Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2021 / Article

Research Article | Open Access

Volume 2021 |Article ID 6623010 | https://doi.org/10.1155/2021/6623010

Xinmiao Wang, Haoyu Yang, Lili Zhang, Lin Han, Sha Di, Xiuxiu Wei, Haoran Wu, Haiyu Zhang, Linhua Zhao, Xiaolin Tong, "Network Pharmacology-Based Prediction of Mechanism of Shenzhuo Formula for Application to DKD", Evidence-Based Complementary and Alternative Medicine, vol. 2021, Article ID 6623010, 13 pages, 2021. https://doi.org/10.1155/2021/6623010

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

Academic Editor: Daniel Dias Rufino Arcanjo
Received03 Oct 2020
Revised19 Feb 2021
Accepted12 Apr 2021
Published21 Apr 2021

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 [79]. 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.


Serial numberTargetCommon nameUniprot ID

1Aldose reductaseAKR1B1P15121
2Acyl coenzyme A:cholesterol acyltransferaseCES1P23141
3Signal transducer and activator of transcription 3STAT3P40763
4Protein-tyrosine phosphatase 1CPTPN6P29350
5Vascular endothelial growth factor receptor 2KDRP35968
6Epidermal growth factor receptor erbB1EGFRP00533
7PI3-kinase p110-alpha subunitPIK3CAP42336
8c-Jun N-terminal kinase 1MAPK8P45983
9LXR-alphaNR1H3Q13133
10Estrogen receptor alphaESR1P03372
11Testis-specific androgen-binding proteinSHBGP04278
12Cytochrome P450 2C19CYP2C19P33261
13Protein-tyrosine phosphatase 1BPTPN1P18031
14ButyrylcholinesteraseBCHEP06276
15Vitamin D receptorVDRP11473
16Glucose-6-phosphate 1-dehydrogenaseG6PDP11413
17Peroxisome proliferator-activated receptor alphaPPARAQ07869
18Peroxisome proliferator-activated receptor deltaPPARDQ03181
19Peroxisome proliferator-activated receptor gammaPPARGP37231
20UDP-glucuronosyltransferase 2B7UGT2B7P16662
2111-Beta-hydroxysteroid dehydrogenase 2HSD11B2P80365
22NADPH oxidase 4NOX4Q9NPH5
23Tyrosine-protein kinase SYKSYKP43405
24Glycogen synthase kinase-3 betaGSK3BP49841
25Matrix metalloproteinase 9MMP9P14780
26Matrix metalloproteinase 2MMP2P08253
27Matrix metalloproteinase 12MMP12P39900
28ATP-binding cassette sub-family G member 2ABCG2Q9UNQ0
29P-glycoprotein 1ABCB1P08183
30Arachidonate 12-lipoxygenaseALOX12P18054
31Cyclooxygenase-2PTGS2P35354
32Insulin-like growth factor I receptorIGF1RP08069
33MyeloperoxidaseMPOP05164
34Matrix metalloproteinase 3MMP3P08254
35Serine/threonine-protein kinase AKTAKT1P31749
36Beta-secretase 1BACE1P56817
37Tyrosine-protein kinase receptor UFOAXLP30530
38NUAK family SNF1-like kinase 1NUAK1O60285
39Aldehyde reductaseAKR1A1P14550
40PlasminogenPLGP00747
41PI3-kinase p110-delta subunitPIK3CDO00329
42PI3-kinase p110-gamma subunitPIK3CGP48736
43Hematopoietic prostaglandin D synthaseHPGDSO60760
44Serine-protein kinase ATMATMQ13315
45Cytochrome P450 24A1CYP24A1Q07973
46Mineralocorticoid receptorNR3C2P08235
47Cannabinoid receptor 1CNR1P21554
48Hepatocyte nuclear factor 4-alphaHNF4AP41235
49C-C chemokine receptor type 1CCR1P32246
50Histone-lysine N-methyltransferase EZH2EZH2Q15910

