0.29 Huangqi
HGWD46 9,10-dimethoxypterocarpan-3-O-β-D-glucoside 462.49 1.18 4 10 5
36.74 0.92 Huangqi
HGWD47 (Z)-1-(2,4-dihydroxyphenyl)-3-(4-hydroxyphenyl)prop-2-en-1-one
256.27 3.04 3 4 3 87.51 0.15 Huangqi
HGWD48 (3R)-3-(2-hydroxy-3,4-dimethoxyphenyl)chroman-7-ol 302.35 2.76 2
5 3 67.67 0.26 Huangqi
[95]Open in a new tab
Figure 2.
Figure 2
[96]Open in a new tab
Distribution map of active components in DSD, GFD, and HGWD.
C-T Network Construction
To facilitate analysis of the complex relationships between active
components and their targets of three formulas, component-target
networks were constructed by using Cytoscape ([97] Figures S1–S3 ). The
results revealed that the DSD network consisted of 124 active
components, 846 target proteins, and 3758 interactions; the GFD network
contained120 active components, 821 target proteins, and 3759
interactions; the HGWD network consisted of 48 active components, 612
target proteins, and 1373 interactions.
We further analyzed the topology parameters of these C-T networks using
NetworkAnalyzer and found that the average degree of components and
targets in DSD were 30.31 and 5.20; the average degree of components
and targets in GFD were 31.33 and 5.36; the average degree of
components and targets in HGWD were 28.6 and 2.43. These results
indicate that there exist interactions between one component and
multiple targets in three formulas, and also exist phenomenon that
different components act on the same target, which is in line with the
characteristics of multi-component and multi-target mediated
synergistic effect of TCM, and also reflects the complexity of the
mechanism of TCM.
KNMSs Predication and Validation
KNMSs Predication
These C-T networks are complex and huge. How to quickly extract
important information from these complex networks is the key step to
decode underlying molecular mechanism. Here, we introduced the infomap
algorithm in the network pharmacology model for the first time based on
the random walk theory combined with Huffman-encoding. The algorithm
performs to optimize the discovery of KNMSs in C-T network
heuristically by using a reasonable global metric. 7, 10, and 10 KNMSs
were predicted in DSD, GFD, and HGWD, respectively (p value < 0.05)
([98] Figures 3 –[99] 5 ). The detail information of network KNMSs were
shown in [100]Table S2 .
Figure 3.
[101]Figure 3
[102]Open in a new tab
The predicated KNMSs of C-T network of DSD. The red nodes represent the
specific components of DSD, and the yellow nodes represent related
targets.
Figure 5.
[103]Figure 5
[104]Open in a new tab
The predicated KNMSs of C-T network of HGWD. The blue nodes represent
the specific components of HGWD, and the yellow nodes represent related
targets.
Figure 4.
[105]Figure 4
[106]Open in a new tab
The predicated KNMSs of C-T network of GFD. The green nodes represent
the specific components of GFD, and the yellow nodes represent related
targets.
KNMSs Validation
In order to validate whether predicted KNMSs in each formula can
represent corresponding full C-T networks in treating RA. Three
strategies were used to verify the accuracy and reliability and of
KNMSs. The first strategy was used to see whether the number of RA
pathogenic genes in KNMSs are close to the number of RA pathogenic
genes in CT network. The coverage was defined as the percentage of the
number of pathogenic genes in KNMSs to the number of pathogenic genes
in C-T network. High coverage indicated that KNMSs could retain most
formula-targeted RA pathogenic genes that included in the corresponding
C-T network. The second strategy was designed to see whether the gene
enrichment pathways in KNMSs covers the gene enrichment pathways in C-T
network as much as possible. High coverage indicated that KNMSs could
cover most genes enriched pathways of the corresponding C-T network.
The third strategy was employed to calculate the percentage of
cumulative contribution of important nodes in KNMSs to that of nodes in
C-T network. High percentage means KNMSs can retain the important nodes
in the corresponding C-T network. The detail results are as follows:
Validated the Number and Coverage of Pathogenic Genes in KNMSs
To assess whether the number of RA pathogenic genes in KNMSs are close
to the number of RA pathogenic genes in corresponding C-T network. The
known pathogenic genes of RA reported by published literature and
databases were collected, and the pathogenic genes confirmed by more
than 5 literatures were selected for further analysis ([107] Table S3
). We found that the C-T network of DSD, GFD, and HGWD contains 50, 52,
and 39 pathogenic genes, respectively. While the KNMSs of DSD, GFD, and
HGWD contains 39, 40, and 30 pathogenic genes. The number of pathogenic
genes in KNMSs compared to that in C-T network of DSD, GFD, and HGWD
reached 78%, 76.9%, and 76.9%, which confirmed that the predicted KNMSs
with high coverage of pathogenic genes ([108] Figures 6A–C ). These
results demonstrate that KNMSs have a high coincidence degree with C-T
network in the number and coverage of pathogenic genes, it also
indicated that our proposed KNMS detection model can maximize the
coincidence degree of pathogenic genes in the C-T network of formulas.
