Work

Multi-omics characterization of diabetic nephropathy in the db/db mouse model of type 2 diabetes

Year: 2025

Type: article

Abstract: Background Despite optimized blood pressure control and glycemic management reducing the incidence of diabetic nephropathy (DN), significant residual risk remains, suggesting the contribution of pathogenic factors independent of glucose metabolism and hemodynamic disturbances. Methods Renal tissues from db/db mice underwent integrative multi-omics analysis, encompassing transcriptomics, metabolomics, and lipidomics. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) was applied to identify significant metabolic perturbations, while bidirectional O2PLS integration elucidated metabolic-transcriptomic correlations. Lipid reaction networks were reconstructed using LINEX2, followed by local topology exploration to identify highly interconnected modules. Mechanistic pathways governing gene-metabolite-lipid interactions were inferred via random walk with restart algorithms and validated by gene set enrichment analysis (GSEA). Results Transcriptomics revealed extensive dysregulation of metabolic and lipid regulatory pathways in db/db. Metabolomic integration pinpointed perturbations within glycine-serine-threonine (Gly-Ser-Thr) metabolism as the most significantly perturbed pathway (P < 0.001), with cross-omics validation identifying GLUL as a pivotal regulatory gene through. Lipidomics uncovered pronounced abnormalities in cardiolipin species composition and plasmalogen profiles. Transcriptome-lipidome integration demonstrated impaired phosphatidylcholine (PC) biosynthesis, mechanistically linked to dysregulation of choline phosphotransferase 1 ( chpt1 ), which correlated significantly with compromised tissue regeneration capacity. Conclusion This multi-omics study systematically delineates the molecular landscape of DN pathogenesis, uncovering previously underappreciated metabolic perturbations and distinct lipid dysregulation patterns. Our findings elucidate mechanistic insights into extra-glycemic disease drivers and propose potential therapeutic targets for DN management.

Source: Computational and Structural Biotechnology Journal

DOI: 10.1016/j.csbj.2025.07.037