# Research on the Mechanism of Guizhi to Treat Nephrotic Syndrome Based on Network Pharmacology and Molecular Docking Technology

Authors: Dan He; Qiang Li; Guangli Du; Jijia Sun; Guofeng Meng; Shaoli Chen
Journal: BioMed Research International
 (2021)

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## Abstract

Objective. Nephrotic syndrome (NS) is a common glomerular disease caused by a variety of causes and is the second most common kidney disease. Guizhi is the key drug of Wulingsan in the treatment of NS. However, the action mechanism remains unclear. In this study, network pharmacology and molecular docking were used to explore the underlying molecular mechanism of Guizhi in treating NS. Methods. The active components and targets of Guizhi were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Hitpick, SEA, and Swiss Target Prediction database. The targets related to NS were obtained from the DisGeNET, GeneCards, and OMIM database, and the intersected targets were obtained by Venny2.1.0. Then, active component-target network was constructed using Cytoscape software. And the protein-protein interaction (PPI) network was drawn through the String database and Cytoscape software. Next, Gene Ontology (GO) and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. And overall network was constructed through Cytoscape. Finally, molecular docking was conducted using Autodock Vina. Results. According to the screening criteria, a total of 8 active compounds and 317 potential targets of Guizhi were chosen. Through the online database, 2125 NS-related targets were identified, and 93 overlapping targets were obtained. In active component-target network, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were the important active components. In PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 were the core targets. GO and KEGG analyses showed that the main pathways of Guizhi in treating NS involved VEGF, Toll-like receptor, and MAPK signaling pathway. In molecular docking, the active compounds of Guizhi had good affinity with the core targets. Conclusions. In this study, we preliminarily predicted the main active components, targets, and signaling pathways of Guizhi to treat NS, which could provide new ideas for further research on the protective mechanism and clinical application of Guizhi against NS.


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## Body

## 1. Introduction

Nephrotic syndrome (NS) is a clinical syndrome defined as massive proteinuria, hypoalbuminemia, hyperlipidemia, and edema [1]. According to the epidemiological survey, the incidence of NS is about 2-10/100000, and it mostly occurs in male children [2]. NS greatly affects people’s health and life quality with poor prognosis and high recurrence rate [3]. At present, the etiopathogenesis of NS is incompletely understood, but relevant reports have shown that it is related to inflammatory response and immune suppression.According to traditional Chinese medicine (TCM), NS belongs to “edema,” and the pathogenesis of NS lies in the dysfunction of the lung, spleen, and kidney [4]. TCM has been used to treat kidney disease and its complications, for that it can protect the kidney from dysfunction and delay the renal failure [5]. Guizhi is a Chinese herbal medicine that is commonly used to treat edema [6]. Currently, there are many reports on the pharmacological effects of Guizhi, but it is not comprehensive and systematic in the mechanism studies of Guizhi to treat NS.Network pharmacology integrates diseases and drugs into the biomolecular network, to predict the active components and the action mechanism [7]. As a new idea approach of TCM research, network pharmacology has been widely applied in the research of the complex network relationship between TCM and disease [8–10].In the present study, network pharmacology and molecular docking were used to explore the potential action mechanism of Guizhi to treat NS. It is hoped to provide theoretical foundation and scientific evidence for the clinical treatment of NS, and the workflow of our study is shown in Figure1.Figure 1
A flow diagram based on a cohesive integration strategy of network pharmacology and molecular docking.


## 2. Materials and Methods

### 2.1. Screening of Active Compounds

The active compounds of Guizhi were searched by TCMSP database (https://tcmspw.com/tcmsp.php). The PubChem ID, molecular formula, and canonical SMILES of each component were collected through PubChem database (https://pubchem.ncbi.nlm.nih.gov/). And the main active components were obtained by the screening criteria of oralbioavailabilityOB≥30%, drug−likeDL≥0.18, and cellpermeabilityCaco−2≥−0.4.


### 2.2. Predicting Drug Targets

The targets of active compounds of Guizhi were predicted by Hitpick (http://mips.helmholtz-muenchen.de/hitpick/cgi-bin/index.cgi?content=help.html), SEA (http://sea.bkslab.org/), and Swiss Target Prediction database (http://swisstargetprediction.ch/). The duplicates were deleted after the predicted targets of the 3 databases were merged.


### 2.3. Screening of Disease Targets

Using “Nephrotic syndrome” and “Adriamycin Nephropathy” as the keywords, the NS-related targets were collected from DisGeNET, GeneCards, and OMIM databases. After the removal of repeated targets, Venny2.1.0 was used to screening the intersection of drug targets and disease targets to obtain the potential targets of Guizhi in the treatment of NS.


### 2.4. Construction of Active Component-Target Network

The potential targets were imported into Cytoscape to construct the components-target-network.


### 2.5. Construction of PPI Network

The PPI network was constructed using the String database and Cytoscape software. In this process, the potential targets were input into the String database to obtain the protein interactions, and the interactions were visualized by Cytoscape software in a form of PPI network.


