Abstract
Objective
Chinese medicine formulae possess the potential for cholestasis
treatment. This study aimed to explore the underlying mechanisms of
San-Huang-Chai-Zhu formula (SHCZF) against cholestasis.
Methods
The major chemical compounds of SHCZF were identified by
high-performance liquid chromatography. The bioactive compounds and
targets of SHCZF, and cholestasis-related targets were obtained from
public databases. Intersected targets of SHCZF and cholestasis were
visualized by Venn diagram. The protein-protein interaction and
compound-target networks were established by Cytoscape according to the
STRING database. The biological functions and pathways of potential
targets were characterized by Gene Ontology and Kyoto Encyclopedia of
Genes and Genomes enrichment analysis. The biological
process-target-pathway network was constructed by Cytoscape. Finally,
the interactions between biological compounds and hub target proteins
were validated via molecular docking.
Results
There 7 major chemical compounds in SHCZF. A total of 141 bioactive
compounds and 83 potential targets were screened for SHCZF against
cholestasis. The process of SHCZF against cholestasis was mainly
involved in AGE-RAGE signaling pathway in diabetic complications, fluid
shear stress and atherosclerosis, and drug metabolism-cytochrome P450.
ALB, IL6, AKT1, TP53, TNF, MAPK3, APOE, IL1B, PPARG, and PPARA were the
top 10 hub targets. Molecular docking showed that bioactive compounds
of SHCZF had a good binding affinity with hub targets.
Conclusions
This study predicted that the mechanisms of SHCZF against cholestasis
mainly involved in AGE-RAGE signaling pathway in diabetic
complications, fluid shear stress and atherosclerosis, and drug
metabolism-cytochrome P450. Moreover, APOE, AKT1, and TP53 were the
critical hub targets for bioactive compounds of SHCZF.
Introduction
Cholestasis is a common clinical manifestation of liver disease mainly
derived from the reduction or obstruction of bile flow [[30]1]. The
long-term cholestasis in liver can lead to hepatocyte dysfunctions,
thereby causing severe liver diseases such as primary biliary
cirrhosis, primary sclerosing cholangitis and secondary sclerosing
cholangitis [[31]2]. At present, although some drugs, such as
rosiglitazone, obeticholic acid, and ursodeoxycholic acid, have been
developed for cholestasis treatment, the therapeutic effect is still
limited and may contribute to pruritus, dyslipidemia, and
gastrointestinal symptoms [[32]3, [33]4]. Therefore, the discovery of
effective drugs for cholestasis treatment is of great significance.
Accumulating evidence indicated that Chinese medicines exert beneficial
therapeutic effects in liver diseases and cholestasis [[34]5, [35]6].
San-Huang-Chai-Zhu formula (SHCZF) is a Chinese herbal formula, which
consists of five herbs, namely, Dahuang (Rhei Radix Et Rhizome),
Huangbai (Phellodendri Chinrnsis Cortex), Huangzhizi (Gardeniae
Fructus), Chaihu (Radix Bupleuri), and Baizhu (Atractylodes
Macrocephala Koidz.). Previous studies indicated that these five herbs
all possess the hepatoprotective effect on liver diseases. Cao et al.
[[36]7] reported that Dahuang had extensive pharmacological effects in
hepatoprotective, anti-inflammatory, anticancer and so on. Huangbai and
Huangzhizi were widely used to ameliorate inflammation and
hepatotoxicity as a core component of herbal formula [[37]8, [38]9].
Saikosaponins extracted from Chaihu showed valuable pharmacological
activities of anti-inflammatory and liver protection [[39]10]. Baizhu
in Xiaoyao San formula was also validated its pharmacological effects
of hepatoprotection [[40]11]. However, the underlying pharmacological
mechanism of SHCZF against cholestasis is still illusive.
Network pharmacology is a favorable method to reveal the
pharmacological mechanism of Chinese medicine formulae against specific
diseases and identify the relevant drugs, targets, and pathways
[[41]12–[42]14]. This approach comprehensively investigates the
interactions of bioactive ingredients, targets, and diseases, and the
relationship are visualized by interaction networks. For instance, by
combining the network pharmacology with the pathological examination,
Xiaoyaosan decoction was proved the therapeutic effects on alleviating
liver fibrosis [[43]15]. The potential biological mechanisms of
GegenQinlian decoction also were unveiled to improve insulin resistance
in liver, adipose, and muscle tissue by network pharmacology analysis
[[44]16]. Therefore, network pharmacology is a commendable approach for
exploring the underlying mechanisms of SHCZF against cholestasis.
In this article, the underlying mechanisms of SHCZF against cholestasis
were uncovered by identifying bioactive compounds and potential target
genes. Moreover, the interactions between major bioactive compounds and
hub target proteins were validated by molecular docking. This study
provides an essential foundation for further experimental
investigations and clinical application of SHCZF against cholestasis.