Figure 6.
[109]Figure 6
[110]Open in a new tab
The number of overlap pathogenic genes between C-T network and KNMSs in
DSD, GFD and HGWD (A–C). (A–C) use venn diagram to visualize the
overlap number between C-T network and KNMSs in DSD, GFD, and HGWD,
respectively.
Validated the Genes Enriched Pathways in KNMSs
An additional metric for evaluating the importance of the inferred
motifs is determined by their functional coherence, which can be
accessed via their related genes enrichment pathways from KEGG
([111]Kanehisa and Goto, 2000). Here, we used this method to detect
whether KNMSs found in each formula can represent their full C-T
networks at functional level. Our analysis shown that genes enriched
pathways of KNMSs in DSD accounts for 85.8% of genes enriched pathways
of the full C-T network in DSD; genes enriched pathways of KNMSs in GFD
accounts for 86.6% genes enriched pathways of the full C-T network in
GFD; genes enriched pathways of KNMSs in HGWD accounts for 81.9% genes
enriched pathways of the full C-T network in HGWD ([112] Figures 7A, B
). It was encouraged that the gene enriched pathways involved in KNMSs
of 3 formulas are highly compatible with gene enriched pathways of
their C-T networks. This result confirmed that KNMSs have a high
coincidence degree with C-T network at the gene functional level and
also suggested that our proposed KNMS detection model can maximize the
retention of functional pathways in the formulas of TCM.
Figure 7.
[113]Figure 7
[114]Open in a new tab
The functional similarity analysis between C-T network and KNMSs in
DSD, GFD, and HGWD, respectively. (A, B) represent the functional
similarity visualized by venn diagram and bubble diagram, respectively.
Validated the Cumulative Contribution of Important Nodes in KNMSs
The degree of nodes is a key topological parameter that characterizes
the influence of nodes in a network ([115]Lv et al., 2014). Here, a
mathematical model was established to evaluate the importance of KNMSs
in each formula based on the degree of nodes. According to the
calculation results, each KNMS was assigned a CC value. The detailed
information was shown in [116]Figure 8 and [117]Table S4 . The sum of
CC of 7, 10, and 10 KNMSs in each formula reached 80.44%, 79.88%, and
70.76% of that in C-T networks of DSD, GFD, and HGWD, respectively. The
results confirmed that KNMSs have a high coincidence degree with C-T
network on the topological structure and also indicated that our
proposed KNMS detection model could maximize the coverage of important
network topological structures compared with C-T network in each
formula.
Figure 8.
[118]Figure 8
[119]Open in a new tab
The contribution coefficient of network topological between C-T network
and KNMSs in DSD, GFD and HGWD (A–C). (A–C) use bar diagram to
visualize the cumulative contribution rate between C-T network and
KNMSs in DSD, GFD and HGWD, respectively.
