Abstract
Non-alcoholic fatty liver disease (NAFLD) has become the most prevalent
liver disease in China. Sinisan (SNS) is a traditional Chinese medicine
formula that has been widely used in treating chronic liver diseases,
including NAFLD. However, its underlying biological mechanisms are
still unclear. In this study, we employed a network pharmacology
approach consisting of overlapped terms- (genes or pathway terms-)
based analysis, protein-protein interaction (PPI) network-based
analysis, and PPI clusters identification. Unlike the previous network
pharmacology study, we used the shortest path length-based network
proximity algorithm to evaluate the efficacy of SNS against NAFLD. And
we also used random walk with restart (RWR) algorithm and Community
Cluster (Glay) algorithm to identify important targets and clusters.
The screening results showed that the mean shortest path length between
genes of SNS and NAFLD was significantly smaller than degree-matched
random ones. Six PPI clusters were identified and ten hub targets were
obtained, including STAT3, CTNNB1, MAPK1, MAPK3, AGT, NQO1, TOP2A,
FDFT1, ALDH4A1, and KCNH2. The experimental study indicated that SNS
reduced hyperlipidemia, liver steatosis, and inflammation. Most
importantly, JAK2/STAT3 signal was inhibited by SNS treatment and was
recognized as the most important signal considering the network
pharmacology part. This study provides a systems perspective to study
the relationship between Chinese medicines and diseases and helps to
discover potential mechanisms by which SNS ameliorates NAFLD.
Keywords: network pharmacology, NAFLD, Sinisan, protein-protein
interaction (PPI) network, topological analysis, JAK2/STAT3
Introduction
Non-alcoholic fatty liver disease (NAFLD) is the most common liver
disease with a global prevalence of 25% ([50]Cotter and Rinella, 2020).
Although NAFLD was traditionally considered as a benign condition and
only patients with obesity had been more likely to receive medical
care, recent evidence has shown it to be a far more complex disease.
Increased NAFLD-related mortality has been observed in several studies
from the past years ([51]Allen et al., 2019; [52]Targher et al., 2020).
And even simple steatosis might finally result in increased all-cause
mortality because of the progression of this disease after a follow-up
observation over decades ([53]Tilg and Targher, 2020). NAFLD includes
various clinical phenotypes ranging from simple steatosis (a benign
condition called fatty liver) to non-alcoholic steatohepatitis (NASH)
and hepatic fibrosis. And these conditions increase the risks of
liver-related complications, such as cirrhosis, liver failure, and
hepatocellular carcinoma. NAFLD is also closely associated with
important extra-hepatic manifestations, such as cardiovascular disease
and chronic kidney disease, which further increases its disease burden.
Therefore, it has to be acknowledged that control of this disease would
have a major impact on the benefits of health care.
Sinisan (SNS), also called Shigyaku-san in Japan, is a classic Chinese
medicine formula originating from the book of Treatise on Febrile
Diseases (Shang Han Lun) of the Han Dynasty (200–201 AD) China. It
comprises four botanical drugs: Bupleurum chinense DC. (Apiaceae;
Bupleuri radix) (Chaihu), Paeonia lactiflora Pall. (Paeoniaceae;
Paeoniae radix alba) (Shaoyao), Gardenia jasminoides J. Ellis
(Rubiaceae; Aurantii fructus immaturus) (Zhishi), and Glycyrrhiza
uralensis Fisch. (Leguminosae; Glycyrrhizae radix et rhizoma) (Gancao)
with a dose proportion of 1:1:1:1. For centuries, SNS has been widely
applied in treatment of chronic liver diseases in the clinic. Our
previous study indicated that SNS decreased liver steatosis and
inflammation in NAFLD rats ([54]Mu et al., 2020). However, for the
well-accepted multi-component and multi-target effects of TCM formulas,
it is difficult to understand the potential biological mechanisms by
the traditional pharmacology approach. Network pharmacology was a
recently proposed TCM research strategy to use a “network target” as a
mathematical and computable representation of various connections
between botanical formulae and diseases ([55]Li and Zhang, 2013; [56]Li
et al., 2014). Therefore, we used this system biology-based approach to
describe the association of multiple components with multiple targets
and multiple pathways and recovery of the potential mechanisms of SNS
against NAFLD at a system level.
In the present work, we, firstly, investigated the effect of SNS
against NAFLD in high fat diet- (HFD-) induced NAFLD rat model. Next,
we collected information about compounds, compound-related targets, and
NAFLD-related genes from extensive databases and identified important
targets and pathways using a protein-protein interaction network-based
method. Finally, a series of in vivo experiments were conducted to
validate important targets of SNS against NAFLD ([57]Figure 1).
FIGURE 1.
[58]FIGURE 1
[59]Open in a new tab
Integrated workflow for discovery of the potential mechanisms of SNS
against NAFLD.