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

No.TargetCommon nameUniprot ID

1Aldose reductaseAKR1B1P15121
2Acyl coenzyme A:cholesterol acyltransferaseCES1P23141
3Signal transducer and activator of transcription 3STAT3P40763
4Protein-tyrosine phosphatase 1CPTPN6P29350
5Vascular endothelial growth factor receptor 2KDRP35968
6Epidermal growth factor receptor erbB1EGFRP00533
7PI3-kinase p110-alpha subunitPIK3CAP42336
8c-Jun N-terminal kinase 1MAPK8P45983
9LXR-alphaNR1H3Q13133
10Estrogen receptor alphaESR1P03372
11Testis-specific androgen-binding proteinSHBGP04278
12Cytochrome P450 2C19 13CYP2C19P33261
13Protein-tyrosine phosphatase 1BPTPN1P18031
14ButyrylcholinesteraseBCHEP06276
15Vitamin D receptorVDRP11473
16Glucose-6-phosphate 1-dehydrogenaseG6PDP11413
17Peroxisome proliferator-activated receptor alphaPPARAQ07869
18Peroxisome proliferator-activated receptor deltaPPARDQ03181
19Peroxisome proliferator-activated receptor gammaPPARGP37231
20UDP-glucuronosyltransferase 2B7UGT2B7P16662
2111-beta-hydroxysteroid dehydrogenase 2HSD11B2P80365
22NADPH oxidase 4NOX4Q9NPH5
23Tyrosine-protein kinase SYKSYKP43405
24Glycogen synthase kinase-3 betaGSK3BP49841
25Matrix metalloproteinase 9MMP9P14780
26Matrix metalloproteinase 2MMP2P08253
27Matrix metalloproteinase 12MMP12P39900
28ATP-binding cassette sub-family G member 2ABCG2Q9UNQ0
29P-glycoprotein 1ABCB1P08183
30Arachidonate 12-lipoxygenaseALOX12P18054
31Cyclooxygenase-2PTGS2P35354
32Insulin-like growth factor I receptorIGF1RP08069
33MyeloperoxidaseMPOP05164
34Matrix metalloproteinase 3MMP3P08254
35Serine/threonine-protein kinase AKTAKT1P31749
36Beta-secretase 1BACE1P56817
37Tyrosine-protein kinase receptor UFOAXLP30530
38NUAK family SNF1-like kinase 1NUAK1060285
39Aldehyde reductase (by homology)AKR1A1P14550
40PlasminogenPLGP00747
41PI3-kinase p110-delta subunitPIK3CDO00329
42PI3-kinase p110-gamma subunitPIK3CGP48736
43Hematopoietic prostaglandin D synthaseHPGDSO60760
44Serine-protein kinase ATMATMQ13315
45Cytochrome P450 24A1CYP24A1Q07973
46Mineralocorticoid receptorNR3C2P08235
47Cannabinoid receptor 1CNR1P21554
48Hepatocyte nuclear factor 4-alphaHNF4AP41235
49C-C chemokine receptor type 1CCR1P32246
50Histone-lysine N-methyltransferase EZH2EZH2Q15910
51MAP kinase p38 alphaMAPK14Q16539
52Bromodomain-containing protein 2BRD2P25440
53Aldehyde dehydrogenaseALDH2P05091
54Fatty acid binding protein adipocyteFABP4P15090
55Fatty acid-binding protein, liverFABP1P07148
56Acyl-CoA desaturaseSCDO00767
57MAP kinase ERK1MAPK3P27361
58Short transient receptor potential channel 6TRPC6Q9Y210
59Mitogen-activated protein kinase kinase kinase 5MAP3K5Q99683
