Evidence-Based Complementary and Alternative Medicine
Volume 2022 (2022), Article ID 3747285, 20 pages
https://doi.org/10.1155/2022/3747285

Research Article

Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach

Sijie Li,1,2 Yong Yang,³ Wei Zhang,1,2,3 Haiyan Li,1,2 Wantong Yu,1,2 Chen Gao,1,2 Jiali Xu,1,2 Wenbo Zhao,1,2 and Changhong Ren1,2

¹Beijing Key Laboratory of Hypoxia Translational Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
²Center of Stroke, Beijing Institute for Brain Disorder, Capital Medical University, Beijing 100053, China
³School of Chinese Medicine, Beijing University of Chines Medicine, Beijing 100029, China

Correspondence should be addressed to Changhong Ren; rench@xwhosp.org

Received 8 September 2021; Accepted 7 December 2021; Published 5 January 2022

Academic Editor: Feng Zhang

Copyright © 2022 Sijie Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Purpose. Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment. Methods. All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot. Results. Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis () and 119 signaling pathways () in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS. Conclusions. This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.

1. Introduction

Stroke has the characteristics of high incidence, high recurrence rate, high fatality rate, high disability rate, and high economic burden, which seriously threatens human health and quality of life. Ischemic stroke (IS) accounts for 70% to 80% of all strokes, which results from sudden interruption of the blood supply to areas of the brain [1]. Neurological deficits such as disturbances to consciousness, cognitive and behavioral changes, paralysis, dysphagia, and aphasia are the major sequels of stroke. In China, stroke is the leading cause of death and contributes to a heavy disease burden [2]. The most effective treatment is currently recognized as intravenous thrombolysis and/or endovascular treatment, which prevent irreversible brain tissue damage by restoring blood flow reperfusion in time [3]. Although in situ clot retrieval can improve the recanalization rate of patients with ischemic stroke (mTICI of 2b/3) to 80–90%, it still exceeds 73% of patients who are left with functional disability or death (90 days of mRS ≥ 2) [4]. Therefore, it is necessary to explore neuroprotective drug to promote neurological function after IS and improve the prognosis of stroke [5].

Traditional Chinese medicine (TCM) has played an important part in maintaining health for thousands of years. In recent years, traditional Chinese drugs have been reported to possess protective effects on the nervous system, which has attracted the attention of researchers worldwide [6]. Danggui Shaoyao San (DSS) is a famous herbal formula composed of the following 6 raw herbs: Paeoniae Radix Alba (PRA), Atractylodes macrocephala Koidz. (AMK), Chuanxiong Rhizoma (CR), Angelicae Sinensis Radix (ASR), Poria cocos (Schw.) Wolf (PCW), and Alisma orientale (Sam.) Juz. (AOJ), which has been widely used in the treatment of various gynecological diseases [7, 8]. Recently, it was found that DSS is a potential therapeutic agent for the treatment of cognitive impairment and depression [9]. It is reported that DSS not only prevents cognitive impairment from Alzheimer’s disease (AD) but also improves microcirculation in patients with asymptomatic cerebral infarction [10]. A number of studies indicate the potentials of DSS for improving neurological functions in poststroke treatment [11, 12]. DSS treatment also promotes focal angiogenesis and neurogenesis, attenuates neurological deficit scores, and improves memory functions in experimental rat models of cerebral ischemic-reperfusion injury [13, 14]. However, the underlying neuroprotection mechanisms of DSS against IS remain largely unknown.

Compared with the single-target therapeutic effect of chemical drugs, TCM compound ingredients have the overall therapeutic effect, which is usually modulated through various pathways and targets [15]. System biology, such as network pharmacology, contributes to reveal the biological networks in which drugs work. The integration strategy of network biology and multidirectional pharmacology is conducive to expand the available drug target space and is expected to enhance therapeutic efficacy, elevate clinical trial success rate, and decrease drug discovery costs [16]. In recent years, it has been well applied for drug discovery, especially in the field of research and development of Chinese medicine formulae [17].

According to the description above, DSS is an ideal TCM in the application of treatment to IS. However, the underlying pharmacological mechanisms of DSS on IS treatment remain unclear. In this study, we used the emerging traditional Chinese medicine network pharmacology method to predict the targets of DSS and systematically predict the mechanism of action of DSS in IS. An outline of the method is shown in Figure 1. Our results offered the systematic mechanism of DSS in the treatment of IS and clearly clarified the synergy mechanism of DSS multicomponent and multi-target.

Figure 1: Flowchart of this study.

2. Materials and Methods

2.1. DSS Ingredient Collection and Target Gene Prediction

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP, https://tcmsp-e.com/) was used for the active ingredient screening [18]. The names of herbs were used as the keywords to retrieve all components. We filtered active compounds by setting the pharmacokinetic index that the oral bioavailability (OB) was greater than 30% and the drug-like (DL) index was >0.18. Target genes were predicted using TCMSP, the PubChem website (pubchem.ncbi.nlm.nih.gov), and the PharmMapper database (http://www.lilabecust.cn/pharmmapper) after identifying the active ingredients [19].