Methods
Collection of Bioactive Compounds and Prediction of Corresponding Targets
All compounds of SNS decoction and their corresponding absorption,
distribution, metabolism, and excretion (ADME) parameters were obtained
from the Traditional Chinese Medicine Systems Pharmacology Database and
Analysis Platform (TCMSP). OB value (systemic bioavailability after
oral absorption and distribution) ≥30% and DL value (structural
similarity between compounds and clinically used drugs in the DrugBank
database) ≥ 0.18 were employed as criteria to filter bioactive
compounds. A total of 137 distinct bioactive compounds with literature
or other database validation were identified for further analysis
([60]Table 1). The putative targets of bioactive compounds in SNS
decoction were predicted using TCMSP database as we previously
mentioned. After removing duplicate genes, we obtained 155 SNS targets
for further analysis.
TABLE 1.
Top 10 genes ranked by random walk with start algorithm.
Rank Gene Score
1 STAT3 0.005377152
2 CTNNB1 0.004535756
3 MAPK1 0.004386918
4 MAPK3 0.004010912
5 AGT 0.003464108
6 NQO1 0.002783255
7 TOP2A 0.002570216
8 FDFT1 0.00255187
9 ALDH4A1 0.002469136
10 KCNH2 0.002469136
[61]Open in a new tab
Prediction of Non-Alcoholic Fatty Liver Disease Genes
Disease genes were obtained using the recently updated DisGeNET
database with keywords “Non-alcoholic Fatty Liver Disease”, “FATTY
LIVER DISEASE, NONALCOHOLIC”, “Nonalcoholic Steatohepatitis”, and
“Fibrosis, Liver” ([62]Piñero et al., 2020). After removing BEFREE text
mining genes, we obtained 306 unduplicated genes related to NAFLD and
the details are provided in [63]Table 2.
TABLE 2.
Identified bioactive compounds in SNS from UPLC-ESI-MS/MS Analysis.
ID Name Status Ion mode
MOL000098 Quercetin Confirmed Positive
MOL000211 Mairin Confirmed Negative
MOL000239 Jaranol Confirmed Negative
MOL000354 Isorhamnetin Confirmed Negative
MOL000359 Sitosterol Confirmed Positive
MOL000392 Formononetin Confirmed Negative
MOL000417 Calycosin Confirmed Positive
MOL000422 Kaempferol Confirmed Negative
MOL000497 Licochalcone A Confirmed Positive
MOL000500 Vestitol Confirmed Negative
MOL001484 Inermine Confirmed Negative
MOL001792 DFV Confirmed Positive
MOL002311 Glycyrol Confirmed Positive
MOL002565 Medicarpin Confirmed Negative
MOL004328 Naringenin Confirmed Positive
MOL004841 Licochalcone B Confirmed Positive
MOL004848 Licochalcone G Confirmed Positive
MOL004855 Licoricone Confirmed Positive
MOL004879 Glycyrin Confirmed Positive
MOL004883 Licoisoflavone Confirmed Positive
MOL004884 Licoisoflavone B Confirmed Negative
MOL004885 Licoisoflavanone Confirmed Positive
MOL004908 Glabridin Confirmed Positive
MOL004910 Glabranin Confirmed Positive
MOL004911 Glabrene Confirmed Negative
MOL004912 Glabrone Confirmed Positive
MOL004915 Eurycarpin A Confirmed Positive
MOL004917 Glycyroside Confirmed Positive
MOL004948 Isoglycyrol Confirmed Negative
MOL004949 Isolicoflavonol Confirmed Positive
MOL004957 HMO Confirmed Positive
MOL004961 Quercetin der Confirmed Negative
MOL005000 Gancaonin G Confirmed Negative
MOL005020 Dehydroglyasperins C Confirmed Negative
MOL002776 Baicalin Confirmed Positive
MOL004609 Areapillin Confirmed Positive
MOL004702 Saikosaponin c_qt Confirmed Negative
MOL013187 Cubebin Confirmed Negative
MOL000492 (+)-Catechin Confirmed Negative
MOL001918 Paeoniflorgenone Confirmed Positive
MOL001921 Lactiflorin Confirmed Positive
MOL001924 Paeoniflorin Confirmed Negative
MOL001925 paeoniflorin_qt Confirmed Negative
MOL001928 albiflorin_qt Confirmed Positive
MOL001930 Benzoyl paeoniflorin Confirmed Positive
MOL000006 Luteolin Confirmed Positive
MOL001798 neohesperidin_qt Confirmed Negative
MOL001803 Sinensetin Confirmed Positive
MOL001941 Ammidin Confirmed Positive
MOL005100 5,7-Dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one
Confirmed Negative
MOL005828 Nobiletin Confirmed Positive
MOL005849 Didymin Confirmed Negative
MOL007879 Tetramethoxyluteolin Confirmed Positive
MOL013276 Poncirin Confirmed Negative
MOL013277 Isosinensetin Confirmed Positive
MOL013279 5,7,4′-Trimethylapigenin Confirmed Positive
MOL013352 Obacunone Confirmed Negative
MOL013428 Isosakuranetin-7-rutinoside Confirmed Negative
[64]Open in a new tab
Construction of Protein-Protein Interaction Networks
We used two highly cited human protein-protein interaction data for
background network constructions, dataset constructed by Professor
Barabasi’s team ([65]Cheng et al., 2018) and the Search Tool for the
Retrieval of Interacting Genes (STRING) database ([66]Szklarczyk et
al., 2019). The first dataset was derived from 15 commonly used
databases with experimental evidence and the in-house confirmed data
without inferred information. We constructed a PPI network containing
16,677 nodes and 243,603 edges and called it Bnet. The second dataset
provided functional associations for proteins, which were sorted by a
confidence score. Data only for “Homo sapiens” with a confidence score
≥0.9 was used for Snet construction, containing 9,941 nodes and 227,186
edges.
