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Integrated metabolomics coupled with pattern recognition and pathway analysis to reveal molecular mechanism of cadmium-induced diabetic nephropathy.

ABSTRACT: Diabetic nephropathy (DN) is becoming a worldwide public health problem and its pathophysiological mechanism is not well understood. Emerging evidences indicated that cadmium (Cd), an industrial material but also an environmental toxin, may be involved in the development and progression of diabetes and diabetes-related kidney disease. However, the underlying mechanism is still unclear. Herein, a DN animal model was constructed by exposing to Cd, the metabolomic profiling of DN mice were obtained by using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), pattern recognition and pathway analysis were performed to screen potential biomarker. Moreover, western blotting was employed to verify the possible mechanism involved in the occurrence of Cd-induced DN. A total of 66 metabolites in serum have been screened out and identified as biomarkers, including free fatty acids, phospholipids, sphingomyelins, glycerides, and others. Significant differences were demonstrated between the metabolic profiles, including decreased levels of phospholipid and increased content of triglyceride, diacylglycerols, ceramide, lysophosphatidylcholine in Cd-induced DN mice compared with control. Protein expression level of p38 MAPK and Wnt/β-catenin were significantly increased. UPLC-Q-TOF/MS-based serum metabolomics coupled with pattern recognition methods and pathway analysis provide a powerful approach to identify potential biomarkers and is a new strategy to predict the underlying mechanism of disease caused by environmental toxicant.

SUBMITTER: Gong P

PROVIDER: S-EPMC8403815 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundImmunoglobulin A nephropathy (IgAN), a globally common primary chronic glomerulopathy, is one of the leading causes of end-stage renal disease. However, the underlying mechanisms of IgAN have yet to be demonstrated. There were no adequate and reliable plasma biomarkers for clinical diagnosis, especially at the early stage. In the present study, integrative proteomics and metabolomics were aimed at exploring the mechanism of IgAN and identifying potential biomarkers.MethodsPlasma from IgAN and healthy individuals were collected and analyzed in a randomized controlled manner. Data-independent acquisition quantification proteomics and mass spectrometry based untargeted metabolomics techniques were used to profile the differentially expressed proteins (DEPs) and differentially abundant metabolites (DAMs) between two groups and identify potential biomarkers for IgAN from health at the early stage. Disease-related pathways were screened out by clustering and function enrichment analyses of DEPs and DAMs. And the potential biomarkers for IgAN were identified through the machine learning approach. Additionally, an independent cohort was used to validate the priority candidates by enzyme-linked immunosorbent assay (ELISA).ResultsProteomic and metabolomic analyses of IgAN plasma showed that the complement and the immune system were activated, while the energy and amino acid metabolism were disordered in the IgAN patients. PRKAR2A, IL6ST, SOS1, and palmitoleic acid have been identified as potential biomarkers. Based on the AUC value for the training and test sets, the classification performance was 0.994 and 0.977, respectively. The AUC of the external validation of the four biomarkers was 0.91.ConclusionIn this study, we combined proteomics and metabolomics techniques to analyze the plasma of IgAN patients and healthy individuals, constructing a biomarker panel, which could provide new insights and provide potential novel molecular diagnoses for IgAN.

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Integrated metabolomics coupled with pattern recognition and pathway analysis to reveal molecular mechanism of cadmium-induced diabetic nephropathy.

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