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

Authors: Xinmiao Wang; Haoyu Yang; Lili Zhang; Lin Han; Sha Di; Xiuxiu Wei; Haoran Wu; Haiyu Zhang; Linhua Zhao; Xiaolin Tong
Journal: Evidence-Based Complementary and Alternative Medicine
 (2021)

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## 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.


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## Body

## 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.


## 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 Table1. 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 numberTargetCommon nameUniprot ID1Aldose reductaseAKR1B1P151212Acyl coenzyme A:cholesterol acyltransferaseCES1P231413Signal transducer and activator of transcription 3STAT3P407634Protein-tyrosine phosphatase 1CPTPN6P293505Vascular endothelial growth factor receptor 2KDRP359686Epidermal growth factor receptor erbB1EGFRP005337PI3-kinase p110-alpha subunitPIK3CAP423368c-Jun N-terminal kinase 1MAPK8P459839LXR-alphaNR1H3Q1313310Estrogen receptor alphaESR1P0337211Testis-specific androgen-binding proteinSHBGP0427812Cytochrome P450 2C19CYP2C19P3326113Protein-tyrosine phosphatase 1BPTPN1P1803114ButyrylcholinesteraseBCHEP0627615Vitamin D receptorVDRP1147316Glucose-6-phosphate 1-dehydrogenaseG6PDP1141317Peroxisome proliferator-activated receptor alphaPPARAQ0786918Peroxisome proliferator-activated receptor deltaPPARDQ0318119Peroxisome proliferator-activated receptor gammaPPARGP3723120UDP-glucuronosyltransferase 2B7UGT2B7P166622111-Beta-hydroxysteroid dehydrogenase 2HSD11B2P8036522NADPH oxidase 4NOX4Q9NPH523Tyrosine-protein kinase SYKSYKP4340524Glycogen synthase kinase-3 betaGSK3BP4984125Matrix metalloproteinase 9MMP9P1478026Matrix metalloproteinase 2MMP2P0825327Matrix metalloproteinase 12MMP12P3990028ATP-binding cassette sub-family G member 2ABCG2Q9UNQ029P-glycoprotein 1ABCB1P0818330Arachidonate 12-lipoxygenaseALOX12P1805431Cyclooxygenase-2PTGS2P3535432Insulin-like growth factor I receptorIGF1RP0806933MyeloperoxidaseMPOP0516434Matrix metalloproteinase 3MMP3P0825435Serine/threonine-protein kinase AKTAKT1P3174936Beta-secretase 1BACE1P5681737Tyrosine-protein kinase receptor UFOAXLP3053038NUAK family SNF1-like kinase 1NUAK1O6028539Aldehyde reductaseAKR1A1P1455040PlasminogenPLGP0074741PI3-kinase p110-delta subunitPIK3CDO0032942PI3-kinase p110-gamma subunitPIK3CGP4873643Hematopoietic prostaglandin D synthaseHPGDSO6076044Serine-protein kinase ATMATMQ1331545Cytochrome P450 24A1CYP24A1Q0797346Mineralocorticoid receptorNR3C2P0823547Cannabinoid receptor 1CNR1P2155448Hepatocyte nuclear factor 4-alphaHNF4AP4123549C-C chemokine receptor type 1CCR1P3224650Histone-lysine N-methyltransferase EZH2EZH2Q15910Organism: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.TargetCommon nameUniprot ID1Aldose reductaseAKR1B1P151212Acyl coenzyme A:cholesterol acyltransferaseCES1P231413Signal transducer and activator of transcription 3STAT3P407634Protein-tyrosine phosphatase 1CPTPN6P293505Vascular endothelial growth factor receptor 2KDRP359686Epidermal growth factor receptor erbB1EGFRP005337PI3-kinase p110-alpha subunitPIK3CAP423368c-Jun N-terminal kinase 1MAPK8P459839LXR-alphaNR1H3Q1313310Estrogen receptor alphaESR1P0337211Testis-specific androgen-binding proteinSHBGP0427812Cytochrome P450 2C19 13CYP2C19P3326113Protein-tyrosine phosphatase 1BPTPN1P1803114ButyrylcholinesteraseBCHEP0627615Vitamin