Network-Based Efficacy Evaluation of Sinisan Against Non-Alcoholic Fatty
Liver Disease
Here, we used the network proximity index proposed by Prof. Barabasi’s
group to evaluate the efficacy of drugs against diseases in the
background of Bnet ([67]Cheng et al., 2019). Specifically, we marked T
and V as the set of SNS genes and the set of NAFLD genes, respectively.
We defined the shortest path length between nodes v ∈ V and t ∈ T in
the network as [68]Equation 1.
[MATH:
dc(V,<
/mo>T)=1‖T‖<
/mfrac>∑t∈T<
/mi>minv∈Vd(v
,t). :MATH]
(1)
We also compared the shortest path length between genes of SNS and
NAFLD with the expected shortest path length between two random and
size-matched groups of genes. As shown in [69]Equation 2, we calculated
the mean µ [d(V,T)] and standard deviation s [d(V,T)] of the reference
distribution and converted the absolute distance d[c] to a relative
distance Z[dc.]
[MATH:
Zdc=dc−<
msub>μdc(
V,T)σdc(V,T). :MATH]
(2)
Network-Based Important Genes Identification of Sinisan against Non-Alcoholic
Fatty Liver Disease
In this study, the random walk with restart (RWR) algorithm, a classic
ranking algorithm, was adopted to select important genes in the
sub-network based on Snet. As shown in [70]Equation 3, the vector p[0]
is the initial probability distribution. Therefore, in p[0], only the
seeds have values different from zero. After several iterations, the
difference between the vectors p[t+1] and p[t] becomes negligible, the
stationary probability distribution is reached, and the elements in
these vectors represent a proximity measure from every graph node to
the seeds. In this work, iterations are repeated until the difference
between p[t] and p[t+1] falls below 10^–10 ([71]Valdeolivas et al.,
2019).
[MATH:
pTt+1=<
/mo>(1−r)
MpTt
+rpT0. :MATH]
(3)
In detail, before the algorithm was executed, a sub-network of SNS
genes and NAFLD genes was obtained from the Snet and considered to be
the core PPI network of SNS against NAFLD. The RWR algorithm was
employed with seed genes as SNS genes and a restarting probability (r)
of 0.75 by RandomWalkRestartMH R package, as in previous studies
([72]Yang et al., 2020). The top 10 genes with the highest affinity
scores were identified. The affinity score referred to the proximity
between two nodes and the parameter “r” is the probability of moving to
seed nodes.
Network Construction and Enrichment Analysis
Compound-gene networks and PPI networks were constructed by Cytoscape
software (Version 3.8). To identify clusters of the sub-PPI network of
SNS genes and NAFLD genes, ClusterMaker 2 plugin for Cytoscape was
applied by the Community Cluster (Glay) algorithm ([73]Morris et al.,
2011). For gene enrichment analysis, gene ontology (GO) enrichment of
these genes includes a biological process (BP), molecular function
(MF), and cellular component (CC). For comparison between the SNS gene
set and NAFLD gene set, the ClusterProfiler package of R 3.5.0 software
is adopted to conduct GO enrichment ([74]Yu et al., 2012). Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of
core targets is also carried out and visualized using the ggplot2 R
package. P value < 0.05 was set to be significant. A suite of KEGG
mapping tools, KEGG Mapper, was used for specific pathway visualization
([75]Kanehisa and Sato, 2020).
Preparation of Sinisan
The mixture of Chaihu, Zhishi, Baishao, and Gancao (1:1:1:1) was
purchased from Beijing Tongrentang (Beijing, China) and authenticated
by our team. The mix was immersed in water for 30 min and extracted
twice with boiling water as we previously mentioned ([76]Wei et al.,
2016). The extraction was filtered and concentrated in a rotary
evaporator under reduced pressure. Ultimately, the dry powder was
manufactured by a freeze dryer at a relatively low temperature
condition (−80°C).
