Exploring the Mechanisms Underlying the Therapeutic Effect of Salvia Miltiorrhiza Against Diabetic Nephropathy Using Network Pharmacology and Molecular Docking

The mechanisms underlying the therapeutic effect of Salvia Miltiorrhiza (SM) against diabetic nephropathy (DN) using systematic network pharmacology and molecular docking methods were examined. TCMSP database was used to screen the active ingredients of SM. Gene targets were obtained using Swiss Target Prediction and TCMSP databases. Related targets of DN were retrieved from the Genecards and DisGeNET databases. Next, a PPI network was constructed using the common targets of SM-DN in the STRING database. The Metascape platform was used for GO function analysis and Cytoscape plug-in ClueGO was used for KEGG pathway enrichment analysis. Molecular docking was performed using iGEMDOCK and AutoDock Vina software. Pymol and LigPlos were used for mapping the network.


Background
Diabetic nephropathy (DN) is a serious complication common in diabetic patients. As the incidence and mortality of patients with diabetes increases year by year, the prevalence of patients with DN rises sharply [ 1 ] and has become one of the leading causes of chronic renal failure [ 2 ]. According to reports, DN accounts for approximately 40% of end-stage kidney disease [ 3 ]. DN mainly manifests as glomerular hyper ltration, renal hypertrophy, albuminuria, and even sepsis [ 4 ], with persistent proteinuria and gradual decline in renal function being its main characteristics [ 5 ]. However, its speci c molecular mechanism is complicated and unclear, leading to a lack of effective therapies.
In recent years, research on the effects of traditional Chinese medicine (TCM) in regulating blood sugar and lipid metabolism, reducing kidney damage, delaying kidney disease, and preventing glomerular sclerosis and brosis have gradually been discovered. Salvia Miltiorrhiza (SM) has a longstanding history as a commonly used TCM for promoting blood circulation in China. Its main functions are reducing blood viscosity, improving hemorheological characteristics, accelerating brin degradation [ 6 ], antioxidant activity [ 7 ], anti-infection [ 8 ], and improving glucose metabolism disorders [ 9 ]. It is often used for microvascular-related diseases such as DN and diabetic retinopathy.
TCM have a multi-target and multi-path intervention strategy that can exert an overall regulatory and synergistic effects, this has certain advantages for DN prevention and individualized treatment. However, the mechanism of SM against DN is unclear.
Network pharmacology is an effective method for studying and clarifying the mechanisms behind drug actions. These methods include chemoinformatics, bioinformatics, network biology, and pharmacology [ 10 , 11 ]. The research strategy of network pharmacology is in line with the understanding of disease integrity in TCM [ 12 , 13 ], and provides new ideas and methods for research on TCM [ 14 ]. In this study, we attempted to utilize network pharmacology technology to explore the main bioactive components of SM, and predict their effective targets and potential mechanisms involved in the treatment of DN at the molecular level. The owchart is as follows (Fig 1).

Materials And Methods
Determination and Screening of Active Components of SM SM components were retrieved from the Traditional Chinese Medicine System Pharmacology Database [TCMSP, http://tcmspw.com/ tcmsp. Php]. We used pharmacokinetic information retrieval lters for absorption, distribution, metabolism, and excretion (ADME) screening based on oral bioavailability (OB) greater than or equal to 30% and Drug-likeness (DL) greater than or equal to 0.18. and active compounds selected without potential target information were excluded. Supplementing the unpredicted active compounds based on published literature reports.

Construction of an Active Component-Target Network
Obtain targets from the TCMSP and Swiss Target Prediction database (http://www. Swisstargetprediction. ch). Afterwards, the targets were standardized in the UniProt (https://www.uniprot.org) database with the property set to "reviewed" and "human" [ 15 ]. After removing duplicates, a database of SM compounds and their targets was constructed. Finally, a visual network was established using Cytoscape v.3.6.0 software.
Determination of Potential DN-Related Targets DN-related targets were retrieved from the Human Gene Database (GeneCards, https: //www.genecards. org/), as well as from the DisGeNET Database (https://www. disgenet.org/home/). The keyword used was "diabetic nephropathy".

