Multiple Reaction Monitoring (MRM)-Based Targeted Kidney Metabolite Profiling of a Mouse Model of Hyperuricemia

Background/Objectives: Chronic urate nephropathy (CUN), also referred to as gouty nephropathy, represents a severe renal disease primarily precipitated by long-term hyperuricemia (HUA) and gout. However, the precise molecular mechanisms underlying its pathogenesis remain poorly understood. The present study was designed to explore these mechanisms from the perspective of targeted metabolomics. Methods: The HUA mice constructed by urate oxidase (Uox) gene knockout (KO) and their corresponding wild-type controls were employed for the present study. Serum clinical biochemical parameters were determined, and renal histopathological changes were evaluated using hematoxylin-eosin (HE) staining and Masson’s trichrome staining. A targeted metabolomic strategy based on multiple reaction monitoring (MRM) was utilized to profile the renal metabolic landscape of Uox-KO mice, and potential metabolic biomarkers for CUN were identified via multivariate data analysis. Results: Clinical biochemical analysis revealed a significant elevation in serum uric acid, creatinine, and urea nitrogen levels in Uox-KO mice compared with control mice. Histopathological observations confirmed a typical CUN phenotype in Uox-KO mice, characterized by renal tubular vacuolar degeneration and dilatation, desquamation of tubular epithelial cells into the lumen, neutrophil infiltration, glomerular crowding, and renal interstitial fibrosis. Metabolomic analysis identified a total of 291 differentially regulated metabolites in Uox-KO mice relative to control animals. These perturbed metabolites were involved in multiple key biochemical pathways, including amino acid biosynthesis, ABC transporter signaling pathway, purine metabolism, aminoacyl-tRNA biosynthesis, protein digestion and absorption, glycerophospholipid metabolism, and serotonergic synaptic transmission. Notably, pathological parameters, including biochemical measurements and histological observations, were significantly correlated with key differential metabolites associated with CUN progression. Furthermore, eleven differential metabolites (pyroglutamic acid, fructose, riboflavin, dimethyl-L-arginine, glucaric acid, indoxyl sulfate, palmitoylethanolamide, trimethylamine N-oxide, 3-hydroxyanthranilic acid, spermidine, and hippuric acid) were identified as potential metabolic biomarkers for the diagnosis and prognosis of CUN. Conclusions: These findings illustrate that targeted tissue metabolomic analysis constitutes a powerful tool for deciphering the molecular mechanisms of diseases, thus offering novel insights into the pathogenesis of CUN.