### 2.6. GO and KEGG Pathway Enrichment Analysis

DAVID database was used for GO and KEGG pathway enrichment analysis. The GO and KEGG enriched terms were collected for biological process (BP), cell component (CC), and molecular function (MF), at a cutoff ofP<0.05, and the corresponding bubble diagram were drawn.


### 2.7. Construction of Overall Network

The active compounds of Guizhi, the top 30 KEGG signaling pathways, and the corresponding targets were used to construct the drug-compound-target-pathway-disease network through Cytoscape.


### 2.8. Molecular Docking

The top 5 important targets with high network connection degrees were selected for molecular docking analysis using Autodock Vina. The smaller the binding energy (affinity) was, the more stable the interaction between the target protein and the active ingredient was.


## 2.1. Screening of Active Compounds

The active compounds of Guizhi were searched by TCMSP database (https://tcmspw.com/tcmsp.php). The PubChem ID, molecular formula, and canonical SMILES of each component were collected through PubChem database (https://pubchem.ncbi.nlm.nih.gov/). And the main active components were obtained by the screening criteria of oralbioavailabilityOB≥30%, drug−likeDL≥0.18, and cellpermeabilityCaco−2≥−0.4.


## 2.2. Predicting Drug Targets

The targets of active compounds of Guizhi were predicted by Hitpick (http://mips.helmholtz-muenchen.de/hitpick/cgi-bin/index.cgi?content=help.html), SEA (http://sea.bkslab.org/), and Swiss Target Prediction database (http://swisstargetprediction.ch/). The duplicates were deleted after the predicted targets of the 3 databases were merged.


## 2.3. Screening of Disease Targets

Using “Nephrotic syndrome” and “Adriamycin Nephropathy” as the keywords, the NS-related targets were collected from DisGeNET, GeneCards, and OMIM databases. After the removal of repeated targets, Venny2.1.0 was used to screening the intersection of drug targets and disease targets to obtain the potential targets of Guizhi in the treatment of NS.


## 2.4. Construction of Active Component-Target Network

The potential targets were imported into Cytoscape to construct the components-target-network.


## 2.5. Construction of PPI Network

The PPI network was constructed using the String database and Cytoscape software. In this process, the potential targets were input into the String database to obtain the protein interactions, and the interactions were visualized by Cytoscape software in a form of PPI network.


## 2.6. GO and KEGG Pathway Enrichment Analysis

DAVID database was used for GO and KEGG pathway enrichment analysis. The GO and KEGG enriched terms were collected for biological process (BP), cell component (CC), and molecular function (MF), at a cutoff ofP<0.05, and the corresponding bubble diagram were drawn.


## 2.7. Construction of Overall Network

The active compounds of Guizhi, the top 30 KEGG signaling pathways, and the corresponding targets were used to construct the drug-compound-target-pathway-disease network through Cytoscape.


## 2.8. Molecular Docking

The top 5 important targets with high network connection degrees were selected for molecular docking analysis using Autodock Vina. The smaller the binding energy (affinity) was, the more stable the interaction between the target protein and the active ingredient was.


## 3. Results

### 3.1. Active Compounds of Guizhi

A total of 220 compounds were collected from the TCMSP database, withOB≥30%, DL≥0.18, and Caco−2≥−0.4, and 7 active compounds were identified. Cinnamaldehyde, OB=31.99%, DL=0.02, and Caco−2=1.35, was not included in the results of TCMSP screening. However, our previous study found that cinnamaldehyde might be an important active compound of Guizhi [11]. Therefore, cinnamaldehyde was selected as a candidate active component in this study, as shown in Table 1.Table 1
The main active compounds of Guizhi.
Mol IDMolecule nameOB (%)DLCaco-2MOL000073Ent-Epicatechin48.960.240.02MOL000358Beta-sitosterol36.910.751.32MOL000359Sitosterol36.910.751.32MOL000492(+)-catechin54.830.24-0.03MOL000991Cinnamaldehyde31.990.021.35MOL001736(-)-taxifolin60.510.27-0.24MOL004576Taxifolin57.840.27-0.23MOL011169Peroxyergosterol44.390.820.86


### 3.2. Drug Target Prediction

8 active components of Guizhi were input into Hitpick, SEA, and Swiss Target Prediction databases. After combining the predicted targets from the 3 databases, the duplicates were deleted, and a total of 317 potential targets were selected.


