# Combined Network Pharmacology and Molecular Docking to Verify the Treatment of Type 2 Diabetes withPueraria Lobata Radix and Salviae Miltiorrhizae Radix

Authors: Jingxin Mao; Guowei Wang; Lin Yang; Lihong Tan; Cheng Tian; Lijing Tang; Ling Fang; Zhenqiang Mu; Zhaojing Zhu; Yan Li
Journal: Computational and Mathematical Methods in Medicine
 (2023)

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## Abstract

Objective. To explore the potential molecular mechanism of Pueraria Lobata Radix (RP) and Salviae Miltiorrhizae Radix (RS) in the treatment of type 2 diabetes mellitus (T2DM) based on network pharmacology and molecular docking. Methods. The chemical constituents and core targets of RP and RS were searched by Traditional Chinese Medicine System Pharmacology (TCMSP); target genes related to T2DM were obtained through GeneCards database, component target network diagram was constructed, intersection genes of active compounds and T2DM were synthesized, protein-protein interaction (PPI) relationship was obtained, and core targets were screened by using Cytoscape 3.7.2. Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed utilizing R studio 4.0.4 according to David database. Based on molecular docking, the screened active components of RP and RS were verified by molecular docking with the core target using Discovery Studio 2019. Results. There were totally 92 components and 29 corresponding targets in the component target network of RP and RS drug pair, of which 6 were the core targets of RP and RS in the treatment of T2DM. Molecular docking results showed that the active compounds of puerarin, formononetin, tanshinone iia, and luteolin had better binding activity with AKT1, VEGFA, NOS3, PPARG, MMP9, and VCAM1, respectively. Among them, puerarin showed significant effects in activating NOS3 pathway and luteolin exhibited significant effects in activating MMP9 pathway, respectively. The main biological processes mainly including xenobiotic stimulus, response to peptide, gland development, response to radiation, cellular response to chemical stress, response to oxygen levels, and the main signal pathways include response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, and response to oxygen levels. Conclusion. Network pharmacology is an effective tool to explain the action mechanism of Traditional Chinese Medicine (TCM) from the overall perspective. RP and RS pair could alleviate T2DM via the molecular mechanism predicted by the network pharmacology, which provided new ideas and further research on the molecular mechanism of T2DM.


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## Body

## 1. Introduction

Diabetes mellitus (DM) is a group of metabolic diseases characterized by increased blood glucose due to genetic and environmental effects, resulting in defective insulin secretion and reduced sensitivity of target cells to insulin. Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, mainly associated with the accumulation of lipids in the insulinβ-cells can lead to elevated blood glucose levels and abnormal glucose tolerance due to the dysfunction of the pancreatic β-cells, and the clinical symptoms are persistent hyperglycemia. In recent decades, the prevalence of T2DM has gradually increased worldwide. The prevalence of DM in the world has increased by 102.9% from 1990 to 2010 and is the country with the largest number of diabetic patients in the world [1]. T2DM is a common clinical endocrine and metabolic disease, and the prevalence of DM in Chinese population is about 11.6%, and the prevalence of pre-DM is 50.1% [2], which is a chronic metabolic disease that seriously affects human health.The important cause of T2DM is the disorder of lipid metabolism, therefore, it is especially important to control blood sugar and regulate blood lipid in the process of T2DM treatment in time [3]. At present, the main methods of T2DM drug treatment are subcutaneous insulin injection and oral hypoglycemic drugs (such as double pulse, sulfonyl artery, and a glyoxylase inhibitors); but with the development of the disease, single-target development of drugs is difficult to treat T2DM and prevent the occurrence of complications such as glucose disorders and gastrointestinal tract, thus reducing the quality of life of patients [4].Traditional Chinese Medicine (TCM) treatment of T2DM is based on single-target superposition and, multicomponent and multitarget synergistic toxicity dispersion effect to achieve better efficacy and lower toxicity group, which is a complex theoretical system to achieve multidimensional regulation from a whole [5]. DM belongs to the category of “thirst disorders” in TCM, and the main treatment of DM in TCM is to clear heat and moisten dryness, and to nourish yin and produce fluid [6]. After the TCM, doctors have formulated prescriptions according to the above treatment rules, the overall regulating advantages of TCM, which can play a role in treating both the symptoms and the root cause, and TCM is milder and more durable than western medicine, with fewer side effects [7].Pueraria Lobata Radix (RP) and Salviae Miltiorrhizae Radix (RS) are a common pair of medicine contained in the famous Chinese medicine book “Shi Jinmo on Medicine.” RP is the dried root of Pueraria lobata (Willd.) Ohwi, which has the functions of quenching thirst, raising yang, relieving diarrhea, relieving muscle and fever, and activating meridians [8]. RS is also called “DanShen” that derived from the dried root and rhizome of Salvia miltiorrhiza Bunge, which has the effects of activating blood circulation, cooling the blood and clearing the heart, and removing irritation and calming the mind [9]. The combination of RP and RS exhibits the effect of promoting “qi,” resolving blood stasis, promoting blood circulation, and relieving pain, and the two are used as a pair to treat diabetes [10, 11]. Modern medicinal chemistry and pharmacology research shows that the main chemical components of RP are isoflavones, triterpenoids, flavonoids, coumarins, etc., which have pharmacological effects such as hypoglycemic and hypolipidemic, anti-inflammatory and antioxidant, and hepatoprotective [12]. The main chemical components of RS mainly including tanshinones, tanshin acids, and volatile oils, which have antioxidant, anti-inflammatory, and antithrombotic pharmacological effects [13].Previous studies have been demonstrated thatPueraria Lobata Radix and Salviae Miltiorrhizae Radix (RP-RS) paired exhibits varies effects on T2DM or diabetes related diseases [10, 14–17]. However, the research on effective compounds of RP-RS is not in-depth, and the analytical methods are not comprehensive in these studies. Therefore, based on the idea of multicomponent and multitarget research, the present study was conducted to predict the mechanism of action and targets of RP-RS drug for the treatment of T2DM through network pharmacology and molecular docking methods, to find the active chemical components, disease therapeutic targets and signaling pathways, and to provide a scientific basis for the experimental research and clinical application of RP-RS drug for the treatment of T2DM.


## 2. Materials and Methods

### 2.1. Screening of RP and RS for Active Components and Potential Targets

The chemical components of RP and RS were obtained with the help of the Traditional Chinese Medicine System Pharmacology Analysis Platform (TCMSP,http://temspw.com/temsp.php), according to the set the bioavailability OB>20 and drug-like properties DL>0.18, screening out eligible active ingredients and related targets.


