Abstract
Complex disease is a cascade process which is associated with
functional abnormalities in multiple proteins and protein-protein
interaction (PPI) networks. One drug one target has not been able to
perfectly intervene complex diseases. Increasing evidences show that
Chinese herb formula usually treats complex diseases in the form of
multi-components and multi-targets. The key step to elucidate the
underlying mechanism of formula in traditional Chinese medicine (TCM)
is to optimize and capture the important components in the formula. At
present, there are several formula optimization models based on network
pharmacology has been proposed. Most of these models focus on the 2D/3D
similarity of chemical structure of drug components and ignore the
functional optimization space based on relationship between
pathogenetic genes and drug targets. How to select the key group of
effective components (KGEC) from the formula of TCM based on the
optimal space which link pathogenic genes and drug targets is a
bottleneck problem in network pharmacology. To address this issue, we
designed a novel network pharmacological model, which takes Lang Chuang
Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case.
We used the weighted gene regulatory network and active components
targets network to construct disease-targets-components network, after
filtering through the network attribute degree, the optimization space
and effective proteins were obtained. And then the KGEC was selected by
using contribution index (CI) model based on knapsack algorithm. The
results show that the enriched pathways of effective proteins we
selected can cover 96% of the pathogenetic genes enriched pathways.
After reverse analysis of effective proteins and optimization with CI
index model, KGEC with 82 components were obtained, and 105 enriched
pathways of KGEC targets were consistent with enriched pathways of
pathogenic genes (80.15%). Finally, the key components in KGEC of LCW
were evaluated by in vitro experiments. These results indicate that the
proposed model with good accuracy in screening the KGEC in the formula
of TCM, which provides reference for the optimization and mechanism
analysis of the formula in TCM.
Keywords: Lang Chuang Wan, systemic lupus erythematosus, network
pharmacology, optimization space, effective proteins, contribution
index
Introduction
The key group of effective components (KGEC) in a formula of
traditional Chinese medicine (TCM) refer to the pharmacologically
active components that are closely related to the positive response to
the therapy of the diseases. How to determine the KGEC that play
leading roles in the treatment of specific disease is a difficult
problem due to the high complexity of the chemical composition and the
incompletely understanding the complex multi-targets mechanism of TCM.
Selecting the KGEC in the formula of TCM is an important direction in
the reduction of non-active components and analysis of the treatment
mechanism of formula. At present, there are several formula
optimization models based on network pharmacology have been proposed.
Most of these models focus on the 2D/3D similarity of chemical
structure of drug or components, and ignore the optimized functional
space, which couldrepresent effective relationships between drug
targets and pathogenetic genes ([41]Wang et al., 2018; [42]Xie et al.,
2018; [43]Duan et al., 2019). Increasing evidences confirm that the
monomer component of TCM exerts important pharmacological effects
through the protein-protein interactions (PPI) ([44]Chen and Cui, 2017;
[45]Gan et al., 2018; [46]Guo et al., 2019). Thus, it is desirable to
design a module to detect the KGEC and infer the potential mechanisms
of formula on complex disease based on chemical properties analysis,
targets prediction, and construction of functional optimization space.
Systemic Lupus Erythematosus (SLE) is an autoimmune disease involving
multiple systems and organs, with complicated clinical manifestations
and persistent. Most of them are found in young women, and the
incidence ratio of male to female is 1: 5 ~ 10 ([47]Veeranki and
Choubey, 2010; [48]Dorner and Furie, 2019). Previous researches
indicate that the SLE may be related to heredity, immune disorder,
endocrine abnormality and environmental factors. Currently, western
medicine mainly adopts non-steroidal anti-inflammatory drugs,
antimalarial drugs, glucocorticoids, immunosuppressive agents, plasma
treatment, and systemic lymph node irradiation therapy for the
treatment of SLE ([49]Loram et al., 2015; [50]Wallace, 2015). However,
so far these drugs and methods can only temporarily control the
disease. At the same time, some toxic and side effects of western
medicine are becoming increasingly apparent. Intervention therapy of
TCM can not only effectively relieve clinical symptoms, but also reduce
the toxic and side effects of western medicine ([51]Huang et al., 2016;
[52]Ma et al., 2016). Increasing evidences proved that TCM play
important roles in both acute and remission phases of SLE.
In recent years, TCM has been widely used in the treatment of SLE and
has achieved remarkable results. With the continuous improvement of its
therapeutic effect, it has gradually attracted the attention of medical
experts and scholars at home and abroad ([53]Ma et al., 2016). At
present, the TCM formula, Lang Chuang Wan (LCW) is widely used in the
treatment of SLE with TCM. LCW generally comprised of 16 herbs:
Lonicera japonica Thunb. (Jinyinhua, 53.6 g), Forsythia suspensa
(Thunb.) Vahl (Lianqiao, 53.6 g), Taraxacum mongolicum Hand.
(Pugongying, 53.6 g), Coptis chinensis Franch. (Huanglian, 13.4 g),
Rehjnannia glutinosa Libosch. (Dihuang, 53.6 g), Rheum officinale
Baill. (Dahuang, 20.1 g), Glycyrrhiza uralensis Fisch. (Gancao, 13.4
g), Scolopendra subspinipes mutilans L. Koch (Wugong, 2.42 g), Paeonia
ladiflora Pall. (Chishao, 26.8 g), Angelica sinensis (Oliv.) Diels
(Danggui, 13.4 g), Salvia miltiorrhiza Bge. (Danshen, 13.4 g),
Scrophularia ningpoensis Hemsl. (Xuanshen, 53.6 g), Prunus persica (L.)
Batsch (Taoren, 26.8 g), Carthamus tinctorius L. (Honghua, 20.1 g),
Cryptotympana pustulata Fabricius (Chantui, 53.6 g), and Fritillaria
thunbergii Miq. (Zhebeimu, 26.8 g). In this Chinese formula, Flos
Lonicerae, Fructus Forsythiae and Herba Taraxaci have the function of
clearing heat-toxin, eliminating carbuncles, and resolving masses,
Coptidis Rhizoma has the function of clearing heart fire, the
Rehmanniae Radix mainly focus on cooling blood, nourishing yin and
promoting fluid production, Radix et Rhizoma Rhei play roles in
clearing heat cooling blood promoting blood circulation; Glycyrrhrizae
Radix used for clearing heat-toxin harmonizing various drugs; Radix
Paeoniae Rubra, Saviae Miltiorrhizae Radix and Carthami Flos usually
used for clearing heat cooling blood; Scolopendra used for removing
toxic substance resolving masses, detumescence, and relieving pain;
Radix Angelicae Sinensis used for nourishing and activating blood;
Radix Scrophulariae used for clearing heat-toxin nourishing yin for
lowering fire; Semen Persicae used for promoting blood circulation
removing blood stasis; Periostracum Cicadae used for clearing heat;
Bulbus Fritillariae Thunbergii used for clearing heat dissipating
phlegm and resolving masses. The whole recipe has the functions of
clearing away heat and toxic materials, cooling blood and promoting
blood circulation. In pharmacologic studies, LCW may has the effect of
inhibiting humoral immune function and enhancing cellular immune
function. Additionally, LCW also obvious inhibitory effect on acute and
chronic inflammation and allergy in rats, and can promote fibrinolytic
activity. These studies confirmed that the LCW could be beneficial in
the treatment of patients with SLE at comprehensive level.
Nevertheless, not any document expounds the key components and
underlying therapeutic mechanism of LCW for clinically benefit to SLE
([54]Wang, 1989; [55]Wang et al., 1991).
Currently, a novel network pharmacology module was designed to detect
the KGEC and elucidate the therapeutic mechanisms of LCW in the
treatment of SLE. During this process, the weighted gene regulatory
network of SLE disease was constructed and used for constructing of
optimization space. All LCW components were collected from databases
and literature. The potential active components were selected from all
components based on published ADME-related models; the targets of these
active components were predicted by three published prediction tools.
The active components and their targets were used for building the
components-targets (C-T) network. The weighted gene regulatory network
and C-T networks were used to construct optimization space to determine
the effective proteins. The effective proteins selected from
optimization space were used to screen the effective components. The
contribution index (CI) module was employed to optimize effective
components and get the KGEC, which would be used to illustrate the
molecular mechanism of LCW in the therapy of SLE. Finally, the key
components in KGEC of LCW were evaluated by in vitro experiments.