### 3.3. Disease Target Prediction

The keywords “Nephrotic syndrome” and “Adriamycin Nephropathy” were searched in DisGeNET, GeneCards, and OMIM databases. A total of 2125 targets were identified. Venny 2.1.0 was used to intersect disease targets with drug targets. Finally, 93 potential targets (Figure2) were selected and further confirmed by UniProt database, as shown in Table 2.Figure 2
The Venny plot of 93 potential targets.Table 2
93 potential targets and UniProt information.
No.Gene namesProtein namesUniProt ID1ABCB1ATP-dependent translocase ABCB1P081832ABCC2ATP-binding cassette subfamily C member 2Q928873ABCG2Broad substrate specificity ATP-binding cassette transporter ABCG2Q9UNQ04ACEAngiotensin-converting enzymeP128215ACHEAcetylcholinesteraseP223036ACP1Low molecular weight phosphotyrosine protein phosphataseP246667AKR1B1Aldo-keto reductase family 1 member B1P151218AKR1B10Aldo-keto reductase family 1 member B10O602189ALKALK tyrosine kinase receptorQ9UM7310APPAmyloid-beta precursor proteinP0506711ARAndrogen receptorP1027512AVPR1AVasopressin V1a receptorP3728813CASRExtracellular calcium-sensing receptorP4118014CCR1C-C chemokine receptor type 1P3224615CD4T-cell surface glycoprotein CD4P0173016CDK4Cyclin-dependent kinase 4P1180217CTSBCathepsin BP0785818CTSLProcathepsin LP0771119CYP11B2Cytochrome P450 11B2P1909920CYP19A1Cytochrome P450 19A1P1151121CYP1A1Cytochrome P450 1A1P0479822CYP1B1Cytochrome P450 1B1Q1667823CYP2A6Cytochrome P450 2A6P1150924CYP2C19Cytochrome P450 2C19P3326125CYP3A4Cytochrome P450 3A4P0868426DNMT1DNA (cytosine-5)-methyltransferase 1P2635827DUSP1Dual specificity protein phosphatase 1P2856228ELANENeutrophil elastaseP0824629ELAVL1ELAV-like protein 1Q1571730ESR1Estrogen receptorP0337231ESR2Estrogen receptor betaQ9273132F10Coagulation factor XP0074233F2Coagulation factor IIP0073434F2RCoagulation factor II receptorP2511635F3Coagulation factor IIIP1372636FABP1Fatty acid-binding protein 1P0714837G6PDGlucose-6-phosphate 1-dehydrogenaseP1141338GCGroup-specific componentP0277439GLO1Glyoxalase IQ0476040HDAC2Histone deacetylase 2Q9276941HIF1AHypoxia-inducible factor 1-alphaQ1666542HMGCR3-Hydroxy-3-methylglutaryl-coenzyme A reductaseP0403543HSD11B1Corticosteroid 11-beta-dehydrogenase isozyme 1P2884544HSD11B2Corticosteroid 11-beta-dehydrogenase isozyme 2P8036545HTR2B5-Hydroxytryptamine receptor 2BP4159546ICAM1Intercellular adhesion molecule 1P0536247IGF1RInsulin-like growth factor 1 receptorP0806948ITGALIntegrin alpha-LP2070149ITGB2Integrin beta-2P0510750KAT2BHistone acetyltransferase KAT2BQ9283151KCNH2Potassium voltage-gated channel subfamily H member 2Q1280952KDRKinase insert domain receptorP3596853MAP2K1Dual specificity mitogen-activated protein kinase kinase 1Q0275054MAPK14Mitogen-activated protein kinase 14Q1653955MAPK3Mitogen-activated protein kinase 3P2736156MAPK8Mitogen-activated protein kinase 8P4598357MAPTMicrotubule-associated protein tauP1063658MCL1Induced myeloid leukemia cell differentiation protein Mcl-1Q0782059MDM2E3 ubiquitin-protein ligase Mdm2Q0098760METHepatocyte growth factor receptorP0858161MMP12Matrix metalloproteinase-12P3990062MPOMyeloperoxidaseP0516463NFE2L2Nuclear factor erythroid 2-related factor 2Q1623664NOS1Peptidyl-cysteine S-nitrosylase NOS1P2947565NOS2Peptidyl-cysteine S-nitrosylase NOS2P3522866NOS3NOS type IIIP2947467NR1I2Nuclear receptor subfamily 1 group I member 2O7546968NR3C1Nuclear receptor subfamily 3 group C member 1P0415069PARP1Poly [ADP-ribose] polymerase 1P0987470PLA2G1BPhosphatidylcholine 2-acylhydrolase 1BP0405471PLA2G7Platelet-activating factor acetylhydrolaseQ1309372PPARAPeroxisome proliferator-activated receptor alphaQ0786973PRKCBProtein kinase C beta typeP0577174PRKCDProtein kinase C delta typeQ0565575PSEN1Presenilin-1P4976876PTGS2Prostaglandin G/H synthase 2P3535477PTK2BProtein-tyrosine kinase 2-betaQ1428978PTPN11Tyrosine-protein phosphatase nonreceptor type 11Q0612479PTPN2Tyrosine-protein phosphatase nonreceptor type 2P1770680PTPRCReceptor-type tyrosine-protein phosphatase CP0857581RELATranscription factor p65Q0420682SLC6A2Sodium-dependent noradrenaline transporterP2397583SNCAAlpha-synucleinP3784084SRCProtooncogene tyrosine-protein kinase SrcP1293185TGM2Protein-glutamine gamma-glutamyltransferase 2P2198086TLR4Toll-like receptor 4O0020687TOP1DNA topoisomerase 1P1138788TRPA1Transient receptor potential cation channel subfamily A member 1O7576289TYMPThymidine phosphorylaseP1997190TYMSThymidylate synthaseP0481891VDRVitamin D3 receptorP1147392VEGFAVascular endothelial growth factor AP1569293XDHXanthine dehydrogenase/oxidaseP47989