D receptorVDRP1147316Glucose-6-phosphate 1-dehydrogenaseG6PDP1141317Peroxisome proliferator-activated receptor alphaPPARAQ0786918Peroxisome proliferator-activated receptor deltaPPARDQ0318119Peroxisome proliferator-activated receptor gammaPPARGP3723120UDP-glucuronosyltransferase 2B7UGT2B7P166622111-beta-hydroxysteroid dehydrogenase 2HSD11B2P8036522NADPH oxidase 4NOX4Q9NPH523Tyrosine-protein kinase SYKSYKP4340524Glycogen synthase kinase-3 betaGSK3BP4984125Matrix metalloproteinase 9MMP9P1478026Matrix metalloproteinase 2MMP2P0825327Matrix metalloproteinase 12MMP12P3990028ATP-binding cassette sub-family G member 2ABCG2Q9UNQ029P-glycoprotein 1ABCB1P0818330Arachidonate 12-lipoxygenaseALOX12P1805431Cyclooxygenase-2PTGS2P3535432Insulin-like growth factor I receptorIGF1RP0806933MyeloperoxidaseMPOP0516434Matrix metalloproteinase 3MMP3P0825435Serine/threonine-protein kinase AKTAKT1P3174936Beta-secretase 1BACE1P5681737Tyrosine-protein kinase receptor UFOAXLP3053038NUAK family SNF1-like kinase 1NUAK106028539Aldehyde reductase (by homology)AKR1A1P1455040PlasminogenPLGP0074741PI3-kinase p110-delta subunitPIK3CDO0032942PI3-kinase p110-gamma subunitPIK3CGP4873643Hematopoietic prostaglandin D synthaseHPGDSO6076044Serine-protein kinase ATMATMQ1331545Cytochrome P450 24A1CYP24A1Q0797346Mineralocorticoid receptorNR3C2P0823547Cannabinoid receptor 1CNR1P2155448Hepatocyte nuclear factor 4-alphaHNF4AP4123549C-C chemokine receptor type 1CCR1P3224650Histone-lysine N-methyltransferase EZH2EZH2Q1591051MAP kinase p38 alphaMAPK14Q1653952Bromodomain-containing protein 2BRD2P2544053Aldehyde dehydrogenaseALDH2P0509154Fatty acid binding protein adipocyteFABP4P1509055Fatty acid-binding protein, liverFABP1P0714856Acyl-CoA desaturaseSCDO0076757MAP kinase ERK1MAPK3P2736158Short transient receptor potential channel 6TRPC6Q9Y21059Mitogen-activated protein kinase kinase kinase 5MAP3K5Q9968360Disintegrin and metalloproteinase domain-containing protein 17ADAM17P7853661Hexokinase type IVGCKP3555762Intercellular adhesion molecule-1ICAM1P0536263P-selectinSELPP1610964Leukocyte adhesion molecule-1SELLP1415165Matrix metalloproteinase 1MMP1P0395666Matrix metalloproteinase 8MMP8P2289467Endothelin-converting enzyme 1ECE1P4289268Integrin beta-3ITGB3P0510669Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoformPIK3CBP4233870Sorbitol dehydrogenaseSORDQ0079671MAP kinase ERK2MAPK1P2848272Vascular endothelial growth factor receptor 1FLT1P1794873Matrix metalloproteinase 7MMP7P0923774Type-1 angiotensin II receptorAGTR1P3055675Glucose transporterSLC2A1P1116676Nerve growth factor receptor Trk-ANTRK1P0462977Tyrosine-protein kinase JAK1JAK1P2345878Tyrosine-protein kinase JAK2JAK2O6067479Sodium/glucose cotransporter 2SLC5A2P3163980Serine/threonine-protein kinase receptor R3ACVRL1P3702381Epoxide hydrataseEPHX2P3491382Cytochrome P450 11B2CYP11B2P1909983Endothelin receptor ET-AEDNRAP2510184Glutathione S-transferase Mu 1GSTM1P0948885Interleukin-1 betaILIBP0158486Insulin receptorINSRP0621387Protein tyrosine kinase 2 betaPTK2BQ1428988Cyclooxygenase-1PTGS1P2321989Cytochrome P450 2C9CYP2C9P1171290Cytochrome P450 3A4CYP3A4P0868491Trypsin IPRSS1P0747792C-C chemokine receptor type 5CCR5P5168193Dopamine D2 receptorDRD2P1441694Cholesteryl ester transfer proteinCETPP1159795Calcitonin gene-related peptide type 1 receptorCALCRLQ1660296Serotonin 2a (5-HT2a) receptorHTR2AP2822397Disintegrin and