Methods
Construct Weighted Gene Regulatory Network of SLE
In order to construct comprehensive weight gene network of SLE, the PPI
data were derived from public databases BioGRID, STRING, Dip, HPRD,
Intact, Mint and Reactome ([56]Guan et al., 2014). Genes from DisGeNET
([57]Pinero et al., 2017) were reported to be related to SLE were
extracted and mapped to the PPI network to construct the weighted gene
regulatory network of SLE. Cytoscape (Version 3.5.1) was utilized to
visualize the network.
LCW Content Determination
Reagents and Chemicals
High performance liquid chromatography (HPLC)-grade acetonitrile and
HPLC grade formic acid were obtained from Thermo-Fisher (USA).
Reference standards provided by the National Institute on Drug Abuse of
China:Hydroxysafflor-Yellow-A (batch number: 111637-200502), amygdalin
(batch number: 110820-200403), paeoniflorin (batch number: 0905-9805),
caffeic acid (batch number: 110728-200506), phillyrin (batch number:
120908-200914), liquiritin (batch number:111610-200503), peimisine
(batch number: 0750-9303), harpagoside (batch number: 712-9403), rhein
(batch number: 0902-200207), Z-Ligustilide (batch number: 927-9908),
berberine (batch number: 0713-200107), tanshinone II a (batch number:
0766-200011), catalpol (batch number: 0808-9602). The purities of all
standards were no less than 98% and suitable for liquid
chromatography-tandem mass spectrometry (LC-MS/MS) analysis. LCW was
purchased from Changchun Overseas Pharmaceutical Co. Ltd.
Preparation of Samples and Standard Solution
LCW (batch number: 20190601) was grinded into powder. Each sample of
LCW powder (0.5 g) was weighed precisely and ultrasonically extracted
in 50 ml hydrochloric-acid methanol (1:100) for 30 min. Supplement the
weight lost with hydrochloric acid-methanol (1:100) solution and then
filtered through 0.22 μm nylon membrane filters. The filtrate was
analyzed directly by UPLC-ESI-MS/MS. At the same time, a stock solution
containing 13 reference standards was prepared in methanol. All
solutions were stored at 4°C prior to analysis.
Instrument and UPLC-ESI-MS/MS Conditions
Chemical profiling of LCW and reference standards were detected by an
Agilent ultra-performance liquid chromatography system (UHPLC)
(Agilent, USA) coupled to a Q-trap 3200 (AB SCIEX, USA). Chromatography
separation was carried out on a Waters ACQUITYUPLC HSS T3 column
(2.1 mm × 100 mm, 1.8 μm) maintained at 40°C. The mobile phase
consisted of 0.1% formic acid in water (A) and 0.1% formic acid in
acetonitrile (B), and run under the following program: 0 ~ 1 min, 15%
B; 1 ~ 4 min, 15 ~ 45% B; 4 ~ 10 min, 45 ~ 60% B, 10 ~ 15 min, 60 ~ 90%
B; 15 ~ 18 min, 90 ~ 95% B. The sample injection volume was 5 μl and
the flow rate was set at 0.2 ml/min. The mass spectrometer was fitted
with an electrospray ionization source and the mass detection was
operated in both positive and negative ion modes with the following
setting: ion source temperature, 500°C; Sheath gas velocity, 50 psi;
Auxiliary gas flow rate, 12 L/min; scan range, m/z 150–900.
Collect and Select Chemical Components of LCW
All components of LCW were collected from four published natural
product data sources: TCMSP database ([58]Ru et al., 2014), Traditional
Chinese Medicine integrated database ([59]Xue et al., 2013),
Traditional Chinese Medicine database@Taiwan ([60]Chen, 2011), and
YaTCM ([61]Li et al., 2018). For all components, the initial structure
formats (e.g., mol2 and SDF) were transformed into unified SDF format
using Open Babel toolkit (version 2.4.1). Subsequently, the properties
of components were retrieved from TCMSP, including molecular weight
(MW), oral bioavailability (OB), Caco-2 permeability (Caco-2), and DL
(drug-likeness).
Three ADME-related models, including OB, Caco-2, and DL, were employed
to screen the bioactive molecules. OB (%F) represents the percentage of
an orally administered dose of unchanged drug that reaches the systemic
circulation, which reveals the convergence of the ADME process ([62]Xu
et al., 2012). High oral bioavailability is often a key indicator to
determine the drug-like property of bioactive molecules as therapeutic
agents. The components with suitable OB≥30% were chosen as candidate
components for further research. Human intestinal cell line Caco-2 is
used as an efficient in vitro model to study the passive diffusion of
drugs across the intestinal epithelium, the ingredients’ transport
rates (nm/s) in Caco-2 monolayers represent the intestinal epithelial
permeability in TCMSP. Components with Caco-2>-0.4 were chosen as the
candidate components, because the components with a Caco-2 value less
than -0.4 were not permeable. Drug-likeness is a qualitative concept
used in drug design for an estimate on how “drug-like” a prospective
compound is, which helps to optimize pharmacokinetic and pharmaceutical
properties, such as solubility and chemical stability. The “drug-like”
level of the components is 0.18, which is used as a selection criterion
for the “drug-like” components in the traditional Chinese herbs
([63]Tao et al., 2013). After ADME screening, some components that did
not meet the three screening criteria were also selected because of
their high content and high biological activity. These will be used in
conjunction with ADME screening as a follow-up study.
Predict Targets of Active Components
To obtain the targets of active components in LCW, the commonly used
prediction tools, i.e., Similarity Ensemble Approach (SEA) ([64]Tao
et al., 2013), HitPick ([65]Liu X. et al., 2013), and Swiss Target
Prediction ([66]Gfeller et al., 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).
Define the Optimization Space and Evaluate the Effective Proteins
Construction of the optimization space is able to maximize the search
for targets of small molecules that are highly relevant to pathogenetic
genes. Firstly, we used the weighted gene regulatory network and active
components targets network to construct the disease-targets-components
network. Degree is an important topological property in the network
that can be used to evaluate the importance of nodes in the network.
The nodes with higher degree than the average degree of all nodes in
the disease-targets-components network were identified as hub nodes.
Following this rule, the passed nodes and their edges in the
disease-targets-components network were kept and defined as
optimization space. The whole process can be described as follows:
We defined Net[ppi] = {N, E}, N means nodes that represent proteins, E
means edges that represent protein–protein interactions derived from
public databases BioGRID, STRING, Dip, HPRD, Intact, Mint, and
Reactome. T[tar] = {P [1] [tar], P [2] [tar]… … P[ntar]} means the
predicted targets of active components. D[dis] = {G [1] [dis], G [2]
[dis]… … G[ndis]} means the pathogenic genes of SLE. The optimization
space can be calculated by the following steps.
[MATH:
Ltar={P1t<
/mi>ar,P<
mn>2tar......Pntar} :MATH]
[MATH:
Ldis={P1d<
/mi>is,P<
mn>2dis......Pndis} :MATH]
[MATH:
Nettarppi=
{Ntar
,Etar}=Netppi∩
Ltar
, Net
tar∈N;
Etar
∈E :MATH]
[MATH:
Netdisppi=
{Ndis
,Edis}=Netppi∩
Ldis
, Net
dis∈N;
Edis
∈E :MATH]
[MATH:
Nettar-dispp
i=Netppi∩Ne<
/mi>tdispp
i∩Net<
/mi>tarppi<
/mrow> :MATH]
[MATH:
Davg=(∑i=1<
/mn>kdi<
/mrow>)/k :MATH]
[MATH: OptS=∪i=1<
/mn>kd(<
/mo>Nettar−dispp
i)i>Davg<
/msub> :MATH]
Where
[MATH:
Nettarppi<
/msub> :MATH]
is the network of predicted targets, the
[MATH:
Netdisppi<
/msub> :MATH]
is the network of pathogenic genes.
[MATH:
Nettar−disppi :MATH]
represents the disease-targets-components network. D[avg] is the
average degree of all nodes in the disease-targets-components network.
k means the number of nodes in the disease-targets-components network.
OptS represent the optimization space. The nodes in the optimization
space were identified as effective proteins.
Develop a CI Model to Select KGEC
To optimize effective components and get the KGEC, which would be used
to illustrate the molecular mechanism of LCW in the therapy of SLE.
Active components which are associated with effective proteins were
extracted as λ = {λ[1], λ[2], λ[3]… … λ[m]}, The target number of each
active compounds be defined as ω = {ω[1], ω[2], ω[3]… …ω[m]}, then
coverage of the target number for each active compounds defined as ν =
{v[1], v[2], v[3]… … v[m]}, the dynamic-0-1 knapsack algorithm can be
described as:
Input: ν and ω, the number of active components m and the number of
effective ν proteins W.
