Evidence-Based Complementary and Alternative Medicine
Volume 2021 (2021), Article ID 6623010, 13 pages
https://doi.org/10.1155/2021/6623010

Research Article

Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD

Xinmiao Wang,¹ Haoyu Yang,1,2 Lili Zhang,¹ Lin Han,¹ Sha Di,¹ Xiuxiu Wei,1,2 Haoran Wu,1,2 Haiyu Zhang,1,2 Linhua Zhao,³ and Xiaolin Tong⁴

¹Department of Endocrinology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
²Graduate College, Beijing University of Traditional Chinese Medicine, Beijing 100029, China
³Laboratory of Molecular and Biology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
⁴Department of Endocrinology, Affiliated Hospital to Changchun University of Chinese Medicine, Changchun 130021, China

Correspondence should be addressed to Linhua Zhao; melonzhao@163.com and Xiaolin Tong; tongxiaolin@vip.163.com

Received 3 October 2020; Revised 19 February 2021; Accepted 12 April 2021; Published 21 April 2021

Academic Editor: Daniel Dias Rufino Arcanjo

Copyright © 2021 Xinmiao Wang 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.

Background. Shenzhuo formula (SZF) is a traditional Chinese medicine (TCM) prescription which has significant therapeutic effects on diabetic kidney disease (DKD). However, its mechanism remains unknown. Therefore, this study aimed to explore the underlying anti-DKD mechanism of SZF. Methods. The active ingredients and targets of SZF were obtained by searching TCMSP, TCMID, SwissTargetPrediction, HIT, and literature. The DKD target was identified from TTD, DrugBank, and DisGeNet. The potential targets were obtained and PPI network were built after mapping SZF targets and DKD targets. The key targets were screened out by network topology and the “SZF-key targets-DKD” network was constructed by Cytoscape. GO analysis and KEGG pathway enrichment analysis were performed by using DAVID, and the results were visualized by Omicshare Tools. Results. We obtained 182 potential targets and 30 key targets. Furthermore, a “SZF-key targets-DKD” network topological analysis showed that active ingredients like M51, M21, M5, M71, and M28 and targets like EGFR, MMP9, MAPK8, PIK3CA, and STAT3 might play important roles in the process of SZF treating in DKD. GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, and other biological processes. KEGG showed that DKD-related pathways like TNF signaling pathway and PI3K-Akt signaling pathway were at the top of the list. Conclusion. This research reveals the potential pharmacological targets of SZF in the treatment of DKD through network pharmacology and lays a foundation for further studies.

1. Introduction

Diabetic kidney disease (DKD) is one of the most common chronic microvascular complications of diabetes. It may be caused and shaped by the interaction of many factors such as endoplasmic reticulum dysfunction, high sugar-mediated generation of terminal advanced glycation endproducts (AGE), increased activation of the renin angiotensin aldosterone system, increased generation of reactive oxygen species (ROS), and activation of extracellular matrix (ECM) and protein kinase C [1, 2]. It is reported that the incidence of DKD is about 40% in the diabetic population [3]. Furthermore, with the increasing incidence of diabetes, the incidence of DKD is increasing yearly [4]. Therefore, it is important to intensify studies of the pathogenesis of DKD and the search for effective intervention targets.

Shenzhuo formula (SZF) as a traditional Chinese medicine (TCM) prescription has certain advantages in the treatment of DKD [5].It is created by Tong Xiaolin, an academician at the Chinese Academy of Sciences, and his team. This formula was based on the pathogenesis of qi deficiency blood stasis, and the classic prescription of Didang decoction. Years of clinical studies have shown that SZF can effectively increase the glomerular filtration rate, reduce 24-hour urinary protein and kidney damage, and reverse kidney disease when used early [5, 6]. However, due to the diversity of TCM compounds and complexity of in vivo processes, the systematic mechanism research of SZF has been hindered.

Recently, network pharmacology has been developed rapidly with the use of multiomics, high-throughput screening, network visualization and analysis, or other techniques [7–9]. It can help to reveal the network structure of drug action [10] and provide possibilities for exploring the mechanism of action of TCM compounds. Therefore, this study aimed to shed light on the underlying mechanisms of SZF in DKD treatment using a network pharmacology approach.