Determination of DN-related Targets of the Active Components
The screened targets of the active components and DN-related proteins were imported into the Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) platform for analysis, and the common ones were identi ed as DN-related targets of the active components for further analysis.

Protein-Protein Interaction (PPI) Network of DN-related Targets of the Active Components
In order to study the interactions between the active components of SM and their target proteins, the drugdisease intersection target genes were searched using the interaction database platform STRING v.11.0 (https://string-db.org/), and a PPI network was constructed. STRING is a comprehensive multifunctional data platform [ 16 , 17 ] and aims to provide protein-protein interaction evaluation and integration [ 18 ]. In our database search, the species was limited to "Homo sapiens", the con dence score cutoff was set at 0.4, and the rest of the settings were default.

Network Construction and Analysis
The targets of SM among DN-related proteins identi ed using STRING were further analyzed using the Cytoscape v.3.6.0 software to visualize and analyze the interaction network. Using the network analysis plug-in in the software to count the nodes in the network graph and analyze its role in the graph.

Gene Ontology (GO) Functional Analysis
The Metascape Platform (http://metascape. Org/gp /index. html) has a comprehensive annotation function that updates the gene annotation data every month [ 19 ]. The SM regulates DN abnormalities entered into the Metascape platform, set P<0.01, analyzes their main biological processes, and performs enrichment analysis. It saves the data results and uses biological online tools to visualize the data.
Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Enrichment Analysis KEGG pathway enrichment analysis was conducted using the Cytoscape plug-in ClueGO. The candidate DN-related genes targeted by SM were entered into the ClueGO plug-in, with P set to <0.01 and kappa score set to ≥0.53.

Molecular Docking
Using KEGG pathway enrichment analysis, we identi ed the potential DN-related genes targeted by SM components. These targets were further con rmed by molecular docking with the experimentally veri ed active components. These components were Tanshinone IIA, which can improve kidney hypertrophy and 24-hour urine protein excretion [ 20 ], and salvianolic acid B, which can inhibit the proliferation of mesangial cells and the production of extracellular matrix induced by high glucose in a dose-dependent manner in SM [ 21 ]. The crystal structure of the core genes was obtained from the RCSB Protein Data Bank (PDB, https://www.rcsb.org/) [ 22 ]. The compound structure was saved as a docking ligand in MOL2 format. The iGEMDOCK software was used for molecular docking. The software automatically defaults parameters when setting the standard docking.
From the molecular docking results of the iGEMDOCK software, we selected the top 5 receptor proteins with the lowest energy value and the ligand that bound to these receptor proteins most stably and ran AutoDock Vina for docking. Pymol and LigPlos softwares were used for visualization and construction of the network, respectively.

Results
Screening for Active Components of SM Using the TCMSP database to obtain 202 known active compounds in SM, we screened these through the conditions of OB≥30%, DL≥0.18, and obtained 65 active ingredients that met the conditions. According to published literature reports, we also selected the known active compound Salvianolic acid B, which was screened out by ADME. Finally, 66 active components were selected. ( Table 1).

Determination of Targets of SM Active Components and Active Component-Target Network Construction
Candidate targets of active components in SM were searched from the databases of TCMSP and Swiss Target Prediction. Then, after uniprot standardization, deduplication occurred. As a result, 189 targets were identi ed after removal of duplicate data (Table S1). Afterwards, we used cytoscape 3.6.0 software to build a "herbs-ingredients-targets" interaction Network, as shown in Figure 2.

Determination of DN-Related Targets
The DN-related disease targets obtained from the GeneCards and DisGeNET databases were 1189 and 3084, respectively.

Drug-Disease Intersection Targets
Venn analysis was performed on 189 targets of SM components, 1189 and 3084 DN target genes, and 64 drug-disease intersection gene targets were obtained for further analysis, as shown in Figure 3 and Table  2. These targets information were provided in Supplementary Table S2.