2.2. Acquisition of Potential Therapeutic Targets for DSS Anti-IS

Online Mendelian Inheritance in Man (OMIM, http://omim.org) database, Comparative Toxicogenomics Database (CTD, http://ctdbase.org/) [20], and Therapeutic Target Database (TTD, http://db.idrblab.net/ttd/) were selected to obtain IS-related target gene [21]. All the databases used “ischemic stroke” as the keyword. The potential therapeutic targets of DSS in the treatment of IS were derived from the overlapping targets of DSS-related targets and IS-related targets. Subsequently, we used Cytoscape software (version 3.7.2) to construct a network diagram of “Drug active ingredient-target gene interaction network.” The topology analysis of the composition-target network was carried out using the function of “Network Analyzer” in the software.

2.3. Protein-Protein Interaction (PPI) Analysis

The obtained intersection targets were inputted to the STRING database (http://string-db.org/cgi/inpup.pl) with the species set to “Homo sapiens” to construct a protein-protein interaction (PPI) network [22]. The minimum required interaction score was set to “highest confidence (0.900),” and the hide disconnected nodes were set. Then, we obtained the PPI network. Next, the interaction files were downloaded and imported into Cytoscape software (version 3.7.2) to visualize the PPI network and analyze the topological value [23]. Then, the targets with the degree and betweenness centrality (BC), which were greater than the average value, were recognized as the key targets of DSS for the treatment of IS.

2.4. Enrichment Analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)

The predicted targets were imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID database, https://david.ncifcrf.gov) for GO and KEGG enrichment analyses [24]. GO enrichment analysis includes biological process (BP), cellular component (CC), and molecular function (MF). The enrichment results of GO and KEGG were obtained by setting the FDR value, and the bubble map was made according to the enrichment results [25].

2.5. Molecular Docking

The top three key compounds analyzed by “Network Analyzer” were obtained in the TCMSP database in mol2 format, and the top three key target proteins in the PPI network were obtained in the PDB database (http://www.rcsb.org). The software AutoDock Vina was used to carry out molecular docking between the key active ingredients and the key targets [26]. The binding energy was used as the evaluation index to evaluate the docking results, and the results were displayed by the software PyMOL [27].

2.6. Animal Model and Drug Administration

All animal experiments were approved by the Institutional Animal Care and Use Committee of Capital Medical University (XW-20210228-1) and in accordance with the principles outlined in the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Male C57/BL6 mice (21–23 g) were used. Transient focal ischemia was induced by right middle cerebral artery occlusion (MCAO) using the intraluminal vascular occlusion method as previously described [28]. In brief, the right common carotid artery and the right external carotid artery (ECA) were exposed. The ECA was then dissected distally, ligated, and coagulated. The right MCA was occluded using a heparinized intraluminal filament. After 60 min, the suture was withdrawn. During surgery, rectal temperature was maintained at 37 ± 0.5°C with a thermostat-controlled heating pad. Sham-operated mice underwent an identical surgery except that the MCA was not occluded. Sixty minutes after occlusion, the filament was removed and a laser speckle contrast imaging (PSI System, Perimed Inc.) was used to observe the local cerebral blood flow. The mice were randomly assigned to the sham, MCAO, and MCAO + DSS groups. The MCAO + DSS group was administered DSS (20 g/kg) via the intragastric route at the time of reperfusion.

2.7. Preparation of DSS

The materials of DSS were purchased from Tong Ren Tang Pharmaceutical Company (Beijing, China) and were then authenticated by Dr. Weipeng Yang in the China Academy of Chinese Medical Sciences. The DSS dilution was prepared as described previously [14]. In brief, the 6 raw herbs were mixed in their dry weight ratios of 3 : 16 : 3:8 : 4:4 (ASR : PRA : CR : AOJ: PCW : AMK). The mixture was soaked in distilled water (1 : 8 w/v) for 30 minutes at room temperature, boiled for 1.5 h, and the extract was filtered thereafter. The boiling and extraction procedures were repeated three times. The extracted filtrate was concentrated using a rotary evaporator, and the final concentration of the extract is 1 g/ml (equivalent to the dry weight of the raw materials). The DSS extract was then stored at 4°C.

2.8. Two-Dimensional Laser Speckle Imaging

Regional cerebral blood flow (CBF) was monitored by the two-dimensional laser speckle imaging before ischemia and after the onset of ischemia (10 min after MCAO). We calculated the blood flow ratio of the two cerebral hemispheres (right/left), and at a ratio less than 20%, the MCAO model was considered successful (Figures 2(a)–2(b)).

(a)

(b)

(c)

(d)

Figure 2: DSS treatment reduced infarct volume. (a) Regional cerebral blood flow (CBF) was monitored by the two-dimensional laser speckle imaging. (b) The blood flow ratio of two cerebral hemispheres (right/left), and a ratio less than 20% considered that the MCAO model is successful (). (c) Representative brain slices with infarcts stained by triphenyltetrazolium chloride from each group at 24 h after MCAO. (d) Quantification of infarct size at 24 h after MCAO. N = 7 per group. , vs. MCAO group, by Student’s t-test.

2.9. Infarct Size Measurement

Infarct size was measured according to previous methods [28]. Twenty-four hours after surgery, the mice (n = 7 per group) were anesthetized with 1% pentobarbital sodium, and then, the brains were removed and sectioned coronally at 1-mm intervals to generate 6 slices. The slices were then incubated with 2% solution of 2, 3, 4-triphenyltetrazolium chloride (TTC). The infarct area and the corresponding contralateral area were measured by a blinded observer using the Image-Pro Plus software 5.0 (Rockville, MD, USA). Infarct size was calculated as a percentage of the size of contralateral hemisphere.