### 3.4. Analysis of Active Component-Target Network

The 93 potential targets were analyzed by Cytoscape to construct the active component-target interaction network (Figure3). The result included 101 nodes and 169 edges. And different components indicated different targets. Among them, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were 37, 37, 36, and 29, respectively (Table 3), which might be the important active components in the network.Figure 3
The active component-target network. The network formed with 101 nodes and 169 edges. The dark purple circles represented active compounds; the light purple inverted triangles represented intersecting targets. The edges represented the connection between active component and targets.Table 3
Degree value of 8 main active components of Guizhi.
Mol IDMolecule nameDegreeMOL000358Beta-sitosterol37MOL000359Sitosterol37MOL000991Cinnamaldehyde36MOL011169Peroxyergosterol29MOL001736(-)-taxifolin12MOL004576Taxifolin12MOL000492(+)-catechin3MOL000073Ent-Epicatechin3


### 3.5. PPI Network Analysis

A total of 93 nodes and 1478 edges were involved in the PPI network (Figure4(a)). The bar chart of the top 10 target proteins was drawn based on the degree value (Figure 4(b)). Among them, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 degree values were 58, 52, 49, 43, and 42, respectively, which were the core nodes of the network, suggesting that Guizhi might play a significance role in the protection of NS through them.Figure 4
The PPI network diagram (a) and the bar graph of the top 10 intersecting targets with degree values in PPI network (b). In PPI network, nodes represented intersecting targets, and edges represented interactions between targets, and the size reflected the value of degree. In the bar graph, the top 10 targets were selected according to the degree value.
(a)(b)


### 3.6. GO and KEGG Analysis

93 potential targets were analyzed using DAVID 6.8, and the GO terms (BP, CC, and MF) and KEGG signaling pathway were selected. Targets in the BP were closely related to response to organic substance and positive regulation of molecular function and response to hormone stimulus. In the CC, Guizhi had great effect on cell surface, cell fraction, and plasma membrane part. At the MF level, drug components of Guizhi were mainly related to steroid binding, heme binding, and tetrapyrrole binding (Figure5(a)). A total of 71 KEGG pathways were mainly involved, including VEGF, Toll-like receptor, and MAPK signaling pathway (Figure 5(b)).Figure 5
Go (a) and KEGG enrichment analysis (b). The top 10 items of BP, CC, and MF and the top 30 KEGG signal pathways were selected according to theP value to draw the GO and KEGG bubble diagram. The color of the bubbles changes from purple to red indicating that the P value decreases from large to small. Gene ratio is the number of targets that located in the pathway. The higher the gene ratio is, the more targets were enriched.
(a)(b)


### 3.7. Overall Network Analysis

To further investigate the molecular mechanism of Guizhi against NS, overall network was constructed based on the top 30 significant KEGG signaling pathways and their corresponding targets (Figure6). 90 nodes (1 drug, 8 compound, 50 targets, 30 pathways, and 1disease) were contained in this network. Among these targets, MAPK3, MAPK8, MAPK14, VEGFA, and TLR4 were identified as high-degree targets, and in these pathways, Toll-like receptor, VEGF, and MAPK signaling pathways were the most important signaling pathways. Therefore, the network analysis suggests that the action mechanism of Guizhi to treat NS might be related to Toll-like receptor, VEGF, and MAPK signaling pathways.Figure 6
The overall network of the top 30 significant KEGG signaling pathways with their corresponding targets. The yellow represents Guizhi, pink represents active compounds, purple represents core targets, green represents signaling pathways, and dark green represents NS.


### 3.8. Molecular Docking

The top 5 targets, including VEGFA, MAPK3, SRC, PTGS2, and MAPK8 (Table4) in the PPI network were analyzed for the molecular docking with the active compounds of Guizhi. It was found that the top 5 targets had good binding affinity with the active components of Guizhi (Table 5; Figure 7). And the binding site of compounds-targets are shown in Figure 8.Table 4
The top 5 targets in PPI network.
No.Target namePDB IDDegree1VEGFA6BFT582MAPK32ZOQ523SRC6HTY494PTGS25IKV435MAPK84L7F42Table 5
Molecular docking results of targets and active components.
Target namePDB IDMolecule nameAffinity (kcal/Mol)VEGFA6BFTPeroxyergosterol-6.8Beta-sitosterol-6.4Sitosterol-6.3Ent-Epicatechin-5.9(-)-taxifolin-5.7Taxifolin-5.6(+)-catechin-5.4Cinnamaldehyde-4.9MAPK32ZOQ(-)-taxifolin-9.0Beta-sitosterol-8.7Taxifolin-8.5(+)-catechin-8.4Peroxyergosterol-8.1Ent-Epicatechin-7.9Sitosterol-7.3Cinnamaldehyde-5.4SRC6HTYPeroxyergosterol-8.7Beta-sitosterol-8.5Sitosterol-8.4(+)-catechin-8.1Taxifolin-8.1(-)-taxifolin-7.9Ent-Epicatechin-7.8Cinnamaldehyde-6.6PTGS25IKV(+)-catechin-7.6Ent-Epicatechin-7.5(-)-taxifolin-7.3Taxifolin-6.8Cinnamaldehyde-6.1Peroxyergosterol-3.8Beta-sitosterol-3.7Sitosterol-3.7MAPK84L7FBeta-sitosterol-9.8Sitosterol-9.8(-)-taxifolin-9.3(+)-catechin-9.1Taxifolin-8.7Ent-Epicatechin-8.5Peroxyergosterol-8.5Cinnamaldehyde-6.0Figure 7
The binding energy of the main active components of Guizhi and core targets.Figure 8
The molecular docking of active compounds and core targets: (a) cinnamaldehyde-PTGS2, (b) (-)-taxifolin-PTGS2, (c) (+)-catechin-PTGS2, (d) (-)-taxifolin-VEGFA, (e) (+)-catechin-SRC, (f) (+)-catechin-VEGFA, (g) ent-Epicatechin-PTGS2, (h) ent-Epicatechin-SRC, (i) peroxyergosterol-PTGS2, (j) taxifolin-PTGS2, (k) taxifolin-VEGFA, and (l) taxifolin-SRC.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 3.1. Active Compounds of Guizhi

