Aim of the study
In this study, a network pharmacology approach and bioinformatic analysis were adopted to predict the pharmacological mechanisms of SM in DN therapy. Based on the predicted
Conclusion
This study revealed the multi-component, multi-target and multi-pathway characteristics of SM therapeutic DN. SM inhibited the inflammatory response and improved renal pathological damage in DN rats, which was related to the regulation of the PI3K-Akt and VEGFA-NOS3 signaling pathways.
Methods
In this study, the candidate bioactive ingredients of SM were obtained via Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and supplementing according to the literature. SM putative targets and the verified targets were acquired from TCMSP and SiwssTartgetPrediction Database. DN-related target genes were collected from GeneCards, OMIM, DisGeNET databases, and microarray data analysis. Biological function and pathway analysis were performed to further explore the pharmacological mechanisms of SM in DN therapy. The protein-protein interaction (PPI) network was established to screen the hub gene. The Receiver Operating Characteristic (ROC) analysis and the molecular docking simulations were performed to validate the potential target-drug interactions. The fingerprint spectrum of multi-components of the SM was characterized by UPLC-MS/MS. The signaling pathways associated with inflammation and hub genes were partially validated in SD rats.
Results
A total of 36 bioactive ingredients were contained, and 666 component-related targets were screened from SM, of which 50 intersected with DN targets and were considered potential therapeutic targets. GO analyses revealed that the 50 intersection targets were mainly enriched in the inflammatory response, positive regulation of angiogenesis, and positive regulation of phosphatidylinositol 3-kinase(PI3K) signaling. KEGG analyses indicated that the PI3K-Akt signaling pathway was considered as the most important pathway for SM antagonism to the occurrence and development of DN, with the highest target count enrichment. PPI network results showed that the top 15 protein targets in degree value, VEGFA, JAK2, CSF1R, NOS3, CCR2, CCR5, TLR7, FYN, BTK, LCK, PLAT, NOS2, TEK, MMP1 and MCL1, were identified as hub genes. The results of ROC analysis showed that VEGFA and NOS3 were valuable in the diagnosis of DN. The molecular docking confirmed that the core bioactive ingredients had well-binding affinity for VEGFA and NOS3. The in vivo experiments confirmed that SM significantly inhibited the over-release of inflammatory cytokines such as interleukin (IL)-6 and tumor necrosis factor receptor (TNF)-α in DN rats, while regulating the PI3K-AKT and VEGFA-NOS3 signaling pathways.