2.10. Western Blot

Tissue samples were collected from the ischemic hemisphere at 24 hours and 7 days, respectively, after reperfusion for the Western blot analysis. The samples at 24 hours after reperfusion were used for the detection of phosphorylated MAPK1 (p-ERK1/2) and phosphorylated RAC-alpha serine/threonine-protein kinase (p-AKT). The samples at 7 days after reperfusion were used for the detection of phosphorylated proto-oncogene tyrosine-protein kinase Src (p-SRC). Protein (40 μg) was electrophoresed on 10% SDS polyacrylamide gels (Beijing Biotides Biotechnology Co., Ltd, Beijing, China) and then transferred to a polyvinylidene fluoride membrane (Millipore Corporation, USA). The membrane was probed with primary antibody: anti-p-ERK1/2 antibody (Cell Signaling; 1 : 1000 dilution), anti-p-AKT antibody (Cell Signaling; 1 : 1000 dilution), and anti-p-SRC antibody (Cell Signaling; 1 : 1000 dilution). The specific reaction was visualized through the use of a chemiluminescent substrate (GE Healthcare, UK) [29]. β-Actin was used to verify equal loading. The optical density of protein was measured using ImageJ software (NIH, Bethesda, MD, USA) according to the manufacturer’s instructions (n = 7 per group).

2.11. Statistical Analysis

For two groups, the differences were analyzed for statistical significance by Student’s t-test. For three or more groups, the differences were analyzed for statistical significance by the one-way ANOVA followed by Tukey’s post hoc test. All the data were expressed as mean ± SD. The statistical analysis was performed with SPSS for Windows, version 21.0 (SPSS Inc.). The p-value <0.05 was considered significant.

3. Results

3.1. DSS Ingredient Collection and Target Gene Prediction

According to the two screening conditions of OB value and DL index, 54 active ingredients of DSS were obtained from TCMSP, including 13 ingredients in PRA, 7 ingredients in CR, 7 ingredients in AMK, 2 ingredients in ASR, 15 ingredients in PCW, and 10 ingredients in AOJ. ASR and PRA share one ingredient. PRA, AOJ, and CR share one ingredient (Table 1). Then, the target genes of 54 active ingredients were collected for target gene prediction in TCMSP and PubChem. A total of 14978 predicted targets were obtained. After removing the other species gene targets and duplicate values, 458 relevant human gene targets were obtained, including ASR (232), PRA (432), AOJ (377), CR(433), PCW (409), and AMK (359).

Table 1: Pharmaceutical ingredients.

Chinese name
Latin name
Molecule ID
Active ingredients
Ingredient code

Danggui
Angelicae Sinensis Radix
MOL000358
Beta-sitosterol
ASR-1

MOL000449
Stigmasterol
ASR-2

Baishao
Paeoniae Radix Alba
MOL001921
Lactiflorin
PRA-1

MOL001924
Paeoniflorin
PRA-2

MOL000211
Mairin
PRA-3

MOL000358
Beta-sitosterol
ASR-1

MOL000359
Sitosterol
PRA-4

MOL001930
Benzoyl paeoniflorin
PRA-5

MOL001919
Palbinone
PRA-6

MOL001925
Paeoniflorin
PRA-7

MOL001910
11alpha,12alpha-epoxy-3beta-23-Dihydroxy-30-nor--olean-20-en-28,12beta-olide
PRA-8

MOL001918
Paeoniflorin genome
PRA-9

MOL001928
Albiflorin
PRA-10

MOL000492
Cianidanol
PRA-11

MOL000422
Kaempferol
PRA-12

Zexie
Alisma Orientale (Sam.) Juz.
MOL000856
Alisol C monoacetate
AOJ-1

MOL000853
Alisol B
AOJ-2

MOL000832
Alisol B 23-acetate
AOJ-3

MOL000830
Alisol B
AOJ-4

MOL000854
Alisol C
AOJ-5

MOL000862
Alisol B 23-acetate
AOJ-6

MOL000831
Alisol B monoacetate
AOJ-7

MOL000849
16β-Methoxyalisol B monoacetate
AOJ-8

MOL000359
Sitosterol
PRA-4

MOL002464
1-Monolinolein
AOJ-9

Chuanxiong
Chuanxiong Rhizoma
MOL000359
Sitosterol
PRA-4

MOL002157
Wallichilide
CR-1

MOL000433
Folsaeure
CR-2

MOL002135
Myricanone
CR-3

MOL002140
Perlolyrine
CR-4

MOL002151
Senkyunone
CR-5

MOL001494
Mandenol
CR-6

Fuling
Poria cocos (Schw.) Wolf.
MOL000300
Dehydroeburicoic acid
PCW-1

MOL000285
Polyporenic acid C
PCW-2

MOL000280
Dehydrotumulosic acid
PCW-3

MOL000273
16alpha-Hydroxydehydrotrametenolic acid
PCW-4

MOL000283
Ergosterol peroxide
PCW-5

MOL000276
7, 9 (11)-dehydropachymic acid
PCW-6

MOL000289
Pachymic acid
PCW-7

MOL000287
Eburicoic acid
PCW-8

MOL000275
Trametenolic acid
PCW-9

MOL000279
Cerevisterol
PCW-10

MOL000290
Poricoic acid A
PCW-11

MOL000296
Hederagenin
PCW-12

MOL000292
Poricoic acid C
PCW-13

MOL000291
Poricoic acid B
PCW-14

MOL000282
Stellasterol
PCW-15

Baizhu
Atractylodes macrocephala Koidz.
MOL000033
(24S)-24-Propylcholesta-5-ene-3beta-ol
AMK-1