PPI Network Analysis
The drug-disease intersection gene targets (64) were analyzed using the STRING database PPI network, as shown in Figure 4A. From the analysis results, a total of 64 nodes and 704 edges were acquired, and the average node degree was 21.3, with a PPI enrichment p-value of <1.0e -16 (Fig 4 A). Then, the Import STRING analysis results were analyzed by Cytoscape software. The network analysis plugin was used to count the nodes in the network graph and analyze its role in the graph according to degree. The greater the degree of freedom, the more biological functions the node has in the network. The network was constructed as shown in Figure 4B. Among them, the top ten targets AKT serine/threonine kinase 1 (AKT1), vascular endothelial growth factor A (VEGFA), Interleukin 6 (IL6), Tumor necrosis factor (TNF), Mitogen-activated protein kinase 1 (MAPK1), Tumor protein p53 (TP53), Epidermal growth factor receptor (EGFR), signal transducer and activator of transcription 3 (STAT3), Mitogen-activated protein kinase 14 (MAPK14), and Transcription factor AP-1 (JUN) explain their signi cance in the network (Fig 4 B).

GO Functional Analysis
The Metascape data platform was used to enrich and analyze the 64 relevant DN-related targets of SM, and the results were visualized using biological online tools.
A total of 1557 biological processes (BP) were enriched, and the rst 20 signi cantly enriched BP terms were selected for analysis. The results showed that the biological processes involved in SM mainly included cytokine-mediated signaling pathway, apoptotic signaling pathway, positive regulation of cell migration, reactive oxygen species metabolic process, regulation of in ammatory response, regulation of cell-cell adhesion, response to oxygen levels, cellular response to growth factor stimulus, and regulation of protein serine/threonine kinase activity (Fig5 A).
A total of 90 molecular functions (MF) GO terms were enriched, and the rst 19 signi cantly enriched MF terms were selected for analysis based on P<0.01. The results showed that the intersection genes were mainly enriched in protein kinase binding, transcription factor binding, cytokine receptor binding, integrin binding, cysteine-type endopeptidase activity involved in apoptotic process, kinase regulator activity, heme binding, protein domain speci c binding, endopeptidase activity, nitric-oxide synthase regulator activity, and many other molecular functions related to the above genes (Fig5 B).
A total of 38 cell components (CC) GO terms were enriched, and the rst 13 signi cantly enriched CC terms were selected for analysis according to P<0.01. The results showed that the intersection genes were mainly enriched in membrane rafts, RNA polymerase II transcription factor complex, external side of plasma membrane, extracellular matrix, adherens junction, and glutamatergic synapse (Fig5 C). Details of the node attribute information of GO analysis results are provided in the supplementary materials (Table S3).

KEGG Pathway Enrichment Analysis
In order to further reveal the potential mechanism underlying the therapeutic effect of SM against DN, we performed KEGG pathway enrichment analysis on 64 intersection gene targets using the Cytoscape plug-in ClueGO. The screen was based on a p<0.01 and kappa score≥0.53, in order to visualize the results of KEGG enrichment (Fig 6 A), and we used a pie chart to describe the percentage of genes involved in the different biological functions and signal pathways among the total number of genes that are intersected (Fig 6 B). The results showed that a total of 38 terms were enriched, including the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, JAK-STAT signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. In addition, we also found some other pathways, such as Fluid shear stress and atherosclerosis, Platelet activation, Relaxin signaling pathway and so on. These results revealed that SM alleviated DN by improving the organism's immunity, anti-in ammation, relieving advanced glycation end products, antioxidant stress response, and other harmful alien organism-related pathways. Details of node attribute information of the KEGG analysis results are provided in the supplementary materials (Table S4).