A total of 220 compounds were collected from the TCMSP database, withOB≥30%, DL≥0.18, and Caco−2≥−0.4, and 7 active compounds were identified. Cinnamaldehyde, OB=31.99%, DL=0.02, and Caco−2=1.35, was not included in the results of TCMSP screening. However, our previous study found that cinnamaldehyde might be an important active compound of Guizhi [11]. Therefore, cinnamaldehyde was selected as a candidate active component in this study, as shown in Table 1.Table 1
The main active compounds of Guizhi.
Mol IDMolecule nameOB (%)DLCaco-2MOL000073Ent-Epicatechin48.960.240.02MOL000358Beta-sitosterol36.910.751.32MOL000359Sitosterol36.910.751.32MOL000492(+)-catechin54.830.24-0.03MOL000991Cinnamaldehyde31.990.021.35MOL001736(-)-taxifolin60.510.27-0.24MOL004576Taxifolin57.840.27-0.23MOL011169Peroxyergosterol44.390.820.86


## 3.2. Drug Target Prediction

8 active components of Guizhi were input into Hitpick, SEA, and Swiss Target Prediction databases. After combining the predicted targets from the 3 databases, the duplicates were deleted, and a total of 317 potential targets were selected.


## 3.3. Disease Target Prediction

The keywords “Nephrotic syndrome” and “Adriamycin Nephropathy” were searched in DisGeNET, GeneCards, and OMIM databases. A total of 2125 targets were identified. Venny 2.1.0 was used to intersect disease targets with drug targets. Finally, 93 potential targets (Figure2) were selected and further confirmed by UniProt database, as shown in Table 2.Figure 2
The Venny plot of 93 potential targets.Table 2
93 potential targets and UniProt information.
No.Gene namesProtein namesUniProt ID1ABCB1ATP-dependent translocase ABCB1P081832ABCC2ATP-binding cassette subfamily C member 2Q928873ABCG2Broad substrate specificity ATP-binding cassette transporter ABCG2Q9UNQ04ACEAngiotensin-converting enzymeP128215ACHEAcetylcholinesteraseP223036ACP1Low molecular weight phosphotyrosine protein phosphataseP246667AKR1B1Aldo-keto reductase family 1 member B1P151218AKR1B10Aldo-keto reductase family 1 member B10O602189ALKALK tyrosine kinase receptorQ9UM7310APPAmyloid-beta precursor proteinP0506711ARAndrogen receptorP1027512AVPR1AVasopressin V1a receptorP3728813CASRExtracellular calcium-sensing receptorP4118014CCR1C-C chemokine receptor type 1P3224615CD4T-cell surface glycoprotein CD4P0173016CDK4Cyclin-dependent kinase 4P1180217CTSBCathepsin BP0785818CTSLProcathepsin LP0771119CYP11B2Cytochrome P450 11B2P1909920CYP19A1Cytochrome P450 19A1P1151121CYP1A1Cytochrome P450 1A1P0479822CYP1B1Cytochrome P450 1B1Q1667823CYP2A6Cytochrome P450 2A6P1150924CYP2C19Cytochrome P450 2C19P3326125CYP3A4Cytochrome P450 3A4P0868426DNMT1DNA (cytosine-5)-methyltransferase 1P2635827DUSP1Dual specificity protein phosphatase 1P2856228ELANENeutrophil elastaseP0824629ELAVL1ELAV-like protein 1Q1571730ESR1Estrogen receptorP0337231ESR2Estrogen receptor betaQ9273132F10Coagulation factor XP0074233F2Coagulation factor IIP0073434F2RCoagulation factor II receptorP2511635F3Coagulation factor IIIP1372636FABP1Fatty acid-binding protein 1P0714837G6PDGlucose-6-phosphate 1-dehydrogenaseP1141338GCGroup-specific componentP0277439GLO1Glyoxalase IQ0476040HDAC2Histone deacetylase 2Q9276941HIF1AHypoxia-inducible factor 1-alphaQ1666542HMGCR3-Hydroxy-3-methylglutaryl-coenzyme A reductaseP0403543HSD11B1Corticosteroid 11-beta-dehydrogenase isozyme 1P2884544HSD11B2Corticosteroid 11-beta-dehydrogenase isozyme 2P8036545HTR2B5-Hydroxytryptamine receptor 2BP4159546ICAM1Intercellular adhesion molecule 1P0536247IGF1RInsulin-like growth factor 1 receptorP0806948ITGALIntegrin alpha-LP2070149ITGB2Integrin beta-2P0510750KAT2BHistone acetyltransferase KAT2BQ9283151KCNH2Potassium voltage-gated channel subfamily H member 2Q1280952KDRKinase insert domain receptorP3596853MAP2K1Dual