MOL000028
Α-Amyrin
AMK-2

MOL000021
14-Acetyl-12-senecioyl-2E, 8E, 10E-atractylentriol
AMK-3

MOL000022
14-Acetyl-12-senecioyl-2E, 8Z, 10E-atractylentriol
AMK-4

MOL000020
12-Senecioyl-2E, 8E, 10E-atractylentriol
AMK-5

MOL000049
3β-Acetoxyatractylone
AMK-6

MOL000072
8β-Ethoxy atractylenolide III
AMK-7

3.2. Drug Active Ingredient-Target Gene Interaction Network

The overlapping targets of DSS-related targets and IS-related targets were considered as potential therapeutic targets for DSS anti-IS. As shown in Figure 1, a total of 315 intersection targets were obtained by screening drug ingredient targets and IS targets (Figure 3(a)). Six kinds of drugs have 182 common targets (Table 2) (Figure 3(b)). Drug active component-target gene interaction network contained 366 nodes (including 51 herb compound nodes and 315 target gene nodes) and 11146 edges. The degree value of a node represents the number of lines connected to the node in the network. The larger node means more importance. Among 315 target gene nodes, the higher the degree of disease correlation, the redder the color (Figure 4). In addition, in the network pharmacological map of a single herb, the higher the degree value of the target gene to the herb, the closer it is to the herb component (Figure 5). The topology analysis showed that the average degree value of each node in the network was 208.4706, and the average medium was 0.016732. There were 17 compound nodes with both degree and intermediate values above the mean (Table 3), suggesting that these compounds may be key compounds in the treatment of stroke.

(a)

(b)

Figure 3: Identification of the drug-target interaction. (a) Venn diagram. (b) Drug-disease target analysis of traditional Chinese medicine. 182 represents the common targets of six Chinese medicines, and other figures indicate the unique targets of each Chinese medicine. PRA, Paeoniae Radix Alba. AMK, Atractylodes macrocephala Koidz., CR, Chuanxiong Rhizoma. ASR, Angelicae Sinensis Radix. PCW, Poria cocos (Schw.) Wolf. AOJ, Alisma orientale (Sam.) Juz.

Table 2: Drug active ingredient-target gene (common targets of six drugs).

