Development of quantitative metabolomics analyses to understand cystic kidney diseases

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Chronic kidney disease (CKD) is one of the most important risk factor for cardiovascular events and requires dialysis or renal transplantation. One frequent cause of CKD is autosomal dominant polycystic kidney disease. Autosomal dominant polycystic kidney disease is characterized by an altered metabolism which drives the proliferation of hundreds and thousands of small fluid-filled cysts, which ultimately destroy the kidney. The metabolic pathways which lead to this phenotype, however, are not completely understood. To answer this question, I will use untargeted, mass-spectrometry based analyses of the entitity of metabolites, the metabolome. Although metabolomic analysis are largely utilized in biomarker studies, the potential of the method to understand (renal) diseases mechanisms is largely unexplored, and the effect of phyisological perturbation is not well characterized. In the project proposed here, I will apply targeted and untargeted metabolomics to understand physiology and pathology of the distal nephron. First, I intend to use nanostructure imaging mass spectrometry to discover which metabolites specifically occur in which region of the kidney and how they change by physiological stimuli (urine concentration). Second, I aim to use a global metabolomics strategy to learn how metabolism changes in the kidneys which are developing cysts. Third, I want to utilize targeted metabolomics analysis to characterize a substrates of an enzyme I found to be upregulated in aged and cystic kidneys. Alltogether, the data generated in this project will generate new insights into renal metabolism and complement previously generated proteomic data in a "multi-omics" approach. The study suggested here will be among the first applications of metabolomics to distal nephron pathophysiology.