Potential Mechanisms Analysis of Different Formulas Treats the Same Disease
In order to reveal the potential mechanism of KNMSs in different
formula for treating rheumatoid arthritis, pathway enrichment analysis
of KNMS-related genes in each formula were performed. In the DSD, genes
in total 7 KNMSs were enriched in 165 pathways, genes in two KNMSs,
DSD1, and DSD2 were enriched in 158 pathways, accounting for 95.8% of
that in 7 KNMSs. The arthritis-related signaling pathways corresponding
to DSD1 and DSD2 were partially complementary, for example, genes in
DSD1 were mainly enriched in JAK-STAT signaling pathway and AMPK
signaling pathway, genes in DSD2 mainly enriched in NF-kappa B
signaling pathway, p53 signaling pathway and Wnt signaling pathway. In
GFD, we total got 10 KNMSs. Genes in these 10 KNMSs were enriched in
151 pathways. Four of 10 KNMSs, GFD1, GFD3, GFD4, and GFD5 related
genes are enriched in 144 pathways, accounting for 95.4% of enrichment
pathways in 10 KNMSs of GFD. Moreover, some of their corresponding
arthritis-related signaling pathways are complementary. For example,
GFD1 mainly includes TNF signaling pathway, IL-17 signaling pathway and
AMPK signaling pathway, and GFD3 mainly includes Inflammatory mediator
regulation of TRP channels and GnRH signaling pathway. In HGWD, genes
in 10 predicted KNMSs were enriched in 110 pathways, genes in HGWD4 and
HGWD5, HGWD6, and HGWD8 covered 102 pathways, accounting for 92.7% of
all KNMSs gene enrichment pathways. Consistent with DSD and GFD
results, some of their corresponding arthritis-related signal pathways
were also complementary. For example, HGWD4 mainly includes TNF
signaling pathway, Hedgehog signaling pathway and IL-17 signaling
pathway, HGWD5 mainly includes VEGF signaling pathway, NF-kappa B
signaling pathway and mTOR signaling pathway, HGWD6 mainly includes
cAMP signaling pathway and cGMP-PKG signaling pathway ([120] Figure 9 ,
[121]Table S5 ). These results show that KNMSs in different formulas
have distinct roles and synergistic effects in the treatment of
rheumatoid arthritis.
Figure 9.
[122]Figure 9
[123]Open in a new tab
The enrichment pathway map of KNMSs in the C-T network of DSD, GFD, and
HGWD.
In order to further explore the potential mechanism of the three
formulas in treating RA, besides the difference analysis of each KNMS
in different formulas, KEGG enrichment analysis of all KNMSs in each
formula were also implemented and found that 3 formulas play the
therapeutic effect on RA through the following five common pathways:
Rap1 signaling pathway, cAMP signaling pathway, MAPK signaling pathway,
EGFR Tyrosine Kinase Inhibitor Resistance, Calcium signaling pathway
and Neuroactive ligand-receptor interaction. Except the common
pathways, we found that the three formulas can play the role of
treating RA through their specific pathways ([124] Figure 10 ). For
example, DSD can play the role of treating RA by regulating VEGF
signaling pathway. GFD can play a role in treating RA by regulating
HIF-1 signaling pathway. HGWD can play a role in treating RA by
regulating PI3K-Akt signaling pathway. These results indicate that 3
formulas can play the role of treating RA through different and common
pathways, which may act as the essence of different formulas treat the
same disease.
Figure 10.
[125]Figure 10
[126]Open in a new tab
Gene enrichment analysis of all KNMSs from DSD, GFD, and HGWD,
respectively.
Through PubMed literature search, we found that among the common
pathways, MAPK signaling pathway and cAMP signaling pathway have the
most correlation records with rheumatoid arthritis. We selected MAPK
signaling pathway and cAMP signaling pathway which were reported
closely related to inflammation to illustrate the mechanism of
different formulas treat the same disease in detail. Firstly, a
comprehensive inflammatory pathway was constructed by integrating the
two pathways. And then, the genes in KNMSs of three formulas were
mapped to the comprehensive inflammatory pathway ([127] Figure 11 ).
Results show that genes in the KNMSs of DSD mainly distributed in the
downstream of the comprehensive inflammatory pathway, such as MAPK14,
MAPK8, and JUND; Genes in the KNMSs of GFD mainly distributed in the
downstream of the comprehensive inflammatory pathway, such as AKT3,
RAF1, and TAOK3; while genes in the KNMSs of HGWD distributed both in
the upstream and downstream of the comprehensive inflammatory pathway,
such as CSF1R, ADCYAP1R1, CHRM1, NFKB1, MAPT, and JUN. The results
suggest that different formulas play therapeutic roles through
targeting different genes in the comprehensive inflammatory pathway.
Figure 11.
Figure 11
[128]Open in a new tab
Distribution of specific targets enriched pathways of KNMSs in three
formulas on the comprehensive inflammatory pathway. (A–C) represent the
distribution of targets enriched pathways of KNMSs in DSD, GFD and HGWD
on the comprehensive inflammatory pathway, respectively.
Experimental Validation In Vitro
Effects of isoliquiritigenin, isorhamnetin and quercetin with different
concentrations on cell viabilities of RAW264.7 cells were detected by
MTT assay. Compared with control group, 1, 5, 10, and 20 μM
isoliquiritigenin, isorhamnetin, and quercetin had no effects on
RAW264.7 cells viabilities ([129] Figures 12A–C ). Therefore, four
concentrations were used (1, 5, 10, and 20 μM) for subsequent
experiments.