### 2.2. T2DM-Related Target Prediction

Through the GeneCards (http://www.genecards.org/) database, with “Type 2 diabetes mellitus or T2DM” as the search term, the targets related to T2DM were obtained, and the correlation value (relevance score>18) was used as the screening condition, and the screening results were used as candidate target genes of T2DM.


### 2.3. Construction of Protein Interaction Network (PPI)

The effect of drugs on disease treatment is ultimately manifested in protein interactions. The Venn diagram (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to obtain the intersection genes of the active ingredient target and the T2DM target, and imported into STRING11.0 (http://www.string-db.org/) to obtain the protein interaction relationship. Using Cytoscape 3.7.2 draws the interaction network, analyzes the key targets by degree value, and constructs the interaction network of RP-RS in the treatment of T2DM.


### 2.4. Construction of Chemical Components-Target Network of TCM

The screened candidate compounds of RP-RS that predicted component target genes were imported into Cytoscape 3.7.2 software to construct a compound-target network, and the main active ingredients/components of RP and RS were analyzed, respectively, according to the degree value in the network.


### 2.5. Analysis of Biological Function and Pathway Enrichment of RP-RS Pair in the Treatment of T2DM

GO enrichment is a commonly used method for analysis of omic data, which is usually used to discover the enrichment degree of GO term in differential genes. Through GO enrichment analysis, the genes in the difference table can be classified according to their functions to achieve the purpose of annotation and classification of genes [18]. KEGG is a practical program database for understanding advanced functions and biological systems (such as cells, organisms, and ecosystems) from molecular level information, especially genome sequencing and other high-throughput experimental technologies generated from large molecular data sets. It can predict the role of PPI networks in various cell activities [19]. Based on the David database (https://david.ncifcrf.gov/), the targets of RP-RS for the treatment of T2DM were collected and imported into R studio 4.0.4 (The species was limited to “Human”). GO functional annotation and KEGG pathway enrichment analysis were performed on the intersection target genes of RP-RS and T2DM, respectively (P<0.05). Potential targets were analyzed for biological process (BP), cellular component (CC), and molecular function (MF), and KEGG was used for pathway enrichment analysis of potential targets. To validate the anti-T2DM mechanism of RP-RS across the key targets and multiple pathways, the KEGG mapper functional analysis was used to mark the target genes on the pathway associated with T2DM.


### 2.6. Molecular Docking Prediction of Key Targets of Active Ingredient Intervention in the Treatment of T2DM by RP-RS

According to the analysis of network pharmacology results, molecular docking software was used to predict the key targets of RP-RS intervention on the main active components. The 3D structure of the compound was constructed using Chem 3D of ChemOffice software and saved in.mol2 format. Download the protein structure of the target from the PDB (https://www.rcsb.org/) database, use PyMOL 2.5 to perform protein and ligand separation, dehydrogenation, water addition, and other operations on the original PDB protein molecule, and use AutoDock 1.5.7 software. The above compound and protein formats were converted to pdbqt format using Discovery Studio 2019 software for molecular docking and mapping of compounds and core targets.


## 2.1. Screening of RP and RS for Active Components and Potential Targets

The chemical components of RP and RS were obtained with the help of the Traditional Chinese Medicine System Pharmacology Analysis Platform (TCMSP,http://temspw.com/temsp.php), according to the set the bioavailability OB>20 and drug-like properties DL>0.18, screening out eligible active ingredients and related targets.


## 2.2. T2DM-Related Target Prediction

Through the GeneCards (http://www.genecards.org/) database, with “Type 2 diabetes mellitus or T2DM” as the search term, the targets related to T2DM were obtained, and the correlation value (relevance score>18) was used as the screening condition, and the screening results were used as candidate target genes of T2DM.


## 2.3. Construction of Protein Interaction Network (PPI)

The effect of drugs on disease treatment is ultimately manifested in protein interactions. The Venn diagram (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to obtain the intersection genes of the active ingredient target and the T2DM target, and imported into STRING11.0 (http://www.string-db.org/) to obtain the protein interaction relationship. Using Cytoscape 3.7.2 draws the interaction network, analyzes the key targets by degree value, and constructs the interaction network of RP-RS in the treatment of T2DM.


## 2.4. Construction of Chemical Components-Target Network of TCM

The screened candidate compounds of RP-RS that predicted component target genes were imported into Cytoscape 3.7.2 software to construct a compound-target network, and the main active ingredients/components of RP and RS were analyzed, respectively, according to the degree value in the network.


## 2.5. Analysis of Biological Function and Pathway Enrichment of RP-RS Pair in the Treatment of T2DM

GO enrichment is a commonly used method for analysis of omic data, which is usually used to discover the enrichment degree of GO term in differential genes. Through GO enrichment analysis, the genes in the difference table can be classified according to their functions to achieve the purpose of annotation and classification of genes [18]. KEGG is a practical program database for understanding advanced functions and biological systems (such as cells, organisms, and ecosystems) from molecular level information, especially genome sequencing and other high-throughput experimental technologies generated from large molecular data sets. It can predict the role of PPI networks in various cell activities [19]. Based on the David database (https://david.ncifcrf.gov/), the targets of RP-RS for the treatment of T2DM were collected and imported into R studio 4.0.4 (The species was limited to “Human”). GO functional annotation and KEGG pathway enrichment analysis were performed on the intersection target genes of RP-RS and T2DM, respectively (P<0.05). Potential targets were analyzed for biological process (BP), cellular component (CC), and molecular function (MF), and KEGG was used for pathway enrichment analysis of potential targets. To validate the anti-T2DM mechanism of RP-RS across the key targets and multiple pathways, the KEGG mapper functional analysis was used to mark the target genes on the pathway associated with T2DM.


## 2.6. Molecular Docking Prediction of Key Targets of Active Ingredient Intervention in the Treatment of T2DM by RP-RS

According to the analysis of network pharmacology results, molecular docking software was used to predict the key targets of RP-RS intervention on the main active components. The 3D structure of the compound was constructed using Chem 3D of ChemOffice software and saved in.mol2 format. Download the protein structure of the target from the PDB (https://www.rcsb.org/) database, use PyMOL 2.5 to perform protein and ligand separation, dehydrogenation, water addition, and other operations on the original PDB protein molecule, and use AutoDock 1.5.7 software. The above compound and protein formats were converted to pdbqt format using Discovery Studio 2019 software for molecular docking and mapping of compounds and core targets.