UPLC-ESI-MS/MS Analysis
To confirm the important bioactive compounds in SNS, the SNS samples
were analyzed using a UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2;
MS, Applied Biosystems 6500 Q TRAP). An Agilent SB-C18 column (1.8 µm,
2.1 mm*100 mm) was used. The mobile phase was pure water with 0.1%
formic acid (A) and acetonitrile with 0.1% formic acid (B). Sample
measurements were performed with a gradient program that employed the
starting conditions of 95% A, 5% B. Within 9 min, a linear gradient to
5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept
for 1 min. Subsequently, a composition of 95% A, 5% B was adjusted
within 1.10 min and kept for 2.9 min. The flow rate was 0.35 ml/min,
and 2 μl of the filtrate was injected into the system for analysis. The
ESI source operation parameters were as follows: ion source, turbo
spray; source temperature 550°C; ion spray voltage (IS) 5500 V
(positive ion mode)/−4500 V (negative ion mode); ion source gas I
(GSI), gas II(GSII), and curtain gas (CUR) set at 50, 60, and 25.0 psi,
respectively; and the collision-activated dissociation (CAD) was high.
Instrument tuning and mass calibration were performed with 10 and
100 μmol/L polypropylene glycol solutions in QQQ and LIT modes,
respectively. QQQ scans were acquired as MRM experiments with collision
gas (nitrogen) set to medium. DP and CE for individual MRM transitions
were done with further DP and CE optimization. A specific set of MRM
transitions were monitored for each period according to the metabolites
eluted within this period.
Animals and Treatments
All animal experiments were approved by the Institutional Animal Care
and Use Committee of the Department of Laboratory Animal Sciences,
Capital Medical University. Animal Experimental Ethics number
(AEEI-2020–165). Twenty-seven male Wistar rats (280–320 g) were
purchased from Charles River Inc., (Vital River Ltd., Beijing, China)
and kept at the SPF animal room of the Department of Laboratory Animal
Sciences, Capital Medical University. All rats were randomly
distributed into three groups after 1 week of acclimatization and were
fed either standard chow (Chow group) or high fat diet (HFD and SNS
group). After 4 weeks of feeding, rats in the SNS group (n = 9) were
intragastrically administered with SNS exact per day (0.368 g/kg), and
the Chow group (n = 9) and HFD group (n = 9) were given the same volume
of distilled water to mimic the effects of oral gavage administration
while remaining on the previous diet, respectively, for another
4 weeks. The dose of SNS was determined based on the body surface area
index between humans and rats ([77]Nair et al., 2018).
Sample Collection
At the end of 8 weeks, the final body weights of rats were recorded
before sacrificing. After anesthetized with pentobarbital sodium
(25 mg/kg; IP), blood was obtained from the abdominal aorta and
separated by centrifugation (3,000 rpm, 15 min) for serum collection
after being kept at room temperature for 30 min, and the serum was
collected from the supernatant of blood and stored at −80°C for the
further determination. The liver was quickly removed, weighed, and
thoroughly washed with phosphate buffer saline (PBS). A portion of the
liver was stored separately in a 4% paraformaldehyde for
histopathological examination. The rest of the liver was snap-frozen
using liquid nitrogen for further investigation.
Biochemical Indicators of Hepatic Function
The serum alanine aminotransferase (ALT), aspartate aminotransferase
(AST), free fatty acids (FFA), total triglyceride (TG), total
cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and
high-density lipoprotein cholesterol (HDL-C) were detected by a Fully
Automatic Biochemical Analyzer (Beckman Coulter, Indianapolis, IN,
United States) according to manufacturer’s protocol.
Measurement of Serum and Liver Cytokines
Serum and liver levels of TNFα, IL6, and IL1β were detected using ELISA
kits of TNFα, IL6, and IL1β, following the manufacturer’s instructions,
respectively. Absorbance at 450 nm was measured using a microplate
spectrophotometer (Thermo Scientific Multiskan GO). Total protein was
used to normalize these cytokine levels in the liver.
Measurement of Advanced Glycation End-Products and Receptor for Advanced
Glycation End-Products
Serum and liver levels of AGEs and RAGE were detected using ELISA kits
of AGEs and RAGE, following the manufacturer’s instructions,
respectively. Absorbance at 450 nm was measured using a microplate
spectrophotometer (Thermo Scientific Multiskan GO). Total protein was
used to normalize these levels in the liver.
Histological Examination
The liver was immediately fixed in 4% paraformaldehyde overnight at
room temperature, washed with ddH[2]O, rehydrated with gradient ethanol
solutions, and embedded in paraffin for Hematoxylin–Eosin (H&E)
staining. For the detection of lipids, frozen liver sections were
stained with Oil Red O.