metalloproteinase domain-containing protein 10ADAM10O1467298TGF-beta receptor type ITGFBR1P3689799Nitric-oxide synthase, brainNOS1P29475100Cathepsin (B and K)CTSBP07858101Bradykinin B1 receptorBDKRB1P46663102Potassium voltage-gated channel subfamily KQT member 1KCNQ1P51787103Leukotriene A4 hydrolaseLTA4HP09960104Apoptosis regulator Bcl-2BCL2P10415105Kininogen-1KNG1P01042106Solute carrier family 22 member 2SLC22A2O15244107Plasma retinol-binding proteinRBP4P02753108Histone deacetylase 4HDAC4P56524109Dopamine D3 receptorDRD3P35462110C-C chemokine receptor type 2CCR2P41597111Solute carrier family 22 member 12SLC22A12Q96S37112Glucagon-like peptide 1 receptorGLP1RP43220113Dual specificity mitogen-activated protein kinase kinase 2MAP2K2P36507114Death-associated protein kinase 2DAPK2Q9UIK4115Bile acid receptor FXRNR1H4Q96RI1116Interleukin-6IL6P05231117Transcription factor AP-1JUNP05412118Vascular endothelial growth factor AVEGFAP15692119Interleukin-10IL10P22301120Endothelin-1EDN1P05305121Nitric oxide synthase, endothelialNOS3P29474122Urotensin II receptorUTS2RQ9UKP612378 kDa glucose-regulated proteinHSPA5P11021124Galectin-3LGALS3P17931125Macrophage migration inhibitory factorMIFP14174126Serum paraoxonase/arylesterase 1PON1P27169127Kallikrein 1KLK1P06870128Rho-associated protein kinase 1ROCK1Q13464129Sphingosine kinase 1SPHK1Q9NYA1130Serine/threonine-protein kinase Sgk1SGK1O00141131Low affinity sodium-glucose cotransporterSLC5A4Q9NY91132Neutrophil cytosol factor 1NCF1P14598133AntileukoproteinaseSLPIP03973134Signal transducer and activator of transcription 1-alpha/betaSTAT1P42224135Protein kinase C beta typePRKCBP05771136Gap junction alpha-1 proteinGJA1P17302137C-X-C motif chemokine 11CXCL11O14625138Interleukin-8CXCL8P10145139Superoxide dismutase [Cu-Zn]SOD1P00441140C-C motif chemokine 2CCL2P13500141Hypoxia-inducible factor 1-alphaHIF1AQ16665142Caveolin-1CAV1Q03135143Interleukin-1 alphaIL1AP01583144Nuclear factor erythroid 2-related factor 2NFE2L2Q16236145C-X-C motif chemokine 10CXCL10P02778146Plasminogen activator inhibitor 1SERPINE1P05121147OsteopontinSPP1P10451148Bone morphogenetic protein 2BMP2P12643149Transforming growth factor beta-1 proproteinTGFB1P01137150Cyclin-dependent kinase inhibitor 2ACDKN2AP42771151Transcription factor E2F1E2F1Q01094152ThrombomodulinTHBDP07204153Insulin-like growth factor IIIGF2P01344154CatalaseCATP04040155Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENPTENP60484156Pro-epidermal growth factorEGFP01133157ATP synthase subunit beta, mitochondriaATP5F1BP06576158NAD-dependent protein deacetylase sirtuin-1SIRT1Q96EB6159Angiotensin-converting enzymeACEP12821160Matrix metalloproteinase 10MMP10P09238161TransketolaseTKTP29401162Dipeptidyl peptidase IVDPP4P27487163Nuclear factor NF-kappa-B p65 subunitRELAQ04206164Nitric oxide synthase, inducibleNOS2P35228165Protein kinase C alpha typePRKCAP17252166Tumor necrosis factorTNFP01375167Protein kinase C epsilon typePRKCEQ02156168ReninRENP00797169Axin1/beta-cateninCTNNB1P35222170FibronectinFN1P02751171C-X-C chemokine receptor type 4CXCR4P61073172HeparanaseHPSEQ9Y251173GlucagonGCGP01275174Tumor necrosis factor receptor superfamily member 11BTNFRSF11BO00300175Metalloproteinase inhibitor 1TIMP1P01033176Metalloproteinase inhibitor 2TIMP2P16035177Fibroblast growth factor 2FGF2P09038178Lipoprotein lipaseLPLP06858179Coagulation factor VF5P12259180Cyclic AMP-responsive element-binding protein 1CREB1P16220181Phosphatidylinositol 3,4,5-trisphosphate 5- phosphatase 2INPPL1O15357182Tumor 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 (Figure1). 