Methods
Main ingredients analysis of SHCZF
SHCZF was prepared by mixing five herbs (Dahuang, Huangbai, Huangzhizi,
Chaihu, and Baizhu) in the ratio of 4:4:3:3:4. The extract of SHCZF was
obtained by adding 10 times the amount of water, soaking for 30 min,
and boiling for 1.5 h. After filtering out the liquid, samples were
added 8 times the amount of water and decocted for 0.5 h after boiling.
Then, the obtained extract was concentrated into 2 g/mL for
high-performance liquid chromatography (HPLC) determination. Samples
were analyzed using a LC-20AT HPLC system (Shimadzu, Japan) and
separated using an Extend-C18 column (250 mm × 4.6 mm, 5 μm) (Agilent,
CA, USA) with a mobile phase consisting of 0.1% phosphoric acid (A) and
acetonitrile (B). The elution gradient was as follows: 0–10 min with
90% A and 10% B, 10–20 min with 30% A and 30% B, 20–30 min with 40% A
and 60% B, 30–53 min with 30% A and 70% B, 53–54 min with 90% A and 10%
B, and 59 min controller stop. The molecular structures of these seven
compounds of SHCZF were downloaded from ZINC
([45]https://zinc15.docking.org/) [[46]17].
Screening for bioactive ingredients and targets of SHCZF
All ingredients from 5 herbs of SHCZF were retrieved from the
traditional Chinese medicine integrated database (TCMID,
[47]http://www.megabionet.org/tcmid/) [[48]18], the traditional Chinese
medicine systems pharmacology database and analysis platform (TCMSP,
[49]https://old.tcmsp-e.com/tcmsp.php) [[50]19], and herb ingredients’
targets (HIT, [51]http://lifecenter.sgst.cn/hit/) database [[52]20].
Totally, 227 compounds were obtained after eliminating those compounds
without targets. In addition, Search tool for interacting chemicals
(STITCH, [53]http://stitch.embl.de/) database [[54]21] and the above
data sources were used to retrieve targets associated with 227
compounds from SHCZF with a setting of minimum required interaction
score = 0.400 in STITCH. A total of 5216 targets was collected and the
Gene ID of these targets was normalized by National Center for
Biotechnology Information (NCBI) database
([55]https://www.ncbi.nlm.nih.gov/).
Drug-likeness calculation of SHCZF compounds
The 227 compounds of SHCZF were screened by drug-likeness evaluation.
The assessment of drug-likeness properties is mainly determined by
absorption, distribution, metabolism, and elimination (ADME) features
of compounds [[56]22]. The quantitative estimate of drug-likeness (QED)
value is an important parameter to assess ADME characteristics. In this
work, we calculated QED value described by Bickerton [[57]23] to screen
pharmaceutically active compounds in SHCZF. The equation of QED
calculation was shown as follows:
[MATH: QED=exp(1n∑i=1<
/mn>nlndi) :MATH]
In this equation, desirability functions (d) were obtained by
integrating 8 physicochemical properties of compounds, including
molecular weight (MW), the number of hydrogen bond acceptors (HBAs),
the number of hydrogen bond donors (HBDs), the octanol-water partition
coefficient (ALogP), the number of rotatable bonds (ROTBs), the number
of aromatic rings (AROMs), molecular polar surface area (PSA), and the
number of structural alerts (ALERTS). Compounds in SHCZF with QED ≥ 0.2
referring to the DrugBank database ([58]https://go.drugbank.com/) were
included for following analyses.
Target selection of active compounds in SHCZF
To precisely define compound-target interaction, the enrichment scoring
algorithm based on a binomial statistical model was used to screen core
targets of compounds [[59]24, [60]25]. The target that interacts with
most of active compounds can be considered as a core target of SHCZF.
The probability of being a core target was calculated as follows:
[MATH: Pi(X≥k)=∑m=knCnm(p)m(1<
mo>−p)n−m :MATH]
where, n is the total number of compounds in SHCZF, p is the ratio of
the average number of compounds simultaneously interacting with the
same target in the total target of SHCZF compounds, and P[i] (X ≥ k)
represents the probability of a target gene (i) simultaneously
interacting with more than k active compounds. The investigated target
with P < 0.01 can be regarded as a core target for SHCZF compounds.
Screening of targets associated with cholestasis
The cholestasis-related targets were retrieved from the GeneCards
database ([61]https://www.genecards.org/) [[62]26, [63]27], the online
mendelian inheritance in man (OMIM, [64]https://www.omim.org/) database
[[65]28], and the DisGeNET database
([66]https://www.disgenet.org/home/) [[67]29]. Accordingly, 56, 28, and
420 cholestasis-related targets were collected from GeneCards, OMIM,
and DisGeNET databases, respectively. A total of 449 targets was
obtained after removing duplicates ([68]S1 Table).