Triglyceride and Cholesterol Evaluation in Liver
Liver samples of appropriate weight were homogenized with RIPA lysis
buffer; homogenate was collected to assay intrahepatic triglyceride and
cholesterol using commercial kits (Applygen, China). Triglyceride and
cholesterol values were normalized by total protein contents.
Immunoblotting Analysis
The liver tissues were homogenized with lysis buffer (Applygen, China)
according to the manufacturer’s protocol. The protein extracts were
separated by 8% SDS-PAGE electrophoresis and electro-transferred to
PVDF membranes (Millipore, MA, United States). After blocking with 5%
BSA, the membranes were incubated overnight at 4°C with specific
primary antibodies against Phospho-JAK2 (Cell Signaling Technology,
Danvers, MA, United States), Phospho-STAT3 (Cell Signaling Technology,
Danvers, MA, United States), and β-actin (Cell Signaling Technology,
Danvers, MA, United States) separately. Following four washes with
Tris-buffered saline tween-20 (TBST), the membranes were incubated with
the corresponding secondary antibodies as Goat Anti-Rabbit IgG
(Lablead, China) and for 1 h at room temperature. Membranes were then
washed four times with TBST, and the detection was carried out with an
electrochemiluminescence (ECL) reagent (Applygen, China). The blot was
imaged using EvolutionCapt FX6 and subjected to quantification analysis
using Image J software. The results are expressed as the ratio of the
relative intensity of the target proteins to that of the internal
standard. JAK2 and STAT3 blots were performed by stripping and
re-probing the blots with corresponding antibodies, respectively (Cell
Signaling Technology, Danvers, MA, United States).
Statistical Analysis
All statistical analyses were performed using one-way ANOVA followed by
Dunnett’s Multiple Comparison Test, and all the bar plots were
generated by GraphPad Prism 9 software; the data were expressed in
terms of mean ± standard division (SD). Statistical significance was
set at p < 0.05.
Results
Compounds and Targets of Sinisan
After ADME screening, 137 unduplicated compounds and 155 unduplicated
targets were obtained, which included 2,670 compound-target
relationships ([78]Figure 2A). Among four botanical drugs of Chaihu,
Baishao, Zhishi, and Gancao, 17, 13, 22, and 92 compounds,
respectively, were obtained ([79]Figure 2B), which corresponded to 95,
77, 104, and 139 targets ([80]Figure 2C). We found a potential
synergistic effect of these four botanical drugs in the target level
without many overlaps in the compound level. Gancao was the botanical
drug that contributed to the highest proportion of collected compounds
and targets.
FIGURE 2.
[81]FIGURE 2
[82]Open in a new tab
Compounds in SNS and their corresponding targets. (A) Compound-target
network of SNS. (B) The SNS compound amount in each botanical drug. (C)
The SNS target amount in each botanical drug.
Relationship Between Sinisan Targets and Non-Alcoholic Fatty Liver Disease
Genes
After screening all of the compounds and targets, we want to recognize
the potential corresponding compounds and targets of SNS anti-NAFLD
effect. And we gathered genes related to NAFLD by database mining and
removed predicted ones. As shown in [83]Figure 3A, only 28 genes were
overlapped between SNS genes and NAFLD genes. GO enrichment indicated
that SNS targets and NAFLD genes could be enriched in the same GO terms
with statistically significant difference ([84]Figures 3B–D). These
overlapped terms may involve lipid metabolism, oxidative stress,
inflammatory progress, and transcription. After KEGG enrichment of each
gene set, 83 KEGG signals were overlapped. [85]Figure 3E exhibited a
complex relationship among SNS targets, NAFLD genes, and representative
KEGG signals. These results showed similar terms enriched with SNS
targets and NAFLD genes, indicating potential therapeutic effects of
SNS against NAFLD.
FIGURE 3.
[86]FIGURE 3
[87]Open in a new tab
Overlapped terms-based analysis. (A) Veen diagram of compound targets
of SNS and NAFLD-related targets. Overlapped gene ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) terms of SNS and NAFLD:
(B) biological processes (BPs), (C) cellular components (CCs), (D)
molecular functions (MFs), and (E) KEGG pathways.
Overlapped Gene-Based Enrichment Analysis
We firstly constructed a compound-target network based on overlapped
genes between SNS targets and NAFLD genes ([88]Figure 4A). GO
enrichment of these overlapped genes indicated the potential mechanisms
of SNS against NAFLD related to response to oxidative stress, response
to lipopolysaccharide (LPS), phosphatidylinositol 3-kinase (PI3K)
signaling, cytokine activity, and antioxidant activity ([89]Figures
4B,C).
FIGURE 4.