60Disintegrin and metalloproteinase domain-containing protein 17ADAM17P78536
61Hexokinase type IVGCKP35557
62Intercellular adhesion molecule-1ICAM1P05362
63P-selectinSELPP16109
64Leukocyte adhesion molecule-1SELLP14151
65Matrix metalloproteinase 1MMP1P03956
66Matrix metalloproteinase 8MMP8P22894
67Endothelin-converting enzyme 1ECE1P42892
68Integrin beta-3ITGB3P05106
69Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoformPIK3CBP42338
70Sorbitol dehydrogenaseSORDQ00796
71MAP kinase ERK2MAPK1P28482
72Vascular endothelial growth factor receptor 1FLT1P17948
73Matrix metalloproteinase 7MMP7P09237
74Type-1 angiotensin II receptorAGTR1P30556
75Glucose transporterSLC2A1P11166
76Nerve growth factor receptor Trk-ANTRK1P04629
77Tyrosine-protein kinase JAK1JAK1P23458
78Tyrosine-protein kinase JAK2JAK2O60674
79Sodium/glucose cotransporter 2SLC5A2P31639
80Serine/threonine-protein kinase receptor R3ACVRL1P37023
81Epoxide hydrataseEPHX2P34913
82Cytochrome P450 11B2CYP11B2P19099
83Endothelin receptor ET-AEDNRAP25101
84Glutathione S-transferase Mu 1GSTM1P09488
85Interleukin-1 betaILIBP01584
86Insulin receptorINSRP06213
87Protein tyrosine kinase 2 betaPTK2BQ14289
88Cyclooxygenase-1PTGS1P23219
89Cytochrome P450 2C9CYP2C9P11712
90Cytochrome P450 3A4CYP3A4P08684
91Trypsin IPRSS1P07477
92C-C chemokine receptor type 5CCR5P51681
93Dopamine D2 receptorDRD2P14416
94Cholesteryl ester transfer proteinCETPP11597
95Calcitonin gene-related peptide type 1 receptorCALCRLQ16602
96Serotonin 2a (5-HT2a) receptorHTR2AP28223
97Disintegrin and metalloproteinase domain-containing protein 10ADAM10O14672
98TGF-beta receptor type ITGFBR1P36897
99Nitric-oxide synthase, brainNOS1P29475
100Cathepsin (B and K)CTSBP07858
101Bradykinin B1 receptorBDKRB1P46663
102Potassium voltage-gated channel subfamily KQT member 1KCNQ1P51787
103Leukotriene A4 hydrolaseLTA4HP09960
104Apoptosis regulator Bcl-2BCL2P10415
105Kininogen-1KNG1P01042
106Solute carrier family 22 member 2SLC22A2O15244
107Plasma retinol-binding proteinRBP4P02753
108Histone deacetylase 4HDAC4P56524
109Dopamine D3 receptorDRD3P35462
110C-C chemokine receptor type 2CCR2P41597
111Solute carrier family 22 member 12SLC22A12Q96S37
112Glucagon-like peptide 1 receptorGLP1RP43220
113Dual specificity mitogen-activated protein kinase kinase 2MAP2K2P36507
114Death-associated protein kinase 2DAPK2Q9UIK4
115Bile acid receptor FXRNR1H4Q96RI1
116Interleukin-6IL6P05231
117Transcription factor AP-1JUNP05412
118Vascular endothelial growth factor AVEGFAP15692
119Interleukin-10IL10P22301
120Endothelin-1EDN1P05305
121Nitric oxide synthase, endothelialNOS3P29474