Construction of protein-protein interaction (PPI) network and compound-target
(C-T) network
The intersection targets of SHCZF and cholestasis were visualized by a
Venn diagram. PPI network of common target proteins was established and
analyzed using Search Tool for the Retrieval of Interacting
Genes/Proteins (STRING) dataset ([69]https://string-db.org) [[70]30],
where each node in the network represented a target, and the node with
higher degree means the more important target in the network. The C-T
network of SHCZF against cholestasis was constructed using Cytoscape
(v3.8.2) [[71]31].
Biological function enrichment analyses
In order to further explore the biological functions of SHCZF acting on
cholestasis, core targets were integrated for Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
[[72]32]. GO enrichment analysis included molecular function (MF),
biological process (BP), and cellular component (CC) analyses according
to the GO database. KEGG enrichment analysis were performed according
to the KEGG database. A hypergeometric distribution model was used to
assess whether the core target genes were significantly related to
specific GO terms and KEGG pathways [[73]33], showed as follows:
[MATH: P=1−∑i=0k−1<
mrow>(Mi)(
N−Mn
−i)(<
mtable>Nn
) :MATH]
where, N is the total number of genes, M is the number of genes
annotated in GO and KEGG databases, n is the number of investigated
target genes of SHCZF, and k is the number of intersection genes of
SHCZF and annotated genes. P-values that corrected by the Bonferroni
method reflected the relevance between potential targets and GO terms
or KEGG pathways. GO terms and KEGG pathways with P-value < 0.01 were
considered as significant relevance. Bubble charts and histograms were
drawn based on the cluster profiler package R 3.15.4.
Construction of a target-pathway network for SHCZF against cholestasis
To elucidate the pharmacological mechanism of SHCZF in cholestasis
treatment, Cytoscape was used to construct a BP-target-pathway network.
The degree, betweenness and centeredness of potential target were
calculated by a CytoHubba plugin [[74]34]. The core targets, top 15
KEGG pathways, and top 15 BPs were included in the network. Targets
with flesh-colored circles, pathways with green circles, and BPs with
purple circles were presented as nodes, and the interactions between
nodes were expressed as edges.
Molecular docking
Molecular docking was conducted to validate the interactions between
bioactive compounds and target proteins of SHCZF against cholestasis.
The top 10 hub target proteins were selected for molecular docking and
used for PPI network construction by a CytoHubba plugin in Ctytoscape.
The 3D structures of target proteins were obtained from Protein Data
Bank (PDB, [75]https://www.rcsb.org/) [[76]35]. After deleting water
molecules using PyMol (v2.3.0) [[77]36], the obtained protein
structures were imported into AutoDockTools (v1.5.6) to construct
mating pocket of molecular docking. Molecular docking with bioactive
compounds was performed using AutoDock Vina (v1.1.2) [[78]37] based on
the data collected above.
Results
Major ingredients in SHCZF
HPLC was performed to identify the major chemical compounds in SHCZF.
Seven main compounds of SHCZF were obtained, including chrysophanol,
emodin, physcion, rhein, aloe-emodin, berberine chloride, gardenoside
([79]S1A–S1G Fig). The chemical structures of these 7 compounds were
shown in [80]Table 1.
Table 1. Chemical structures of 7 major compounds of San-Huang-Chai-Zhu
formula (SHCZF).
Synonyms Cas Molecular Formula
Chrysophanol 481-74-3 C[15]H[10]O[4]
Emodin 518-82-1 C[15]H[10]O[5]
Physcion 521-61-9 C[16]H[12]O[5]
Rhein 478-43-3 C[15]H[8]O[6]
Aloe-emodin 481-72-1 C[15]H[10]O[5]
Berberine chloride 633-65-8 C[20]H[18]ClNO[4]
Gardenoside 24512-62-7 C[17]H[24]O[11]
[81]Open in a new tab
Bioactive components and targets of SHCZF
QED is a critical indicator to evaluate the drug-likeness of compounds.
According to the QED values, 216 drug-likeness components in SHCZF were
obtained based on the TCMID, TCMSP, and HIT database. Moreover, 162
active compounds and 457 SHCZF compound-related targets were collected
by combining the public databases with a binomial statistical model.
There were 19, 40, 34, 93, and 22 bioactive compounds in Dahuang,
Huangbai, Huangzhizi, Chaihu, and Baizhu of SHCZF, respectively ([82]S2
Table).