Molecular Docking Study
According to the results of KEGG pathway enrichment analysis, we selected the AGE-RAGE signaling pathway in diabetic complications which had the largest percentage of genes involved in different biological functions and signaling pathways among the total number of intersection genes. According to the drug-target correspondence, the target protein in this pathway is molecularly docked with the corresponding drug components. The 16 target proteins enriched by the AGE-RAGE signaling pathway in diabetic complications are AKT1, BCL2, CASP3, EDN1, ICAM1, IL6, JUN, MAPK1, MAPK14, MMP2, NOS3, NOS2, RELA, STAT3, TNF, and VEGFA. We selected the experimentally veri ed tanshinone IIA and salvianolic acid B from among the SM active molecules, and molecularly docked with 16 target proteins. The matching energy of small molecules and large molecules determines the degree of binding. If the matching energy is relatively low, it indicates that the conformation of small molecules and large molecules is stable. The 16 potential targets of DN had better binding stability to salvianolic acid B than the tanshinone IIA (Table 3).
Using AutoDock Vina software, the ve target proteins (AKT1, NOS2, TNF, JUN, and RELA) with the lowest energy value in the molecular docking by iGEMDOCK software were molecularly docked with Salvianolic acid B. Figure 7 shows the best docking combination for the target protein and the active component salvianolic acid B, including TNF, NOS2, and AKT1. These have the best combination with salvianolic acid B, and the binding energy is -9.3 kcal/mol, -6.6 kcal/mol, and -6.4 kcal/mol respectively. This shows that salvianolic acid B has good binding ability with the targets.