2. Methods

2.1. Research Tools

The Chinese Traditional Medicine System Pharmacological Database Analysis Platform (TCMSP, http://lsp.nwu.edu.cn/tcmsp.php) [11], Traditional Chinese Medicine Integrated Database (TCMID, http://www.megabionet.org/tcmid/) [12], SwissTargetPrediction (http://www.swisstargetprediction.ch/) [13], and HIT (http:lifecenter.biosino.org/hit/) [14] were used to access to SZF ingredients and targets. (2) The Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/cjttd/) [15], DrugBank (https://www.drugbank.ca/) [16], and DisGeNet (http://www.disgenet.org/) [17] were used to get the targets’ proteins of DKD. (3) The protein-protein interaction (PPI) network was obtained online using STRING (http://string-db.org) [18]. Compositional software Cytoscape 3.2.1 (http://www.cytoscape.org/) [19] was used to carry out network topology analysis and construct SZF-key targets-DKD network. The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.ncifcrf.Gov) [20] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Omicshare Tools (https://www.Omicshare.com/) were used for visual analysis of GO and KEGG results.

2.2. Collection of Major Chemical Constituents

We relied on TCMSP, TCMID database, and literatures mining to search for the chemical constituents of SZF (Hedysarum Multijugum Maxim, Radix Salviae, Hirudo, and Radix Rhei Et Rhizome).

2.3. Screening of Active Compounds

As we all know, TCM drugs enter human body and then take effect through absorption, distribution, metabolism, and excretion (ADME) processes. Among them, oral bioavailability (OB) and drug similarity (DL), the key parameters of ADME components, were used as the screening criteria for active ingredients in this study. In this section, we used TCMSP to collect active compounds and their ADME properties. And then the active compounds that meet “OB ≥ 30%, DL ≥ 0.18” were selected as potential active ingredients.

2.4. Prediction of Targets

SwissTargetPrediction and HIT databases were used to collect the drug targets. In addition, TTD, DrugBank and DisGeNet databases were used to search for DKD targets by entering the key words of “diabetic kidney disease” and “diabetic nephropathy.” Further, we matched SZF targets with DKD targets to obtain common targets.

2.5. Network Construction and Analysis

PPI network of common targets was obtained using STRING. Furthermore, the PPI network topology analysis was carried out using Cytoscape 3.2.1 software and then key targets were obtained. To further explore the interactions between the active ingredients and their related targets at a system level, a “SZF-key targets-DKD” network was constructed by Cytoscape3.2.1.

2.6. GO and KEGG Analysis

GO analysis is widely used for gene function classification and mainly includes the molecular function (MF), biological processes (BP), and cellular components (CC) [21]. In this step, we used the DAVID tool for GO and KEGG pathway analysis. Then, we used Omicshare Tools for visual display.

3. Results

3.1. Screening of Candidate Components in SZF

Through TCMSP and TCMID database, a total of 87 active compounds of Hedysarum Multijugum Maxim, 210 of Radix Salviae, 35 of Hirudo, and 92 of Radix Rhei Et Rhizome were obtained. Then by ADME (OB ≥ 30%, DL ≥ 0.18) screening, a total of 101 active compounds were selected, including 20 active compounds of Hedysarum Multijugum Maxim, 65 of Radix Salviae, and 16 of Radix Rhei Et Rhizome (in this section, because Hirudo could not be found in TCMSP database, its ADME parameters could not be obtained and did not participate in screening). In addition, through literature mining, another 4 active compounds were collected, including 2 active compounds of Hedysarum Multijugum Maxim [22, 23], 1 of Radix Salviae [24], and 1 of Radix Rhei Et Rhizome [25].

3.2. Target Prediction

After matching SZF targets with DKD genes, a total of 182 common targets of SZF were obtained. We only show 50 of them in Table 1. And full information of 182 common targets is displayed in Table 2.

Table 1: Common targets of SZF and DKD (50 of 182 targets).