Potential targets of SHCZF active compounds for cholestasis treatment
According to the GeneCards, OMIM, and DisGeNET databases, a total of
449 cholestasis-related target genes were obtained after eliminating
duplicates ([83]S1 Table). The intersection between 457 SHCZF targets
and 449 cholestasis-related targets was presented by a Venn diagram. As
a result, there were 83 overlapping targets considered as core targets
associated with both SHCZF compounds and cholestasis ([84]Fig 1A &
[85]Table 2). Furthermore, 83 potential targets were input into the
STRING database to construct a PPI network. Nodes and edges in the PPI
network represent targets and protein-protein associations,
respectively. The PPI network included 83 nodes and 1034 edges. Green
and yellow circles in the PPI network stood for 83 potential targets.
The degree of targets represents the number of links to nodes, and the
target with higher degree can be regarded as the more important target.
In this PPI network, the darker green circles mean the targets with
higher degree and yellow circles mean less importance. The average node
degree of this PPI network was 24.9, and ALB, IL6, AKT1, TP53, TNF,
MAPK3, APOE, IL1B, PPARG, and PPARA were top 10 targets with high
degrees ([86]Fig 1B).
Fig 1. The 83 potential targets for San-Huang-Chai-Zhu formula (SHCZF) in
cholestasis treatment.
[87]Fig 1
[88]Open in a new tab
(A) Intersection of SHCZF and cholestasis targets was visualized by
Venn diagram. (B) Protein-protein interaction (PPI) network of 83
common targets. Each node represents a common target for SHCZF and
cholestasis, and each edge represents the association between two
targets. The darker green means the higher degree value, and the
average degree is 24.9.
Table 2. 83 potential targets of SHCZF against cholestasis.
Gene ID Target Name Gene ID Target Name Gene ID Target Name Gene ID
Target Name
19 ABCA1 1559 CYP2C9 4846 NOS3 7157 TP53
183 AGT 1565 CYP2D6 4988 OPRM1 7376 NR1H2
207 AKT1 1576 CYP3A4 5122 PCSK1 7412 VCAM1
213 ALB 1581 CYP7A1 5243 ABCB1 8856 NR1I2
216 ALDH1A1 1728 NQO1 5290 PIK3CA 9002 F2RL3
219 ALDH1B1 2099 ESR1 5319 PLA2G1B 9370 ADIPOQ
335 APOA1 2147 F2 5320 PLA2G2A 9429 ABCG2
345 APOC3 2149 F2R 5443 POMC 9970 NR1I3
348 APOE 2539 G6PD 5444 PON1 10062 NR1H3
551 AVP 2641 GCG 5465 PPARA 10891 PPARGC1A
567 B2M 2950 GSTP1 5468 PPARG 23411 SIRT1
570 BAAT 3162 HMOX1 5594 MAPK1 54575 UGT1A10
596 BCL2 3383 ICAM1 5595 MAPK3 54576 UGT1A8
841 CASP8 3480 IGF1R 5599 MAPK8 54577 UGT1A7
847 CAT 3552 IL1A 5603 MAPK13 54578 UGT1A6
885 CCK 3553 IL1B 5970 RELA 54658 UGT1A1
1080 CFTR 3569 IL6 6288 SAA1 54659 UGT1A3
1432 MAPK14 3576 CXCL8 6822 SULT2A1 64240 ABCG5
1544 CYP1A2 4129 MAOB 6863 TAC1 64241 ABCG8
1555 CYP2B6 4313 MMP2 7097 TLR2 94233 OPN4
1557 CYP2C19 4843 NOS2 7124 TNF
[89]Open in a new tab
Compound-target (C-T) network of SHCZF against cholestasis
According to 83 potential targets, 141 SHCZF compounds were identified
as the major ingredients acting on cholestasis ([90]Table 3). The
interactions between 83 potential targets and 141 SHCZF compounds were
exhibited by a C-T network. In the C-T network, red diamonds
represented 5 herbs in SHCZF, including Dahuang (Rhei Radix Et
Rhizome), Huangzhizi (Gardeniae Fructus), Baizhu (Atractylodes
Macrocephala Koidz.), Huangbai (Phellodendri Chinrnsis Cortex), and
Chaihu (Radix Bupleuri). Circles with 5 different colors stood for
distinct compounds from 5 herbs, among which, there were 17 compounds
from Rhei Radix Et Rhizome, 19 from Gardeniae Fructus, 17 from
Atractylodes Macrocephala Koidz., 15 from Phellodendri Chinrnsis
Cortex, and 49 from Radix Bupleuri. Besides, 24 common compounds were
displayed using blue circles. The parallelograms in the network
represented 83 potential targets of SHCZF against cholestasis and
darker orange indicated higher degree ([91]Fig 2).
Table 3. 141 bioactive compounds of SHCZF against cholestasis.