122Urotensin II receptorUTS2RQ9UKP6
12378 kDa glucose-regulated proteinHSPA5P11021
124Galectin-3LGALS3P17931
125Macrophage migration inhibitory factorMIFP14174
126Serum paraoxonase/arylesterase 1PON1P27169
127Kallikrein 1KLK1P06870
128Rho-associated protein kinase 1ROCK1Q13464
129Sphingosine kinase 1SPHK1Q9NYA1
130Serine/threonine-protein kinase Sgk1SGK1O00141
131Low affinity sodium-glucose cotransporterSLC5A4Q9NY91
132Neutrophil cytosol factor 1NCF1P14598
133AntileukoproteinaseSLPIP03973
134Signal transducer and activator of transcription 1-alpha/betaSTAT1P42224
135Protein kinase C beta typePRKCBP05771
136Gap junction alpha-1 proteinGJA1P17302
137C-X-C motif chemokine 11CXCL11O14625
138Interleukin-8CXCL8P10145
139Superoxide dismutase [Cu-Zn]SOD1P00441
140C-C motif chemokine 2CCL2P13500
141Hypoxia-inducible factor 1-alphaHIF1AQ16665
142Caveolin-1CAV1Q03135
143Interleukin-1 alphaIL1AP01583
144Nuclear factor erythroid 2-related factor 2NFE2L2Q16236
145C-X-C motif chemokine 10CXCL10P02778
146Plasminogen activator inhibitor 1SERPINE1P05121
147OsteopontinSPP1P10451
148Bone morphogenetic protein 2BMP2P12643
149Transforming growth factor beta-1 proproteinTGFB1P01137
150Cyclin-dependent kinase inhibitor 2ACDKN2AP42771
151Transcription factor E2F1E2F1Q01094
152ThrombomodulinTHBDP07204
153Insulin-like growth factor IIIGF2P01344
154CatalaseCATP04040
155Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENPTENP60484
156Pro-epidermal growth factorEGFP01133
157ATP synthase subunit beta, mitochondriaATP5F1BP06576
158NAD-dependent protein deacetylase sirtuin-1SIRT1Q96EB6
159Angiotensin-converting enzymeACEP12821
160Matrix metalloproteinase 10MMP10P09238
161TransketolaseTKTP29401
162Dipeptidyl peptidase IVDPP4P27487
163Nuclear factor NF-kappa-B p65 subunitRELAQ04206
164Nitric oxide synthase, inducibleNOS2P35228
165Protein kinase C alpha typePRKCAP17252
166Tumor necrosis factorTNFP01375
167Protein kinase C epsilon typePRKCEQ02156
168ReninRENP00797
169Axin1/beta-cateninCTNNB1P35222
170FibronectinFN1P02751
171C-X-C chemokine receptor type 4CXCR4P61073
172HeparanaseHPSEQ9Y251
173GlucagonGCGP01275
174Tumor necrosis factor receptor superfamily member 11BTNFRSF11BO00300
175Metalloproteinase inhibitor 1TIMP1P01033
176Metalloproteinase inhibitor 2TIMP2P16035
177Fibroblast growth factor 2FGF2P09038
178Lipoprotein lipaseLPLP06858
179Coagulation factor VF5P12259
180Cyclic AMP-responsive element-binding protein 1CREB1P16220
181Phosphatidylinositol 3,4,5-trisphosphate 5- phosphatase 2INPPL1O15357
182Tumor necrosis factor ligand superfamily member 6FASLGP48023