Serial number
Target
Common name
Uniprot ID

1
Aldose reductase
AKR1B1
P15121

2
Acyl coenzyme A:cholesterol acyltransferase
CES1
P23141

3
Signal transducer and activator of transcription 3
STAT3
P40763

4
Protein-tyrosine phosphatase 1C
PTPN6
P29350

5
Vascular endothelial growth factor receptor 2
KDR
P35968

6
Epidermal growth factor receptor erbB1
EGFR
P00533

7
PI3-kinase p110-alpha subunit
PIK3CA
P42336

8
c-Jun N-terminal kinase 1
MAPK8
P45983

9
LXR-alpha
NR1H3
Q13133

10
Estrogen receptor alpha
ESR1
P03372

11
Testis-specific androgen-binding protein
SHBG
P04278

12
Cytochrome P450 2C19
CYP2C19
P33261

13
Protein-tyrosine phosphatase 1B
PTPN1
P18031

14
Butyrylcholinesterase
BCHE
P06276

15
Vitamin D receptor
VDR
P11473

16
Glucose-6-phosphate 1-dehydrogenase
G6PD
P11413

17
Peroxisome proliferator-activated receptor alpha
PPARA
Q07869

18
Peroxisome proliferator-activated receptor delta
PPARD
Q03181

19
Peroxisome proliferator-activated receptor gamma
PPARG
P37231

20
UDP-glucuronosyltransferase 2B7
UGT2B7
P16662

21
11-Beta-hydroxysteroid dehydrogenase 2
HSD11B2
P80365

22
NADPH oxidase 4
NOX4
Q9NPH5

23
Tyrosine-protein kinase SYK
SYK
P43405

24
Glycogen synthase kinase-3 beta
GSK3B
P49841

25
Matrix metalloproteinase 9
MMP9
P14780

26
Matrix metalloproteinase 2
MMP2
P08253

27
Matrix metalloproteinase 12
MMP12
P39900

28
ATP-binding cassette sub-family G member 2
ABCG2
Q9UNQ0

29
P-glycoprotein 1
ABCB1
P08183

30
Arachidonate 12-lipoxygenase
ALOX12
P18054

31
Cyclooxygenase-2
PTGS2
P35354

32
Insulin-like growth factor I receptor
IGF1R
P08069

33
Myeloperoxidase
MPO
P05164

34
Matrix metalloproteinase 3
MMP3
P08254

35
Serine/threonine-protein kinase AKT
AKT1
P31749

36
Beta-secretase 1
BACE1
P56817

37
Tyrosine-protein kinase receptor UFO
AXL
P30530

38
NUAK family SNF1-like kinase 1
NUAK1
O60285

39
Aldehyde reductase
AKR1A1
P14550

40
Plasminogen
PLG
P00747

41
PI3-kinase p110-delta subunit
PIK3CD
O00329

42
PI3-kinase p110-gamma subunit
PIK3CG
P48736

43
Hematopoietic prostaglandin D synthase
HPGDS
O60760

44
Serine-protein kinase ATM
ATM
Q13315

45
Cytochrome P450 24A1
CYP24A1
Q07973

46
Mineralocorticoid receptor
NR3C2
P08235

47
Cannabinoid receptor 1
CNR1
P21554

48
Hepatocyte nuclear factor 4-alpha
HNF4A
P41235

49
C-C chemokine receptor type 1
CCR1
P32246

50
Histone-lysine N-methyltransferase EZH2
EZH2
Q15910

Organism: Homo sapiens. Only 50 potential targets’ information is shown here, and the whole is in Table 3.

Table 2: A total of 182 common targets.