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 numberNodeDegreeCloseness centralityBetweenness centrality1PIK3CA400.495081970.093702142STAT3400.50.08630863AKT1350.490259740.153119214KNG1330.440233240.061281855VEGFA330.491856680.069534426JUN320.480891720.072294497MAPK3300.46177370.022404768MAPK1300.46894410.067144779EGF270.46177370.033667210EDN1270.466049380.0518007711EGFR260.440233240.0179442912JAK1260.449404760.0225490513IL6260.452095810.0253262214CXCL8250.437681160.0319174315RELA240.457575760.0403524116FN1230.43515850.0146482817JAK2230.449404760.0162085218CTNNB1230.454819280.0648899719TNF220.442815250.027263120TGFB1210.442815250.0327072421MMP9200.409214090.0320051222CXCR4190.410326090.0140265223TIMP1190.417127070.0079814624MAPK14190.444117650.0162841625BDKRB1180.39947090.0072530826PIK3CB180.409214090.0073215527MAPK8180.422969190.0309969728ITGB3180.422969190.0105083429CCR5160.398416890.0059239230PLG160.402666670.02168017Figure 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 Table4.Table 4
The information of active ingredients.
No.Active ingredientsCode name1IsoimperatorinM121,2,5,6-TetrahydrotanshinoneM235,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-oneM34(E)-3-[2-(3,4-Dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylicM452-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehydeM56Przewaquinone cM67CryptotanshinoneM78DihydrotanshinlactoneM89Isotanshinone IIM910MiltipoloneM1011MiltironeM1112TanshinaldehydeM1213Danshenol BM1314Danshenol AM1415DeoxyneocryptotanshinoneM1516Dihydrotanshinone IM1617Miltionone IM1718Miltionone IIM1819Neocryptotanshinone iiM1920NeocryptotanshinoneM2021LuteolinM2122Salvilenone IM2223SalvioloneM2324EpidanshenspiroketallactoneM2425Tanshinone iiaM2526α-AmyrinM2627Dan-shexinkum dM2728SclareolM2829Dehydrotanshinone II AM2930BaicalinM30312-Isopropyl-8-methylphenanthrene-3,4-dioneM3132FormyltanshinoneM32333-Beta-HydroxymethyllenetanshiquinoneM3334MethylenetanshinquinoneM3435(2R)-3-(3,4-Dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acidM3536(6S)-6-(Hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dioneM3637Tanshinone VIM3738Przewalskin bM38396-o-Syringyl-8-o-acetyl shanzhiside methyl esterM3940Prolithospermic acidM4041(Z)-3-[2-[(E)-2-(3,4-Dihydroxyphenyl)vinyl]-3,4-dihydroxyphenyl]acrylic acidM4142Salvianolic acid gM4243Salvianolic acid jM4344DanshenspiroketallactoneM44451-Methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trioneM45463,9-di-O-MethylnissolinMM4647(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-olM4748(3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-olM4849IsorhamnetinM4950KaempferolM5051QuercetinM5152JaranolM5253BifendateM5354FormononetinM5455IsoflavanoneM5556CalycosinM5657HederageninM5758Sennoside E_qtM5859ToralactoneM5960Palmidin AM6061Daucosterol_qtM6162EupatinM6263Procyanidin B-5,3’-O-gallateM6364RheinM6465Beta-sitosterolM6566Aloe-emodinM6667LipaseM6768Gardnerilin aM6869HirudinM6970o-Desulfated heparinM7071Ursolic acidM7172HeparinM7273Genioisidic acidM7374Genipinic acidM7475NadroparinM75


### 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 (Figure3). 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 Figure4, 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.