specificity mitogen-activated protein kinase kinase 1Q0275054MAPK14Mitogen-activated protein kinase 14Q1653955MAPK3Mitogen-activated protein kinase 3P2736156MAPK8Mitogen-activated protein kinase 8P4598357MAPTMicrotubule-associated protein tauP1063658MCL1Induced myeloid leukemia cell differentiation protein Mcl-1Q0782059MDM2E3 ubiquitin-protein ligase Mdm2Q0098760METHepatocyte growth factor receptorP0858161MMP12Matrix metalloproteinase-12P3990062MPOMyeloperoxidaseP0516463NFE2L2Nuclear factor erythroid 2-related factor 2Q1623664NOS1Peptidyl-cysteine S-nitrosylase NOS1P2947565NOS2Peptidyl-cysteine S-nitrosylase NOS2P3522866NOS3NOS type IIIP2947467NR1I2Nuclear receptor subfamily 1 group I member 2O7546968NR3C1Nuclear receptor subfamily 3 group C member 1P0415069PARP1Poly [ADP-ribose] polymerase 1P0987470PLA2G1BPhosphatidylcholine 2-acylhydrolase 1BP0405471PLA2G7Platelet-activating factor acetylhydrolaseQ1309372PPARAPeroxisome proliferator-activated receptor alphaQ0786973PRKCBProtein kinase C beta typeP0577174PRKCDProtein kinase C delta typeQ0565575PSEN1Presenilin-1P4976876PTGS2Prostaglandin G/H synthase 2P3535477PTK2BProtein-tyrosine kinase 2-betaQ1428978PTPN11Tyrosine-protein phosphatase nonreceptor type 11Q0612479PTPN2Tyrosine-protein phosphatase nonreceptor type 2P1770680PTPRCReceptor-type tyrosine-protein phosphatase CP0857581RELATranscription factor p65Q0420682SLC6A2Sodium-dependent noradrenaline transporterP2397583SNCAAlpha-synucleinP3784084SRCProtooncogene tyrosine-protein kinase SrcP1293185TGM2Protein-glutamine gamma-glutamyltransferase 2P2198086TLR4Toll-like receptor 4O0020687TOP1DNA topoisomerase 1P1138788TRPA1Transient receptor potential cation channel subfamily A member 1O7576289TYMPThymidine phosphorylaseP1997190TYMSThymidylate synthaseP0481891VDRVitamin D3 receptorP1147392VEGFAVascular endothelial growth factor AP1569293XDHXanthine dehydrogenase/oxidaseP47989


## 3.4. Analysis of Active Component-Target Network

The 93 potential targets were analyzed by Cytoscape to construct the active component-target interaction network (Figure3). The result included 101 nodes and 169 edges. And different components indicated different targets. Among them, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were 37, 37, 36, and 29, respectively (Table 3), which might be the important active components in the network.Figure 3
The active component-target network. The network formed with 101 nodes and 169 edges. The dark purple circles represented active compounds; the light purple inverted triangles represented intersecting targets. The edges represented the connection between active component and targets.Table 3
Degree value of 8 main active components of Guizhi.
Mol IDMolecule nameDegreeMOL000358Beta-sitosterol37MOL000359Sitosterol37MOL000991Cinnamaldehyde36MOL011169Peroxyergosterol29MOL001736(-)-taxifolin12MOL004576Taxifolin12MOL000492(+)-catechin3MOL000073Ent-Epicatechin3


## 3.5. PPI Network Analysis

A total of 93 nodes and 1478 edges were involved in the PPI network (Figure4(a)). The bar chart of the top 10 target proteins was drawn based on the degree value (Figure 4(b)). Among them, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 degree values were 58, 52, 49, 43, and 42, respectively, which were the core nodes of the network, suggesting that Guizhi might play a significance role in the protection of NS through them.Figure 4
The PPI network diagram (a) and the bar graph of the top 10 intersecting targets with degree values in PPI network (b). In PPI network, nodes represented intersecting targets, and edges represented interactions between targets, and the size reflected the value of degree. In the bar graph, the top 10 targets were selected according to the degree value.
(a)(b)


