Integrated network pharmacology and bioinformatics analysis reveals MME as key target of Notoginsenoside R1 in diabetic nephropathy
Abstract
Background: Diabetic nephropathy (DN) is a major diabetes complication and a primary cause of end-stage renal failure. Notoginsenoside R1 (NGR1) is known to reduce proteinuria, exert hypoglycemic effects, and enhance renal function in DN patients. However, the exact mechanisms by which NGR1 affects DN are not well understood.
Methods: An integrative approach combining multi-omics bioinformatics and experimental validation was employed. We analyzed GEO datasets (GSE30122, GSE96804) to identify differentially expressed genes and performed WGCNA to define key modules. Network pharmacology predicted NGR1 targets, which were refined via machine learning (LASSO and Random Forest). Immune infiltration was assessed by CIBERSORT. Molecular docking and dynamics simulations evaluated binding interactions. In vitro functional validation used high glucose (HG)-injured MPC5 podocytes, with MME-specific inhibitor (Thiorphan), confirming target necessity.
Results: Bioinformatic analysis identified three core targets, MME, PTGS2, and S100A9, within the DN pathological network. Functional enrichment revealed their involvement in oxidative stress detoxification, TGF-β-mediated fibrosis, and AGE-RAGE signaling pathways. Immune infiltration analysis indicated an aberrant increase in M0/M2 macrophages and activated mast cells, alongside a reduction in protective γδ T cells, with core targets showing strong correlations with specific immune subsets. Subsequent molecular docking and dynamics…
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