## 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 Table1. 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 numberTargetCommon nameUniprot ID1Aldose reductaseAKR1B1P151212Acyl coenzyme A:cholesterol acyltransferaseCES1P231413Signal transducer and activator of transcription 3STAT3P407634Protein-tyrosine phosphatase 1CPTPN6P293505Vascular endothelial growth factor receptor 2KDRP359686Epidermal growth factor receptor erbB1EGFRP005337PI3-kinase p110-alpha subunitPIK3CAP423368c-Jun N-terminal kinase 1MAPK8P459839LXR-alphaNR1H3Q1313310Estrogen receptor alphaESR1P0337211Testis-specific androgen-binding proteinSHBGP0427812Cytochrome P450 2C19CYP2C19P3326113Protein-tyrosine phosphatase 1BPTPN1P1803114ButyrylcholinesteraseBCHEP0627615Vitamin D receptorVDRP1147316Glucose-6-phosphate 1-dehydrogenaseG6PDP1141317Peroxisome proliferator-activated receptor alphaPPARAQ0786918Peroxisome proliferator-activated receptor deltaPPARDQ0318119Peroxisome proliferator-activated receptor gammaPPARGP3723120UDP-glucuronosyltransferase 2B7UGT2B7P166622111-Beta-hydroxysteroid dehydrogenase 2HSD11B2P8036522NADPH oxidase 4NOX4Q9NPH523Tyrosine-protein kinase SYKSYKP4340524Glycogen synthase kinase-3 betaGSK3BP4984125Matrix metalloproteinase 9MMP9P1478026Matrix metalloproteinase 2MMP2P0825327Matrix metalloproteinase 12MMP12P3990028ATP-binding cassette sub-family G member 2ABCG2Q9UNQ029P-glycoprotein 1ABCB1P0818330Arachidonate 12-lipoxygenaseALOX12P1805431Cyclooxygenase-2PTGS2P3535432Insulin-like growth factor I receptorIGF1RP0806933MyeloperoxidaseMPOP0516434Matrix metalloproteinase 3MMP3P0825435Serine/threonine-protein kinase AKTAKT1P3174936Beta-secretase 1BACE1P5681737Tyrosine-protein kinase receptor UFOAXLP3053038NUAK family SNF1-like kinase 1NUAK1O6028539Aldehyde reductaseAKR1A1P1455040PlasminogenPLGP0074741PI3-kinase p110-delta subunitPIK3CDO0032942PI3-kinase p110-gamma subunitPIK3CGP4873643Hematopoietic prostaglandin D synthaseHPGDSO6076044Serine-protein kinase ATMATMQ1331545Cytochrome P450 24A1CYP24A1Q0797346Mineralocorticoid receptorNR3C2P0823547Cannabinoid receptor 1CNR1P2155448Hepatocyte nuclear factor 4-alphaHNF4AP4123549C-C chemokine receptor type 1CCR1P3224650Histone-lysine N-methyltransferase EZH2EZH2Q15910Organism: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.TargetCommon nameUniprot ID1Aldose reductaseAKR1B1P151212Acyl coenzyme A:cholesterol acyltransferaseCES1P231413Signal transducer and activator of transcription 3STAT3P407634Protein-tyrosine phosphatase 1CPTPN6P293505Vascular endothelial growth factor receptor 2KDRP359686Epidermal growth factor receptor erbB1EGFRP005337PI3-kinase p110-alpha subunitPIK3CAP423368c-Jun N-terminal kinase 1MAPK8P459839LXR-alphaNR1H3Q1313310Estrogen receptor alphaESR1P0337211Testis-specific androgen-binding proteinSHBGP0427812Cytochrome P450 2C19 13CYP2C19P3326113Protein-tyrosine phosphatase 1BPTPN1P1803114ButyrylcholinesteraseBCHEP0627615Vitamin D receptorVDRP1147316Glucose-6-phosphate 1-dehydrogenaseG6PDP1141317Peroxisome proliferator-activated receptor alphaPPARAQ0786918Peroxisome proliferator-activated receptor deltaPPARDQ0318119Peroxisome proliferator-activated receptor gammaPPARGP3723120UDP-glucuronosyltransferase 2B7UGT2B7P166622111-beta-hydroxysteroid dehydrogenase 2HSD11B2P8036522NADPH oxidase 4NOX4Q9NPH523Tyrosine-protein