## 3. Results

### 3.1. Screening of Active Compounds from RP-RS

Taking OB and DL properties as screening criteria, qualified compounds were screened from TCMSP database. After setting the screening conditions, 7 and 85 chemical components of RP and RS were obtained, respectively (Table1).Table 1
A total of possible active ingredient information of RP and RS. (a) Intersection genes between T2DM and RS. (b) Intersection genes between T2DM and RP. (c) Intersection genes among T2DM, RP, and RS.
MOL_IDClassificationMolecule_nameObMwDlMOL000358RPBeta-sitosterol36.9139058327414.7900.75123MOL012297RPPuerarin24.0308979964416.4100.69099MOL002959RP3′-Methoxydaidzein48.5690937439284.2800.24261MOL004631RP7,8,4′-Trihydroxyisoflavone20.6680736276270.2500.21583MOL000392RPFormononetin69.6738806088268.2800.21202MOL000390RPDaidzein19.4410626585254.2500.18694MOL007140RS(Z)-3-[2-[(E)-2-(3,4-dihydroxyphenyl)vinyl]-3,4-dihydroxy-phenyl]acrylic acid88.5360210103314.3100.25869MOL007150RS(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-quinone75.3858784662312.3400.4551MOL007058RSFormyltanshinone73.4446220045290.2800.41736MOL007120RSMiltionone II71.0297032063312.3900.43711MOL007105RSEpidanshenspiroketallactone68.2731592943284.3800.30549MOL007155RS(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione65.25893771310.3700.44871MOL007130RSProlithospermic acid64.3709620672314.3100.31017MOL007050RS2-(4-hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.7841472598356.4000.39628MOL007068RSPrzewaquinone B62.2400596208292.3000.41374MOL000569RSDigallate61.8486180263322.2400.25635MOL007081RSDanshenol B57.950875299354.4800.55764MOL007082RSDanshenol A56.9652489923336.4100.52172MOL007069RSPrzewaquinone c55.7416730964296.3400.40408MOL007108RSIsocryptotanshi-none54.9819324596296.3900.39449MOL007125RSNeocryptotanshinone52.4879970089314.4100.32306MOL007079RSTanshinaldehyde52.4747043036308.3500.45196MOL007088RSCryptotanshinone52.3419622629296.3900.39555MOL007094RSDanshenspiroketallactone50.4312810266282.3600.3067MOL007111RSIsotanshinone II49.9160257407294.3700.39674MOL007154RSTanshinone iia49.8873000352294.3700.39781MOL007119RSMiltionone I49.6843943314312.3900.32125MOL007098RSDeoxyneocryptotanshinone49.4003470541298.4100.28555MOL007048RS(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.2436324414312.2900.31229MOL007156RSTanshinone VI45.6373060194296.3400.29549MOL007141RSSalvianolic acid g45.5648557799340.3000.60602MOL001942RSIsoimperatorin45.4642467387270.3000.22524MOL007115RSManool45.0443163606304.5700.20208MOL007101RSDihydrotanshinone I45.0432791888278.3200.36015MOL007123RSMiltirone II44.9510664818272.3200.23537MOL007045RS3α-hydroxytanshinone II A44.92933597310.3700.44272MOL001659RSPoriferasterol43.8298515785412.7700.75596MOL002651RSDehydrotanshinone II A43.7622859945292.3500.40019MOL007077RSSclareol43.6706845842308.5600.2058MOL007152RSPrzewaquinone E42.8548520397312.3400.45301MOL007151RSTanshindiol B42.6658104891312.3400.45303MOL007070RS(6S,7R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione41.3104570553312.3400.453MOL007041RS2-isopropyl-8-methylphenanthrene-3,4-dione40.8601540814264.3400.22897MOL007071RSPrzewaquinone F40.3078839947312.3400.45925MOL002776RSBaicalin40.1236099599446.3900.75264MOL007118RSMicrostegiol39.6122945749298.4600.27734MOL006824RSα-Amyrin39.5120897831426.8000.76221MOL007124RSNeocryptotanshinone II39.4629911416270.3500.23157MOL007093RSDan-shexinkum D38.8830210096336.4100.55453MOL007122RSMiltirone38.7569863502282.4100.25418MOL001601RS1,2,5,6-Tetrahydrotanshinone38.7453867227280.3400.35791MOL007100RSDihydrotanshinlactone38.6847682981266.3100.32227MOL007063RSPrzewalskin A37.1065006596398.4900.64901MOL007061RSMethylenetanshinquinone37.07319368278.3200.36017MOL001771RSPoriferast-5-en-3beta-ol36.9139058327414.7900.75034MOL007121RSMiltipolone36.5561120639300.4300.36803MOL000006RSLuteolin36.1626293429286.2500.24552MOL002222RSSugiol36.113534855300.4800.27648MOL007107RSC0909236.069489858286.5000.2474MOL007127RS1-methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trione34.7208221315280.2900.36634MOL007149RSNSC 12242134.4929230859300.4800.27645MOL007049RS4-Methylenemiltirone34.348675885266.3600.22726MOL007036RS5,6-dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.7652523626298.4100.28585MOL007143RSSalvilenone I32.4347085631270.4000.22895MOL007059RS3-beta-Hydroxymethyllenetanshiquinone32.1610337648294.3200.40894MOL007145RSSalviolone31.7241503895268.3800.23568MOL007085RSSalvilenone30.3836538737292.4000.37639


### 3.2. Intersection Genes of RP-RS with T2DM

By database analysis, 356, 159, and 99 targets were identified for T2DM, RP, and RS, respectively. The online Venn diagram analysis showed that there were 28 intersecting genes between the active ingredient target of RS and T2DM (Figure1(a)), 10 intersecting genes between the active ingredient target of RP and T2DM (Figure 1(b)), and 8 intersecting genes among RP, RS, and T2DM (Figure 1(c)), which were the main potential targets of RP-RS for the treatment of T2DM.Figure 1
Venn diagram of intersection genes of active ingredient related targets of RP and RS with anti-T2DM targets.
(a)(b)(c)