Gene
Gene ID

CASP3
836

SOD2
6648

NOS3
4846

HMOX1
3162

MAPK1
5594

F2
2147

MMP2
4313

GSK3B
2932

MAPK8
5599

IL-2
3558

PPARG
5468

NR3C1
2908

MAPK14
1432

REN
5972

F3
2152

IGF-1
3479

BCL2L1
598

PARP1
142

EGFR
1956

CASP7
840

ALB
213

CYP19A1
1588

PPARA
5465

GSR
2936

FABP4
2167

IGF-1R
3480

MMP3
4314

MAOA
4128

NQO1
1728

MMP13
4322

HSP90AA1
3320

CCNA2
890

AKR1C3
8644

MDM2
4193

TGFBR1
7046

CALM1
801

PGR
5241

TGFB2
7042

CYP2C9
1559

ESR1
2099

NR3C2
4306

KDR
3791

GSTP1
2950

F7
2155

AR
367

NR1I2
8856

AURKA
6790

MAP2K1
5604

B2M
567

CASP1
834

HSPA8
3312

MAOB
4129

FGFR1
2260

HSP90AB1
3326

HMGCR
3156

JAK2
3717

MME
4311

CTSB
1508

LCN2
3934

HSD11B1
3290

SRC
6714

XIAP
331

SULT1E1
6783

VDR
7421

GSTA1
2938

GCK
2645

INSR
3643

CDK2
1017

NCOA2
10499

S100A9
6280

FABP5
2171

BMP2
650

TEK
7010

MET
4233

CDK6
1021

CYP2C8
1558

CTSK
1513

PSAP
5660

THRB
7068

PDPK1
5170

ADAM17
6868

KIT
3815

EPHX2
2053

FKBP1A
2280

MMP12
4321

CTSS
1520

MAPK10
5602

FABP3
2170

RARA
5914

MAPKAPK2
9261

PIK3R1
5295

PDE4D
5144

LSS
4047

AKT2
208

PIM1
5292

AKR1B1
231

PLA2G2A
5320

NR1I3
9970

OAT
4942

DUSP6
1848

PGF
5228

CHEK1
1111

ESRRA
2101

PPIA
5478

TNNC1
7134

AKR1C1
1645

GSTM1
2944

HCK
3055

SULT2A1
6822

PDK2
5164

F10
2159

RORA
6095

NR1H3
10062

AKR1C2
1646

EIF4E
1977

BRAF
673

CA2
760

AMD1
262

MMP8
4317

JAK3
3718

NCOA1
8648

NR1H4
9971

THRA
7067

PDE4B
5142

PTPN11
5781

GART
2618

RBP4
5950

PDE5A
8654

BACE1
23621

WAS
7454

GRB2
2885

PTPN1
5770

PROCR
10544

SDS
10993

PPP5C
5536

PRKCQ
5588

BCHE
590

ACADM
34

DHFR
1719

FGFR2
2263

TTR
7276

PYGL
5836

GC
2638

ANXA5
308

CES1
1066

LCK
3932

DPP4
1803

FKBP1B
2281

FABP7
2173

FECH
2235

STS
412

ELANE
1991

BAG1
573

ERBB4
2066

GM2A
2760

TPX2
22974

PLK1
5347

TYMS
7298

DCK
1633

BHMT
635

CTNNA1
1495

CRABP2
1382

ESRRG
2104

RXRA
6256

MTAP
4507

SHBG
6462

NCOA5
57727

PPARD
5467

ABL1
25

TTPA
7274

FKBP3
2287

GRB14
2888

PCTP
58488

CTSF
8722

ADK
132

HSD17B1
3292

KIF11
3832

RFK
55312

FNTB
2342

YARS1
8565

HNMT
3176

BCAT2
587

Figure 4: DSS prescription compositions of active ingredient-target pathway network. Rhombus represents the active ingredients of herbs contained in DSS. The degree value of a node represents the number of lines connected to the node in the network. The higher the degree value, the more important the node. The round nodes represent disease targets. The higher the degree of disease correlation, the redder the color is.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 5: The network pharmacological diagram of a single herb. (a) PRA, Paeoniae Radix Alba. (b) AMK, Atractylodes macrocephala Koidz. (c) CR, Chuanxiong Rhizoma. (d) ASR, Angelicae Sinensis Radix. (e) PCW, Poria cocos (Schw.) Wolf. (f) AOJ, Alisma orientale (Sam.) Juz. Rhombus represents the active ingredients of herbs contained in DSS. The round nodes represent disease targets. The higher the degree of disease correlation, the redder the color is. The higher the degree value of the target gene to the herb, the closer it is to the herb component.

Table 3: Key compounds. (The average degree value is 208.4706, and the average medium is 0.016732.)

Compound
Compound ID
Degree
Betweenness centrality

11alpha,12alpha-epoxy-3beta-23-Dihydroxy-30-nor-olean-20-en-28,12beta-olide
PRA-8
228
0.024789

Eburicoic acid
PCW-8
226
0.017975

12-Senecioyl-2E, 8E, 10E-atractylentriol
AMK-5
225
0.021314

Dehydroeburicoic acid
PCW-1
225
0.01739

14-Acetyl-12-senecioyl-2E, 8Z, 10E-atractylentriol
PRA-11
223
0.028611

Cianidanol
AMK-4
223
0.01698

Alisol C
AOJ-5
221
0.017782

16alpha-Hydroxydehydrotrametenolic acid
PCW-4
221
0.017585

Poricoic acid C
PCW-13
220
0.021088

Palbinone
PRA-6
219
0.024345

Cerevisterol
PCW-10
217
0.017087

Poricoic acid A
PCW-11
216
0.02044

Kaempferol
PRA-12
215
0.032249

Paeoniflorin
PRA-2
215
0.017069

Lactiflorin
PRA-1
214
0.016884

Poricoic acid B
PCW-14
213
0.021272

Myricanone
CR-3
209
0.041457

3.3. Construction of the PPI Network and Core Target Screening

315 selected target genes were introduced into the STRING database to construct PPI network (Figure 6(a)). The PPI network contains 268 nodes and 1184 edges, indicating that 268 targets can interact with each other in the network, resulting in a total of 1184 interactions. After the analysis of topology parameters, the average node degree is 8.670412, and the average medium is 0.20796. There are 30 nodes with degree and intermediate values above the average, which were considered to be core targets of DSS in the treatment of IS (Figure 6(b), Table 4). According to the degree values, the top 10 core targets were identified as MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, RXRA, and FN1.

(a)

(b)

Figure 6: PPI network and top 30 hub genes for DSS anti-IS. (a) PPI network constructed with the STRING database. (b) Top 30 hub genes for DSS anti-IS were obtained using the degree and betweenness centrality algorithm. The node size and color indicate the degree value. The heavier the color, the greater the degree value and the wider the line and the heavier its color, indicating the closer the connection between the two proteins.

Table 4: Drug targets of IS. (The average degree value is 8.670412, and the average medium is 0.20796.)