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).


Serial numberNodeDegreeCloseness centralityBetweenness centrality

1PIK3CA400.495081970.09370214
2STAT3400.50.0863086
3AKT1350.490259740.15311921
4KNG1330.440233240.06128185
5VEGFA330.491856680.06953442
6JUN320.480891720.07229449
7MAPK3300.46177370.02240476
8MAPK1300.46894410.06714477
9EGF270.46177370.0336672
10EDN1270.466049380.05180077
11EGFR260.440233240.01794429
12JAK1260.449404760.02254905
13IL6260.452095810.02532622
14CXCL8250.437681160.03191743
15RELA240.457575760.04035241
16FN1230.43515850.01464828
17JAK2230.449404760.01620852
18CTNNB1230.454819280.06488997
19TNF220.442815250.0272631
20TGFB1210.442815250.03270724
21MMP9200.409214090.03200512
22CXCR4190.410326090.01402652
23TIMP1190.417127070.00798146
24MAPK14190.444117650.01628416
25BDKRB1180.39947090.00725308
26PIK3CB180.409214090.00732155
27MAPK8180.422969190.03099697
28ITGB3180.422969190.01050834
29CCR5160.398416890.00592392
30PLG160.402666670.02168017


No.Active ingredientsCode name

1IsoimperatorinM1
21,2,5,6-TetrahydrotanshinoneM2
35,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-oneM3
4(E)-3-[2-(3,4-Dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylicM4
52-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehydeM5
6Przewaquinone cM6
7CryptotanshinoneM7
8DihydrotanshinlactoneM8
9Isotanshinone IIM9
10MiltipoloneM10
11MiltironeM11
12TanshinaldehydeM12
13Danshenol BM13
14Danshenol AM14
15DeoxyneocryptotanshinoneM15
16Dihydrotanshinone IM16
17Miltionone IM17
18Miltionone IIM18
19Neocryptotanshinone iiM19
20NeocryptotanshinoneM20
21LuteolinM21
22Salvilenone IM22
23SalvioloneM23
24EpidanshenspiroketallactoneM24
25Tanshinone iiaM25
26α-AmyrinM26
27Dan-shexinkum dM27
28SclareolM28
29Dehydrotanshinone II AM29
30BaicalinM30
312-Isopropyl-8-methylphenanthrene-3,4-dioneM31
32FormyltanshinoneM32
333-Beta-HydroxymethyllenetanshiquinoneM33
34MethylenetanshinquinoneM34
35(2R)-3-(3,4-Dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acidM35
36(6S)-6-(Hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dioneM36
37Tanshinone VIM37
38Przewalskin bM38
396-o-Syringyl-8-o-acetyl shanzhiside methyl esterM39
40Prolithospermic acidM40
41(Z)-3-[2-[(E)-2-(3,4-Dihydroxyphenyl)vinyl]-3,4-dihydroxyphenyl]acrylic acidM41
42Salvianolic acid gM42
43Salvianolic acid jM43
44DanshenspiroketallactoneM44
451-Methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trioneM45
463,9-di-O-MethylnissolinMM46
47(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-olM47
48(3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-olM48
49IsorhamnetinM49
50KaempferolM50
51QuercetinM51
52JaranolM52
53BifendateM53
54FormononetinM54
55IsoflavanoneM55
56CalycosinM56
57HederageninM57
58Sennoside E_qtM58
59ToralactoneM59
60Palmidin AM60
61Daucosterol_qtM61
62EupatinM62
63Procyanidin B-5,3’-O-gallateM63
64RheinM64
65Beta-sitosterolM65
66Aloe-emodinM66
67LipaseM67
68Gardnerilin aM68
69HirudinM69
70o-Desulfated heparinM70
71Ursolic acidM71
72HeparinM72
73Genioisidic acidM73
74Genipinic acidM74
75NadroparinM75

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.

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.

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 [3238].

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).