### 3.3. Protein Interaction PPI Network Construction

In this study, we analyzed the interaction between the targets of RP and RS for the treatment of T2DM based on the STRING database, and constructed the target interaction network (Figures2(a) and 2(b)) by importing compound-disease shared genes into STRING with the interaction confidence setting, medium confidence (0.4), and performed the network topology analysis. The top 6 nodes in the interaction network were AKT1 (26), VEGFA (25), NOS3 (24), PPARG (22), MMP9 (22), and VCAM1 (20), (Table 2, Figure 2(b)), which may play an important role in the treatment of T2DM with RS. These 6 key targets may play an important role in the treatment of T2DM with RP.Figure 2
Active ingredients of RP and RS with anti-T2DM target network.
(a)(b)Table 2
Main ingredients from RP and RS based on number of corresponding targets. (a) PPI visualization network diagram of 120 targets; (b) PPI visualization network diagram of 29 core targets.
MOL_IDMolecule_nameClassificationObMwDlMOL012297PuerarinRP24.0308979964416.4100.69099MOL000392FormononetinRP69.6738806088268.2800.21202MOL007154Tanshinone iiaRS49.8873000352294.3700.39781MOL000006LuteolinRS36.1626293429286.2500.24552


### 3.4. Compound-Target Interaction Network

The network diagram visually reflects the interaction between compounds and targets, the nodes represent compounds, and the edges represent the relationship between compounds and targets, which can reflect the synergistic superposition of multicomponent and multitarget in Chinese medicine. The top 4 compounds in terms of degree value were MOL000006-luteolin (degreevalue=54), MOL012297-puerarin (degree value=53), MOL07154-tanshinone iia (degree value=40), and MOL000392-formononetin (degree value=32), respectively (Table 3, Figure 3).Table 3
Corresponding core targets genes of main ingredient based on the degree value.
Gene nameDegreeBetweenness centralityCloseness centralityAKT1260.097220440.93333333VEGFA250.082972520.90322581NOS3240.07483130.875PPARG220.060899270.82352941MMP9220.046166440.82352941VCAM1200.042754450.77777778CAT200.03174740.77777778STAT3190.02241570.75675676IL4180.024620190.73684211HIF1A160.007004570.7CTNNB1160.011648710.7ESR1150.013814350.68292683TGFB1150.00434850.68292683SIRT1140.005830730.66666667GPT130.007908260.65116279NOS2130.002401880.65116279SOD1100.002545870.60869565AGTR1100.004724110.60869565DPP4100.004594920.60869565AR90.001763670.58333333SELP90.002626610.59574468CD40LG700.57142857AKR1B177.94E-040.57142857ACE276.42E-040.57142857CYP19A165.93E-040.56LEPR61.89E-040.56PLAT500.54901961PON152.04E-040.53846154ADRB252.94E-040.54901961Figure 3
Protein interaction network of RP and RS with anti-T2DM.


### 3.5. Core Target Pathway Analysis

A total of 2015 BPs, 68 CCs, and 171 MFs of key targets were obtained by GO functional annotation, respectively (P<0.05) (Figure 4). BP mainly involved response to xenobiotic stimulus, response to peptide, gland development, response to radiation, cellular response to chemical stress, response to oxygen levels, response to metal ion, response to decreased oxygen levels, response to hypoxia, and response to UV. CC is mainly membrane raft, membrane microdomain, transcription requlator complex, postsynaptic membrane, protein kinase compley, caveola, serine/threonine protein kinase complex, plasma membrane raft, cyclin-dependent protein kinase holoenzyme complex, and integral component of presynaptic membrane. MF mainly involves DNA-binding transcription factor binding, RNA polymerase I-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, ubiquitin protein ligase binding, phosphatase binding, kinase regulator activity, protein phosphatase binding, cyclin-dependent protein serine/threonine kinase regulator activity, G protein-coupled amine receptor activity, and G protein-coupled neurotransmitter receptor activity. The KEGG analysis results showed that a total of 136 signaling pathways were enriched (Figure 5), mainly related to response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, response to oxygen levels, response to radiation, response to decreased oxygen levels, response to UV, response to metalion, response to hypoxia, response to reactive oxygen species, response to estradiol, response to oxidative stress, response to light stimulus, regulation of apoptotic signaling pathway, reproductive structure development, neuron death, reproductive system development, cellular response to oxidative stress, and cellular response to peptide. Based on the GO functional annotation and KEGG pathway enrichment analysis of these two main aspects, the RP-RS drug pair mainly treats T2DM through the synergistic effect of multiple pathways and multiple targets. Annotated map of the key target genes locations of RP-RS in T2DM-related pathways was presented in Figure 6. It was found that most of the key target genes are closely with AGE-RAGE signaling pathway in T2DM.Figure 4
RP and RS prevention and treatment of T2DM gene GO enrichment analysis diagram.Figure 5
KEGG pathway enrichment bar chart of the active ingredients of RP and RS preventing and treating T2DM.Figure 6
Annotated map of the target genes related the main active components of RP-RS on T2DM-related signal pathways.


### 3.6. Molecular Docking Prediction of Key Targets for Active Ingredient Intervention in the Treatment of T2DM by RP-RS