Figure 12.
[130]Figure 12
[131]Open in a new tab
Effects of isoliquiritigenin (A, D), isorhamnetin (B, E), and quercetin
(C, F) on cell viabilities and NO of LPS induced RAW264.7 cells. ^*p <
0.05, ^**p < 0.01, ^***p < 0.001 compared with control group. ^#p <
0.05, ^###p < 0.001 compared with the LPS group.
NO is a regulator of information transmission between cells and has the
function of mediating cellular immune and inflammatory reactions. In
order to further evaluate the results obtained by the network
pharmacology model, the key components in KNMSs of each formula were
selected for experimental validation. Isoliquiritigenin from motif 1
(DSD1) of DSD, isorhamnetin from motif 1 (GFD1) of GFD, and quercetin
from motif 5 (HGWD5) of HGWD were chose to detect potential
anti-inflammatory effects using LPS induced RAW264.7 cells. Compared
with control group, the NO level was significantly increased by 275.34%
in the culture medium of LPS treated cells, however, isoliquiritigenin
(10 and 20 μM) markedly decreased the extracellular NO levels by
107.94% and 151.04%, isorhamnetin (10 and 20 μM) markedly decreased the
extracellular NO levels by 81.59% and 137.94%, quercetin (5, 10 and 20
μM) markedly decreased the extracellular NO levels by 56.68%, 106.57%
and 174.59%, respectively, in a concentration-dependent manner ([132]
Figures 12D–F ). Our results demonstrated that isoliquiritigenin,
isorhamnetin, and quercetin inhibited NO production in LPS induced
RAW264.7 cells.
Discussion
The therapeutic effect of current synthetic agents in treating RA is
not satisfactory and most of them have undesirable side effects. In
China, some classical formulas have a long history of clinical
application to treat RA and have shown significant curative effects.
However, TCM formulas is a multi-component and multi-target agent from
the molecular perspective ([133]Corson and Crews, 2007; [134]Bo et al.,
2013). Based on the characteristics of complex components and unclear
targets of TCM formula, the development of novel methods became an
urgent issue needed to be solved.
TCM network pharmacology emerging recently has become a flourishing
field in TCM modern studies along with the rapid progress of
bioinformatics ([135]Guo et al., 2017; [136]Gao et al., 2018; [137]Wang
et al., 2018). So, using the method, combined with the rich experience
of TCM treatment, could hopefully decode the underlying mechanism of
TCM formula in the treatment of complex disease with the characteristic
of “multi-targets, multi-component”. Network pharmacology approach
could help us search for putative active components and targets of
herbs based on widely existing databases and shows the network of
drug-targets by a visual way ([138]Gao et al., 2016). Moreover, it
abstracts the interaction between drugs and target genes into a network
model and investigates the effects of drugs on biological networks from
a holistic perspective. It can help us to further understand potential
action mechanisms of TCM within the context of interactions at the
system level. However, in the decode process of complex networks, there
are still exist redundancies and noises in current network pharmacology
study.
In order to solve this problem, we introduced the infomap algorithm
based on huffman encoding and the random walk theory for the first
time. The algorithm performs to optimize the discovery of motif in C-T
network heuristically by using a reasonable global metric. The results
of optimized KNMSs are used to analyze the mechanism of different
formulas for the treatment of RA. During this process the contribution
coefficient model was used to validate the predicted KNMSs, which
confirm the accuracy and reliability of our proposed strategy.
In this study, 230 active components of three formulas were found in
total after ADME screening, 31 of these components are common to the
three formulas, and 93, 89, and 17 components are specific to each
formula. It suggested that the three formulas play therapeutic effect
on rheumatoid arthritis through both common and specific components. In
order to analyze the key component groups and mechanisms of the three
formulas in the treatment of rheumatoid arthritis, we used target
prediction tools to predict the targets of active components in
different formulas and construct C-T networks. The degree distribution
in the C-T network shows that the same components could act on
different targets, and different components could also act on the same
targets, which fully reflects the multi-component and multi-target
complexity of TCM in treating complex diseases.