Output: The optimal KGEC.
[MATH: for ω←0 to<
mtext> W do c[0,ω]←0e<
/mi>nd for<
/mtd>fori<
mo>←0 to m do c[i,0 ]←0 for ω←1 to W do if ωi≪ω t<
mi>hen
if vi+c[i−1, ω−ωi]>c[i−1,ω] then c[i,ω]←vi+
c[i−1, ω−ωi] else c[i,ω]←c[i−1,ω] end if else c[i,ω]←c[i−1,ω] end if end
forend forreturn CI=c[k,W] :MATH]
Gene Ontology and Pathway Analysis
To analyze the main function of the targets, the clusterProfiler
package of R software was used to perform Gene Ontology (GO) analysis.
p-values were set at 0.05 as the cut-off criterion. The clusterProfiler
package of R software ([67]Yu et al., 2012) was employed to classify
the biological terms and to analyze the gene cluster enrichment
automatically. The latest pathway data were obtained from the Kyoto
Encyclopedia of Genes and Genomes (KEGG) database ([68]Draghici et al.,
2007) for KEGG pathway enrichment analyses. p-values were set at 0.05
as the cut-off criterion. The ggplot2 package was used to create graphs
in R statistical programming language (version 3.4.2). The results of
analysis were annotated by Pathview ([69]Luo et al., 2017) in the R
Bioconductor package ([70]https://www.bioconductor.org/).
Experimental Validation
Chemicals and Reagents
Liquiritin and ferulic acid (≧98% purity by HPLC, PUYI BIOLOGY; Jangsu,
China). Resiquimod (≧99.6%, Topscience Co., Ltd. China). Fetal bovine
serum (FBS) (Sangon Biotech (Shanghai) Co., Ltd., China). RPMI-1640
(Sangon Biotech (Shanghai) Co., Ltd., China). β-actin, Phospho-ERK1/2,
Phospho-AKT, and Phospho-PI3K (CST, USA).
Cell Culture and Drug Treatment
RAW 264.7 cells (Cell Bank of Chinese Academy of Sciences, China) were
cultured in DMEM-H with 10% FBS at 37°C, in an atmosphere containing 5%
CO[2,] humidified 95%. RAW 264.7 cells were seeded on 96-well plates
with 1×10^4 per/well, 100 mm dishes with 1×10^6 per/dishes, and
cultured for 24 h. After incubation, the RAW 264.7 cells were
co-incubated with different concentrations of liquiritin, ferulic acid,
and resiquimod for 24 h. The concentration of resiquimod was selected
at 0.1 μg/ml.
Cell Viability Assay
RAW 264.7 cells were seeded in 96-well plates. After drug treatment,
the culture medium was removed, 100 μl 0.5 mg/ml MTT (Sangon Biotech
(Shanghai) Co., Ltd., China) solution was added. After 4 h, the culture
medium was removed and 100 μl DMSO (Sangon Biotech (Shanghai) Co.,
Ltd., China) was added. The absorbance at 570 nm was measured with a
microplate reader (BioTek Epoch, USA). Cell viability is expressed as a
percentage of the control.
Measurement of IL-6 Levels
The cells were cultured in 6-well plates. After relevant treatment, the
cells were collected and centrifuged to obtain a cell pellet and
supernatant. The cell pellet and supernatant were stored at −80°C until
required for analysis. The level of IL-6 was determined by commercial
assay kits (Nanjing Jiancheng, China).
Western Blot Analyses
RAW 264.7 cells were seeded in 100 mm dishes. At the end of the
treatments, the cells were harvested and washed twice with cold PBS.
The cells were lysed with RIPA lysis buffer (Beyotime, China)
containing 1% phenylmethylsulfonylfluoride (PMSF, Beyotime, China). The
whole-cell lysates were centrifuged at 12,000 rpm/min for 15 min at
4°C, and the supernatants were collected. Protein concentrations were
determined by bicinchoninic acid assay. Equal amounts of protein (50
μg) were separated by electrophoresis on 12% sodium dodecyl sulphate
polyacrylamide gels and transferred onto PVDF membranes. These
membranes were incubated with 5% (w/v) non-fat milk powder in
Tris-buffered saline containing 0.1% (v/v) Tween-20 (TBST) for 2 h to
block nonspecific binding sites. The membranes were then incubated
overnight at 4°C with the primary antibodies. After washing with TBST,
the membranes were incubated for 2 h at room temperature with the
fluorescent secondary antibodies. After rewashing with TBST, the
membranes were scanning by a fluorescent scanner (Odclyssey CLX, Gene
company limi ed, USA).
Results
In this report, a novel network pharmacology module was designed to
detect the KGEC and elucidate the therapeutic mechanisms of LCW in the
treatment of SLE ([71] Figure 1 ). Firstly, all LCW components were
collected from databases and literature. Next, the potential active
components were selected from all LCW components based on published
ADME-related models. The targets of these active components were
predicted by three online prediction tools. Then the weighted gene
regulatory network and active components targets network were used to
construct optimization space for determining the effective proteins.
The effective proteins were used to select the KGEC based on CI module
and then the KGEC was used to infer the underlying molecular mechanism
of LCW in treating SLE. Finally, the key components in KGEC of LCW were
evaluated by in vitro experiments.
Figure 1.
[72]Figure 1
[73]Open in a new tab
The flowchart of our proposed network pharmacology approach.
Experimental methods include gene regulatory network of SLE,
compound-target netowork of LCW, optimization space construction,
effective proteins analysis and mechanism analysis. SLE represents
systemic lupus erythematosus, LCW represents Lang Chuang Wan, KGEC
represents key group of effective components.
Construct Weighted Gene Regulatory Network of SLE
Construction and analysis of weighted gene regulatory network is the
basis and key step to understand the pathogenesis and provide
intervention strategies of SLE. In order to construct a comprehensive
weighted gene network of SLE, the PPI data sets from public databases
BioGRID, STRING, Dip, HPRD, Intact, Mint and Reactome were used to
construct the PPI network. 1201 genes from DisGeNET which confirmed
associated with SLE were extracted and mapped to the PPI network to
construct the weighted gene regulatory network of SLE. The weighted
gene regulatory network contains 950 nodes and 6,984 edges ([74] Figure
2 ). The number of literature reports of one node represent the weight
of the node ([75] Table S1 ). Eight out of top 30 genes with the
highest weight in the gene regulatory network enriched in the common
SLE pathways (hsa05322), including TNF ([76]Geng et al., 2014),
HLA-DRB1 ([77]Shimane et al., 2013), IFNG ([78]Leng et al., 2016),
CD40LG ([79]Wu et al., 2016), IL10 ([80]Liu P. et al., 2013), FCGR3A
([81]Kyogoku et al., 2013), FCGR2A ([82]Bentham et al., 2015) and
TRIM21 ([83]Kyriakidis et al., 2014). These genes are also enriched in
cytokine-cytokine receptor interaction, T cell receptor signaling
pathway and Th17 cell differentiation, which are closely related to SLE
([84] Figure 3 ). These results indicate that the weight gene
regulatory network and weight genes can reflect the pathogenesis of
SLE, which also provides a reliable reference for the next step to
construct the optimization space.
Figure 2.
[85]Figure 2
[86]Open in a new tab
Weighted gene regulatory network of SLE. The weighted gene regulatory
network contains 950 nodes and 6,984 edges. The number of literature
reports of one node represent the weight of the node. The red nodes
list the top 30 of SLE pathogenetic genes in the weighted gene
regulatory network.
Figure 3.
[87]Figure 3
[88]Open in a new tab
Pathway enrichment analysis of top 30 weighted genes of SLE. The
ordinate represents genes and the abscissa represents enriched
pathways. Orange nodes represent genes enriched in a given pathway and
no nodes represent genes not enriched in this pathway.
Chemical Components Analysis
Thirteen components in the chromatograms of the LCW sample were
identified and assigned by comparing the retention time with those of
the reference compounds ([89] Figure 4 ). The 13 components are
hydroxysafflor-Yellow-A, amygdalin, paeoniflorin, caffeic acid,
phillyrin, liquiritin, peimisine, harpagoside, rhein, Z-Ligustilide,
berberine, tanshinone II a, and catalpol ([90] Table 1 ). The
pharmacopoeia defines that the content of berberine should not be less
than 0.4 mg/g, and we get the content of berberine as 0.57 mg/g by
chemical analysis. These results confirmed that the content of
berberine in LCW meets the requirements of pharmacopoeia. Chemical
Components analysis provides a reference for the screening of active
components in LCW for further analysis.