Compound Name QED Compound Name QED
(-)-Epicatechin-pentaacetate 0.3317 Istidina 0.4207
(+)-trans-Carveol 0.5719 jatrorrhizine 0.7352
(Z,Z)-farnesol 0.6157 kaempferol 0.6372
vanillin 0.5173 lauric acid 0.3925
2-heptanone 0.5103 l-carvone 0.5247
3,4,5-trihydroxybenzoic acid 0.4656 L-Ile 0.4718
acetic acid 0.4199 limonin 0.4519
adonitol 0.3082 linalool 0.6172
aloe-emodin 0.7330 linolenic acid 0.3326
alpha-humulene 0.4851 L-Limonene 0.4838
alpha-limonene 0.4838 L-Lysin 0.2814
alpha-linolenic acid 0.3326 LPG 0.3562
angelicin 0.4354 Lutein 0.2035
angelicin 0.4670 L-Valin 0.4120
Apocynin 0.6736 L-valine 0.4266
Auraptene 0.4124 MAE 0.4992
Azole 0.4642 menthyl acetate 0.6510
Baicalin 0.3617 Methose 0.3101
berberine 0.6633 Methyl naphthalene 0.5294
berberine 0.8245 methyl palmitate 0.2468
beta-elemene 0.5799 Methyleugenol 0.6599
beta-sitosterol 0.4354 MTL 0.2704
caffeic acid 0.4750 myristic acid 0.4490
caprylic acid 0.5818 naphthalene 0.5114
capsaicin 0.5370 nonanoic acid 0.5775
chrysin 0.8206 obaculactone 0.4519
cinnamic acid 0.6504 Obacunone 0.4784
cis-Carveol 0.5719 o-caffeoylquinic acid 0.2356
citric acid 0.4243 octanoic acid 0.5818
coumarin 0.4124 octanol 0.5480
crocetin 0.5030 oleanolic acid 0.4460
Cyclopentenone 0.4228 oleic acid 0.2030
DBP 0.4752 OYA 0.3958
DEP 0.6925 PAC 0.6684
DIBP 0.6761 paeonol 0.5478
DLA 0.4605 palmatine 0.6613
d-limonene 0.4838 palmitic acid 0.3653
DTY 0.5110 PCR 0.5390
EIC 0.2944 PEA 0.6259
emodin 0.6835 pentadecylic acid 0.4059
esculetin 0.3579 PHA 0.5664
EUG 0.6993 phenylalanine 0.5664
eugenol 0.6955 PHPH 0.5905
farnesol 0.6157 PIT 0.4834
fructose 0.3101 PLO 0.7502
Fumarine 0.7258 poriferast-5-en-3beta-ol 0.4354
Furol 0.4792 Prolinum 0.3867
gallicacid 0.4656 puerarin 0.4049
genipin 0.5093 py 0.4453
geniposide 0.2532 quercetin 0.5064
geraniol 0.6172 rhapontigenin 0.7399
Germacron 0.4329 rhein 0.7375
GLB 0.3046 rottlerin 0.2140
glutamate 0.3835 rutaecarpine 0.6889
guaiacol 0.5771 scoparone 0.5470
guanidine 0.2426 scopoletin 0.5425
Guasol 0.5771 Scopoletol 0.5425
Gulutamine 0.3835 Serotonin 0.6456
Hemo-sol 0.4838 serotonine 0.6456
Heptadekan 0.2688 stearic acid 0.3017
heptanoic acid 0.5128 Stigmasterol 0.4599
Heptanol 0.5465 succinic acid 0.5303
hexanal 0.2939 TDA 0.4900
hexanoic acid 0.5687 tetradecane 0.3217
histidine 0.4184 thymol 0.6510
Hyacinthin 0.4290 trans-2-nonenal 0.3144
IFP 0.3920 tridecanoic acid 0.4900
IPH 0.5172 trihydroxybenzoic acid 0.4656
isoimperatorin 0.4856 UND 0.4133
isoliquiritigenin 0.5824 ursolic acid 0.4433
isorhamnetin 0.6678
[92]Open in a new tab
Fig 2. Compound-target (C-T) network of 141 bioactive compounds and 83
potential targets for SHCZF against cholestasis.
[93]Fig 2
[94]Open in a new tab
There were 229 nodes in the C-T network, including 5 red diamonds for
herbs from SHCZF, 83 orange (higher degree) and green (lower degree)
parallelograms for potential targets, and 141 circles for bioactive
compounds.
GO and KEGG enrichment analyses
To elaborate the biological functions of 83 potential targets, targets
were characterized by GO and KEGG pathway enrichment analyses. In the
GO analysis, a total of 1617 GO terms were found, including 91 of MF,
1498 of BP, and 28 of CC (p value < 0.01). The top 15 terms of MF, BP,
and CC were ranked according to the adjusted p value and gene count
([95]Fig 3). Lower p value with red color and higher count with bigger
circle indicated greater enrichment of GO terms. The bubble chart and
histogram showed that MF was significantly enriched in heme binding,
tetrapyrrole binding, carboxylic acid binding, receptor agonist
activity, and organic acid binding, etc. ([96]Fig 3A and 3B). The main
GO terms of BP were related to response to lipopolysaccharide,
regulation of lipid localization, cellular response to biotic stimulus,
regulation of inflammatory response, and response to oxidative stress,
etc. ([97]Fig 3C and 3D). CC were mainly enriched in membrane
microdomain, high-density lipoprotein particle, blood microparticle,
nuclear transcription factor complex, and RNA polymerase II
transcription factor complex, etc. ([98]Fig 3E and 3F).