In order to quickly extract important information from complex C-T
networks, motif prediction and validation strategy were used to rapidly
discover the KNMSs of different formulas in the treatment of RA by
using multidimensional data. More and more evidences show that network
motif is an effective method to extract functional units and find core
elements in complex networks. Radicchi et al. has confirmed that
network motif offers an effective and manageable approach for
characterizing rapidly the main functional unit of disease progression
([139]Radicchi et al., 2004). Yang has reported that identifying
overlapping motifs is crucial for understanding the structure as well
as the function of real-world networks ([140]Yang and Leskovec, 2012).
Cai et al. indicate that uncovering motif structures of a complex
network can help us to understand how the network play functions
([141]Cai et al., 2014). Utilizing the network motif prediction model,
7, 10, and 10 KNMSs (p<0.05) were predicted in DSD, GFD and HGWD,
respectively.
Coverage of RA pathogenic genes, coverage of functional pathways and
cumulative contribution of key nodes were employed to evaluate the
accuracy and reliability of KNMSs. The verification results show that
KNMSs has a high coincidence degree with C-T network at the pathogenic
genes, gene functional and topological structure level. It suggests
that our proposed KNMS detection model can maximize the retention of
functional pathways, the coverage of network topological structure and
the coincidence degree of pathogenic genes in the formulas of TCM.
Through the analysis of KNMSs gene enrichment pathways in different
formulas, we found that the percentage of gene enrichment pathways of
different KNMSs is distinct compare to the gene enrichment pathways of
all KNMSs in each formula. In DSD, gene enrichment pathways of DSD1,
DSD2 account for more than 95% of gene enrichment pathways of 7 KNMSs.
In GFD, the gene enrichment pathways of 4 KNMSs, GFD1, GFD3, GFD4, and
GFD5 account for 95.4% of gene enrichment pathways of 10 KNMSs. In
HGWD, the gene enrichment pathways of 4 KNMSs, HGWD4, HGWD5, HGWD6, and
HGWD8 account for 92.7% of gene enrichment pathways of 10 KNMSs. These
KNMSs in each formula play different roles by targeting on common and
complementary inflammation-related signaling pathways. These
complementary inflammatory signaling pathways include: DSD1
specifically related JAK-STAT signaling pathway and AMPK signaling
pathway, DSD2 specifically related NF-kappa B signaling pathway, p53
signaling pathway and Wnt signaling pathway. GFD1 specifically related
TNF signaling pathway, IL-17 signaling pathway, GFD3 specifically
related Inflammatory mediator regulation of TRP channels and GnRH
signaling pathway, HGWD4 specifically related TNF signaling pathway,
Hedgehog signaling pathway, HGWD5 specifically related VEGF signaling
pathway and mTOR signaling pathway, HGWD6 specifically related cAMP
signaling pathway and cGMP-PKG signaling pathway. These results
indicate that KNMSs in different formulas have distinct roles and
synergistic effects in the treatment of rheumatoid arthritis.
In addition to the difference analysis of each KNMS in different
formulas, KEGG enrichment analysis of all KNMSs in each formula were
also implemented and revealed that 3 formulas exert the therapeutic
effect of RA through common pathway, such as MAPK signaling pathway,
cAMP signal pathway etc. or specific pathway, such as VEGF signaling
pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway etc. Among
them, MAPK signaling pathway plays an important role in the
pathological process of RA ([142]Schett and Zwerina, 2008). Its
over-activation is closely related to inflammatory hyperplasia of
synovial tissue and destruction of articular cartilage tissue. As an
inducible transcription factor, MAPK regulates the expression of many
genes and has been considered as a promising target for the treatment
of RA ([143]Rubbert-Roth, 2012). Studies have shown that
collagen-induced arthritis rats administrated with MAPK signal
transduction pathway inhibitor have significant differences in
inhibiting synovitis, bone destruction and articular cartilage
destruction compared with the group without signal pathway inhibitor
([144]Adelheid et al., 2014). cAMP signal pathway is an important
signal pathway for peripheral blood lymphocytes of RA patients. The
study found that the cAMP level in peripheral blood lymphocytes (PBL)
of RA patients increased, and its proliferation response was
significantly lower than that of PBL in normal patients. It was also
found that the abnormal activation of adenylate cyclase in RA patients
was related to the low function of Gi protein ([145]Dai and Wei, 2003).
The formation of RA neovascularization depends on the expression of
various angiogenic factors, especially VEGF and its receptor in RA
([146]Mi-La et al., 2006). It has been confirmed that VEGF expression
is upregulated in synovial macrophages and fibroblasts of RA patients,
and VEGF expression is positively correlated with RA disease activity
and joint destruction ([147]Kanbe et al., 2015). The articular cavity
of RA is anoxic microenvironment. Recent studies have shown that the
increased expression of HIF-1 in synovium of RA joint is closely
related to the occurrence and development of RA ([148]Xu et al., 2010).