Gene
Protein name
Degree
Betweenness centrality

MAPK1
Mitogen-activated protein kinase 1
53
0.126385

SRC
Proto-oncogene tyrosine-protein kinase Src
50
0.052449

PIK3R1

47
0.047873

HRAS
GTPase HRas
44
0.033552

AKT1
Phosphatidylinositol 3-kinase regulatory subunit alpha
40
0.109791

RHOA
Transforming protein RhoA
35
0.047081

RAC1
Ras-related C3 botulinum toxin substrate 1
34
0.0268

HSP90AA1
Heat shock protein HSP 90-alpha
33
0.059285

RXRA
Retinoic acid receptor alpha， RAR-alpha
30
0.067159

FN1
Fibronectin 1
29
0.023024

MAPK8
Mitogen-activated protein kinase 8
29
0.022235

ESR1
Estrogen receptor
28
0.091184

MAPK14
Mitogen-activated protein kinase 14
28
0.058202

NCOA1
Nuclear receptor coactivator 1
27
0.039326

CREBBP
CREB binding protein
26
0.03777

IGF-1
Insulin-like growth factor I
25
0.024056

ALDOA
Fructose-bisphosphate aldolase A
22
0.065989

RAP1A
Ras-related protein Rap-1A
21
0.026304

B2M
Beta-2-microglobulin
19
0.034377

PLG
Plasminogen
19
0.023626

F2
Prothrombin
16
0.028418

ARG1
Arginase-1
16
0.021575

CTSD
Cathepsin D
15
0.037545

PPARA
Peroxisome proliferator-activated receptor alpha
15
0.028961

CDA
Cytidine deaminase
14
0.059555

TYMS
Thymidylate synthase
11
0.037145

NOS3
Nitric oxide synthase 3
11
0.036816

DUT
Deoxyuridine triphosphatase
9
0.057749

NT5M
5′(3′)-Deoxyribonucleotidase, mitochondrial
9
0.048718

G6PD
Glucose-6-phosphate dehydrogenase
9
0.027099

3.4. GO and KEGG Enrichment Analyses

315 targets screened by the DAVID database were used for GO functional enrichment analysis and KEGG signaling pathway enrichment analysis (FDR < 0.01). As a result, we obtained 417 GO terms, including 288 biological processes (BPs), 86 molecular functions (MFs), and 43 cellular components (CCs). BP mainly includes negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, and cellular response to oxidative stress. MF mainly includes the activity of steroid hormone receptor and protein tyrosine kinase. CC mainly includes cytoplasm and extracellular bodies. The top 10 screened BP, MF, and CC were selected as bubble charts through R language-related procedures (Figure 7).

Figure 7: Functional classification of drug-disease targets by bioinformatic analysis. The biological process (BP), cellular component (CC), and molecular function (MF). Gene ratio refers to the ratio of enriched genes to all target genes, and counts refer to the number of the enriched genes.

The enrichment and screening of KEGG pathways resulted in 119 signaling pathways (FDR < 0.01) and 16 pathways related to stroke (Figure 8). The main pathway included VEGF signaling pathway, estrogen signaling pathway, neurotrophin signaling pathway, HIF-1 signaling pathway, and thyroid hormone signaling pathway.

Figure 8: KEGG signaling pathway enrichment of screened genes. The bar chart showed the 16 pathways related to stroke. “Rich factor” represents the ratio of the number of target genes belonging to a pathway and the number of the annotated genes located in the pathway. A higher rich factor represents a higher level of enrichment. The size of the dot indicates the number of target genes in the pathway, and the color of the dot reflects the different P values.

3.5. Molecular Docking

The top 3 key target proteins (MAPK1, PIK3R1, and SRC) in PPI network were selected to dock with the top 3 key active compounds (“11alpha, 12alpha-epoxy-3beta-23-dihydroxy-30-nor-olean-20-en-28,12beta-olide,” “eburicoic acid,” “12-senecioyl-2E, 8E, 10E-atractylentriol”). The PyMOL software was used to visualize the docking results of key active ingredients and core targets, as shown in Figure 9 and Table 5.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 9: Docking results of the key ingredient and three hub target proteins. (a) 12-senecioyl-2E, 8E, 10E-atractylentriol and MAPK1; (b) eburicoic acid and MAPK1; (c) 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-nor-olean-20-en-28,12beta-olide and MAPK1; (d) 12-senecioyl-2E, 8E, 10E-atractylentriol and PI3K; (e) eburicoic acid and PI3K; (f) 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-nor-olean-20-en-28,12beta-olide and PI3K; (g) 12-senecioyl-2E, 8E, 10E-atractylentriol and SRC; (h) eburicoic acid and SRC; (i) 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-nor-olean-20-en-28,12beta-olide and SRC.

Table 5: Binding energy between the key ingredients and target proteins.

NO.
Receptor protein
Molecule ID
Ligand component
Binding energy (kcal·mol-1)

1
MAPK1
MOL000020
12-Senecioyl-2E, 8E, 10E-atractylentriol
−5.99

2
MAPK1
MOL000287
Eburicoic acid
−8.64

3
MAPK1
MOL001910
11alpha,12alpha-epoxy-3beta-23-Dihydroxy-30-nor-olean-20-en-28,12beta-olide
−9.32

4
PIK3R1
MOL000020
12-Senecioyl-2E, 8E, 10E-atractylentriol
−6.66

5
PIK3R1
MOL000287
Eburicoic acid
−9.24

6
PIK3R1
MOL001910
11alpha,12alpha-epoxy-3beta-23-Dihydroxy-30-nor-olean-20-en-28,12beta-olide
−10.18

7
SRC
MOL000020
12-Senecioyl-2E, 8E, 10E-atractylentriol
−7.57

8
SRC
MOL000287
Eburicoic acid
−9.42

9
SRC
MOL001910
11alpha,12alpha-epoxy-3beta-23-Dihydroxy-30-nor-olean-20-en-28,12beta-olide
−9

3.6. DSS Treatment Reduced Infarct Size

To confirm the neuroprotection of DSS in focal ischemic stroke, the MCAO mice were treated with DSS, and 24 hours later, the infarct size was detected by TTC stain. Compared with the MCAO group (56.47 ± 3.30), the infarct size was reduced by 39% in the MCAO + DSS group (34.49 ± 2.94) () (Figure 2).