Discussion
TCM mechanisms of action are complicated with multiple components and targets. When the pathogenesis of a disease is not clear yet, it becomes more di cult to analyze the mechanism of action of Chinese medicine. Network pharmacology is a method that combines system network analysis and pharmacology. It can systematically study the effective components, targets, and pathways of drugs at the molecular level, so as to understand the interaction between components, targets, and pathways. Therefore, network pharmacology research methods provide new ideas and methods for TCM research.
In this study, the results of the TCM-component-target network analysis showed that luteolin, tanshinone IIA, salviolone, salvianolic acid B, dihydrotanshinlactone, and other active ingredients can act on multiple targets in the network. This nding suggests that these components may be important for the therapeutic effect of SM against DN and warrant further exploration. Luteolin has the most potential targets, followed by Tanshinone IIA. According to reports, luteolin can not only increase insulin-mediated glucose uptake and enhance insulin sensitivity [ 23 ], but also inhibit high glucose-induced vascular endothelial growth factor (VEGF) [ 24 ], reducing reactive oxygen species (ROS) generation, and reducing lipid accumulation To predict the mechanism underlying the therapeutic effect of SM against DN, we performed GO enrichment analysis of 64 potential targets. As shown in Figure 5A, the rst 20 terms of BP are mainly related to cytokines, apoptosis, reactive oxygen species, and in ammation regulation. For example, cytokine-mediated signaling pathway, apoptosis signaling pathway, reactive oxygen species metabolic process, regulation of in ammatory response, etc. Relevant studies have shown that the occurrence and development of DN is related to cell dysfunction and damage [ 40 , 41 ], chronic in ammatory in ltration [ 42 ], cell apoptosis, and oxidative stress [ 43 ]. This indicates that the main target genes are important for multiple BP. MF enrichment analysis mainly included cytokine receptor binding, integrin binding, endopeptidase activity, transcription factor binding, protein kinase binding, and heme binding ( Figure 5B).
The target genes involved mainly include VEGFA, PTGS2, DDP4, TNF, and NOS2, which mainly focused on oxidative stress, in ammatory response, and immune regulation. Among them, DDP4 inhibitors is of great signi cance for reducing the blood sugar levels in diabetic patients and delaying the occurrence and development of DN [ 44 , 45 ]. In addition, as shown in Figure 5C, cellular components mainly include membrane raft, RNA polymerase II transcription factor complex, external side of plasma membrane, extracellular matrix, and adherens junction. These enriched functions also involve top targets, such as TNF and JUN. At the same time, this also illustrates the complexity of the pathological mechanism of DN.
To further explore the potential mechanism of SM in treating DN, we conducted KEGG analysis on 64 potential targets of SM acting on DN. As shown in Figure 6, the pathways related to DN, including the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, JAK-STAT signaling pathway, and FoxO signaling pathway, mainly involve three aspects: (1) accumulation of advanced glycation end products: Normally, the glycation reaction proceeds very slowly. However, the response is obviously accelerated in the hyperglycemic state, and the aggregation of AGEs in tissues and their binding with RAGE, a speci c receptor, produces cytotoxic effects and damages the kidneys, which may be the key factors contributing to DN. Most studies have shown that the AGE/RAGE signaling pathway can promote the expression of NF-κB [ 46 ], upregulate TGF-beta 1, VEGF [ 47 ], and activate NADPH oxidases, etc., cause the expression and release of in ammatory factors and adhesion factors, increase vascular permeability, increase the expression of connective tissue growth factor, and enhance oxidative stress, thus increasing proteinuria, promoting renal brosis, leading to the occurrence and development of DN. In addition, other studies have shown that the interaction between AGEs and RAGE leads to vasoconstriction, procoagulant state [ 48 ], accelerates renal vascular aging and injury [ 49 ], and further promotes DN progression. (2) Immune in ammation regulation: TNF has immunomodulatory and proin ammatory effects [37]. TNF-α stimulates the aggregation and adhesion of in ammatory cells, increases the permeability of microvessels, and impairs glomeruli through an in ammatory response [ 50 ]. Some studies have con rmed that TNF-α levels are signi cantly increased in DN patients and positively correlated with the course of disease [ 51 , 52 ]. In addition, many studies have shown that activation of the JAK/STAT signaling pathway can cause immune in ammation in renal tissue [ 53 , 54 ], and mediates mesangial proliferation and renal tissue brosis associated with DN [ 55 ]. (3) Oxidative stress: FoxO mainly regulates oxidative stress, apoptosis, and immune response through transcription and transmission of various growth factors and cytokine signals, among which FoxO1 plays an important role in the pathogenesis of kidney disease [ 56 ]. FoxO1 activation can inhibit podocyte epithelialmesenchymal cell transformation induced by high glucose and improve proteinuria and renal damage in diabetic mice [ 57 ]. In addition, we found other pathways, such as proteoglycans in cancer, uid shear stress, and atherosclerosis. This indicates that SM has potential applications in tumors, arteriosclerosis, and other diseases. Based on the above multiple pathways, it is speculated that SM may delay the progression of DN and protect renal function by participating in advanced glycation end-products, oxidative stress, in ammatory response, immune regulation, and other processes.
To further explore the potential molecular mechanism of SM in the treatment of DN, we conducted molecular docking studies on 16 target genes closely related to DN screened by KEGG, using the corresponding experimentally validated key components Tanshinone IIA and Salvianolic acid B as ligands. The results showed that 16 potential target genes had good binding with salvianolic acid B, and their stability was better than the Tanshinone IIA. The binding energies of these docking results further helped to re ne the targets for SM.

Conclusions
In conclusion, this study analyzed the mechanisms underlying the therapeutic effect of SM against DN using network pharmacology and analysis of a PPI network showing the interactions between SM active components and targeted DN-related proteins and determined the synergistic effects between the herbs in SM. Our ndings revealed that SM exerted its pharmacological effects against DN through "multi components-multi targets-multi pathways" that were mainly involved in advanced glycation end-products, oxidative stress, in ammatory response, and immune regulation. Further, our ndings offer a reference for further investigation of the mechanism underlying the therapeutic effect of SM against DN. This study also had some limitations. We only explored the effect of SM on DN at the level of network pharmacology. However, the current network information technology is not comprehensive, and the accuracy of database data and real-time updates needs to be improved. Therefore, the results obtained from this analysis need further veri cation with respect to the corresponding pharmacodynamics, and mechanistic experiments are needed to explain the complex multi-target, multi-pathway, and synergistic interactions involved in the therapeutic effects of TCM.

Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.