kinase SYKSYKP4340524Glycogen synthase kinase-3 betaGSK3BP4984125Matrix metalloproteinase 9MMP9P1478026Matrix metalloproteinase 2MMP2P0825327Matrix metalloproteinase 12MMP12P3990028ATP-binding cassette sub-family G member 2ABCG2Q9UNQ029P-glycoprotein 1ABCB1P0818330Arachidonate 12-lipoxygenaseALOX12P1805431Cyclooxygenase-2PTGS2P3535432Insulin-like growth factor I receptorIGF1RP0806933MyeloperoxidaseMPOP0516434Matrix metalloproteinase 3MMP3P0825435Serine/threonine-protein kinase AKTAKT1P3174936Beta-secretase 1BACE1P5681737Tyrosine-protein kinase receptor UFOAXLP3053038NUAK family SNF1-like kinase 1NUAK106028539Aldehyde reductase (by homology)AKR1A1P1455040PlasminogenPLGP0074741PI3-kinase p110-delta subunitPIK3CDO0032942PI3-kinase p110-gamma subunitPIK3CGP4873643Hematopoietic prostaglandin D synthaseHPGDSO6076044Serine-protein kinase ATMATMQ1331545Cytochrome P450 24A1CYP24A1Q0797346Mineralocorticoid receptorNR3C2P0823547Cannabinoid receptor 1CNR1P2155448Hepatocyte nuclear factor 4-alphaHNF4AP4123549C-C chemokine receptor type 1CCR1P3224650Histone-lysine N-methyltransferase EZH2EZH2Q1591051MAP kinase p38 alphaMAPK14Q1653952Bromodomain-containing protein 2BRD2P2544053Aldehyde dehydrogenaseALDH2P0509154Fatty acid binding protein adipocyteFABP4P1509055Fatty acid-binding protein, liverFABP1P0714856Acyl-CoA desaturaseSCDO0076757MAP kinase ERK1MAPK3P2736158Short transient receptor potential channel 6TRPC6Q9Y21059Mitogen-activated protein kinase kinase kinase 5MAP3K5Q9968360Disintegrin and metalloproteinase domain-containing protein 17ADAM17P7853661Hexokinase type IVGCKP3555762Intercellular adhesion molecule-1ICAM1P0536263P-selectinSELPP1610964Leukocyte adhesion molecule-1SELLP1415165Matrix metalloproteinase 1MMP1P0395666Matrix metalloproteinase 8MMP8P2289467Endothelin-converting enzyme 1ECE1P4289268Integrin beta-3ITGB3P0510669Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoformPIK3CBP4233870Sorbitol dehydrogenaseSORDQ0079671MAP kinase ERK2MAPK1P2848272Vascular endothelial growth factor receptor 1FLT1P1794873Matrix metalloproteinase 7MMP7P0923774Type-1 angiotensin II receptorAGTR1P3055675Glucose transporterSLC2A1P1116676Nerve growth factor receptor Trk-ANTRK1P0462977Tyrosine-protein kinase JAK1JAK1P2345878Tyrosine-protein kinase JAK2JAK2O6067479Sodium/glucose cotransporter 2SLC5A2P3163980Serine/threonine-protein kinase receptor R3ACVRL1P3702381Epoxide hydrataseEPHX2P3491382Cytochrome P450 11B2CYP11B2P1909983Endothelin receptor ET-AEDNRAP2510184Glutathione S-transferase Mu 1GSTM1P0948885Interleukin-1 betaILIBP0158486Insulin receptorINSRP0621387Protein tyrosine kinase 2 betaPTK2BQ1428988Cyclooxygenase-1PTGS1P2321989Cytochrome P450 2C9CYP2C9P1171290Cytochrome P450 3A4CYP3A4P0868491Trypsin IPRSS1P0747792C-C chemokine receptor type 5CCR5P5168193Dopamine D2 receptorDRD2P1441694Cholesteryl ester transfer proteinCETPP1159795Calcitonin gene-related peptide type 1 receptorCALCRLQ1660296Serotonin 2a (5-HT2a) receptorHTR2AP2822397Disintegrin and metalloproteinase domain-containing protein 10ADAM10O1467298TGF-beta receptor type ITGFBR1P3689799Nitric-oxide synthase, brainNOS1P29475100Cathepsin (B and K)CTSBP07858101Bradykinin B1 receptorBDKRB1P46663102Potassium voltage-gated channel subfamily KQT member 1KCNQ1P51787103Leukotriene A4 hydrolaseLTA4HP09960104Apoptosis