3.7. DSS Modulated the Expression of p-AKT, p-ERK1/2, and p-SRC

To evaluate the performance of the core target screening approach used in this study, we selected the three major target proteins for analysis by the Western blot. The level of p-ERK1/2 was significantly increased in the MCAO mice (MCAO vs. sham, ) and further significantly increased after LRIC treatment (MCAO + DSS vs. MCAO, ) (Figure 10). Moreover, compared with the sham group, the expression of p-AKT in the MCAO group was decreased (), but that in the DSS group was significantly upregulated compared with the MCAO group () (Figure 2). We also measured the expression levels of p-SRC at day 7 after MCAO surgery, finding that, when compared with the sham group, the levels of p-SRC in the MCAO mice were significantly increased (). DSS treatment for 7 days increased the levels of p-SRC compared with those found in the MCAO group () (Figure 10).

(a)

(b)

(c)

(d)

Figure 10: Expression of protein detected through the Western blot. (a) Representative Western blots of p-ERK1/2 and p-AKT at day 1 after MCAO surgery and the levels of p-SRC at day 7 after MCAO surgery. (b-d) Quantification of p-ERK1/2, p-AKT, and p-SRC, respectively. Values are mean ± SD. , , . N = 7 per group.

4. Discussion

In this study, the anti-IS mechanism underlying the effect of DSS was explored using network pharmacology through data mining and subsequent computational modeling. It was noteworthy that our findings partially elucidate the complex anti-IS mechanism in the DSS, and they provide insight into the integrated understanding of the therapeutic efficacy and pharmaceutical activity of DSS. First, a total of 534 active ingredients were extracted from DSS. The possible targets of DSS were mined using the TCMSP database. PPI data were used to construct the core PPI network. Then, 30 candidate anti-IS targets of DSS were identified as the pivotal hub genes in the core PPI network according to the specific topological importance. In addition, GO and KEGG pathway analyses were carried out to demonstrate the candidate target biological significance after the pivotal hub genes were incorporated into ClueGO. Finally, we evaluate the neuroprotection role of DSS in IS stroke mice and detected the effect of DSS on the major drug targets in IS treatment using the Western blot.

4.1. DSS May Contribute to the Treatment of IS by Diminishing Apoptosis, Reducing Inflammation, and Oxidative Stress at Acute Stage

Cerebral ischemia-reperfusion injury induces a complex pathophysiological cascade that includes a wide range of aberrant cellular processes [30]. In the ischemic phase, reduced blood supply rapidly leads to failure of ionic gradients, excitotoxicity, and neuronal death. During the reperfusion phase, the return of oxygen contributes to oxidative stress, and the restoration of blood introduces factors that promote inflammation and edema, thereby further increasing the vulnerability of the affected tissue to death [30].

The increasing number of reports shows that apoptosis may contribute to a significant proportion of neuron death following IS [31]. Mechanistic studies have established that decreasing pro-apoptotic proteins or enhancing pro-survival proteins protects the brain after cerebral ischemia. We found that MAPK1 is the key target of DSS in the treatment of IS, and AKT1, GSK3, CASP3, and BCL2 are also among the targets. MAPK includes the extracellular signal-regulated kinase 1/2 (ERK1/2) subfamily. The phosphatidylinositol 3-kinase/Akt (PI3K/Akt) is an important signaling pathway involved in the regulation of cell apoptosis after stroke [32]. Many studies have shown that the activation of AKT and ERK1/2 can effectively inhibit apoptosis by regulating the expression of the anti-apoptosis gene Bcl-2 [33, 34]. Activated Akt promotes cell survival and suppresses apoptosis partly via inhibiting glycogen synthase kinase-3 (GSK3), an apoptosis-related molecule [31]. Caspase-3 is considered to play an executing role at the final step of apoptosis-producing DNA fragmentation [31]. The report showed that DSS reduced the expression of caspase-3 and upregulated the expression of Bcl-2 at the MCAO rat model [11]. In this study, the Western blot results revealed that DSS significantly regulated the expression of p-ERK1/2, p-AKT, and Bcl-2. MAPK1 (ERK1/2) and PI3K, the two key targets, created 3D graphs of docking and analysis. Docking their crystal structures with the compounds, we showed that the two genes could be attached to the active pocket of the protein.

Inflammation after cerebral ischemia is a complicated pathological process starting from the activation of microglia, circulating leukocyte infiltrate (such as neutrophils, lymphocytes, and macrophages), and releasing of pro-inflammatory mediators mediated by ischemic cells and immune cells [30, 35]. Our GO analysis showed that DSS regulated neutrophil activation and neutrophil mediated immunity. Experimental models have shown that the inhibition of neutrophilic inflammatory mechanisms reduces neurodegeneration and improves functional outcome after cerebral ischemia [36]. Based on the KEGG pathway analysis, DSS influences TNF signaling pathway. TNF signaling pathway mediates a wide range of cellular processes including inflammation, proliferation, cell migration, apoptosis, and necrosis [37]. Studies confirm that the inhibition of TNF signaling pathway might be associated with reducing neuroinflammation in IS [38].

