Mechanism diagram of this study (IMAGE)
Caption
This illustration outlines the comprehensive research strategy combining computational biology, machine learning, and experimental validation. Using network toxicology, 238 overlapping targets were identified from 374 PS-related targets and 4,037 kidney injury-related targets, and a protein-protein interaction (PPI) network was constructed. Machine learning was employed to screen for pivotal genes, and molecular docking coupled with molecular dynamics simulations were utilized to validate the binding between PS and core targets (e.g., MMP9, APP). Single-cell transcriptome analysis further uncovered the critical role of intercellular communication. The central mechanistic discovery: PS upregulates Amyloid Precursor Protein (APP), which acts as a ligand engaging CD74 and PTGER2 receptors on endothelial cells. This interaction promotes aberrant communication between endothelial cells and immune cells (e.g., NK cells), ultimately contributing to glomerulonephritis, renal tubular cell damage, and fibrosis. Finally, in vitro experiments—including CCK-8 cell viability assay, wound healing assay, and Western blot—using human renal tubular epithelial cells (HK-2) confirmed the cytotoxicity of PS, its inhibitory effect on cell migration, and the upregulation of APP protein expression.
Credit
Yimao Wu and Meng-Yao Li
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