Exploring the mechanisms underlying the therapeutic effect of Salvia miltiorrhiza in diabetic nephropathy using network pharmacology and molecular docking

Abstract

The mechanisms underlying the therapeutic effect of Salvia miltiorrhiza (SM) on diabetic nephropathy (DN) were examined using a systematic network pharmacology approach and molecular docking. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen active ingredients of SM. Targets were obtained using the SwissTargetPrediction and TCMSP databases. Proteins related to DN were retrieved from the GeneCards and DisGeNET databases. A protein-protein interaction (PPI) network was constructed using common SM/DN targets in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The Metascape platform was used for Gene Ontology (GO) function analysis, and the Cytoscape plug-in ClueGO was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was performed using iGEMDOCK and AutoDock Vina software. Pymol and LigPlos were used for network mapping. Sixty-six active ingredients and 189 targets of SM were found. Sixty-four targets overlapped with DN-related proteins. The PPI network revealed that AKT serine/threonine kinase 1 (AKT1), VEGFA, interleukin 6 (IL6), 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 JUN were the ten most relevant targets. GO and KEGG analyses revealed that the common targets of DN and SM were mainly involved in advanced glycation end-products, oxidative stress, inflammatory response, and immune regulation. Molecular docking revealed that potential DN-related targets, including tumor necrosis factor (TNF), NOS2, and AKT1, more stably bound with salvianolic acid B than with tanshinone IIA. In conclusion, the present study revealed the active components and potential molecular therapeutic mechanisms of SM in DN and provides a reference for the wide application of SM in clinically managing DN.

Keywords: Salvia miltiorrhiza; diabetic nephropathy; molecular docking; molecular mechanism; network pharmacology.

Figure 1. Flowchart of the network pharmacology and molecular docking study

Figure 2. ‘Ingredients–targets’ network construction

The light cyan prism nodes represent the targets, the light purple round nodes represent SM ingredients.

Figure 3. SM/DN common target genes

Figure 4. PPI network analysis

(A) PPI network of targets generated using STRING 11.0. Nodes represent proteins. Edges represent PPIs. (B) Potential targets are arranged counterclockwise according to the degree value from large to small.

Figure 5. GO enrichment analysis

Included are (A) BP terms, (B) molecular function (MF) terms, and (C) cellular component (CC) terms. (A,B) Node color is displayed in a gradient from red to green in descending order of the P-value. The size of the nodes is arranged in ascending order of the number of genes. (C) Sorted by the importance of –log10(P) of each lane.

Figure 6. KEGG pathway analysis of potential targets of SM among DN-related proteins using the ClueGO plug-in

(A) The KEGG term is indicated as a node, and the size of the node indicates its importance. Only the most significant terms in the group are labeled. (B) Pie chart presenting the percentage of genes involved in different biological functions and signaling pathways in the total number of genes that are intersected.

Figure 7. Molecular docking

Molecular models of the binding of salvianolic acid B with (A) TNF, (B) NOS2, and (C) AKT1 shown as 3D and 2D diagrams.

Figure 8. Network structure of ‘SM–component–target pathway–DN’

Ovals represent SM and DN, diamonds represent components, hexagons represent targets, and rectangles represent pathways.