The key targets predicted by network pharmacology were AKT1 (1H10), VEGFA (1BJ1), NOS3 (1M9J), PPARG (2VV4), MMP9 (1GKC), and VCAM1 (1VSC), respectively. Molecular docking results of the top 4 main active ingredients and 6 core targets are shown in Figure7 and Table 4, respectively. The key targets were individually docked to the active compounds puerarin (Figure 8), formononetin (Figure 9), tanshinone iia (Figure 10), and luteolin (Figure 11). Using Discovery Studio 2019 software, the selected compounds were finally docked into the binding site by utilizing the LibDock modules. In addition, the docked pose with the highest LibDock Score was retained for each compound for the LibDock results. The results of molecular docking showed that MMP9 had the best binding energy (LibDock Score) to puerarin, formononetin, tanshinone iia, and luteolin (133.3540, 130.3760, 123.063, and 138.5800), VEGFA to puerarin, formononetin, tanshinone iia, and luteolin (117.1350, 90.4544, 77.7759, and 59.7402), AKT1 to puerarin, formononetin, tanshinone iia, and luteolin (79.3393, 53.2793, 61.7867, and 61.3038), NOS3 to puerarin, formononetin, and luteolin (141.9620, 115.5420, and 87.0491), VCAM1 to puerarin, formononetin, tanshinone iia, and luteolin (121.3260, 93.8531, 96.6147, and 100.627), and PPARG to formononetin and tanshinone iia (60.7435 and 87.5627), respectively, and the key targets were molecularly docked to the active compounds.Figure 7
Heatmap of main active ingredients of RP and RS with core target genes.Table 4
The results of molecular docking.
CompoundTargetPDBLibDock scorePuerarinVEGFA1BJ1117.1350PuerarinMMP91GKC133.3540PuerarinAKT11H1079.3393PuerarinNOS31M9J141.9620PuerarinVCAM11VSC121.3260PuerarinPPARG2VV40FormononetinVEGFA1BJ190.4544FormononetinMMP91GKC130.3760FormononetinAKT11H1053.2793FormononetinNOS31M9J115.5420FormononetinVCAM11VSC93.8531FormononetinPPARG2VV460.7435Tanshinone iiaVEGFA1BJ177.7759Tanshinone iiaMMP91GKC123.0630Tanshinone iiaAKT11H1061.7867Tanshinone iiaNOS31M9J0Tanshinone iiaVCAM11VSC96.6147Tanshinone iiaPPARG2VV487.5627LuteolinVEGFA1BJ159.7402LuteolinMMP91GKC138.5800LuteolinAKT11H1061.3038LuteolinNOS31M9J87.0491LuteolinVCAM11VSC100.627LuteolinPPARG2VV40Figure 8
Molecular docking of puerarin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, (e) VCAM1, and respectively.
(a)(b)(c)(d)(e)Figure 9
Molecular docking of formononetin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, (e) VCAM1, and (f) PPARG, respectively.
(a)(b)(c)(d)(e)(f)Figure 10
Molecular docking of tanshinone iia with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) VCAM1, and (e) PPARG, respectively.
(a)(b)(c)(d)(e)Figure 11
Molecular docking of luteolin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, and (e) VCAM1, respectively.
(a)(b)(c)(d)(e)


## 3.1. Screening of Active Compounds from RP-RS

Taking OB and DL properties as screening criteria, qualified compounds were screened from TCMSP database. After setting the screening conditions, 7 and 85 chemical components of RP and RS were obtained, respectively (Table1).Table 1
A total of possible active ingredient information of RP and RS. (a) Intersection genes between T2DM and RS. (b) Intersection genes between T2DM and RP. (c) Intersection genes among T2DM, RP, and RS.
MOL_IDClassificationMolecule_nameObMwDlMOL000358RPBeta-sitosterol36.9139058327414.7900.75123MOL012297RPPuerarin24.0308979964416.4100.69099MOL002959RP3′-Methoxydaidzein48.5690937439284.2800.24261MOL004631RP7,8,4′-Trihydroxyisoflavone20.6680736276270.2500.21583MOL000392RPFormononetin69.6738806088268.2800.21202MOL000390RPDaidzein19.4410626585254.2500.18694MOL007140RS(Z)-3-[2-[(E)-2-(3,4-dihydroxyphenyl)vinyl]-3,4-dihydroxy-phenyl]acrylic acid88.5360210103314.3100.25869MOL007150RS(6S)-6-hydroxy-1-methyl-6-methylol-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-quinone75.3858784662312.3400.4551MOL007058RSFormyltanshinone73.4446220045290.2800.41736MOL007120RSMiltionone II71.0297032063312.3900.43711MOL007105RSEpidanshenspiroketallactone68.2731592943284.3800.30549MOL007155RS(6S)-6-(hydroxymethyl)-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione65.25893771310.3700.44871MOL007130RSProlithospermic acid64.3709620672314.3100.31017MOL007050RS2-(4-hydroxy-3-methoxyphenyl)-5-(3-hydroxypropyl)-7-methoxy-3-benzofurancarboxaldehyde62.7841472598356.4000.39628MOL007068RSPrzewaquinone B62.2400596208292.3000.41374MOL000569RSDigallate61.8486180263322.2400.25635MOL007081RSDanshenol B57.950875299354.4800.55764MOL007082RSDanshenol A56.9652489923336.4100.52172MOL007069RSPrzewaquinone c55.7416730964296.3400.40408MOL007108RSIsocryptotanshi-none54.9819324596296.3900.39449MOL007125RSNeocryptotanshinone52.4879970089314.4100.32306MOL007079RSTanshinaldehyde52.4747043036308.3500.45196MOL007088RSCryptotanshinone52.3419622629296.3900.39555MOL007094RSDanshenspiroketallactone50.4312810266282.3600.3067MOL007111RSIsotanshinone II49.9160257407294.3700.39674MOL007154RSTanshinone iia49.8873000352294.3700.39781MOL007119RSMiltionone I49.6843943314312.3900.32125MOL007098RSDeoxyneocryptotanshinone49.4003470541298.4100.28555MOL007048RS(E)-3-[2-(3,4-dihydroxyphenyl)-7-hydroxy-benzofuran-4-yl]acrylic acid48.2436324414312.2900.31229MOL007156RSTanshinone VI45.6373060194296.3400.29549MOL007141RSSalvianolic acid g45.5648557799340.3000.60602MOL001942RSIsoimperatorin45.4642467387270.3000.22524MOL007115RSManool45.0443163606304.5700.20208MOL007101RSDihydrotanshinone I45.0432791888278.3200.36015MOL007123RSMiltirone II44.9510664818272.3200.23537MOL007045RS3α-hydroxytanshinone II A44.92933597310.3700.44272MOL001659RSPoriferasterol43.8298515785412.7700.75596MOL002651RSDehydrotanshinone II A43.7622859945292.3500.40019MOL007077RSSclareol43.6706845842308.5600.2058MOL007152RSPrzewaquinone E42.8548520397312.3400.45301MOL007151RSTanshindiol B42.6658104891312.3400.45303MOL007070RS(6S,7R)-6,7-dihydroxy-1,6-dimethyl-8,9-dihydro-7H-naphtho[8,7-g]benzofuran-10,11-dione41.3104570553312.3400.453MOL007041RS2-isopropyl-8-methylphenanthrene-3,4-dione40.8601540814264.3400.22897MOL007071RSPrzewaquinone F40.3078839947312.3400.45925MOL002776RSBaicalin40.1236099599446.3900.75264MOL007118RSMicrostegiol39.6122945749298.4600.27734MOL006824RSα-Amyrin39.5120897831426.8000.76221MOL007124RSNeocryptotanshinone II39.4629911416270.3500.23157MOL007093RSDan-shexinkum D38.8830210096336.4100.55453MOL007122RSMiltirone38.7569863502282.4100.25418MOL001601RS1,2,5,6-Tetrahydrotanshinone38.7453867227280.3400.35791MOL007100RSDihydrotanshinlactone38.6847682981266.3100.32227MOL007063RSPrzewalskin A37.1065006596398.4900.64901MOL007061RSMethylenetanshinquinone37.07319368278.3200.36017MOL001771RSPoriferast-5-en-3beta-ol36.9139058327414.7900.75034MOL007121RSMiltipolone36.5561120639300.4300.36803MOL000006RSLuteolin36.1626293429286.2500.24552MOL002222RSSugiol36.113534855300.4800.27648MOL007107RSC0909236.069489858286.5000.2474MOL007127RS1-methyl-8,9-dihydro-7H-naphtho[5,6-g]benzofuran-6,10,11-trione34.7208221315280.2900.36634MOL007149RSNSC 12242134.4929230859300.4800.27645MOL007049RS4-Methylenemiltirone34.348675885266.3600.22726MOL007036RS5,6-dihydroxy-7-isopropyl-1,1-dimethyl-2,3-dihydrophenanthren-4-one33.7652523626298.4100.28585MOL007143RSSalvilenone I32.4347085631270.4000.22895MOL007059RS3-beta-Hydroxymethyllenetanshiquinone32.1610337648294.3200.40894MOL007145RSSalviolone31.7241503895268.3800.23568MOL007085RSSalvilenone30.3836538737292.4000.37639