Fig 3. Gene Ontology (GO) enrichment analysis for 83 potential targets of
SHCZF against cholestasis.
[99]Fig 3
[100]Open in a new tab
(A, B) The bubble chart and histogram of top 15 molecular function (MF)
enrichment. (C, D) The bubble chart and histogram of top 15 biological
process (BP) enrichment. (E, F) The bubble chart and histogram of top
15 cellular component (CC) enrichment.
The essential signaling pathways of SHCZF in cholestasis were displayed
by KEGG pathway enrichment analysis. A total of 133 pathways were
significantly associated with 83 potential targets (p value < 0.01). In
addition, the top 15 pathways with low adjust p values and high counts
were displayed by the bubble chart and the histogram ([101]Fig 4A and
4B), and listed in [102]Table 4. The results showed that the common
signaling pathways mainly focused on the AGE-RAGE signaling pathway in
diabetic complications, Toll-like receptor signaling pathway, and TNF
signaling pathway, etc. ([103]Fig 4A and 4B). In addition, the
interactions among 83 potential targets, top 15 BP terms, and top 15
pathways were visualized by a BP-target-pathway network ([104]Fig 5A).
Furthermore, the interactions among top 10 hub targets, namely ALB,
IL6, AKT1, TP53, TNF, MAPK3, APOE, IL1B, PPARG, and PPARA, were
visualized by a PPI network. The network showed 10 target nodes
connected by 44 edges with an average degree of 8.8 ([105]Fig 5B).
Fig 4. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for
83 potential targets of SHCZF against cholestasis.
[106]Fig 4
[107]Open in a new tab
(A) The bubble chart of top 15 KEGG pathways. (B) The histogram of top
15 KEGG pathways.
Table 4. Top 15 KEGG pathways for SHCZF against cholestasis.
ID Pathway P. adjust Count
hsa04933 AGE-RAGE signaling pathway in diabetic complications 7.28E-18
19
hsa05418 Fluid shear stress and atherosclerosis 5.18E-14 18
hsa00982 Drug metabolism—cytochrome P450 2.46E-13 14
hsa05142 Chagas disease (American trypanosomiasis) 1.37E-12 15
hsa00980 Metabolism of xenobiotics by cytochrome P450 1.08E-11 13
hsa04620 Toll-like receptor signaling pathway 2.58E-11 14
hsa04668 TNF signaling pathway 6.29E-11 14
hsa05133 Pertussis 1.40E-10 12
hsa01522 Endocrine resistance 1.50E-10 13
hsa05204 Chemical carcinogenesis 3.32E-10 12
hsa05161 Hepatitis B 4.98E-10 15
hsa00830 Retinol metabolism 4.98E-10 11
hsa05152 Tuberculosis 1.93E-09 15
hsa04625 C-type lectin receptor signaling pathway 3.61E-09 12
hsa04931 Insulin resistance 5.28E-09 12
[108]Open in a new tab
Fig 5. BP-target-pathway network and PPI network of top 10 hub genes for
SHCZF against cholestasis.
[109]Fig 5
[110]Open in a new tab
(A) The BP-target-pathway network included 83 potential targets
(flesh-colored circles), top 15 BP terms (purple circles), and top 15
KEGG pathways (green circles). (B) PPI network of top 10 hub targets
(ALB, IL6, AKT1, TP53, TNF, MAPK3, APOE, IL1B, PPARG, and PPARA) for
SHCZF against cholestasis.
Molecular docking between bioactive compounds and hub targets
Molecular docking was performed to validate the interactions between
bioactive compounds and hub targets of SHCZF against cholestasis. Seven
main compounds of SHCZF, including chrysophanol, emodin, physcion,
rhein, aloe-emodin, berberine chloride, and gardenoside, were chosen
for molecular docking based on their high contents analyzed by HPLC.