Figure 4.
[91]Figure 4
[92]Open in a new tab
(A) Chromatograms of LCW and standard samples. (B) Total ion Current
(TIC) chromatograms of LCW and standards samples. (C)
Extracted ion current (XIC) chromatograms of paeoniflorin in LCW and
standard samples. 1) Hydroxysafflor-Yellow-A, 2) Amygdalin, 3)
Paeoniflorin, 4) Caffeic Acid, 5) Phillyrin, 6) Liquiritin, 7)
Peimisine, 8) Harpagoside, 9) Rhein, 10) Z-Ligustilide, 11) Berberine,
12) Tanshinone II a, and 13) Catalpol.
Table 1.
Information of chemical components in LCW.
No TR Name Formula Molecular Weight m/z ion Area (Standard) Area (LCW)
Content (mg)
1 2.56 Hydroxysafflor-Yellow-A C[27]H[32]O[16] 612.17 613.18 M+H
1.06E+07 7.19E+05 0.34
2 3.41 Amygdalin C[20]H[27]NO[11] 457.16 458.17 M+H 1.36E+06 8.70E+04
0.32
3 4.1 Paeoniflorin C[23]H[28]O[11] 480.16 481.17 M+H 2.79E+07 3.15E+05
0.06
4 4.42 Caffeic Acid C[9]H[8]O[4] 180.04 181.05 M+H 3.75E+06 1.14E+06
1.52
5 4.48 Phillyrin C[29]H[36]O[15] 624.20 623.19 M-H 9.24E+07 3.57E+06
0.19
6 4.7 Liquiritin C[21]H[22]O[9] 418.13 419.14 M+H 2.40E+06 4.30E+05
0.89
7 5.1 Peimisine C[27]H[45]NO[3] 431.34 430.33 M-H 6.98E+05 5.28E+04
0.38
8 6.08 Harpagoside C[24]H[30]O[11] 494.18 493.17 M-H 2.31E+07 2.11E+06
0.46
9 8.81 Rhein C[15]H[8]O[6] 284.03 285.04 M+H 1.07E+07 3.30E+06 1.54
10 12.19 Z-Ligustilide C[12]H[14]O[2] 190.10 191.11 M-H 5.77E+06
2.24E+06 1.94
11 15.89 Berberine C[17]H[17]N 235.14 236.15 M-H 5.10E+06 5.84E+05 0.57
12 16.87 Tanshinone II a C[19]H[18]O[3] 294.13 295.14 M+H 1.61E+08
3.94E+06 0.12
13 17.57 Catalpol C[15]H[22]O[10] 362.12 363.13 M+H 7.06E+06 3.35E+05
0.24
[93]Open in a new tab
Select Potential Active Components
By a systematic search of components from public databases of 16 herbs,
a total of 1693 components were retrieved in LCW in [94]Table 2 . The
detail information of these components was provided in [95]Table S2 .
TCM formula usually contains multiple components, only a few components
have satisfactory pharmacodynamic and pharmacokinetic properties. In
this study, three ADME-related models, OB, Caco-2, and DL were employed
to screen the active components. The components with OB values higher
than 30%, Caco-2 values higher than -0.4 and DL values higher than 0.18
were retained for further investigation. After ADME screening, some
components that did not meet the three screening criteria were selected
because of their high content and high biological activity, which has
been reported in the literature and our UPLC-ESI-MS/MS analysis.
Finally, 193 active components were filtered out of the 1,693
components of LCW. The detail information was shown in [96]Table S3 .
Table 2.
The number of LCW components collected in the published databases.
Herbs components Herbs components
Lonicera japonica Thunb. (JYH) 239 Paeonia ladiflora Pall. (CS) 75
Forsythia suspensa (Thunb.) Vahl (LQ) 153 Angelica sinensis (Oliv.)
Diels (DG) 126
Taraxacum mongolicum Hand. (PGY) 77 Salvia miltiorrhiza Bge. (DS) 203
Coptis chinensis Franch. (HL) 33 Scrophularia ningpoensis Hemsl. (XS)
47
Rehjnannia glutinosa Libosch. (DH) 76 Prunus persica (L.) Batsch (TR)
66
Rheum officinale Baill. (DH) 92 Carthamus tinctorius L. (HH) 190
Glycyrrhiza uralensis Fisch. (GC) 280 Cryptotympana pustulata Fabricius
(CT) 8
Scolopendra subspinipes mutilans L. 8 Fritillaria thunbergii Miq. (ZBM)
17
Koch (WG)
Total 1693
[97]Open in a new tab
Shared Components of Herbs in LCW
As can be seen from [98]Table S2 , there exist more than 20 active
components shared by two or more herbs in LCW. For example,
beta-sitosterol (LCW5), a common component of 10 herbs such as CS, DH,
DG, HH, JYH, LQ, TR, XS, ZBM, and PGY, shows an inhibitory effect on
the expression of proinflammatory cytokine interleukin IL-6 and TNF-α,
which display the properties of anti-inflammatory and immune-modulating
in the treatment of SLE ([99]Fraile et al., 2012). Caffeic acid (LCW23)
shared by, DS, JYH, PGY, and LQ was well-known for its pharmacological
properties such as antiviral, anti-inflammatory, anti-carcinogenic, and
immunomodulatory activities ([100]Espindola et al., 2019). It has also
been revealed that the administration of caffeic acid not only protect
rats from cisplatin-induced oxidative stress and gastrointestinal
toxicity but also reverse the activities of enzymes superoxide
dismutase, catalase, glutathione reductase, and glutathione peroxidase
near to their normal level, which are related to the SLE and that may
be used to infer underlying mechanism of LCW on SLE ([101]Iraz et al.,
2006). Kaempferol (LCW77), a shared component of GC, HH, JYH, and LQ,
was a common type of dietary flavonoid with anti-oxidative and
anti-inflammatory properties. Studies also indicated that kaempferol
decreased lipopolysaccharide (LPS)-induced TNF-α and IL-1 expression by
increasing the number of activated macrophages, which has been reported
associating with SLE ([102]Lee et al., 2018). Additionally, quercetin
(LCW97) in HH, JYH, LQ, and HL was one type of flavonoid compound with
anti-cancer, anti-inflammatory and immune-regulating activities. The
treatment of pristane-induced SLE model mice with liposomal quercetin
found that quercetin achieves SLE therapeutic effect is by reducing the
level of autoantibody expression ([103]Dos Santos et al., 2018).
Specific Components of Herbs in LCW
Except the shared components, most of the herbs possess their specific
ingredients. For example, luteolin (LCW25), the specific component of
JYH, protects against vascular inflammation in mice and TNF-α induced
monocyte adhesion to endothelial cells via suppressing IKBα/NF-kappa B
signaling pathway, which has been reported associating with SLE.
Phillyrin (LCW187), one of the most effective constituents in LQ, has
good antibacterial and anti-inflammatory activity, which can regulate
MyD88/IκBα/NF-kappa B signaling pathway by controlling the expression
of IκBα, IL-1β, IL-6 and TNF-α, which would be a benefit to SLE
([104]Yang et al., 2017). As the major component of HL, epiberberine
(LCW178) has broad biological activities, including antihyperlipidemic
and antihyperglycemic effects as well as anti-inflammatory and
antioxidant effects, and inhibits urease activity ([105]Li et al.,
2015) which suggested the MAPK signaling pathway could be used as the
therapy target. Berberine (LCW172) was the quality marker of LCW in
Chinese Pharmacopeia (China, 2015), and has anti-inflammatory effects,
suppresses the expression of proinflammatory cytokines likely due to
its capacity of AMPK activation, which could be used to illustrate the
molecular mechanism of LCW in the therapy of SLE ([106]Liu et al.,
2019). Thus, these components could be considered as curative elements
in treating SLE.
C-T Network Construction of Active Components
To explore the therapeutic mechanism of LCW in the treatment of SLE,
193 active components and 1,220 targets ([107] Table S4 ) were used to
construct the component-target network ([108] Figure 5 ). This network
contain 6,399 component-target associations. The average number of
targets of per component is 33.16. It shows that the multi-targets
characteristics of LCW for treating of SLE. Among these components,
vanillic acid (LCW190, degree = 510) has the highest number of targets,
followed by ferulic acid (LCW75, degree = 480), kaempferol (LCW77,
degree = 300), palmitic acid (LCW100, degree = 252), luteolin (LCW25,
degree = 220), protocatechuic acid (LCW29, degree = 182),
beta-sitosterol (LCW5, degree = 170), stigmasterol (LCW2, degree =
170), and caffeic acid (LCW23, degree = 148). Most of these components
were reported associated with the inflammation and immune-related
pathways of SLE. Such as vanillic acid, a well-known flavonoid,
significantly decreased the increased serum levels of TNFα and IL-6 on
concanavalin a-induced liver injury in mice ([109]Itoh et al., 2010).