No.
Target
Common name
Uniprot ID

1
Aldose reductase
AKR1B1
P15121

2
Acyl coenzyme A:cholesterol acyltransferase
CES1
P23141

3
Signal transducer and activator of transcription 3
STAT3
P40763

4
Protein-tyrosine phosphatase 1C
PTPN6
P29350

5
Vascular endothelial growth factor receptor 2
KDR
P35968

6
Epidermal growth factor receptor erbB1
EGFR
P00533

7
PI3-kinase p110-alpha subunit
PIK3CA
P42336

8
c-Jun N-terminal kinase 1
MAPK8
P45983

9
LXR-alpha
NR1H3
Q13133

10
Estrogen receptor alpha
ESR1
P03372

11
Testis-specific androgen-binding protein
SHBG
P04278

12
Cytochrome P450 2C19 13
CYP2C19
P33261

13
Protein-tyrosine phosphatase 1B
PTPN1
P18031

14
Butyrylcholinesterase
BCHE
P06276

15
Vitamin D receptor
VDR
P11473

16
Glucose-6-phosphate 1-dehydrogenase
G6PD
P11413

17
Peroxisome proliferator-activated receptor alpha
PPARA
Q07869

18
Peroxisome proliferator-activated receptor delta
PPARD
Q03181

19
Peroxisome proliferator-activated receptor gamma
PPARG
P37231

20
UDP-glucuronosyltransferase 2B7
UGT2B7
P16662

21
11-beta-hydroxysteroid dehydrogenase 2
HSD11B2
P80365

22
NADPH oxidase 4
NOX4
Q9NPH5

23
Tyrosine-protein kinase SYK
SYK
P43405

24
Glycogen synthase kinase-3 beta
GSK3B
P49841

25
Matrix metalloproteinase 9
MMP9
P14780

26
Matrix metalloproteinase 2
MMP2
P08253

27
Matrix metalloproteinase 12
MMP12
P39900

28
ATP-binding cassette sub-family G member 2
ABCG2
Q9UNQ0

29
P-glycoprotein 1
ABCB1
P08183

30
Arachidonate 12-lipoxygenase
ALOX12
P18054

31
Cyclooxygenase-2
PTGS2
P35354

32
Insulin-like growth factor I receptor
IGF1R
P08069

33
Myeloperoxidase
MPO
P05164

34
Matrix metalloproteinase 3
MMP3
P08254

35
Serine/threonine-protein kinase AKT
AKT1
P31749

36
Beta-secretase 1
BACE1
P56817

37
Tyrosine-protein kinase receptor UFO
AXL
P30530

38
NUAK family SNF1-like kinase 1
NUAK1
060285

39
Aldehyde reductase (by homology)
AKR1A1
P14550

40
Plasminogen
PLG
P00747

41
PI3-kinase p110-delta subunit
PIK3CD
O00329

42
PI3-kinase p110-gamma subunit
PIK3CG
P48736

43
Hematopoietic prostaglandin D synthase
HPGDS
O60760

44
Serine-protein kinase ATM
ATM
Q13315

45
Cytochrome P450 24A1
CYP24A1
Q07973

46
Mineralocorticoid receptor
NR3C2
P08235

47
Cannabinoid receptor 1
CNR1
P21554

48
Hepatocyte nuclear factor 4-alpha
HNF4A
P41235

49
C-C chemokine receptor type 1
CCR1
P32246

50
Histone-lysine N-methyltransferase EZH2
EZH2
Q15910

51
MAP kinase p38 alpha
MAPK14
Q16539

52
Bromodomain-containing protein 2
BRD2
P25440

53
Aldehyde dehydrogenase
ALDH2
P05091

54
Fatty acid binding protein adipocyte
FABP4
P15090

55
Fatty acid-binding protein, liver
FABP1
P07148

56
Acyl-CoA desaturase
SCD
O00767

57
MAP kinase ERK1
MAPK3
P27361

58
Short transient receptor potential channel 6
TRPC6
Q9Y210

59
Mitogen-activated protein kinase kinase kinase 5
MAP3K5
Q99683

60
Disintegrin and metalloproteinase domain-containing protein 17
ADAM17
P78536

61
Hexokinase type IV
GCK
P35557

62
Intercellular adhesion molecule-1
ICAM1
P05362

63
P-selectin
SELP
P16109

64
Leukocyte adhesion molecule-1
SELL
P14151

65
Matrix metalloproteinase 1
MMP1
P03956

66
Matrix metalloproteinase 8
MMP8
P22894

67
Endothelin-converting enzyme 1
ECE1
P42892

68
Integrin beta-3
ITGB3
P05106

69
Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoform
PIK3CB
P42338

70
Sorbitol dehydrogenase
SORD
Q00796

71
MAP kinase ERK2
MAPK1
P28482

72
Vascular endothelial growth factor receptor 1
FLT1
P17948

73