References

  1. A.-L. Cao, L. Wang, X. Chen et al., “Ursodeoxycholic acid and 4-phenylbutyrate prevent endoplasmic reticulum stress-induced podocyte apoptosis in diabetic nephropathy,” Laboratory Investigation, vol. 96, no. 6, pp. 610–622, 2016. View at: Publisher Site | Google Scholar
  2. G.-D. Sun, W.-P. Cui, Q.-Y. Guo, and L.-N. Miao, “Histone lysine methylation in diabetic nephropathy,” Journal of Diabetes Research, vol. 2014, Article ID 654148, 9 pages, 2014. View at: Publisher Site | Google Scholar
  3. R. Z. Alicic, M. T. Rooney, and K. R. Tuttle, “Diabetic kidney disease,” Clinical Journal of the American Society of Nephrology, vol. 12, no. 12, pp. 2032–2045, 2017. View at: Publisher Site | Google Scholar
  4. C. Magee, D. J. Grieve, C. J. Watson, and D. P. Brazil, “Diabetic nephropathy: a tangled web to unweave,” Cardiovascular Drugs and Therapy, vol. 31, no. 5-6, pp. 579–592, 2017. View at: Publisher Site | Google Scholar
  5. H. Chen, J. Guo, X. Zhao et al., “Retrospective analysis of the overt proteinuria diabetic kidney disease in the treatment of modified Shenzhuo formula for 2 years,” Medicine (Baltimore), vol. 96, no. 12, p. e6349, 2017. View at: Publisher Site | Google Scholar
  6. J. Tian, L. Zhao, Q. Zhou et al., “Retrospective analysis on modified Didang Tang for treating microalbuminuria of diabetic nephropathy,” Journal of Beijing University of Traditional Chinese Medicine, vol. 19, no. 6, pp. 7–10, 2012. View at: Google Scholar
  7. Y. Y. Jiang, M. Q. Sun, B. Ma et al., “The application and thinking of omics technologies in the modern pharmacology research of traditional Chinese medine,” World Science and Technology/Modernization of Traditional Chinese Medicine and Materia Medica, vol. 20, no. 8, pp. 1287–1295, 2018. View at: Google Scholar
  8. M. Isgut, M. Rao, C. Yang, V. Subrahmanyam, P. C. G. Rida, and R. Aneja, “Application of combination high-throughput phenotypic screening and target identification methods for the discovery of natural product-based combination drugs,” Medicinal Research Reviews, vol. 38, no. 2, pp. 504–524, 2018. View at: Publisher Site | Google Scholar
  9. G.-B. Zhang, Q.-Y. Li, Q.-L. Chen, and S.-B. Su, “Network pharmacology: a new approach for Chinese herbal medicine research,” Evidence-based Complementary and Alternative Medicine, vol. 2013, Article ID 621423, 9 pages, 2013. View at: Publisher Site | Google Scholar
  10. A. L. Hopkins, “Network pharmacology: the next paradigm in drug discovery,” Nature Chemical Biology, vol. 4, no. 11, pp. 682–690, 2008. View at: Publisher Site | Google Scholar
  11. J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, p. 13, 2014. View at: Publisher Site | Google Scholar
  12. R. Xue, Z. Fang, M. Zhang, Z. Yi, C. Wen, and T. Shi, “TCMID: traditional Chinese Medicine integrative database for herb molecular mechanism analysis,” Nucleic Acids Research, vol. 41, no. D1, pp. D1089–D1095, 2013. View at: Publisher Site | Google Scholar
  13. A. Daina, O. Michielin, and V. Zoete, “SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules,” Nucleic Acids Research, vol. 47, no. W1, pp. W357–W364, 2019. View at: Publisher Site | Google Scholar
  14. H. Ye, L. Ye, H. Kang et al., “HIT: linking herbal active ingredients to targets,” Nucleic Acids Research, vol. 39, no. suppl_1, pp. D1055–D1059, 2011. View at: Publisher Site | Google Scholar
  15. Y. H. Li, C. Y. Yu, X. X. Li et al., “Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics,” Nucleic Acids Research, vol. 46, no. D1, pp. D1121–D1127, 2018. View at: Publisher Site | Google Scholar