regulator Bcl-2BCL2P10415105Kininogen-1KNG1P01042106Solute carrier family 22 member 2SLC22A2O15244107Plasma retinol-binding proteinRBP4P02753108Histone deacetylase 4HDAC4P56524109Dopamine D3 receptorDRD3P35462110C-C chemokine receptor type 2CCR2P41597111Solute carrier family 22 member 12SLC22A12Q96S37112Glucagon-like peptide 1 receptorGLP1RP43220113Dual specificity mitogen-activated protein kinase kinase 2MAP2K2P36507114Death-associated protein kinase 2DAPK2Q9UIK4115Bile acid receptor FXRNR1H4Q96RI1116Interleukin-6IL6P05231117Transcription factor AP-1JUNP05412118Vascular endothelial growth factor AVEGFAP15692119Interleukin-10IL10P22301120Endothelin-1EDN1P05305121Nitric oxide synthase, endothelialNOS3P29474122Urotensin II receptorUTS2RQ9UKP612378 kDa glucose-regulated proteinHSPA5P11021124Galectin-3LGALS3P17931125Macrophage migration inhibitory factorMIFP14174126Serum paraoxonase/arylesterase 1PON1P27169127Kallikrein 1KLK1P06870128Rho-associated protein kinase 1ROCK1Q13464129Sphingosine kinase 1SPHK1Q9NYA1130Serine/threonine-protein kinase Sgk1SGK1O00141131Low affinity sodium-glucose cotransporterSLC5A4Q9NY91132Neutrophil cytosol factor 1NCF1P14598133AntileukoproteinaseSLPIP03973134Signal transducer and activator of transcription 1-alpha/betaSTAT1P42224135Protein kinase C beta typePRKCBP05771136Gap junction alpha-1 proteinGJA1P17302137C-X-C motif chemokine 11CXCL11O14625138Interleukin-8CXCL8P10145139Superoxide dismutase [Cu-Zn]SOD1P00441140C-C motif chemokine 2CCL2P13500141Hypoxia-inducible factor 1-alphaHIF1AQ16665142Caveolin-1CAV1Q03135143Interleukin-1 alphaIL1AP01583144Nuclear factor erythroid 2-related factor 2NFE2L2Q16236145C-X-C motif chemokine 10CXCL10P02778146Plasminogen activator inhibitor 1SERPINE1P05121147OsteopontinSPP1P10451148Bone morphogenetic protein 2BMP2P12643149Transforming growth factor beta-1 proproteinTGFB1P01137150Cyclin-dependent kinase inhibitor 2ACDKN2AP42771151Transcription factor E2F1E2F1Q01094152ThrombomodulinTHBDP07204153Insulin-like growth factor IIIGF2P01344154CatalaseCATP04040155Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENPTENP60484156Pro-epidermal growth factorEGFP01133157ATP synthase subunit beta, mitochondriaATP5F1BP06576158NAD-dependent protein deacetylase sirtuin-1SIRT1Q96EB6159Angiotensin-converting enzymeACEP12821160Matrix metalloproteinase 10MMP10P09238161TransketolaseTKTP29401162Dipeptidyl peptidase IVDPP4P27487163Nuclear factor NF-kappa-B p65 subunitRELAQ04206164Nitric oxide synthase, inducibleNOS2P35228165Protein kinase C alpha typePRKCAP17252166Tumor necrosis factorTNFP01375167Protein kinase C epsilon typePRKCEQ02156168ReninRENP00797169Axin1/beta-cateninCTNNB1P35222170FibronectinFN1P02751171C-X-C chemokine receptor type 4CXCR4P61073172HeparanaseHPSEQ9Y251173GlucagonGCGP01275174Tumor necrosis factor receptor superfamily member 11BTNFRSF11BO00300175Metalloproteinase inhibitor 1TIMP1P01033176Metalloproteinase inhibitor 2TIMP2P16035177Fibroblast growth factor 2FGF2P09038178Lipoprotein lipaseLPLP06858179Coagulation factor VF5P12259180Cyclic AMP-responsive element-binding protein 1CREB1P16220181Phosphatidylinositol 3,4,5-trisphosphate 5- phosphatase 2INPPL1O15357182Tumor 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 (Figure1). 