Matrix metalloproteinase 7
MMP7
P09237

74
Type-1 angiotensin II receptor
AGTR1
P30556

75
Glucose transporter
SLC2A1
P11166

76
Nerve growth factor receptor Trk-A
NTRK1
P04629

77
Tyrosine-protein kinase JAK1
JAK1
P23458

78
Tyrosine-protein kinase JAK2
JAK2
O60674

79
Sodium/glucose cotransporter 2
SLC5A2
P31639

80
Serine/threonine-protein kinase receptor R3
ACVRL1
P37023

81
Epoxide hydratase
EPHX2
P34913

82
Cytochrome P450 11B2
CYP11B2
P19099

83
Endothelin receptor ET-A
EDNRA
P25101

84
Glutathione S-transferase Mu 1
GSTM1
P09488

85
Interleukin-1 beta
ILIB
P01584

86
Insulin receptor
INSR
P06213

87
Protein tyrosine kinase 2 beta
PTK2B
Q14289

88
Cyclooxygenase-1
PTGS1
P23219

89
Cytochrome P450 2C9
CYP2C9
P11712

90
Cytochrome P450 3A4
CYP3A4
P08684

91
Trypsin I
PRSS1
P07477

92
C-C chemokine receptor type 5
CCR5
P51681

93
Dopamine D2 receptor
DRD2
P14416

94
Cholesteryl ester transfer protein
CETP
P11597

95
Calcitonin gene-related peptide type 1 receptor
CALCRL
Q16602

96
Serotonin 2a (5-HT2a) receptor
HTR2A
P28223

97
Disintegrin and metalloproteinase domain-containing protein 10
ADAM10
O14672

98
TGF-beta receptor type I
TGFBR1
P36897

99
Nitric-oxide synthase, brain
NOS1
P29475

100
Cathepsin (B and K)
CTSB
P07858

101
Bradykinin B1 receptor
BDKRB1
P46663

102
Potassium voltage-gated channel subfamily KQT member 1
KCNQ1
P51787

103
Leukotriene A4 hydrolase
LTA4H
P09960

104
Apoptosis regulator Bcl-2
BCL2
P10415

105
Kininogen-1
KNG1
P01042

106
Solute carrier family 22 member 2
SLC22A2
O15244

107
Plasma retinol-binding protein
RBP4
P02753

108
Histone deacetylase 4
HDAC4
P56524

109
Dopamine D3 receptor
DRD3
P35462

110
C-C chemokine receptor type 2
CCR2
P41597

111
Solute carrier family 22 member 12
SLC22A12
Q96S37

112
Glucagon-like peptide 1 receptor
GLP1R
P43220

113
Dual specificity mitogen-activated protein kinase kinase 2
MAP2K2
P36507

114
Death-associated protein kinase 2
DAPK2
Q9UIK4

115
Bile acid receptor FXR
NR1H4
Q96RI1

116
Interleukin-6
IL6
P05231

117
Transcription factor AP-1
JUN
P05412

118
Vascular endothelial growth factor A
VEGFA
P15692

119
Interleukin-10
IL10
P22301

120
Endothelin-1
EDN1
P05305

121
Nitric oxide synthase, endothelial
NOS3
P29474

122
Urotensin II receptor
UTS2R
Q9UKP6

123
78 kDa glucose-regulated protein
HSPA5
P11021

124
Galectin-3
LGALS3
P17931

125
Macrophage migration inhibitory factor
MIF
P14174

126
Serum paraoxonase/arylesterase 1
PON1
P27169

127
Kallikrein 1
KLK1
P06870

128
Rho-associated protein kinase 1
ROCK1
Q13464

129
Sphingosine kinase 1
SPHK1
Q9NYA1

130
Serine/threonine-protein kinase Sgk1
SGK1
O00141

131
Low affinity sodium-glucose cotransporter
SLC5A4
Q9NY91

132
Neutrophil cytosol factor 1
NCF1
P14598

133
Antileukoproteinase
SLPI
P03973

134
Signal transducer and activator of transcription 1-alpha/beta
STAT1
P42224

135
Protein kinase C beta type
PRKCB
P05771

136
Gap junction alpha-1 protein
GJA1
P17302

137
C-X-C motif chemokine 11
CXCL11
O14625

138
Interleukin-8
CXCL8
P10145

139
Superoxide dismutase [Cu-Zn]
SOD1
P00441

140
C-C motif chemokine 2
CCL2
P13500

141
Hypoxia-inducible factor 1-alpha
HIF1A
Q16665

142
Caveolin-1
CAV1
Q03135

143
Interleukin-1 alpha
IL1A
P01583

144
Nuclear factor erythroid 2-related factor 2
NFE2L2
Q16236

145
C-X-C motif chemokine 10
CXCL10
P02778

146
Plasminogen activator inhibitor 1
SERPINE1
P05121

147
Osteopontin
SPP1
P10451

148
Bone morphogenetic protein 2
BMP2
P12643

149