## 3.2. Intersection Genes of RP-RS with T2DM

By database analysis, 356, 159, and 99 targets were identified for T2DM, RP, and RS, respectively. The online Venn diagram analysis showed that there were 28 intersecting genes between the active ingredient target of RS and T2DM (Figure1(a)), 10 intersecting genes between the active ingredient target of RP and T2DM (Figure 1(b)), and 8 intersecting genes among RP, RS, and T2DM (Figure 1(c)), which were the main potential targets of RP-RS for the treatment of T2DM.Figure 1
Venn diagram of intersection genes of active ingredient related targets of RP and RS with anti-T2DM targets.
(a)(b)(c)


## 3.3. Protein Interaction PPI Network Construction

In this study, we analyzed the interaction between the targets of RP and RS for the treatment of T2DM based on the STRING database, and constructed the target interaction network (Figures2(a) and 2(b)) by importing compound-disease shared genes into STRING with the interaction confidence setting, medium confidence (0.4), and performed the network topology analysis. The top 6 nodes in the interaction network were AKT1 (26), VEGFA (25), NOS3 (24), PPARG (22), MMP9 (22), and VCAM1 (20), (Table 2, Figure 2(b)), which may play an important role in the treatment of T2DM with RS. These 6 key targets may play an important role in the treatment of T2DM with RP.Figure 2
Active ingredients of RP and RS with anti-T2DM target network.
(a)(b)Table 2
Main ingredients from RP and RS based on number of corresponding targets. (a) PPI visualization network diagram of 120 targets; (b) PPI visualization network diagram of 29 core targets.
MOL_IDMolecule_nameClassificationObMwDlMOL012297PuerarinRP24.0308979964416.4100.69099MOL000392FormononetinRP69.6738806088268.2800.21202MOL007154Tanshinone iiaRS49.8873000352294.3700.39781MOL000006LuteolinRS36.1626293429286.2500.24552


## 3.4. Compound-Target Interaction Network

The network diagram visually reflects the interaction between compounds and targets, the nodes represent compounds, and the edges represent the relationship between compounds and targets, which can reflect the synergistic superposition of multicomponent and multitarget in Chinese medicine. The top 4 compounds in terms of degree value were MOL000006-luteolin (degreevalue=54), MOL012297-puerarin (degree value=53), MOL07154-tanshinone iia (degree value=40), and MOL000392-formononetin (degree value=32), respectively (Table 3, Figure 3).Table 3
Corresponding core targets genes of main ingredient based on the degree value.
Gene nameDegreeBetweenness centralityCloseness centralityAKT1260.097220440.93333333VEGFA250.082972520.90322581NOS3240.07483130.875PPARG220.060899270.82352941MMP9220.046166440.82352941VCAM1200.042754450.77777778CAT200.03174740.77777778STAT3190.02241570.75675676IL4180.024620190.73684211HIF1A160.007004570.7CTNNB1160.011648710.7ESR1150.013814350.68292683TGFB1150.00434850.68292683SIRT1140.005830730.66666667GPT130.007908260.65116279NOS2130.002401880.65116279SOD1100.002545870.60869565AGTR1100.004724110.60869565DPP4100.004594920.60869565AR90.001763670.58333333SELP90.002626610.59574468CD40LG700.57142857AKR1B177.94E-040.57142857ACE276.42E-040.57142857CYP19A165.93E-040.56LEPR61.89E-040.56PLAT500.54901961PON152.04E-040.53846154ADRB252.94E-040.54901961Figure 3
Protein interaction network of RP and RS with anti-T2DM.


## 3.5. Core Target Pathway Analysis

A total of 2015 BPs, 68 CCs, and 171 MFs of key targets were obtained by GO functional annotation, respectively (P<0.05) (Figure 4). BP mainly involved response to xenobiotic stimulus, response to peptide, gland development, response to radiation, cellular response to chemical stress, response to oxygen levels, response to metal ion, response to decreased oxygen levels, response to hypoxia, and response to UV. CC is mainly membrane raft, membrane microdomain, transcription requlator complex, postsynaptic membrane, protein kinase compley, caveola, serine/threonine protein kinase complex, plasma membrane raft, cyclin-dependent protein kinase holoenzyme complex, and integral component of presynaptic membrane. MF mainly involves DNA-binding transcription factor binding, RNA polymerase I-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, ubiquitin protein ligase binding, phosphatase binding, kinase regulator activity, protein phosphatase binding, cyclin-dependent protein serine/threonine kinase regulator activity, G protein-coupled amine receptor activity, and G protein-coupled neurotransmitter receptor activity. The KEGG analysis results showed that a total of 136 signaling pathways were enriched (Figure 5), mainly related to response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, response to oxygen levels, response to radiation, response to decreased oxygen levels, response to UV, response to metalion, response to hypoxia, response to reactive oxygen species, response to estradiol, response to oxidative stress, response to light stimulus, regulation of apoptotic signaling pathway, reproductive structure development, neuron death, reproductive system development, cellular response to oxidative stress, and cellular response to peptide. Based on the GO functional annotation and KEGG pathway enrichment analysis of these two main aspects, the RP-RS drug pair mainly treats T2DM through the synergistic effect of multiple pathways and multiple targets. Annotated map of the key target genes locations of RP-RS in T2DM-related pathways was presented in Figure 6. It was found that most of the key target genes are closely with AGE-RAGE signaling pathway in T2DM.Figure 4
RP and RS prevention and treatment of T2DM gene GO enrichment analysis diagram.Figure 5
KEGG pathway enrichment bar chart of the active ingredients of RP and RS preventing and treating T2DM.Figure 6
Annotated map of the target genes related the main active components of RP-RS on T2DM-related signal pathways.