## 3.6. GO and KEGG Analysis

93 potential targets were analyzed using DAVID 6.8, and the GO terms (BP, CC, and MF) and KEGG signaling pathway were selected. Targets in the BP were closely related to response to organic substance and positive regulation of molecular function and response to hormone stimulus. In the CC, Guizhi had great effect on cell surface, cell fraction, and plasma membrane part. At the MF level, drug components of Guizhi were mainly related to steroid binding, heme binding, and tetrapyrrole binding (Figure5(a)). A total of 71 KEGG pathways were mainly involved, including VEGF, Toll-like receptor, and MAPK signaling pathway (Figure 5(b)).Figure 5
Go (a) and KEGG enrichment analysis (b). The top 10 items of BP, CC, and MF and the top 30 KEGG signal pathways were selected according to theP value to draw the GO and KEGG bubble diagram. The color of the bubbles changes from purple to red indicating that the P value decreases from large to small. Gene ratio is the number of targets that located in the pathway. The higher the gene ratio is, the more targets were enriched.
(a)(b)


## 3.7. Overall Network Analysis

To further investigate the molecular mechanism of Guizhi against NS, overall network was constructed based on the top 30 significant KEGG signaling pathways and their corresponding targets (Figure6). 90 nodes (1 drug, 8 compound, 50 targets, 30 pathways, and 1disease) were contained in this network. Among these targets, MAPK3, MAPK8, MAPK14, VEGFA, and TLR4 were identified as high-degree targets, and in these pathways, Toll-like receptor, VEGF, and MAPK signaling pathways were the most important signaling pathways. Therefore, the network analysis suggests that the action mechanism of Guizhi to treat NS might be related to Toll-like receptor, VEGF, and MAPK signaling pathways.Figure 6
The overall network of the top 30 significant KEGG signaling pathways with their corresponding targets. The yellow represents Guizhi, pink represents active compounds, purple represents core targets, green represents signaling pathways, and dark green represents NS.


## 3.8. Molecular Docking

The top 5 targets, including VEGFA, MAPK3, SRC, PTGS2, and MAPK8 (Table4) in the PPI network were analyzed for the molecular docking with the active compounds of Guizhi. It was found that the top 5 targets had good binding affinity with the active components of Guizhi (Table 5; Figure 7). And the binding site of compounds-targets are shown in Figure 8.Table 4
The top 5 targets in PPI network.
No.Target namePDB IDDegree1VEGFA6BFT582MAPK32ZOQ523SRC6HTY494PTGS25IKV435MAPK84L7F42Table 5
Molecular docking results of targets and active components.
Target namePDB IDMolecule nameAffinity (kcal/Mol)VEGFA6BFTPeroxyergosterol-6.8Beta-sitosterol-6.4Sitosterol-6.3Ent-Epicatechin-5.9(-)-taxifolin-5.7Taxifolin-5.6(+)-catechin-5.4Cinnamaldehyde-4.9MAPK32ZOQ(-)-taxifolin-9.0Beta-sitosterol-8.7Taxifolin-8.5(+)-catechin-8.4Peroxyergosterol-8.1Ent-Epicatechin-7.9Sitosterol-7.3Cinnamaldehyde-5.4SRC6HTYPeroxyergosterol-8.7Beta-sitosterol-8.5Sitosterol-8.4(+)-catechin-8.1Taxifolin-8.1(-)-taxifolin-7.9Ent-Epicatechin-7.8Cinnamaldehyde-6.6PTGS25IKV(+)-catechin-7.6Ent-Epicatechin-7.5(-)-taxifolin-7.3Taxifolin-6.8Cinnamaldehyde-6.1Peroxyergosterol-3.8Beta-sitosterol-3.7Sitosterol-3.7MAPK84L7FBeta-sitosterol-9.8Sitosterol-9.8(-)-taxifolin-9.3(+)-catechin-9.1Taxifolin-8.7Ent-Epicatechin-8.5Peroxyergosterol-8.5Cinnamaldehyde-6.0Figure 7
The binding energy of the main active components of Guizhi and core targets.Figure 8
The molecular docking of active compounds and core targets: (a) cinnamaldehyde-PTGS2, (b) (-)-taxifolin-PTGS2, (c) (+)-catechin-PTGS2, (d) (-)-taxifolin-VEGFA, (e) (+)-catechin-SRC, (f) (+)-catechin-VEGFA, (g) ent-Epicatechin-PTGS2, (h) ent-Epicatechin-SRC, (i) peroxyergosterol-PTGS2, (j) taxifolin-PTGS2, (k) taxifolin-VEGFA, and (l) taxifolin-SRC.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 4. Discussion