Top 10 hub targets, including ALB, IL6, AKT1, TP53, TNF, MAPK3, APOE,
IL1B, PPARG, and PPARA, were chosen for molecular docking based on
network pharmacology. The results presented that the molecular docking
affinity of seven active compounds with top 10 hub target proteins were
all less than -5 kcal/mol ([111]S3 Table). The strongest binding
activity between active compounds and hub target proteins were
exhibited in [112]Fig 6. APOE displayed the best binding affinity with
berberine chloride (affinity = -10.5 kcal/mol), physcion (affinity =
-10 kcal/mol), chrysophanol (affinity = -9.9 kcal/mol), emodin
(affinity = -9.8 kcal/mol), and rhein (affinity = -9.8 kcal/mol)
([113]Fig 6A–6E). AKT1 had a strong affinity with berberine chloride
(affinity = -10.4 kcal/mol), chrysophanol (affinity = -9.7 kcal/mol),
physcion (affinity = -9.7 kcal/mol), and rhein (affinity = -9.7
kcal/mol) ([114]Fig 6F–6I). TP53 bound to emodin with a binding energy
of -9.5 kcal/mol ([115]Fig 6J). According to the molecular docking
diagrams, the structures of emodin bound to sites of ALA-260 and
LYS-268, while rhein interacted with LEU-330 in APOE by hydrogen bond
([116]Fig 6D and 6E). Berberine chloride bound to sites of ARG-206 and
SER-205 in AKT1, while chrysophanol bound to sites of SER-205, LYS-268,
and ASN-53 ([117]Fig 6F and 6G). Both physcion and rhein bound to sites
of SER-205 and LYS-268 in AKT1 ([118]Fig 6H and 6I). The structure of
emodin bound to the site of ASP-65 in TP53 ([119]Fig 6J).
Fig 6. Molecular docking of SHCZF compounds and hub target proteins.
[120]Fig 6
[121]Open in a new tab
(A-E) The binding mode of APOE and berberine chloride, physcion,
chrysophanol, emodin, and rhein, respectively. (F-I) The binding mode
of AKT1 and berberine chloride, chrysophanol, physcion, and rhein,
respectively. (J) The binding mode of TP53 and emodin.
Discussion
Cholestasis is clinical condition and pathogenic features caused by the
impairment of bile flow, which is closely associated with hepatocyte
dysfunction and liver diseases [[122]38]. Previous study indicated that
SHCZF had the potential for cholestasis treatment, however, the
pharmacological mechanisms remain unclear [[123]6]. Our study found
that SHCZF possessed 7 major chemical compounds, including
chrysophanol, emodin, physcion, rhein, aloe-emodin, berberine chloride,
and gardenoside. According to the network pharmacology analysis, 141
bioactive compounds and 83 potential targets of SHCZF against
cholestasis were screened. The corresponding biological functions of
potential targets were characterized and presented by Go terms and KEGG
pathways. Furthermore, the interactions between 7 major bioactive
compounds and top 10 hub target proteins were exhibited by molecular
docking.
SHCZF is a Chinese medicine formula, presenting a hepatoprotective
effect on intrahepatic cholestasis [[124]6]. There are five herbs in
SHCZF, including Dahuang (Rhei Radix Et Rhizome), Huangbai
(Phellodendri Chinrnsis Cortex), Huangzhizi (Gardeniae Fructus), Chaihu
(Radix Bupleuri) and Baizhu (Atractylodes Macrocephala Koidz.). Our
study identified 7 major chemical compounds in five herbs of SHCZF,
including chrysophanol, emodin, physcion, rhein, aloe-emodin, berberine
chloride, and gardenoside. Previous studies indicated that these seven
compounds have favorable pharmacological properties including
anticancer, hepatoprotective, anti-inflammatory, etc.
[[125]39–[126]45]. For instance, emodin can suppress liver injury and
bile acids secretion, and exert a protective effect on intrahepatic
cholestasis [[127]40]. Physcion is a novel liver protective agent by
reprogramming the hepatic circadian clock [[128]41]. Rhein may promote
bile acid transport and reduce bile acid accumulation in liver
[[129]42]. As a result, we speculate that these seven compounds from
SHCZF may exert critical effects for SHCZF against cholestasis.
Network pharmacology are widely applied in elucidating the biological
mechanism of traditional Chinese medicine formula by constructing
intricate interaction network based on bioactive compounds, targets,
and biological functions [[130]46]. According to the network
pharmacology analysis, a total of 141 bioactive compounds and 83
potential targets of SHCZF against cholestasis were collected based on
public databases. The interactions among 83 targets were presented by a
PPI network containing 83 target nodes connected by 1034 edges with an
average node degree of 24.9. Besides, the interactions between 141
bioactive compounds and 83 potential targets were visualized by a C-T
network. The top 10 hub targets were ALB, IL6, AKT1, TP53, TNF, MAPK3,
APOE, IL1B, PPARG, and PPARA. Of note, most of them is associated with
the progression of liver diseases [[131]47–[132]51]. For instance, ALB
is a protein produced by liver, which is widely used as a marker for
liver diseases [[133]47]. IL6 and TNF are inflammatory biomarkers for
cholestatic liver injury [[134]48]. AKT1 and TP53 are closely related
to the regulation of liver cancer progression [[135]50, [136]51]. These
results suggest that these top 10 hub targets may act as essential
roles in SHCZF for cholestasis treatment.