An imbalance between free radical production and antioxidant activity leads to oxidative stress, which is a major pathologic mechanism of secondary brain damage after cerebral ischemia [30]. Our GO analysis showed that the mechanism of DSS in the treatment of IS was related to cellular response to oxidative stress and reactive oxygen species metabolic process. Superoxide dismutase 2 (SOD2) and peroxisome proliferator-activated receptor alpha (PPARA) are the core targets of DSS in IS treatment. SOD2 is an enzymatic antioxidant that catalyzes the conversion of H2O2 and helps maintain the redox balance by diffusing the superoxide. Therapeutically increasing the levels of SOD2 could be an important treatment strategy in oxidative stress-induced pathology [39]. PPARA is a member of the PPAR nuclear receptor subfamily. Several studies have demonstrated that PPARA-mediated reduction in oxidative stress correlates with improved outcomes in rodent stroke models [40]. Recently, the study showed that DSS could attenuate oxidative stress against cerebral ischemic-reperfusion injury via SIRT1-dependent manner [11].

4.2. DSS May Contribute to the Treatment of IS by Increased Angiogenesis and Neurogenesis at Chronic Stage

Spontaneous neurogenesis and angiogenesis in the post-acute phase are highly coordinated responses and may contribute to the improvement in neurologic function after stroke. Accumulating experimental studies showed that promoting post-ischemic angiogenesis and neurogenesis can improve neurological function [41]. Ischemic stroke promotes neurogenesis by several growth factors including FGF-2, IGF-1, BDNF, VEGF, and chemokines including SDF-1 and MCP-1. Stroke-induced angiogenesis is similarly regulated by many factors most notably eNOS and CSE, VEGF/VEGFR2, and Ang-1/Tie2 [41]. KEGG enrichment analysis showed that VEGF signaling pathway and FGF signaling pathway were part of the ten major signaling pathways, suggesting that the mechanisms of DSS in the treatment of IS were related to the angiogenesis. SRC is the top three targets of DSS in the IS treatment. SRC is the downstream of VEGF to regulate angiogenesis. Our Western blot analysis confirmed that DSS significantly modulated the activity of SRC. We found that IGF-1, FGFR2, NOX3 (eNOS), MMP13, and EIF4E are the key targets of DSS in the treatment of IS. VEGF confers neuroprotection and promotes neurogenesis and cerebral angiogenesis after ischemic stroke [42]. VEGF signaling promotes endothelial nitric oxide synthase (eNOS) phosphorylation and is related to the angiogenesis [43]. Our previous study showed that DSS treatment promoted focal angiogenesis at 14 days after MCAO. Our previous study showed that DSS promotes angiogenesis and neurogenesis in rat following MCAO via upregulation of VEGF protein expression and increased eNOS activity [14]. The above studies have shown that DSS can not only inhibit apoptosis, inflammatory response, and oxidative stress in the acute phase of stroke but also improve neurological function by increasing neurogenesis and angiogenesis in the chronic recovery period. It suggests that DSS can be used in any period of stroke.

4.3. DSS May Contribute to the Treatment of IS by Modulate Estrogen Level

DSS is a famous herbal formulary from the Synopsis of the Golden Chamber, which was used to treat gynecological disease, especially female abdominal pain. At present, DSS is widely used in neurological diseases [7, 14]. Therefore, it is speculated that DSS might be able to regulate estrogen levels. Both GO and KEGG results showed that DSS responds to steroid hormone and regulates the estrogen signaling pathway. A number of evidence shows that the biological effects of estrogen extend beyond the gonads to other body systems, including the brain and behavior [44]. The results of preclinical studies have shown that estrogen has neuroprotective effects in various experimental models of IS [44]. Many different biological effects of estrogen are modulated by estrogen receptor (ESR), e.g., ESR1 and ESR2. Our result showed that ESR1 is a major target of DSS for IS treatment. The reports showed that estrogen reduces the ischemic oxidative damage via an ESR1-mediated inhibition of NADPH oxidase activation [45]. Dubal et al. showed that ESR1-/-mice have more severe brain damage after MCAO [46]. The above results might in part explain why DSS can treat both gynecological diseases and stroke.

Despite abundant new findings in this study, some limitations still exist. First, the corresponding experimental validation covered only a small number of mechanisms. Second, the compounds, targets, and pathways contained in these databases may not be exhaustive. In conclusion, the study, combined with network pharmacology, molecular docking, and animal experiments, offers the systematic mechanism of DSS in the treatment of IS and clearly clarifies the synergy mechanism of DSS multicomponent and multi-target. DSS may contribute to the treatment of IS by diminishing apoptosis, reducing inflammation and oxidative stress, increasing angiogenesis and neurogenesis, and modulating estrogen level. Our results indicate that DSS can not only reduce brain injury in the acute phase of stroke but also promote neurological recovery in the chronic phase of stroke.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Authors’ Contributions

C. R. and Y. Y. conceived and designed the study. S.L. developed the methodology. S.L., J.X, and W.Z. performed the data acquisition. C. R. performed data analysis and interpretation. S. L., C.G., H. L., W.Y., and W.Z did animal experiments and molecular biology experiments. C. R. drafted, reviewed, and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 81971114, 81573867, 81801313, and 81801142) and the National Key R&D Program of China (2017YFC1308402).

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