[90]FIGURE 4
[91]Open in a new tab
Overlapped genes-based analysis. (A) Compound-target network of SNS
against NAFLD. Gene ontology (GO) enrichment analysis of overlapped
genes: (B) biological processes (BPs) and (C) molecular functions
(MFs).
Protein-Protein Interaction Network-Based Proximity Analysis
According to previously published articles, disease genes tended to
form a neighborhood in the human protein interactome, rather than being
separate randomly ([92]Cheng et al., 2019). And if a drug was effective
for a disease, corresponding targets should be within or in the
immediate vicinity of the corresponding disease module in the human
protein interactome, indicating the role of neighborhood genes of
disease genes in drug discovery. Our data showed the mean shortest path
length between genes of SNS and NAFLD was 1.05, and the Z score was
−9.51, indicating a significant difference between the mean shortest
path lengths of SNS-NAFLD and random ones.
Protein-Protein Interaction Network-Based Cluster Analysis
The above results indicated a close relationship between SNS targets
and NAFLD genes. Therefore, we hypothesized that SNS targets and NAFLD
genes could form a network in the human protein interactome. To obtain
data of high quality, a sub-PPI network with 1425 PPI relationships was
grabbed from Snet. Further, [93]Figure 5 A-F showed six PPI clusters
identified, containing both SNS targets (pink) and NAFLD genes (blue).
Cluster 1 is related to the MAPK signaling pathway and FoxO signaling
pathway, cluster 2 is related to the PI3K-Akt signaling pathway and
HIF-1 signaling pathway, cluster 3 is related to TNF signaling pathway
and AGE-RAGE signaling pathway, cluster 4 is related to Fatty acid
degradation, cluster 5 is related to Arachidonic acid metabolism, and
cluster 6 is related to relaxin signaling pathway.
FIGURE 5.
[94]FIGURE 5
[95]Open in a new tab
Network-based clusters identification and Kyoto Encyclopedia of Genes
and Genomes (KEGG) enrichment. (A) Cluster 1 was related to MAPK and
FoxO pathways. (B) Cluster 2 was related to PI3K-Akt and HIF-1
pathways. (C) Cluster 3 was related to TNF and AGE-RAGE pathways. (D)
Cluster 4 was related to the fatty acid degradation pathway. (E)
Cluster 5 was related to the arachidonic acid metabolism pathway. (F)
Cluster 6 was related to the relaxin pathway.
Protein-Protein Interaction Network-Based Hub Genes Identification
Although proximity analysis evaluated the efficacy of drug targets on
disease genes, it was no help in hub gene identification. Therefore, we
needed a ranking algorithm called RWR. As shown in [96]Table 1, the top
10 genes were identified as hub genes by executing the RWR algorithm on
the above sub-PPI network, including STAT3, CTNNB1, MAPK1, MAPK3, AGT,
NQO1, TOP2A, FDFT1, ALDH4A1, and KCNH2 ([97]Table 1).
Screening of Bioactive Compounds by UPLC-ESI-MS/MS Analysis
The present approach identified 58 bioactive compounds in SNS
([98]Table 2), and some important bioactive compounds in the SNS
compound-target network were identified, such as quercetin, kaempferol,
medicarpin, luteolin, tetramethoxyluteolin, formononetin, isorhamnetin,
naringenin, vestitol, and licochalcone A.
Sinisan Attenuated High Fat Diet-Induced Hyperlipidemia
To evaluate the therapeutic effect of SNS, a high fat diet- (HFD-)
induced NAFLD rat model was used. As expected in the NAFLD model,
HFD-treated rats exhibited an increased body weight. Compared with the
HFD group, SNS treated rats showed significantly lowered body weights
after a 4-week treatment ([99]Figure 6A). The transaminase levels were
assessed to evaluate whether SNS made improvements on liver function of
NAFLD rats. The serum levels of AST and ALT were increased
significantly in the HFD group compared with the Chow group, and these
rises were reduced significantly by the SNS treatment compared with the
HFD group ([100]Figures 6B,C). It showed that levels of FFA were
elevated in HFD rats, which manifests as metabolic disorders; however,
it could be significantly alleviated after SNS intervention
([101]Figure 6D). In addition, compared to the HFD group, SNS treatment
influenced body lipid metabolism, including significantly decreasing
the levels of serum TG and LDL-C ([102]Figures 6E,H) and significantly
increasing the levels of HDL-C ([103]Figure 6G). SNS was also observed
to decrease the levels of serum TC without significant difference
([104]Figure 6F). These results indicated a therapeutic effect of SNS
on HFD-induced hyperlipidemia.
FIGURE 6.
