ArticleComputational and structural biotechnology journal2025
Multi-omics characterization of diabetic nephropathy in the db/db mouse model of type 2 diabetes.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Morphological and Biochemical Insights Into the Renoprotective Effects of Combined N-Acetylcysteine and Glycine Treatment in Experimental Diabetic Nephropathy.Pharmacology research & perspectives · 2026Article
- A novel mouse model for cardiovascular-kidney-metabolic syndrome: Bridging metabolic, renal and cardiac dysfunction.Molecular metabolism · 2026Article
- Integrated multi-omics and machine learning identify an interaction between SLC39A11 and phosphoinositide metabolism in deep vein thrombosis.BMC medical informatics and decision making · 2026Article
- Unbiased Long-Read Whole-Genome Sequencing Enables High-Resolution Mapping of Transgene Concatenation and Off-target Genomic Disruption in a Mouse Model.Computational and structural biotechnology journal · 2026Article
- C3a/C3aR axis is involved in diabetic kidney injury by regulating podocyte mitophagy in diabetic nephropathy.International journal of molecular medicine · 2025Article
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Authors and funding
7 authors.
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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 ( 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.
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