PI3K-AKT signal pathway is an important intracellular signal
transduction pathway, which is closely related to abnormal apoptosis of
RA fibroblast-like synovial cells (RAFLS) ([149]Smith and Walker,
2004). Inhibition of abnormally activated PI3K-AKT signaling pathway or
expression of anti-apoptotic molecules can induce apoptosis in RAFLS
and have therapeutic effect on RA ([150]Liu and Pope, 2003).
Besides the function analysis, we also analysis and validated the key
components in KNMSs of each formula. In DSD, the results suggested that
the key component isoliquiritigenin from motif 1 (DSD1) exert effect on
treatment of RA possibly through acting on MAPK signaling pathway.
Studies have shown that isoliquiritigenin suppresses RANKL-induced
osteoclastogenesis and inflammatory bone loss via RANK-TRAF6, MAPK,
IκBα/NF-κB, and AP-1 signaling pathways ([151]Zhu et al., 2012). In
GFD, the results suggested that the key component isorhamnetin from
motif 1 (GFD1) treats RA possibly through acting on TNF signaling
pathway. Published reports confirmed that isorhamnetin play intervening
roles in the development and progression of RA via anti-inflammatory
and anti-oxidative activities. Previous studies have suggested that
isorhamnetin attenuates collagen-induced arthritis via modulating the
levels of cytokines TNF-α, IL-1β, and IL-6 etc. in the joint tissue
homogenate of mice ([152]Wang and Zhong, 2015). In HGWD, the results
suggested that the key component quercetin from motif 5 (HGWD5) has
therapeutic effect on RA possibly through acting on PI3K-Akt signaling
pathway. This also verified by previous studies, which found that the
mechanisms responsible for the quercetin-induced apoptosis of FLS from
patients with RA are associated with the inhibition of PI3K/AKT pathway
activation ([153]Pan et al., 2016). Cellular experiments were applied
to prove the reliability of the network pharmacology model through
verifying the protective effects of key components in KNMSs of three
formulas on the inflammation of mice RAW264.7 cells induced by LPS. In
addition, in order to better evaluate the reliability of our proposed
network pharmacology model, in vivo study will be conducted in our
future research.
To summarize, a network pharmacology-based approach was established to
extract core components group and decode the mechanisms of different
formulas treat the same disease of TCM. Additionally, our proposed KNMS
prediction and validation strategy provides methodological reference
for optimization of core components group and interpretation of the
molecular mechanism in the treatment of complex diseases using TCM.
Data Availability Statement
The raw data supporting the conclusions of this article will be made
available by the authors, without undue reservation, to any qualified
researcher.
Author Contributions
A-PL, D-GG, and LG provided the concept and designed the study. K-XW
and YG conducted the analyses. K-XW and D-GG wrote the manuscript.
K-XW, YG, CL, YL and B-YZ participated in data analysis. X-MQ, G-HD,
and A-PL provided oversight. A-PL, D-GG, and LG contributed to revising
and proof-reading the manuscript. All authors contributed to the
article and approved the submitted version.
Funding
This study is financially supported by the Startup fund from Southern
Medical University (grant No. G619280010), the Natural Science
Foundation Council of China (grant No. 31501080), Hong Kong Baptist
University Strategic Development Fund [grant No. SDF13-1209-P01,
SDF15-0324-P02(b) and SDF19-0402-P02], the Key Laboratory of Effective
Substances Research and Utilization in TCM of Shanxi Province (No.
201705D111008-21), Hong Kong Baptist University Interdisciplinary
Research Matching Scheme (grant No. RC/IRCs/17-18/04), the General
Research Fund of Hong Kong Research Grants Council (grant No. 12101018,
12100719, 12102518).
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Supplementary Material
The Supplementary Material for this article can be found online at:
[154]https://www.frontiersin.org/articles/10.3389/fphar.2020.01035/full
#supplementary-material
[155]Click here for additional data file.^ (563.8KB, docx)
[156]Click here for additional data file.^ (23KB, docx)
[157]Click here for additional data file.^ (17.2KB, xlsx)
[158]Click here for additional data file.^ (17.6KB, docx)
[159]Click here for additional data file.^ (18KB, xlsx)
References