Moreover, it could down-regulate LPS-induced COX-2 and nitric oxide
production in mouse peritoneal macrophages in vitro ([110]Kim et al.,
2011). Ferulic acid reduced the translocation of NF-E2-related factor 2
(NRF2) and nuclear transcription factor-κB (NF-kappa B) into the nuclei
through a reduction of the expression of phosphorylated IKK and
consequently inhibited IL-6 and NF-kappa B promoter activity. These
data suggested that ferulic acid play anti-inflammatory roles by
regulating IKK/NF-kappa B signaling pathway ([111]Lampiasi and Montana,
2016). Protocatechuic acid inhibits Toll-like receptor-4 dependent
activation of NF-kappa B by suppressing activation of the Akt, mTOR,
JNK, and p38-MAPK ([112]Nam and Lee, 2018). The function of remaining
components in the treatment of SLE has been described in Shared
Components of Herbs in LCW and Specific Components of Herbs in LCW
sections. These results demonstrated that the crucial roles of these
components in the treatment of SLE and further confirmed that these
components work in a multi-target manner to treat SLE.
Figure 5.
[113]Figure 5
[114]Open in a new tab
Optimization space construction. Optimization space includes three
category targets, pink nodes represent essential common targets, orange
nodes represent disease-specific targets, cyan nodes represent
component-specific targets.
In the component-target network, the mean degree of targets for
different components is 5.25. The top 20 targets with larger weight are
ESR1, IL2, IL1B, TLR9, and ACE, etc. Interestingly, majority of these
targets are related to immunity and inflammation, which are confirmed
associated with the pathogenesis of SLE and that maybe indicate
potential therapeutic mechanisms of LCW on SLE. For example, ESR1
polymorphism and its interaction with smoking and drinking contribute
to susceptibility of SLE ([115]Zhou et al., 2017); In addition, IL2
stimulates T cell proliferation and activation and regulates the
adaptive immune response by stimulating both T-regulatory cells and
activation-induced cell death in antigen-activated T cells. Some
research reports indicated that IL2 region seems to play a role in the
response to rituximab in SLE patients ([116]Marquez et al., 2013);
Moreover, TLR9 plays important role in immunopathology of SLE, because
increased apoptosis and/or clearance deficiencies in SLE are considered
to result in increased amounts of circulating plasma DNA, which may act
as TLR agonists and subsequently provide B cell activation signals
([117]Celhar et al., 2012). It is worth mention that 287-bp Alu
insertion/deletion (I/D) of ACE gene was association with SLE and renal
injury ([118]Xu et al., 2007). Some other SNPs, such as A5466C, T3892C,
A240T, C1237T, G2215A, A2350G, and C3409T, of ACE gene may affect the
risk of certain autoimmune diseases including IgA nephropathy and lupus
nephropathy ([119]Li et al., 2010). Overall, these results suggested
that LCW act synergistically to treat SLE by regulating inflammation,
and immunity and further confirmed that targets of LCW were regulated
by multi-components in the treatment of SLE.
Effective Proteins Selection and Validation From Optimization Space
Here, we used the weighted gene regulatory network and active
components targets network to construct disease-targets-components
network. This network contains 1,829 nodes and 24,841 edges. Degree is
an important topological property in the network that can be used to
evaluate the importance of nodes in the network. For each node i in
disease-targets-components network, if the degree of a node is more
than the average degree of all nodes in a network, such node is
believed to play a critical role in the network structure, and can be
treated as a hub node ([120]Liu et al., 2016). Following this rule, the
passed nodes and their edges in the disease-targets-components network
were kept and defined as optimization space. The optimization space
contains 565 nodes and 15,550 edges, each node represents one effective
protein, and thus we identified 565 effective proteins from the
optimization space. There are three categories of effective proteins in
optimization space. The first category is the direct interactions
between the component targets and pathogenic genes. We defined this
category as the essential common targets. The second category is the
interactions of disease-specific targets. The third category is the
interactions of component-specific targets ([121] Figure 5 and
[122]Table S5 ).
To test whether the effective proteins we selected from optimization
space could cover the pathogenic genes of SLE at functional level. We
performed functional pathway analysis using effective proteins and SLE
pathogenic genes, respectively. Among them, the effective proteins
enriched in 178 pathways (p < 0.05), and the pathogenic genes enriched
in 131 pathways (p < 0.05). The effective proteins enriched pathways
were found to cover 96% of the pathogenic genes enriched pathways
([123] Figure 6A ). Additionally, in order to test whether the
effective proteins in optimization space can be replaced by essential
common targets, disease-specific targets or component-specific targets
for further optimization. We performed pathway analysis on essential
common targets, disease-specific targets, and component-specific
targets, respectively. Results show that the coverage proportion of
enriched pathways of three categories compare with the enriched
pathways of pathogenic genes is 89%, 78%, 71%, respectively ([124]
Figures 6B, C ), which are far less than that of the effective
proteins. These results confirmed the accuracy and reliability of our
approach to construct optimization space and further demonstrated that
the effective proteins selected in the optimization space play a key
role in the pathogenesis of SLW.
Figure 6.
[125]Figure 6
[126]Open in a new tab
Validation of optimization space. (A) Venn diagram for pathway
enrichment analysis of effective proteins and SLE related genes. (B)
Venn diagram for pathway enrichment analysis of essential
component-specific targets, disease-specific targets, essential common
targets and SLE related genes. (C) The proportion histogram of enriched
pathways of three categories (component-specific targets,
disease-specific targets, essential common targets) and effective
proteins compare with the enrichment pathways of pathogenic genes. (D)
Bubble diagram for common pathway enrichment analysis of effective
proteins and SLE related genes.
According to the results of KEGG analysis, these effective proteins
were frequently involved in PI3K-Akt signaling pathway (hsa04151), Th17
cell differentiation (hsa04659), T cell receptor signaling pathway
(hsa04660), TNF signaling pathway (hsa04668), MAPK signaling pathway
(hsa04010), Toll-like receptor signaling pathway (hsa04620), NF-kappa B
signaling pathway (hsa04064), IL-17 signaling pathway (hsa04657), and B
cell receptor signaling pathway (hsa04662) ([127] Figure 6D ).
PI3K/Akt/mTOR signaling pathway plays an important role in cellular
proliferation and growth signaling. Increased activity of Akt can
reduce expression of its substrate p27kip1 in SLE ([128]Besliu et al.,
2009). This defect seems to be involved in SLE lymphocytes passage by
G1/S cell cycle checkpoint. Therefore, SLE lymphocytes accumulate in S
and G2/M cell cycle phases towards apoptosis or proliferation. Previous
research found that abnormal activation of the PI3K/AKT signaling
pathway by upregulation of CDKs and downregulation of p27Kip1 and
p21WAF1/CIP1 increased the proliferation of T lymphocytes might
participate in the pathogenesis of SLE in SLE patients ([129]Tang
et al., 2009). The KEGG analysis and literature reported suggested that
majority of them are related to immunity and inflammation, which are
confirmed associated with the pathogenesis of SLE and that may be a
potential therapeutic mechanism of LCW on SLE.
KGEC Selection and Validation
The CI module was established to capture the KGEC, which would be used
to illustrate the molecular mechanism of LCW in the therapy of SLE.
According to the contribution accumulation results, the top 11
components including vanillic acid (LCW190), pinoresinol monomethyl
ether (LCW169), phillyrin (LCW187), oleic acid (LCW160), palmitic acid
(LCW100), stearic acid (LCW139), ferulic acid (LCW75), methyl caffeate
(LCW137), p-coumaric acid (LCW186), ellagic acid (LCW8), and wogonin
(LCW130) contribute to 51.89% target coverage of effective proteins.
For further analysis, 82 components which can contribute to 90.18%
targets coverage of effective proteins were selected as KGEC ([130]
Figure 7 and [131]Table 3 ). Among these components in KGEC, some of
them belongs to different single herbs in LCW have the function of
clearing away heat and toxic materials: JYH (18), LQ (19), PGY (14), HL
(5), and WG (2). And some of them in different herbs have the function
of cooling blood and promoting blood circulation: DH (3), CS (9), DG
(4), DS (25), XS (6), TR (5), HH (21), CT (2), and ZBM (1). Higher
targets coverage of effective proteins proved that the KGEC may play
the leading role and generate combination effects in the treatment of
SLE.