  16. D. S. Wishart, Y. D. Feunang, A. C. Guo et al., “DrugBank 5.0: a major update to the DrugBank database for 2018,” Nucleic Acids Research, vol. 46, no. D1, pp. D1074–D1082, 2018. View at: Publisher Site | Google Scholar
  17. J. Piñero, À. Bravo, N. Queralt-Rosinach et al., “DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants,” Nucleic Acids Research, vol. 45, no. D1, pp. D833–D839, 2017. View at: Publisher Site | Google Scholar
  18. D. Szklarczyk, A. L. Gable, D. Lyon et al., “STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Research, vol. 47, no. D1, pp. D607–D613, 2019. View at: Publisher Site | Google Scholar
  19. P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. View at: Publisher Site | Google Scholar
  20. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at: Publisher Site | Google Scholar
  21. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at: Publisher Site | Google Scholar
  22. L. Zhou, Z. Liu, Z Wang et al., “Astragalus polysaccharides exerts immunomodulatory effects via TLR4-mediated MyD88-dependent signaling pathway in vitro and in vivo,” Scientific Reports, vol. 7, p. 44822, 2017. View at: Publisher Site | Google Scholar
  23. L. Li, X. Hou, R. Xu, C. Liu, and M. Tu, “Research review on the pharmacological effects of astragaloside IV,” Fundamental and Clinical Pharmacology, vol. 31, no. 1, pp. 17–36, 2017. View at: Publisher Site | Google Scholar
  24. X.-Y. Bao, Q. Zheng, Q. Tong et al., “Danshensu for myocardial ischemic injury: precli-nical evidence and novel methodology of quality assessment tool,” Frontiers in Pharmacology, vol. 9, p. 1445, 2018. View at: Publisher Site | Google Scholar
  25. X. Dong, J. Fu, X. Yin et al., “Emodin: a review of its pharmacology, toxicity and pharmacokinetics,” Phytotherapy Research, vol. 30, no. 8, pp. 1217-1218, 2016. View at: Publisher Site | Google Scholar
  26. L. Tang, K. Li, Y. Zhang et al., “Quercetin liposomes ameliorate streptozotocin-induced diabetic nephropathy in diabetic rats,” Scientific Reports, vol. 10, no. 1, p. 2440, 2020. View at: Publisher Site | Google Scholar
  27. M. Zhang, L. He, J. Liu, and L. Zhou, “Luteolin attenuates diabetic nephropathy through suppressing inflammatory response and oxidative stress by inhibiting STAT3 pathway,” Experimental and Clinical Endocrinology and Diabetes, vol. 128, 2020. View at: Publisher Site | Google Scholar
  28. T. Ma, L. Xu, L. Lu et al., “Ursolic acid treatment alleviates diabetic kidney injury by regulating the ARAP1/AT1R signaling pathway,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 12, pp. 2597–2608, 2019. View at: Publisher Site | Google Scholar
  29. J. Chen, J.-K. Chen, and R. C. Harris, “EGF receptor deletion in podocytes attenuates diabetic nephropathy,” Journal of the American Society of Nephrology, vol. 26, no. 5, pp. 1115–1125, 2015. View at: Publisher Site | Google Scholar
  30. L. Liu and J Tan, “The relationship between TIMP-1, MMP-9 and diabetic nephropathy,” Chinese Journal of Clinical Rational Drug Use, vol. 7, no. 29, pp. 173-174, 2014. View at: Google Scholar
  31. E. Said, A. Z. Sawsan, M. Eldosoky, and N. M. Elsherbeny, “Nifuroxazide, a STAT3 inhibitor, mitigates inflammatory burden and protects against diabetes-induced nephropathy in rats,” Chemico-Biological Interactions, vol. 281, pp. 111–120, 2018. View at: Publisher Site | Google Scholar
  32. W. Huang, F. Guo, Y. Long et al., “High glucose and lipopolysaccharide activate NOD1- RICK-NF-kappaB inflammatory signaling in mesangial cells,” Experimental and Clinical Endocrinology and Diabetes, vol. 124, no. 8, pp. 512–517, 2016. View at: Publisher Site | Google Scholar