Transforming growth factor beta-1 proprotein
TGFB1
P01137

150
Cyclin-dependent kinase inhibitor 2A
CDKN2A
P42771

151
Transcription factor E2F1
E2F1
Q01094

152
Thrombomodulin
THBD
P07204

153
Insulin-like growth factor II
IGF2
P01344

154
Catalase
CAT
P04040

155
Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN
PTEN
P60484

156
Pro-epidermal growth factor
EGF
P01133

157
ATP synthase subunit beta, mitochondria
ATP5F1B
P06576

158
NAD-dependent protein deacetylase sirtuin-1
SIRT1
Q96EB6

159
Angiotensin-converting enzyme
ACE
P12821

160
Matrix metalloproteinase 10
MMP10
P09238

161
Transketolase
TKT
P29401

162
Dipeptidyl peptidase IV
DPP4
P27487

163
Nuclear factor NF-kappa-B p65 subunit
RELA
Q04206

164
Nitric oxide synthase, inducible
NOS2
P35228

165
Protein kinase C alpha type
PRKCA
P17252

166
Tumor necrosis factor
TNF
P01375

167
Protein kinase C epsilon type
PRKCE
Q02156

168
Renin
REN
P00797

169
Axin1/beta-catenin
CTNNB1
P35222

170
Fibronectin
FN1
P02751

171
C-X-C chemokine receptor type 4
CXCR4
P61073

172
Heparanase
HPSE
Q9Y251

173
Glucagon
GCG
P01275

174
Tumor necrosis factor receptor superfamily member 11B
TNFRSF11B
O00300

175
Metalloproteinase inhibitor 1
TIMP1
P01033

176
Metalloproteinase inhibitor 2
TIMP2
P16035

177
Fibroblast growth factor 2
FGF2
P09038

178
Lipoprotein lipase
LPL
P06858

179
Coagulation factor V
F5
P12259

180
Cyclic AMP-responsive element-binding protein 1
CREB1
P16220

181
Phosphatidylinositol 3,4,5-trisphosphate 5- phosphatase 2
INPPL1
O15357

182
Tumor necrosis factor ligand superfamily member 6
FASLG
P48023

3.3. Construction and Analysis of Network Maps

The PPI network of the 182 common targets was obtained using STRING (Figure 1). Then, we used Cytoscape 3.2.1 to obtain 30 key targets by network topology analysis with inclusion criteria of “degree ≥ 2 times of the median, closeness centrality ≥ median, betweenness centrality ≥ median” (Table 3). Next, we constructed a “SZF-key targets-DKD” network by Cytoscape3.2.1 (Figure 2).

Figure 1: PPI network of the 182 common targets.

Table 3: Thirty key targets obtained by network topology analysis.

Serial number
Node
Degree
Closeness centrality
Betweenness centrality

1
PIK3CA
40
0.49508197
0.09370214

2
STAT3
40
0.5
0.0863086

3
AKT1
35
0.49025974
0.15311921

4
KNG1
33
0.44023324
0.06128185

5
VEGFA
33
0.49185668
0.06953442

6
JUN
32
0.48089172
0.07229449

7
MAPK3
30
0.4617737
0.02240476

8
MAPK1
30
0.4689441
0.06714477

9
EGF
27
0.4617737
0.0336672

10
EDN1
27
0.46604938
0.05180077

11
EGFR
26
0.44023324
0.01794429

12
JAK1
26
0.44940476
0.02254905

13
IL6
26
0.45209581
0.02532622

14
CXCL8
25
0.43768116
0.03191743

15
RELA
24
0.45757576
0.04035241

16
FN1
23
0.4351585
0.01464828

17
JAK2
23
0.44940476
0.01620852

18
CTNNB1
23
0.45481928
0.06488997

19
TNF
22
0.44281525
0.0272631

20
TGFB1
21
0.44281525
0.03270724

21
MMP9
20
0.40921409
0.03200512

22
CXCR4
19
0.41032609
0.01402652

23
TIMP1
19
0.41712707
0.00798146

24
MAPK14
19
0.44411765
0.01628416

25
BDKRB1
18
0.3994709
0.00725308

26
PIK3CB
18
0.40921409
0.00732155

27
MAPK8
18
0.42296919
0.03099697

28
ITGB3
18
0.42296919
0.01050834

29
CCR5
16
0.39841689
0.00592392

30
PLG
16
0.40266667
0.02168017

Figure 2: “SZF-key targets-DKD” network. The nodes were visualized with degree. The larger and the redder the node, the higher the degree it was. M1-75 stand for the active ingredients whose full names are shown in Table 4.