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 numberNodeDegreeCloseness centralityBetweenness centrality1PIK3CA400.495081970.093702142STAT3400.50.08630863AKT1350.490259740.153119214KNG1330.440233240.061281855VEGFA330.491856680.069534426JUN320.480891720.072294497MAPK3300.46177370.022404768MAPK1300.46894410.067144779EGF270.46177370.033667210EDN1270.466049380.0518007711EGFR260.440233240.0179442912JAK1260.449404760.0225490513IL6260.452095810.0253262214CXCL8250.437681160.0319174315RELA240.457575760.0403524116FN1230.43515850.0146482817JAK2230.449404760.0162085218CTNNB1230.454819280.0648899719TNF220.442815250.027263120TGFB1210.442815250.0327072421MMP9200.409214090.0320051222CXCR4190.410326090.0140265223TIMP1190.417127070.0079814624MAPK14190.444117650.0162841625BDKRB1180.39947090.0072530826PIK3CB180.409214090.0073215527MAPK8180.422969190.0309969728ITGB3180.422969190.0105083429CCR5160.398416890.0059239230PLG160.402666670.02168017Figure 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 Table4.Table 4
The information of active ingredients.
No.Active ingredientsCode name1IsoimperatorinM121,2,5,6-TetrahydrotanshinoneM235,6-Dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-oneM34(E)-3-[2-(3,4-Dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylicM452-(4-Hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehydeM56Przewaquinone cM67CryptotanshinoneM78DihydrotanshinlactoneM89Isotanshinone IIM910MiltipoloneM1011MiltironeM1112TanshinaldehydeM1213Danshenol BM1314Danshenol AM1415DeoxyneocryptotanshinoneM1516Dihydrotanshinone IM1617Miltionone IM1718Miltionone IIM1819Neocryptotanshinone iiM1920NeocryptotanshinoneM2021LuteolinM2122Salvilenone IM2223SalvioloneM2324EpidanshenspiroketallactoneM2425Tanshinone iiaM2526α-AmyrinM2627Dan-shexinkum dM2728SclareolM2829Dehydrotanshinone II AM2930BaicalinM30312-Isopropyl-8-methylphenanthrene-3,4-dioneM3132FormyltanshinoneM32333-Beta-HydroxymethyllenetanshiquinoneM3334MethylenetanshinquinoneM3435(2R)-3-(3,4-Dihydroxyphenyl)-2-[(Z)-3-(3,4-dihydroxyphenyl)acryloyl]oxy-propionic acidM3536(6S)-6-(Hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dioneM3637Tanshinone VIM3738Przewalskin bM38396-o-Syringyl-8-o-acetyl shanzhiside methyl esterM3940Prolithospermic acidM4041(Z)-3-[2-[(E)-2-(3,4-Dihydroxyphenyl)vinyl]-3,4-dihydroxyphenyl]acrylic acidM4142Salvianolic acid gM4243Salvianolic acid jM4344DanshenspiroketallactoneM44451-Methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trioneM45463,9-di-O-MethylnissolinMM4647(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-olM4748(3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-olM4849IsorhamnetinM4950KaempferolM5051QuercetinM5152JaranolM5253BifendateM5354FormononetinM5455IsoflavanoneM5556CalycosinM5657HederageninM5758Sennoside E_qtM5859ToralactoneM5960Palmidin AM6061Daucosterol_qtM6162EupatinM6263Procyanidin B-5,3’-O-gallateM6364RheinM6465Beta-sitosterolM6566Aloe-emodinM6667LipaseM6768Gardnerilin aM6869HirudinM6970o-Desulfated heparinM7071Ursolic acidM7172HeparinM7273Genioisidic acidM7374Genipinic acidM7475NadroparinM75


## 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 (Figure3). 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 Figure4, 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, usingin vitro or in vivo studies.



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*Source: 6623010-2021-04-21.xml*