  33. J. Donate-Correa, D. Luis-Rodríguez, E. Martín-Núñez et al., “Inflammatory targets in diabetic nephropathy,” Journal of Clinical Medicine, vol. 9, no. 2, p. 458, 2020. View at: Publisher Site | Google Scholar
  34. S. Xiao, Q. Li, L. Hu et al., “Soluble guanylate cyclase stimulators and activators: where are we and where to go?” Mini-Reviews in Medicinal Chemistry, vol. 19, no. 18, pp. 1544–1557, 2019. View at: Publisher Site | Google Scholar
  35. K. Sakamoto, K. Kuno, M. Takemoto et al., “Pituitary adenylate cyclase-activating polypeptide protects glomerular podocytes from inflammatory injuries,” Journal of Diabetes Research, vol. 2015, Article ID 727152, 10 pages, 2015. View at: Publisher Site | Google Scholar
  36. G. Wang, Y. Yan, N. Xu, Y. Hui, and D. Yin, “Upregulation of microRNA-424 relieved diabetic nephropathy by targeting Rictor through mTOR complex2/protein kinase B signaling,” Journal of Cellular Physiology, vol. 234, no. 7, pp. 11646–11653, 2019. View at: Publisher Site | Google Scholar
  37. R.-M. Wang, Z.-B. Wang, Y. Wang et al., “Swiprosin-1 promotes mitochondria-dependent apoptosis of glomerular podocytes via P38 MAPK pathway in early-stage diabetic nephropathy,” Cellular Physiology and Biochemistry, vol. 45, no. 3, pp. 899–916, 2018. View at: Publisher Site | Google Scholar
  38. A. Inada, O. Inada, N. L. Fujii et al., “Adjusting the 17β-Estradiol-to-Androgen ratio ameliorates diabetic nephropathy,” Journal of the American Society of Nephrology, vol. 27, no. 10, pp. 3035–3050, 2016. View at: Publisher Site | Google Scholar
  39. K. Omote, T. Gohda, M. Murakoshi et al., “Role of the TNF pathway in the progression of diabetic nephropathy in KK-Ay mice,” American Journal of Physiology-Renal Physiology, vol. 306, no. 11, pp. F1335–F1347, 2014. View at: Publisher Site | Google Scholar
  40. R. Bohuslavova, R. Cerychova, K. Nepomucka, and G. Pavlinkova, “Renal injury is accelerated by global hypoxia-inducible factor 1 alpha deficiency in a mouse model of STZ-induced diabetes,” BMC Endocrine Disorders, vol. 17, no. 1, p. 48, 2017. View at: Publisher Site | Google Scholar
  41. X. Y. Wu, J. Yu, and H. M Tian, “Effect of SOCS1 on diabetic renal injury through regulating TLR signaling pathway,” European Review for Medical and Pharmacological Sciences, vol. 23, no. 18, pp. 8068–8074, 2019. View at: Publisher Site | Google Scholar
  42. Y. A. Hong, J. H. Lim, M. Y. Kim et al., “Extracellular superoxide dismutase attenuates renal oxidative stress through the activation of adenosine monophosphate-activated protein kinase in diabetic nephropathy,” Antioxidants & Redox Signaling, vol. 28, no. 17, pp. 1543–1561, 2018. View at: Publisher Site | Google Scholar
  43. R. Yan, Y. Wang, M. Shi et al., “Regulation of PTEN/AKT/FAK pathways by PPARgamma impacts on fibrosis in diabetic nephropathy,” Journal of Cellular Biochemistry, vol. 120, 2019. View at: Publisher Site | Google Scholar
  44. P. Luan, J. Zhuang, J. Zou et al., “NLRC5 deficiency ameliorates diabetic nephropathy through alleviating inflammation,” FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, vol. 32, no. 2, pp. 1070–1084, 2018. View at: Publisher Site | Google Scholar
  45. W.-Y. Jiang, “Therapeutic wisdom in traditional Chinese medicine: a perspective from modern science,” Trends in Pharmacological Sciences, vol. 26, no. 11, pp. 558–563, 2005. View at: Publisher Site | Google Scholar

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.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views355
Downloads455
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.