Table 4: The information of active ingredients.

No.
Active ingredients
Code name

1
Isoimperatorin
M1

2
1,2,5,6-Tetrahydrotanshinone
M2

3
5,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one
M3

4
(E)-3-[2-(3,4-Dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic
M4

5
2-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde
M5

6
Przewaquinone c
M6

7
Cryptotanshinone
M7

8
Dihydrotanshinlactone
M8

9
Isotanshinone II
M9

10
Miltipolone
M10

11
Miltirone
M11

12
Tanshinaldehyde
M12

13
Danshenol B
M13

14
Danshenol A
M14

15
Deoxyneocryptotanshinone
M15

16
Dihydrotanshinone I
M16

17
Miltionone I
M17

18
Miltionone II
M18

19
Neocryptotanshinone ii
M19

20
Neocryptotanshinone
M20

21
Luteolin
M21

22
Salvilenone I
M22

23
Salviolone
M23

24
Epidanshenspiroketallactone
M24

25
Tanshinone iia
M25

26
α-Amyrin
M26

27
Dan-shexinkum d
M27

28
Sclareol
M28

29
Dehydrotanshinone II A
M29

30
Baicalin
M30

31
2-Isopropyl-8-methylphenanthrene-3,4-dione
M31

32
Formyltanshinone
M32

33
3-Beta-Hydroxymethyllenetanshiquinone
M33

34
Methylenetanshinquinone
M34

35
(2R)-3-(3,4-Dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acid
M35

36
(6S)-6-(Hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione
M36

37
Tanshinone VI
M37

38
Przewalskin b
M38

39
6-o-Syringyl-8-o-acetyl shanzhiside methyl ester
M39

40
Prolithospermic acid
M40

41
(Z)-3-[2-[(E)-2-(3,4-Dihydroxyphenyl)vinyl]-3,4-dihydroxyphenyl]acrylic acid
M41

42
Salvianolic acid g
M42

43
Salvianolic acid j
M43

44
Danshenspiroketallactone
M44

45
1-Methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trione
M45

46
3,9-di-O-MethylnissolinM
M46

47
(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol
M47

48
(3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-ol
M48

49
Isorhamnetin
M49

50
Kaempferol
M50

51
Quercetin
M51

52
Jaranol
M52

53
Bifendate
M53

54
Formononetin
M54

55
Isoflavanone
M55

56
Calycosin
M56

57
Hederagenin
M57

58
Sennoside E_qt
M58

59
Toralactone
M59

60
Palmidin A
M60

61
Daucosterol_qt
M61

62
Eupatin
M62

63
Procyanidin B-5,3’-O-gallate
M63

64
Rhein
M64

65
Beta-sitosterol
M65

66
Aloe-emodin
M66

67
Lipase
M67

68
Gardnerilin a
M68

69
Hirudin
M69

70
o-Desulfated heparin
M70

71
Ursolic acid
M71

72
Heparin
M72

73
Genioisidic acid
M73

74
Genipinic acid
M74

75
Nadroparin
M75

3.4. GO and KEGG Analysis

The DAVID was used to carry out GO analysis. And the GO terms were visualized by the Omicshare Tools (Figure 3). The GO analysis results showed that targets were mainly involved in positive regulation of transcription from RNA polymerase II promoter, inflammatory response, lipopolysaccharide-mediated signaling pathway, positive regulation of peptidyl-serine phosphorylation, and other biological processes. As the top 20 GO enrichment items listed, DKD is relevant to kinds of BP in body abnormalities, and SZF is likely to regulate these items and then play an anti-DKD role.

Figure 3: Top 20 enrichments in GO analysis.

KEGG pathway enrichment analysis showed that a total of 104 pathways were obtained. The top 20 pathways are displayed in Figure 4, which include TNF signaling pathway, HIF-1 signaling pathway, Toll-like receptor signaling pathway, FoxO signaling pathway, NOD-like receptor signaling pathway, and so on.

