Renal Metabolite Handling: from Gene to Function to Disease

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The kidneys have a central role for whole body homeostasis. Chronic kidney disease (CKD) affects >10% of the adult general population. Our understanding of the manifold functions of the kidney is incomplete even under physiological conditions, impeding the development of specific therapies for CKD. Gaining insights into kidney function (patho-)physiology has been complicated by the organ’s complexity: the handling of filtered and secreted solutes varies widely. Many of these solutes are metabolites, small intermediates or products of metabolic processes. Genome-wide genetic screens of metabolite concentrations are an unbiased way to gain insights into such processes, for example by identifying novel metabolite transporters or enzymes. In the first funding period of KO 3598/4-1, we successfully established methods to study human renal metabolite handling in large human populations by linking genomics and metabolomics data. We generated novel insights into metabolite handling in 1,200 patients with CKD. This included findings specific to known uremic toxins, but also more general findings applicable to human renal physiology, such as the identification of substrates of renal enzymes and amino acid exchangers in humans. For a second funding period, we now propose several important advances: first, we will increase the population size from 1,200 to all 5,217 persons in the German Chronic Kidney Disease study. This will lead to improved statistical power especially for the identification of rare genetic variants with large effects on metabolite concentrations. Second, we will increase the number of metabolites from 188 to >1,400. The non-targeted, mass-spectrometry based quantification of urinary metabolite concentrations will allow both for the in-depth evaluation of specific biochemical pathways and for the study of molecules of yet unknown identity. Third, we will perform a systematic comparison of our findings to those from a population-based study of healthy individuals, in order to systematically distinguish CKD-specific mechanisms from more general insights into renal metabolism. And fourth, we will be able to translate the identified mechanisms into clinical insights by evaluating the associations between metabolites and risk variants with >2,000 events of CKD progression, cardiovascular endpoints and all-cause mortality. To identify and characterize mechanisms of renal metabolite handling, we will use unbiased genome-wide association studies and exome-wide rare variant association studies (Aim 1), genome-wide genetic screens of composite metabolite measures such as ratios and clusters of highly correlated metabolites (Aim 2), and epidemiological and experimental follow-up studies of implicated mechanisms (Aim 3). Genetic, metabolomics and endpoint data have already been generated, making the proposed research feasible, cutting-edge and relevant to advance our insights into the role of the kidney in metabolite handling in health and disease.