## 3.6. Molecular Docking Prediction of Key Targets for Active Ingredient Intervention in the Treatment of T2DM by RP-RS

The key targets predicted by network pharmacology were AKT1 (1H10), VEGFA (1BJ1), NOS3 (1M9J), PPARG (2VV4), MMP9 (1GKC), and VCAM1 (1VSC), respectively. Molecular docking results of the top 4 main active ingredients and 6 core targets are shown in Figure7 and Table 4, respectively. The key targets were individually docked to the active compounds puerarin (Figure 8), formononetin (Figure 9), tanshinone iia (Figure 10), and luteolin (Figure 11). Using Discovery Studio 2019 software, the selected compounds were finally docked into the binding site by utilizing the LibDock modules. In addition, the docked pose with the highest LibDock Score was retained for each compound for the LibDock results. The results of molecular docking showed that MMP9 had the best binding energy (LibDock Score) to puerarin, formononetin, tanshinone iia, and luteolin (133.3540, 130.3760, 123.063, and 138.5800), VEGFA to puerarin, formononetin, tanshinone iia, and luteolin (117.1350, 90.4544, 77.7759, and 59.7402), AKT1 to puerarin, formononetin, tanshinone iia, and luteolin (79.3393, 53.2793, 61.7867, and 61.3038), NOS3 to puerarin, formononetin, and luteolin (141.9620, 115.5420, and 87.0491), VCAM1 to puerarin, formononetin, tanshinone iia, and luteolin (121.3260, 93.8531, 96.6147, and 100.627), and PPARG to formononetin and tanshinone iia (60.7435 and 87.5627), respectively, and the key targets were molecularly docked to the active compounds.Figure 7
Heatmap of main active ingredients of RP and RS with core target genes.Table 4
The results of molecular docking.
CompoundTargetPDBLibDock scorePuerarinVEGFA1BJ1117.1350PuerarinMMP91GKC133.3540PuerarinAKT11H1079.3393PuerarinNOS31M9J141.9620PuerarinVCAM11VSC121.3260PuerarinPPARG2VV40FormononetinVEGFA1BJ190.4544FormononetinMMP91GKC130.3760FormononetinAKT11H1053.2793FormononetinNOS31M9J115.5420FormononetinVCAM11VSC93.8531FormononetinPPARG2VV460.7435Tanshinone iiaVEGFA1BJ177.7759Tanshinone iiaMMP91GKC123.0630Tanshinone iiaAKT11H1061.7867Tanshinone iiaNOS31M9J0Tanshinone iiaVCAM11VSC96.6147Tanshinone iiaPPARG2VV487.5627LuteolinVEGFA1BJ159.7402LuteolinMMP91GKC138.5800LuteolinAKT11H1061.3038LuteolinNOS31M9J87.0491LuteolinVCAM11VSC100.627LuteolinPPARG2VV40Figure 8
Molecular docking of puerarin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, (e) VCAM1, and respectively.
(a)(b)(c)(d)(e)Figure 9
Molecular docking of formononetin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, (e) VCAM1, and (f) PPARG, respectively.
(a)(b)(c)(d)(e)(f)Figure 10
Molecular docking of tanshinone iia with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) VCAM1, and (e) PPARG, respectively.
(a)(b)(c)(d)(e)Figure 11
Molecular docking of luteolin with core target genes. (a) VEGFA, (b) MMP9, (c) AKT1, (d) NOS3, and (e) VCAM1, respectively.
(a)(b)(c)(d)(e)