NS is a common glomerular disease caused by various etiologies and is the second most common kidney disease after acute glomerulonephritis. At present, Western medicine mainly focuses on glucocorticoids, cytotoxic drugs, and immunosuppressants for NS [1], such as glucocorticoids, cyclophosphamide, and cyclosporine, which can achieve certain efficacy. However, hormone therapy is prone to infection, hormone resistance [12], withdrawal, and relapse [3], which eventually lead to chronic terminal renal failure [13, 14]. Therefore, to explore the pathogenesis of NS and to find safe and effective treatment drugs are urgent problems [15].Wulingsan is a classic prescription for NS, which has obvious advantages in improving urinary system diseases. Guizhi is an important component of Wulingsan, which has the pharmacological activities of diuresis, improving blood circulation and dilating blood vessels. Our preliminary study found that Guizhi is indeed an important drug of Wulingsan with the protective effect on rats with adriamycin-induced nephropathy [11]. However, the action mechanism remains unknown.In order to further explore the potential mechanism of Guizhi in treating NS and provide more evidence for clinical treatment, the main active components and targets of Guizhi, as well as the possible signaling pathways of Guizhi to treat NS, were predicted through network pharmacology and molecular docking.In the active component-target network, the degree values of beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were much higher than those of other components, which were 37, 37, 36, and 29, respectively, suggesting that they were the key active components in the treatment of NS.Among them, beta-sitosterol and sitosterol belong to plant sterols. Studies have found that beta-sitosterol has antihyperlipemia, anti-inflammatory, and immunomodulatory effects and can treat cholesterol, proteinuria, and edema [16]. Cinnamaldehyde is an organic compound of olefine aldehyde, and it is the main component of Guizhi that plays a diuretic role. Pharmacological studies have shown that cinnamaldehyde has a variety of pharmacological activity of anti-inflammatory [17], antitumor [18], hypotensive [19], lipid-lowering [20], hypoglycemic [21], and vascular endothelial protection [22], that is playing a protective role on kidney in various aspects. Our previous study also found that cinnamaldehyde had a protective effect on renal function in adriamycin nephropathy rat [11]. Peroxyergosterol is a kind of relatively rare sterol, with antioxidant [23], antibacterial [24], immunosuppressive [25], antitumor [26], and other activities and can repair damaged kidney cells through antioxidant action. In conclusion, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol might be the main pharmacodynamic basis of Guizhi against NS.In the PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 had higher degrees than others, indicating that Guizhi might achieve the protective effect on the kidney through the above targets.VEGFA is a receptor in vascular endothelial cells that can induce endothelial cell differentiation and proliferation [27]. It is an important molecule that maintains the function of the glomerular filtration barrier and plays an important role in renal microangiogenesis. The study has shown that the upregulation of VEGF expression is closely related to the occurrence of proteinuria [28]. Its signal conduction runs through the whole life process of podocytes and glomerular vascular endothelial cells. MAPK3 and MAPK8 are both mitogen-activated protein kinases that can mediate the progress of differentiation, proliferation, and apoptosis [29, 30]. SRC is a nonreceptor protein with tyrosine protein kinase activity and plays an important role in mitosis and proliferation in normal cell [31, 32]. SRC can participate in the process of cell differentiation, proliferation, and apoptosis through the MAPK signaling pathway and plays a crucial role in the inflammation and autoimmune diseases. PTGS2 is significant in inhibiting the development of excessive fibrosis and inflammatory response. When cells are stimulated, the expression of PTGS2 is rapidly upregulated, which catalyzes arachidonic acid to produce a variety of prostaglandins and produces anti-inflammatory and antifibrotic effects, thus achieving the protective effect on the kidney [33]. Therefore, the targets of VEGFA, MAPK3, SRC, PTGS2, and MAPK8 might play an essential role in the protective effect of Guizhi against NS.In KEGG and overall network analysis, the key targets were mainly involved in VEGF, Toll-like receptor, and MAPK signaling pathway, which were highly correlated with renal disease, suggesting that the action mechanism of Guizhi to treat NS might be related to VEGF, Toll-like receptor, and MAPK signaling pathways.VEGF signaling pathway plays an irreplaceable role in the whole process of angiogenesis and is directly related to the occurrence of hypertension. When the endothelial function of patients with hypertensive is impaired, blood pressure will significantly increase, and proteinuria is aggravated. Studies have found that the inhibition of VEGF signaling pathway can lead to large amounts of proteinuria accompanied by irreversible renal impairment [34]. In recent years, the role of the innate immune Toll-like receptor (TLR) in kidney disease has attracted more and more attention [35] and is involved in the innate immune and inflammatory response [36]. Studies have shown that TLR can be expressed in a small amount in renal mesangial cells, renal tubular epithelial cells, and podocytes [37]. The increase of TLR expression can cause the pathophysiological changes such as the upregulation of inflammatory factors and cell apoptosis [38]. MAPK signaling pathway is a stress pathway of cellular functional activities, plays a vital role in acute and chronic inflammation, and participates in physiological processes such as cell growth, development, differentiation, and apoptosis [39]. Activation of MAPK signaling pathway can not only trigger renal inflammation [40] but also lead to podocyte injury [41]. Studies have shown that MAPK signaling pathway can cause proteinuria and glomerulosclerosis through oxidative stress, triggering inflammatory factors and activating inflammatory pathways [42]. Munkonda et al. found that MAPK can also promote proximal tubule fibrosis, thus mediating the development of renal disease [43].Based on the network pharmacology results, molecular docking was performed to verify the interactions between the 8 active components of Guizhi and the key targets. The docking results showed that all the compounds had good binding activities with the targets, indicating that they may play an important role in Guizhi to treat NS.


## 5. Conclusion

In conclusion, the potential mechanism of Guizhi to treat NS was predicted based on the network pharmacology and molecular docking. The results indicated that the underlying mechanism of Guizhi in treating NS may be related to VEGF, Toll-like receptor, and MAPK signaling pathway. However, the transmission of each signal pathway is complicated, and the final mechanism needs to be further verified by subsequent experiments.



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*Source: 8141075-2021-11-27.xml*