Figure 7.
[132]Figure 7
[133]Open in a new tab
The CI and CI accumulation for KGEC selection in LCW. The bar diagram
and trend line diagram are used to visualize the targets coverage of
effective proteins of the LCW components and the contribution
accumulation results, respectively.
Table 3.
The information of selected KGEC in LCW.
ID Name MF MW ID Name MF MW
LCW190 Vanillic acid C[8]H[8]O[4] 168.16 LCW43 Danshenol B
C[22]H[26]O[4] 354.48
LCW169 Pinoresinol monomethyl ether C[21]H[24]O[6] 372.45 LCW71
Zinc14719978 C[19]H18O[4] 310.37
LCW187 Phillyrin C[27]H[34]O[11] 534.61 LCW36
(Z)-3-[2-(E)-2-(3,4-Dihydroxyphenyl)
ethenyl]-3,4-Dihydroxy-Phenyl]Acrylic Acid C[17]H[14]O[6] 314.31
LCW160 Oleic acid C[18]H[34]O[2] 282.52 LCW101 6-Hydroxykaempferol
C[15]H[10]O[7] 302.25
LCW100 Palmitic acid C[16]H[32]O[2] 256.48 LCW94 Beta-carotene
C[40]H[56] 536.96
LCW139 Stearic acid C[18]H[36]O[2] 284.54 LCW95 Cholesterol C[27]H[46]O
386.73
LCW75 Ferulic acid C[10]H[10]O[4] 194.2 LCW80 Licoricone C[22]H[22]O[6]
382.44
LCW137 Methyl caffeate C[10]H[10]O[4] 194.2 LCW66 Salviolone
C[18]H[20]O[2] 268.38
LCW186 P-coumaric acid C[9]H[8]O[3] 164.17 LCW147 Gibberellin 7
C[19]H[22]O[5] 330.41
LCW8 Ellagic acid C[14]H[6]O[8] 302.2 LCW175 Chrysanthemaxanthin
C[40]H[56]O[3] 584.96
LCW130 Wogonin C[16]H[12]O[5] 284.28 LCW171 Beta-amyrin acetate
C[32]H[52]O[2] 468.84
LCW114 Mandenol C[20]H[36]O[2] 308.56 LCW164
3-Acetyl-5-Hydroxy-7-Methoxy-2-Methylnaphthalene-1,4-Dione
C[14]H[12]O[5] 260.26
LCW111 (R)-Canadine C[20]H[21]NO[4] 339.42 LCW131 Zinc238769177
C[21]H[24]O[6] 372.45
LCW115 Chryseriol C[16]H[12]O[6] 300.28 LCW178 Epiberberine
C[20]H[18]NO[4] ^+ 336.39
LCW138 Myristic acid C[14]H[28]O[2] 228.42 LCW70 Zinc13341234
C[18]H[16]O[8] 360.34
LCW161 Sitogluside C[35]H[60]O[6] 576.95 LCW1 Baicalin C[21]H[18]O[11]
446.39
LCW129 Rutin C[27]H[30]O[16] 610.57 LCW24 Clionasterol C[29]H[50]O
414.79
LCW174 Cholesteryl ferulate C[37]H[54]O[4] 562.91 LCW41
Cryptotanshinone C[19]H[20]O[3] 296.39
LCW143 Stachyose C[24]H[42]O[21] 666.66 LCW45 Deoxyneocryptotanshinone
C[19]H[22]O[3] 298.41
LCW118 Ethyl Linolenate C[20]H[34]O[2] 306.54 LCW59 Nortanshinone
C[17]H[12]O[4] 280.29
LCW77 Kaempferol C[15]H[10]O[6] 286.25 LCW106 Pyrethrin II
C[22]H[28]O[5] 372.5
LCW170 3beta-Acetyl-20,25-epoxydammarane-24alpha-ol C[32]H[54]O[4]
502.86 LCW97 Quercetin C[15]H[10]O[7] 302.25
LCW38
2-(4-Hydroxy-3-Methoxyphenyl)-5-(3-Hydroxypropyl)-7-Methoxy-3-Benzofura
ncarboxaldehyde C[20]H[20]O[6] 356.4 LCW180 GA121-isolactone
C[23]H[32]O[5]Si 330.41
LCW185 Isoscopoletin C[10]H[8]O[4] 192.18 LCW104 Amygdalin
C[20]H[27]NO[11] 457.48
LCW29 Protocatechuic Acid C[7]H[6]O[4] 154.13 LCW134 Esculetin
C[9]H[6]O[4] 178.15
LCW127 (-)-Phillygenin C[21]H[24]O[6] 372.45 LCW68 Tournefolic acid A
C[17]H[12]O[6] 312.29
LCW126 Isolariciresinol C[20]H[24]O[6] 360.44 LCW110 Magnograndiolide
C[15]H[22]O[4] 266.37
LCW31 Tanshinone II a C[19]H[18]O[3] 294.37 LCW81
Methoxy-7-Hydroxycoumarin C[16]H[12]O[6] 300.28
LCW93 Shinpterocarpin C[20]H[18]O[4] 322.38 LCW84 Glabridin
C[20]H[20]O[4] 324.4
LCW108 Quercetagetin C[15]H[10]O[8] 318.25 LCW135 Ethyl Caffeate
C[11]H[12]O[4] 208.23
LCW113 Palmatine C[21]H[22]NO[4] ^+ 352.44 LCW55 Miltionone II
C[19]H[20]O[4] 312.39
LCW54 Miltionone I C[19]H[20]O[4] 312.39 LCW90 Liquiritin
C[21]H[22]O[9]418.43
LCW116 Corymbosin C[19]H[18]O[7] 358.37 LCW3 3-Epi-Beta-Sitosterol
C[29]H[50]O 414.79
LCW25 Luteolin C[15]H[10]O[6] 286.25 LCW123 Arctiin C[27]H[34]O[11]
534.61
LCW9 Paeoniflorigenone C[17]H[18]O[6] 318.35 LCW181 GA122-isolactone
C[23]H[32]O[5]Si 416.6
LCW11 Paeonol C[9]H[10]O[3] 166.19 LCW79 Licochalcone B C[16]H[14]O[5]
286.3
LCW103 7,8-dimethyl-1H-pyrazino[2,3-g]quinazoline-2,4-dione
C[12]H[10]N[4]O[2] 242.26 LCW188 Protocatechualdehyde C[7]H[6]O[3]
138.13
LCW122 Bicuculline C[20]H[17]NO[6] 367.38 LCW26 Sugiol C[20]H[28]O[2]
300.48
LCW32 3-Hydroxymethylenetanshinquinone C[18]H[14]O[4] 294.32 LCW34
Tanshinone II b C[19]H[18]O[4] 310.37
LCW51 Isotanshinone IIa C[19]H[18]O[3] 294.37 LCW173 Carthamone
C[21]H[20]O[11] 448.41
LCW14 Zinc15211904 C[17]H[18]O[6] 318.35 LCW98 Lignan C[25]H[30]O[8]
458.55
[134]Open in a new tab
In order to investigate the function of LCW in the treatment of SLE, we
performed pathway analysis using KGEC targets and SLE pathogenic genes,
respectively. Among them, the KGEC targets enriched in 181 pathways (p
< 0.05), and the pathogenic genes enriched in 131 pathways (p < 0.05).
The KGEC targets enriched pathways were found to cover 80.15% of the
pathogenic genes enriched pathways ([135] Figure 8 ). These major
targets of KEGG were frequently involved in PI3K-Akt signaling pathway
(hsa04151), HIF-1 signaling pathway (hsa04066), MAPK signaling pathway
(hsa04010), T cell receptor signaling pathway (hsa04660), IL-17
signaling pathway (hsa04657), B cell receptor signaling pathway
(hsa04662), TNF signaling pathway (hsa04668), Toll-like receptor
signaling pathway (hsa04620), NF-kappa B signaling pathway (hsa04064),
JAK-STAT signaling pathway (hsa04630) and Th1 and Th2 cell
differentiation (hsa04658), etc. For example, the PI3K-Akt signaling
pathway (hsa04151) was essential to cellular proliferation and growth.