[105]FIGURE 6
[106]Open in a new tab
Body weight and biochemical assays results after SNS treatment for rat
NAFLD model. (A) Body weight measurement. (B) Serum aspartate
aminotransferase (AST) levels. (C) Serum alanine aminotransferase (ALT)
levels. (D) Serum free fatty acid (FFA) levels. (E) Serum total
triglyceride (TG) levels. (F) Serum total cholesterol (TC) levels. (G)
Serum high-density lipoprotein cholesterol (HDL-C) levels. (H) Serum
low-density lipoprotein cholesterol (LDL-C) levels. Data are shown as
mean ± SD (n = 9). ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 when
compared with chow group. *p < 0.05, **p < 0.01, ***p < 0.001 when
compared with HFD group.
Sinisan Reduced High Fat Diet-Induced Hepatic Steatosis
As shown in [107]Figure 7A, in contrast to normal diet rats, HFD-fed
rats developed a liver enlargement and discoloration, which were
partially recovered with SNS treatment. Markedly, HFD-induced
accumulation of lipid droplets in the liver was reduced by SNS, as
evidenced by hepatic concentration of TC, TG ([108]Figures 7B,C) and
pathological staining ([109]Figure 7D, representative histological
liver H&E and Oil Red O staining). H&E staining revealed inflammatory
cells and numerous lipid droplets in the livers of NAFLD rats,
indicating inflammation and hepatocyte steatosis in the liver. SNS
treatments showed less inflammation and hepatocyte steatosis than those
of the NAFLD group. Oil Red O staining showed that there were many
deposited lipid droplets in the HFD group. Compared with the HFD group,
the lipid droplet quantity was lower in the SNS group ([110]Figure 7E).
FIGURE 7.
[111]FIGURE 7
[112]Open in a new tab
Sinisan reduced liver lipid accumulation. (A) Morphology of the liver
in each group. (B) Liver total cholesterol (TC) levels normalized by
total protein. (C) Liver total triglyceride (TG) levels normalized by
total protein. (D). HE-stained and Oil Red O-stained liver tissue. (E)
Relative evaluation of Oil Red O-staining. Three biological replicates
were performed for each study. ^# p < 0.05, ^## p < 0.01, ^### p <
0.001 when compared with the Chow group, *p < 0.05, **p < 0.01, ***p <
0.001 when compared with HFD group.
Sinisan Ameliorated High Fat Diet-Induced Hepatic Inflammation
To assess the extent of inflammatory, several inflammatory cytokines,
AGEs, and RAGE in serum and liver were measured ([113]Figures 8A–J). As
a result, the HFD group displayed markedly higher expressions of IL6,
IL1β, TNFα, AGEs, and RAGE compared with the Chow group. However, they
were decreased in SNS-intervened rats compared with those in the HFD
group, suggesting that SNS had an inhibitory effect on hepatic
inflammation in NAFLD rats.
FIGURE 8.
[114]FIGURE 8
[115]Open in a new tab
SNS reduced liver pro-inflammatory cytokines, advanced glycation
end-products (AGEs), and its receptor. (A) Serum levels of tumor
necrosis factor alpha (TNFα). (B) Serum levels of interleukin 6 (IL6).
(C) Serum levels of interleukin 1β (IL1β). (D) Serum levels of AGEs.
(E) Serum levels of receptor for advanced glycation end-products
(RAGE). (F) Liver levels of TNFα. (G) Liver levels of IL6. (H) Liver
levels of IL1β. (I) Liver levels of AGEs. (J) Liver levels of receptor
for RAGE. Data are shown as mean ± SD (n = 6). ^# p < 0.05, ^## p <
0.01, ^### p < 0.001 when compared with the Chow group, *p < 0.05, **p
< 0.01, ***p < 0.001 when compared with the HFD group.
Sinisan Inhibited JAK2/Signal Transducer and Activator of Transcription 3
Phosphorylation
To investigate the potential of SNS’s effect on the STAT3 signal in the
NAFLD rat model, immunoblot analysis was performed for JAK2 and STAT3
phosphorylation ([116]Figures 9A–D). It was demonstrated that
expression of JAK2 and STAT3 phosphorylation was enhanced in NAFLD
rats, while SNS reversed this activation to some extent. Therefore, we
suggested that the inhibition of the JAK2/STAT3 signal may be the
potential mechanism of SNS treating NAFLD ([117]Figure 9E).
FIGURE 9.
[118]FIGURE 9
[119]Open in a new tab
SNS inhibited JAK2/STAT3 signal against NAFLD. (A) Expressions of
signal transducer and activator of transcription (STAT3) and
phosphorylated STAT3 (p-STAT3) were determined by Western blotting. (B)
Quantitative analysis of p-STAT3:STAT3 expression ratio. (C)
Expressions of Janus Kinase 2 (JAK2) and phosphorylated JAK2 (p-JAK2)
were determined by Western blotting. (D) Quantitative analysis of
p-JAK2:JAK2 expression ratio. Data are shown as mean ± SD (n = 3). ^# p
< 0.05, ^## p < 0.01, ^### p < 0.001 when compared with the Chow group,
*p < 0.05, **p < 0.01, ***p < 0.001 when compared with HFD group.