## 4. Discussion

Current research on T2DM has focused on several pathological alterations such as obesity-related insulin resistance and defective insulin secretion as well as decreased B-cell mass through B-cell apoptosis, and has a close association with inflammatory alterations, immune gene defects, and impaired mitochondrial function [20, 21]. In this way, the results of the network pharmacological analysis, i.e., the results of the potential mechanism of RP-RS for the treatment of T2DM and the results of the molecular docking of potential pharmacodynamic substances and key targets will be discussed.In this study, a holistic analysis was performed using network pharmacology from multigene, multipassage, and multitarget, and 92 active ingredients of RP-RS for the treatment of T2DM were identified; the most important of which were puerarin, formononetin, tanshinone iia, and luteolin. The phytochemicals are estrogenically active polyphenolic nonsteroidal phytochemicals, which are commonly found in various plants. It has been shown that formononetin has therapeutic effects on diabetes, such as formononetin exhibited hypoglycemic effects in mice with tetraoxacillin-induced type 1 diabetes by inhibiting pancreaticβ-cell apoptosis [22]. It was shown that formononetin significantly increased insulin sensitivity index, decreased HOMA-IR, and improved insulin resistance [23]. Puerarin, as its main active ingredient in the treatment of T2DM may participate in the whole process of inflammatory factor expression in type 2 diabetes patients, reduce the inflammatory response, regulate the body’s internal environment, and improve the state of insulin resistance and disorders of glucose and lipid metabolism, while the symptoms of type 2 diabetes can be improved [24]. It was reported that puerarin may also ameliorated streptozotocin (STZ) pancreatic injury in mice, upregulated insulin receptor substrate 1 and insulin-like growth factor protein expression in the pancreas, inhibited STZ-induced apoptosis of islet β-cells in diabetic mice, and increased serum insulin levels; and its protective effects on β-cells may be mediated by modulating the phosphatidylinositol 3-kinase/protein kinase B pathway, thereby exerting hypoglycemic effects and improving glucose tolerance [25]. In previous study, puerarin may also upregulated the gene expression of retinal vascular endothelial growth factor and hypoxia-inducible factor-1, which had a significant protective effect against STZ-induced diabetic retinopathy in rats, while VEGF, an angiogenic and vascular permeability factor, was significantly increased in the vitreous and aqueous fluids of eyes with proliferative diabetic retinopathy [26]. In addition, tanshinone iia has been reported to antagonize endothelial damage and antioxidant effects to effectively reduce diabetic nephropathy [27] and diabetic neuropathy [28]. Previous studies have shown that luteolin inhibits high-glucose-induced activation of NF-κB in human monocytes and the release of the proinflammatory factor TNF-α, with potential preventive and therapeutic activity in diabetes mellitus [29]. It was revealed that have a regulatory effect on endothelial cell function, and luteolin enhance insulin action in adipocytes by activating the PPAR pathway [30]. Recent studies have revealed the potential protective effect of luteolin on diabetes-related hypertension, which may significantly reduce diabetes-induced vascular complications and hypertension [31]. Therefore, these components of RP-RS may play an important role in the prevention and treatment of T2DM.Core targets including AKT1, VEGFA, NOS3, PPARG, MMP9, and VCAM1 have been identified as important by network pharmacological analysis. AKT1 is involved in several regulatory processes, including glucose metabolism, and AKT mediates insulin signaling and interacts with the transcription factors PGC-1α and FoxO1 to stimulate gluconeogenic gene expression [32]. AKT is part of the insulin-signaling pathway that directly inhibits the expression of PGC-1α protein in hepatocytes [33]. This pathway is able to regulate lipid secretion in type 2 diabetes. Because of AKT-induced inhibition of PGC-1α leads to inhibition of fatty acid oxidation in the liver, promoting PGC-1α activity in insulin-resistant hepatocytes may be able to eliminate lipid imbalance in T2DM patients [34]. It was reported that VEGFA promotes angiogenesis, and abnormal levels of VEGFA expression can exacerbate pathological angiogenesis and the development of diabetic retinopathy [35]. It was shown that reduced local production of VEGFA in the glomeruli of diabetic mouse models promotes endothelial injury and accelerates the progression of glomerular injury. It suggests that upregulation of VEGFA in diabetic kidneys protects microvasculature from injury, while decreased VEGFA in diabetes may be detrimental [36]. The NOS3 gene is an important 21-22 kb long gene located in vascular endothelial cells and contains 26 exons and 25 introns [37]. It has been reported that endothelial cells produce more oxygen radicals and release large amounts of Ca2+ in the early stages of diabetes, which leads to increased intracellular flow and activation of NOS, causing more endothelial cells to be produced. The end effector molecule of B-cell damage is NO, and NO can cause apoptosis by inducing endoplasmic reticulum stress-related apoptotic factors, indirectly damaging islet cells, and affecting insulin synthesis, and the activity of NOS will be reduced in diabetic patients in the later stages, while plasma NO levels will be reduced [38]. PPARG, a member of the nuclear hormone receptor superfamily, which has an important role in controlling lipid and glucose metabolism as well as in T2DM development [39]. Previous studies have shown that high expression of PPARG levels in adipose tissue reduces plasma lipid levels, has a beneficial role in long-term glucolipid homeostasis, reduces the incidence of visceral adipose IR, and is involved in regulating the pathological process of T2DM in obese populations [40]. DM is not only a metabolic disorder, activation of innate immunity and inflammatory response play an important role in the development of diabetes and its onset. MMP9 is a vital effector molecule of inflammatory cells; it will act as a switch in acute and chronic inflammation and is presumptively concerned within the initial part of inflammation and later tissue remodeling [41]. Hyperglycemia promotes the production of proinflammatory cytokines TNF-α and IL-6 production, and the expression of these proinflammatory cytokines induces an increase in MMP9 expression in an autocrine and paracrine manner [42]. Studies have demonstrated that the level of MMP9 directly affects the development of diabetic nephropathy, a complication of diabetes, and that high glucose downregulates the expression of MMP9 protein, thereby affecting its proportional imbalance and also exacerbating the development of diabetic nephropathy [43]. VCAM1 is a cell adhesion molecule that is a member of the immunoglobulin superfamily, and Guillén-Gómez et al. found that urinary VCAM1 levels were significantly higher in DN patients compared to diabetic patients, which could be a marker of renal pathology in diabetic patients [44]. In addition, VCAM1 protein level was significantly correlated with T2DM complications. The enhanced induction of VCAM1 expression in endothelial cells by circulating factors may play a role in the development of atherosclerosis in diabetes [45].Through GO functional annotation analysis of the intersection target genes, it was found that RP-RS may regulate T2DM through various BP, cellular composition CC and MF. The GO enrichment analysis showed that RP-RS exhibits efficacy mainly via xenobiotic stimulus, response to peptide, response to oxygen levels, membrane raft, membrane microdomain, transcription requlator complex, DNA-binding transcription factor binding, RNA polymerase I-specific DNA-binding transcription factor binding, ubiquitin-like protein ligase binding, etc. The KEGG pathway enrichment results revealed that a total of 136 signaling pathways were enriched, mainly related to response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, response to oxygen levels, and so on. The results of molecular docking validation showed that the key components screened by network pharmacology showed stable binding to the core targets NOS3 and MMP9, among which, the docking scores of puerarin and NOS3 were the highest, followed by those of luteolin and MMP9, and the stronger the interaction, the more stable the conformation of the compounds, indicating that puerarin showed good effects in activating NOS3 and luteolin exhibited good effects in activating MMP9 pathways, respectively.


## 5. Conclusion

In conclusion, a total of 92 active ingredients of RP-RS were obtained. The active components of RP-RS mainly including puerarin, formononetin, tanshinone iia, and luteolin which are closely associated with T2DM. A total of 29 intersecting target genes was acquired between drugs (RP-RS) and diseases (T2DM). Among them, the core target genes in treatment of T2DM mainly including VEGFA, MMP9, AKT1, NOS3, VCAM1, and PPARG, respectively. In addition, puerarin showed significant anti-T2DM effects in activating NOS3 and luteolin exhibited significant anti-T2DM effects in activating MMP9 pathways, respectively. The biological function exhibits efficacy mainly via positive regulation of xenobiotic stimulus, response to peptide, response to oxygen levels, membrane raft, and membrane microdomain. The signaling pathways that exerting their therapeutic effect on T2DM mainly including response to xenobiotic stimulus, cellular response to chemical stress, response to peptide, gland development, and response to oxygen levels. From the perspective of modern molecular biology, it was confirmed that RP-RS has good therapeutic effect on T2DM, the multitarget and multipath effects of TCM may rely on its therapeutic characteristics.



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*Source: 9150324-2023-02-11.xml*