Figure 4: Top 20 KEGG pathway enrichments.

4. Discussion

Previous studies have suggested that SZF has a therapeutic effect on DKD [5,6]. However, the potential mechanisms of SZF treating in DKD have not been fully explained. In this study, we mainly applied network pharmacology to explore it. Firstly, a total of 140 potential active compounds and 182 common targets of SZF and DKD were obtained after screening of active compounds and mapping of targets. Then, we constructed two networks, including the PPI network of 182 common targets and SZF-key targets-DKD network, and then applying GO and KEGG enrichment analysis to explore the regulation mechanism of SZF in treating DKD.

Through the SZF-key targets-DKD network, we could know that most active ingredients were linked with no less than one target, which indicated the character of multi-target of TCM active ingredients. In the meanwhile, different active compounds from different herbs acted on the same targets, which demonstrated that SZF had a synergistic effect in treating DKD. In addition, there were 8 active ingredients whose degrees were greater than 2 times of average in SZF-key targets-DKD network topology analysis. Interestingly, 3 of them had been proven to have kidney protection effect by experiments. For example, quercetin liposomes had renal protective effects of reducing oxidative stress, attenuating AGE expression, and delaying the progression of DKD [26]. Luteolin attenuated DKD mainly via suppression of inflammatory response and oxidative response [27]. Ursolic acid alleviated renal damage in type 2 diabetic db/db mice by downregulating proteins in the angiotensin II type 1 receptor-associated protein/angiotensin II type 1 receptor signaling pathway to inhibit extracellular matrix accumulation, renal inflammation, fibrosis, and oxidative stress [28]. These results were coincident with our predictions, which suggested that active ingredients with higher degree might play an important role in the treatment of DKD. Meanwhile, we discovered five active ingredients (M5, M27, M28, M60, and M70) that were likely to have renal protection effect but had not been verified up to now.

Moreover, the results of the SZF-key targets-DKD topology analysis also showed that there were 5 targets whose degrees were greater than 2 times of the average. Particularly, 3 of these had been proven to be closely related with DKD. For instance, EGFR activation had a significant role in activating pathways that mediate podocyte injury and loss in diabetic nephropathy [29]. Downregulated expression of MMP-9 could promote the process of DKD [30]. STAT3 inhibition could hinder the development and progression of DKD in diabetic patients [31].

As shown in GO analysis, the potential targets of SZF acting on DKD were mainly associated with various biological processes, such as lipopolysaccharide-mediated signaling pathway, inflammatory response, positive regulation of cyclase activity, protein kinase B signaling, positive regulation of MAP kinase activity, and response to estradiol, which had a strongly direct correlation with the pathogenesis of DKD [32–38].

Similarly, KEGG pathway enrichment analysis showed that SZF took an anti-DKD effect by multiple pathways. Through further research, we found that some pathways had been already verified to exert anti-DKD potential by experiments, such as TNF signaling pathway [39], HIF-1 signaling pathway [40], Toll-like receptor signaling pathway [41], FoxO signaling pathway [42], focal adhesion [43], and NOD-like receptor signaling pathway [44]. These results were also consistent with what we predicted. In addition, SZF might have potential therapeutic effects on diseases such as cancer, hepatitis, influenza, leishmaniasis, pertussis, and tuberculosis according to the KEGG enrichment analysis. Just as it was reported that different diseases had common or similar pathological changes and could be treated with the same prescription [45], the above results suggested that SZF concentrated more on the systematicness of the body when treating DKD. In other words, SZF possibly regulated the body to reach the balance state, then reaching the aim of treatment.

5. Conclusion

In conclusion, this study based on the network pharmacology had preliminarily explained the anti-DKD mechanism of SZF from the perspective of multi-active ingredients, multi-targets, and multi-pathway. In the future, we will further investigate its mechanism by molecular docking, using in vitro or in vivo studies.

Data Availability

The data used to support the results of this study can be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors’ Contributions

Wang Xin-miao, Yang Hao-yu, and Zhang Li-li contributed equally to this work.

Acknowledgments

This study was supported by the Beijing Natural Science Foundation (7212189), the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ13-ZD-06), and the Fundamental Research Funds for the China Academy of Chinese Medical Sciences (ZZ0808004).

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