Discussion
SNS has been widely applied in the treatment of chronic liver diseases
in the clinic. As we previously mentioned, previous studies reported
that SNS induced a decreased body weight and liver triglyceride
accumulation in animal models ([120]Cheng et al., 2017; [121]Zhu et
al., 2019). However, the underlying mechanisms of SNS against NAFLD are
still difficult to understand, for it includes multiple compounds and
multiple targets. In the present study, we modified our previously
published network pharmacology approach ([122]Ma et al., 2020) and used
it to study the system-wide mechanism of SNS against NAFLD. This
approach started from compounds screening, targets searching, and drug
effect analyzing. Unlike previous studies, we hypothesized that SNS may
influence NAFLD not only directly acting on the targets related to
NAFLD, but also acting on the neighbors of NAFLD targets. Therefore, to
evaluate the relationship between SNS targets and NAFLD targets, we
used a well-performed network-based drug-disease proximity algorithm on
the human interactome, which had an area under the receiver operating
characteristic curve (AUC) of over 70% in a previous study ([123]Cheng
et al., 2018). After comparing with the reference distance distribution
corresponding to the expected network topological distance between two
randomly selected groups of proteins matched to size and degree as the
NAFLD targets and SNS targets, the distance between the NAFLD targets
and SNS targets was more close than the reference distance. These
results indicated a therapeutic effect of SNS against NAFLD. To
evaluate the importance of different targets, we used the RWR algorithm
to sort the targets in a sub-PPI network only consisting of SNS targets
and NAFLD targets. According to the RWR score, STAT3 was the most
important target related to SNS targets. And most importantly, STAT3
played a core role in enriched KEGG pathways, including AGE-RAGE
signal, FoxO signal, HIF-1 signal, and insulin resistance.
Our experimental data indicated that SNS treatment decreased Jak2/STAT3
activation, which could be activated by IL6 and AGEs. Previous studies
showed that STAT3 played important roles in liver inflammation and
interacted with multiple signals. GWAS study validated the genetic
associations of STAT3 with the susceptibility to NAFLD and disease
progression in the Asian population ([124]Sookoian et al., 2008;
[125]Kumar et al., 2019). In vivo study indicated that STAT3 was
related to lipid synthesis by modulating the expression of SREBP1 in a
high-fat diet model ([126]Zeng et al., 2017). In vitro obesity model
showed that STAT3 knockdown significantly attenuated TG content and
expression of SREBP1 in LO2 cells ([127]Chen et al., 2018). Another
function of the STAT3 signal was activating liver inflammation. It has
been reported that hepatocyte-specific STAT3 knockout markedly
inhibited liver inflammation compared with wild-type mice ([128]Miller
et al., 2011). STAT3 was considered highly interconnected with NFκB
signal, a core transcription factor in diverse immune responses. Many
inflammatory factors transcribed by NFκB, such as IL6, are important
STAT3 activators. In some conditions, STAT3 could directly interact
with NFκB and lead to constitutive NFκB activation and numerous
inflammatory genes transcription ([129]Yu et al., 2009). According to
previous reports, IL6, IL1b, and CCL2 were upregulated by STAT3
activation ([130]Yu et al., 2009). And these pro-inflammatory factors
played important roles in the progression of NAFLD ([131]Kazankov et
al., 2019). Recent data showed that STAT3-related lysosomal membrane
permeabilization promoted ferroptosis via CTSB (cathepsin B) ([132]Zhou
et al., 2020). These data supported the therapeutic effect of SNS
against NAFLD.
Our network pharmacology identified three compounds in SNS related to
STAT3 inhibition, including quercetin, paeoniflorin, and luteolin.
Recent studies reported that quercetin interrupted the positive
feedback loop between STAT3 and IL-6 and exhibited an anti-inflammatory
phenotype ([133]Granato et al., 2019). Luteolin was also found to
decrease STAT-binding activity and markedly suppress STAT3
phosphorylation in a dose-dependent manner ([134]Aziz et al., 2018).
Except for these two flavonoids, paeoniflorin, a monoterpene glucoside,
was proved to markedly decrease STAT3 activation in immune cells
([135]Zhang and Wei, 2020). Future studies should test the relationship
between STAT3 and these compounds' treatment in NAFLD models.
Conclusion
In conclusion, in this study, we combined a PPI-network-based
pharmacology analysis with biological validation to study the mechanism
of the actions of SNS against NAFLD at the systemic level. We confirmed
the anti-NAFLD effect of SNS was involved in multiple targets on
Jak2/STAT3 signal pathway. In the future, other pathways or mechanisms
predicted in this study should be further investigated.
Acknowledgments