In addition, it was correlated with autoimmune diseases due to its
activation in lymphocytes, which are developed features of systemic
autoimmunity ([136]Ge et al., 2017). NF-kappa B was an essential
modulator in the pathogenesis of SLE in the context of the increasing
immune deficiencies ([137]Wong et al., 1999). The TLR family in the
NF-kappa B pathway was responsible for sensing microbial pathogens and
occupied an important position in innate immune responses. So TLR
signals in B cells amplified anti-dsDNA autoantibody and enhanced one
SLE characteristic, autoantibody production ([138]Papadimitraki et al.,
2006). This result suggested that the strategy of combining the
optimization space with the CI model to optimize the herbal formula is
reliable and the predicted KGEC may play therapy role by mediating
multiple inflammation-related pathways.
Figure 8.
[139]Figure 8
[140]Open in a new tab
Pathway enrichment analysis of KGEC Targets. Veen diagram for pathway
enrichment analysis of KGEC targets and SLE related genes; The size of
the circle represents the number of genes enriched in the pathways, and
the color change of the circle represents the significant degree of the
enriched genes in the pathways.
GO Enrichment Analysis of KGEC Targets
GO enrichment analysis based on clusterProfiler package of R software
was performed to identify the biological functional of the primary
targets with p-values <0.05. In order to further dissect the
combination effects of LCW, all the targets interacting with KGEC of
LCW were enriched by GO enrichment analysis ([141] Figure 9 ).
Figure 9.
[142]Figure 9
[143]Open in a new tab
GO enrichment analysis of KGEC Targets. The Orange, purple and bright
green color on the circle represent biological processes, cellular
component and molecular function, respectively. The color change of the
bar represents the significant degree of the enriched genes in the GO
items.
GO analysis showed that the targets of KGEC were enriched in biological
processes related to inflammation and immunity. For example, the
pathways of inflammation are regulation of inflammatory response
(GO:0050727, LYN, PTGS2, PPARG, PTPRC, etc.), leukocyte activation
involved in inflammatory response (GO:0002269, PTPRC, LRRK2, TNF, JUN,
TLR2, etc.), production of molecular mediator involved in inflammatory
response (GO:0002532, TNF, KDM6B, CXCR2, ELANE, CNR1, etc.), and
inflammatory response to antigenic stimulus (GO:0002437, LYN, TLR4, F2,
SERPINE1, NOS2, etc.). Important genes involved in inflammation in
these pathways include TNF, NOS2, TLR2, etc. these genes are involved
in inflammation of SLE ([144]Oates et al., 2003; [145]Marques et al.,
2016; [146]Zhao et al., 2017). The pathways of immunity are regulation
of innate immune response (GO:0045088, PTPN22, LYN, PPARG, JAK1, EP300,
etc.), immune response-regulating cell surface receptor signaling
pathway (GO:0002768, PTPN22, BLK, LYN, PTPRC, EP300, etc.), regulation
of production of molecular mediator of immune response (GO:0002700,
PTPRC, IL1B, IL2, TLR9, TNF, etc.), B cell activation involved in
immune response (GO:0002312, PTPRC, IL2, TLR4, LGALS1, ABL1, etc.) and
T cell differentiation involved in immune response (GO:0002292, IL2,
MTOR, STAT3, RORA, RORC, etc.). Important genes involved in
inflammation in these pathways include PTPN22, TLR4, MTOR. These genes
are involved in inflammation of SLE ([147]Lai et al., 2015;
[148]Wong-Baeza et al., 2015; [149]Morris et al., 2016). SLE is an
autoimmune disease characterized by the presence of circulating immune
complexes and inflammation in multiple organs and tissues. GO analysis
confirmed that LCW treat SLE through regulation of inflammation and
immune therapy.
Interestingly, LCW regulates the GO cellular component of SLE,
including mitochondrial respiratory chain complex I (GO: 0005747, SNCA,
NDUFS4, NDUFA4, NDUFA1, NDUFA10, etc.), mitochondrial respiratory chain
(GO:0005746, SNCA, NDUFS4, NDUFA4, NDUFA1, NDUFA10, etc.), and
oxidoreductase complex (GO:1990204, SNCA, NDUFS4, P4HB, NOX1, NOX4,
etc.). Accumulating evidence indicates that mitochondrial dysfunction
plays important roles in the pathogenesis of SLE, including
mitochondrial DNA damage, mitochondrial dynamics change, abnormal
mitochondrial biogenesis and energy metabolism, oxidative stress,
inflammatory reactions ([150]Lee et al., 2016). Given the accumulating
evidence for mitochondrial release during inflammatory pathogenesis,
these observations point to a role for mitochondria both in the
stimulation of the innate immune system and as a potential source of
autoantigens. Our results indicated that LCW may play a role in the
treatment of SLE by modulating targets on the mitochondria.
Moreover, LCW regulates the GO molecular function of SLE including
protein serine/threonine kinase activity (GO: 0004674, LRRK2, MAPK1,
MTOR, EGFR, PRKCB, etc.), transcription factor activity (GO:0098531,
PPARG, RXRA, ESR1, STAT3, VDR, etc.), and protein tyrosine kinase
activity (GO:0004713, BLK, LYN, JAK1, EPHB2, HSP90AA1, etc.).
Increasing evidence confirmed enzymes play different roles in
regulating inflammation and immunity ([151]Cunninghame Graham et al.,
2007; [152]Suarez-Fueyo et al., 2011). Our results indicate that LCW
may affect different types of enzyme functions in the treatment of SLE.
KEGG Enrichment Analysis of KGEC Targets
SLE is a chronic autoimmune disease involving multiple organs and
systems characterized by the production of multiple autoantibodies.
Previous studies confirmed that SLE-associated pathways could be
decomposed into several functional modules such as immune response,
synthesis of inflammatory mediators, autoimmune pathology, neutrophil
recruitment, immunity to extracellular pathogens, cell cycle
progression, protein synthesis, apoptosis, and so forth. Increasing
evidence indicate that PI3K-Akt signaling pathway (hsa04151), TNF
signaling pathway (hsa04668), NF-kappa B signaling pathway (hsa04064)
and IL-17 signaling pathway (hsa04657) response to these functional
modules. Such as, PI3K-Akt signaling pathway (hsa04151) has been
reported involved in the inhibition of apoptosis, cell proliferation
and expression of inflammatory cytokines. TNF signaling pathway
(hsa04668) can induce a wide range of intracellular signal pathways
including apoptosis and cell survival as well as inflammation and
immunity. NF-kappa B signaling pathway (hsa04064) is the generic name
of a family of transcription factors that function as dimers and
regulate genes involved in immunity, inflammation and cell survival.
While IL-17 signaling pathway (hsa04657) work as a subset of cytokines
consisting of IL-17A-F, plays crucial roles in both acute and chronic
inflammatory responses. For exploring the mechanism of LCW in the
treatment of SLE at the system level, we constructed a comprehensive
signaling pathway use four important molecular pathways ([153] Figure
10 ).
Figure 10.
[154]Figure 10
[155]Open in a new tab
Distribution of targets of KGEC on the comprehensive signaling pathway.
Different colors represent different types of targets. Light green
represents the targets of LCW, orange represents the pathogenetic genes
of SLE, and pink represents shared targets, respectively.
These four pathways play important roles in the treatment of SLE. In
order to define the position of LCW targets on the pathways, we
consider the first three columns as upstream and the rest as a
downstream position of the pathway. Among them, PI3K-Akt signaling
pathway (hsa04151) is one of the top pathways in the treatment of SLE
with KGEC in LCW. KGEC regulates 10 targets located upstream of
PI3K-Akt signaling pathway (hsa04151), such as RTK, TLR2/4 and JAK, and
24 targets located downstream pathways, such as PI3K, AKT, and mTOR.
The downstream targets account for more than 70%. KGEC may activate
downstream of the PI3K and AKT proteins through the upstream TLR2/4,
resulting in downstream GSK3, RXRα, and CREB cascade amplification,
which are closely related to SLE immune response, cell proliferation
and protein synthesis ([156]Bentham et al., 2015). Most of the targets
of KGEC regulating TNF signaling pathway (hsa04668) are located
downstream of the pathway, such as JNK, C-JUN and RAF-1. In addition,
it can also be seen ([157] Figure 10 ) that KGEC can also affect the
activation of PI3K and AKT proteins downstream of TNF to play a role in
the treatment of SLE. Therefore, KGEC in LCW plays a key role in the
treatment of SLE by regulating the TNF-PI3K-AKT key cascade to
synergistically affect the process of immunity and inflammation.
NF-kappa B signaling pathway (hsa04064) and IL-17 signaling pathway
(hsa04657) are also important pathways in the treatment of SLE by LCW.