In order to further investigate the underlying mechanisms, the
biological functions of hub targets were enriched via GO and KEGG
analyses. The interactions among 83 potential targets, top 15 related
BP terms, and top 15 KEGG pathways were presented by a
BP-target-pathway network. Our study showed that these targets were
mainly related to the processes of response to molecule of bacterial
origin, response to nutrient levels, response to lipopolysaccharide,
etc. A previous study also found that patients with cholestasis
presented a lack of response to bacterial infections [[137]52]. These
results suggested that these targets may be involved in the regulation
of SHCZF against cholestasis via moderating these biological processes.
In addition, the top 15 KEGG pathways related to hub targets were
AGE-RAGE signaling pathway in diabetic complications, fluid shear
stress and atherosclerosis, drug metabolism-cytochrome P450, TNF
signaling pathway, insulin resistance, etc. According to previous
statistic, the pathways of AGE-RAGE signaling pathway in diabetic
complications, fluid shear stress and atherosclerosis, and insulin
resistance were also enriched in non-alcoholic fatty liver and involved
in the regulation of liver function [[138]53]. Xue et al. [[139]54]
found that Da-Huang-Xiao-Shi decoction could upregulate the expression
of the metabolic enzyme cytochrome P450 in chronic cholestasis. Our
previous study suggested that TNF signaling pathway may be the
important mechanism for SHCZF against cholestasis [[140]6]. Overall,
the above pathways may be closed relevant to SHCZF against cholestasis.
The binding force of a drug with target proteins is a pivotal index for
assessing its mechanistic action on diseases [[141]55]. The binding
models between 7 SHCZF compounds and 10 hub target proteins were
visualized by molecular docking. The results showed that chrysophanol,
physcion, rhein, aloe-emodin, and berberine chloride had a strong
affinity with APOE and AKT1. Emodin had a strong affinity with APOE,
AKT1, and TP53. The structures of emodin and rhein bound to sites of
SER-278 and LEU-330 in APOE, respectively. The structure of berberine
chloride bound to sites of ARG-206 and SER-205 in AKT1, while
chrysophanol bound to sites of SER-205, LYS-268, and ASN-53. Both
physcion and rhein bound to sites of SER-205 and LYS-268 in AKT1. The
structure of emodin bound to the site of ASP-65 in TP53. Differences in
the binding sites may affect the ability of SHCZF compounds to bind
target proteins, thereby exerting regulatory effects on cholestasis.
In conclusion, the interactions of 141 bioactive compounds and 83
potential targets of SHCZF against cholestasis were characterized by
network pharmacology analysis. These targets may be closely related to
the biological processes of response to molecule of bacterial origin,
response to nutrient levels, response to lipopolysaccharide, etc., and
involved in the pathways of AGE-RAGE signaling pathway in diabetic
complications, fluid shear stress and atherosclerosis, drug
metabolism-cytochrome P450, TNF signaling pathway, insulin resistance,
etc. Molecular docking validated the binding of 7 active compounds and
top 10 hub target proteins. Chrysophanol, physcion, rhein, aloe-emodin,
and berberine chloride had a strong affinity with APOE and AKT1, and
emodin had a strong affinity with APOE, AKT1, and TP53. This study
provides essential clues to further explore the underlying mechanisms
of SHCZF against cholestasis. However, in vivo or in vitro experiments
are needed to be performed for validating the mechanisms of SHCZF
against cholestasis through moderating above hub targets and pathways.
Supporting information
S1 Fig. High Performance Liquid Chromatography (HPLC) chromatograms of
7 major chemical compounds in SHCZF.
(A) Chrysophanol. (B) Emodin. (C) Physcion. (D) Rhein. (E) Aloe-emodin.
(F) Berberine chloride. (G) Gardenoside.
(PDF)
[142]Click here for additional data file.^ (188.2KB, pdf)
S1 Table. Cholestasis-related targets from public databases.
(XLSX)
[143]Click here for additional data file.^ (18.3KB, xlsx)
S2 Table. 162 active compounds and 457 corresponding targets of SHCZF.
(XLSX)
[144]Click here for additional data file.^ (23.2KB, xlsx)
S3 Table. Molecular docking of seven bioactive compounds and top 10
targets.
(DOCX)
[145]Click here for additional data file.^ (17KB, docx)
Data Availability
All relevant data are within the paper and its [146]Supporting
Information files.
Funding Statement
This work was supported by Zhejiang science and technology research
fund [No. 2014C33238] and Zhejiang science and technology research fund
of traditional Chinese medicine [No.2020ZB158], funder play the role of
conception and design of the research, analysis and interpretation of
data and revision of manuscript.
References