The targets regulated by KGEC are more downstream of the pathway. For
example, 19 targets such as TAK1, IL-1 β, and COX2 of KGEC are located
downstream of NF-kappa B signaling pathway (hsa04064). KGEC in LCW can
affect upstream BCR, and then activate downstream AP-1, to further
affect a series of inflammatory and immune-related proteins such as
IL-1 β, TNF α, and COX2 related to SLE. The 12 targets of KGEC, such as
MAPKs, AP-1 and LCN2, in the IL-17 signaling pathway (hsa04657) are
located downstream of the pathway. KGEC in LCW can activate downstream
AP-1, through upstream MAPKs, and affect a series of inflammatory and
immune-related proteins such as LCN2, MMP1, and MMP3 related to SLE
([158]Marques et al., 2016). Therefore, KGEC in LCW can also play a
role in the treatment of SLE by regulating the NTF-MAPKs-AP-1 key
cascade to synergistically affect the process of immunity and
inflammation.
Experimental Validation In Vitro
The effect of liquiritin and ferulic acid in KGEC was assessed in RAW
264.7 cells. As shown in [159]Figure S1 , 10–100 μM of liquiritin and
ferulic acid revealed no obvious effects. To evaluate the
anti-inflammatory effect of liquiritin and ferulic acid, the level of
IL-6 was detected. Exposure of RAW 264.7 cells to resiquimod
significantly elevated the level of IL-6 (p < 0.01). In contrast,
pretreatment with liquiritin and ferulic acid significantly attenuated
the phenomenon induced by resiquimod ([160] Figures 11A, B ). According
to the anti-inflammatory effect of liquiritin and ferulic acid, 50 μM
liquiritin and ferulic acid were used in subsequent research.
Therefore, liquiritin and ferulic acid concentration of 50 μM were used
to evaluate the effect on resiquimod-induced RAW 264.7 cells. Exposure
of RAW 264.7 cells to resiquimod significantly elevated the protein
expression of IL- p-ERK1/2, p-AKT, and p-PI3K. In contrast, treatment
with liquiritin and ferulic acid significantly attenuated the
phenomenon induced by resiquimod ([161] Figures 11C–F ). Our results
demonstrated that liquiritin and ferulic acid inhibited the phenomenon
production in resiquimod induced RAW264.7 cells.
Figure 11.
[162]Figure 11
[163]Open in a new tab
Effects of ferulic acid (A) and liquiritin (B) on resiquimod-induced
RAW 264.7 cells IL-6 cytokines expression and the protein expression of
p-ERK1/2, p-AKT, and p-PI3K of resiquimod induced RAW264.7 cells (C–F).
^## p < 0.01, compared with control group. *p < 0.05, **p < 0.01,
compared with the resiquimod group.
Discussion
The main purpose of formula optimization is to reduce the
non-pharmacological factors and improve the curative effect of the
formula. Although different medicinal herbs are composed as the formula
according to the theory of TCM, whether the herbs or the components in
the formula are necessary, especially for a specific indication is
still need analysis and verification. Through optimization of formula,
the medicine herbs or ingredient with effect can be screened, so that
the formula is more simplified and the drug effect is more clarified.
In order to better optimize the classical formula with clinical
curative effect, effective proteins, common targets, disease-specific
targets, component-specific targets and KGEC targets were defined as
changed target-data sets. Mathematical methods and network pharmacology
were employed to investigate the coverage percentage of changed
target-data sets related functional analysis on that of the disease
pathogenesis genes. The changed target-data sets response to different
targets of various herbs and different chemical components in each
formula. To find the relatively optimal KGEC, the strategy of
optimization space definition and reverse searching for components was
applied and evaluated based on changed target-data sets, which provide
a methodological reference for the research and development of new
drugs based on TCM.
At present, how to optimize and obtain the KGEC and analyze their
mechanism of action is the basis for quantification of TCM. The
research of TCM emphasizes the holistic view, integrity and synergy.
Network pharmacology has the characteristics of systematization and
integrity, which is consistent with the philosophy of TCM research.
Network pharmacology emphasizes multi-targets regulation of signal
pathways to improve the therapeutic effect of drugs and reduce toxic
and side effects. At present, network pharmacology is widely used in
the treatment of complex diseases in TCM. For example, to determine the
molecular mechanism of TCM formula in the treatment of complex
diseases, such as “treating different diseases with the same treatment,
treating the same diseases with different treatment”, but there are few
research reports based on network pharmacology to study the
optimization of formula in TCM. Thus, we propose an integrative
strategy to optimize LCW based on network pharmacology, obtain the key
components of LCW in the treatment of SLE, and analyze the potential
mechanism of these components.
In the process of analyzing the therapeutic mechanism, network
pharmacology has formed its own analytical rules, in the first step,
select active components through ADME/T screening based on the chemical
properties of components in TCM. Then predict targets and analyze
potential mechanisms. This flowchart really solves the molecular
mechanism of some formulas of TCM in treating complex diseases. Such
as: Hua Yu Qiang Shen Tong Bi formula treats rheumatoid arthritis
([164]Wang et al., 2019), Shen Qi Wan treats kidney yang deficiency
syndrome ([165]Zhang et al., 2019), and Huo Xiang Zheng Qi
formula treats functional dyspepsia ([166]Zhao et al., 2018). However,
there still exists some problems, such as false positive and noise in
the predicted targets of components. Here we propose a strategy to
optimize active components and decode molecular mechanism of LCW in the
treatment of SLE. In this strategy, we build an optimization space and
extract effective proteins based on the associations of component
targets and pathogenetic genes to reduce the false positive and noise.
The result shows that the enriched functional pathways of effective
protein can cover 96% of the enriched functional pathways of
pathogenetic genes. It proves that our strategy of constructing
target-based optimization space to select effective proteins is
appropriate and reliable. Based on the effective proteins provided by
optimization space, we calculated the accumulation contribution degree
by using the CI model, and the accumulation contribution degree of 82
active components reach more than 90% was finally optimized as KGEC.
KEGG and Go analysis confirmed that the targets of our optimized KGEC
are closely related to the pathogenesis of SLE in pathways and
functional annotation. This proves once again the reliability of our
optimization space and CI model.
Currently, our network pharmacology model provides a powerful tool for
exploring the compatibility and mechanism of TCM formula. Cellular
experiments were applied to prove the reliability of the network
pharmacology model through verifying the protective effects of
components in KGEC of LCW on the inflammation of mice RAW264.7 cells
induced by resiquimod. In addition, in order to better evaluate the
reliability of our proposed network pharmacology model, in vivo study
will be conducted to verify the efficacy and mechanism of active
components in the treatment of SLE in our future research.
Data Availability Statement
The raw data supporting the conclusions of this manuscript will be made
available by the authors, without undue reservation, to any qualified
researcher.
Author Contributions
A-pL, X-mQ, and D-gG provided the concept and designed the study. YG,
K-xW, PW, XL, J-jC, B-yZ, and J-sT conducted the analyses and wrote the
manuscript. YG, K-xW, PW, XL, J-jC, B-yZ, and J-sT participated in data
analysis. A-pL, X-mQ, and D-gG 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], 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] and the
National S&T Major Project for “Major New Drugs Innovation and
Development” [2017ZX09301047].
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:
[167]https://www.frontiersin.org/articles/10.3389/fphar.2020.512877/ful
l#supplementary-material.
Supplementary Table 1
The information of SLE genes.
[168]Click here for additional data file.^ (308KB, xlsx)
Supplementary Table 2
The information of LCW components.
[169]Click here for additional data file.^ (308KB, xlsx)
Supplementary Table 3
The information of LCW active components.
[170]Click here for additional data file.^ (308KB, xlsx)
Supplementary Table 4
The relationship between components and targets.
[171]Click here for additional data file.^ (308KB, xlsx)
Supplementary Table 5
The information of effective proteins.
[172]Click here for additional data file.^ (308KB, xlsx)
Supplementary Figure 1
Effects of ferulic acid (A) and liquiritin (B) on cell viabilities.
[173]Click here for additional data file.^ (270.2KB, tif)
Abbreviations
CI, Contribution index; DL, Drug-likeness; GO, Gene Ontology; KGEC, Key
group of effective components; KEGG, Kyoto Encyclopedia of Genes and
Genomes; LCW, Lang Chuang Wan; OB, Oral bioavailability; PPI,
Protein-protein interactions; SLE, Systemic lupus erythematosus; TCM,
Traditional Chinese medicine.
References