Epigenetics of Chronic Kidney Disease

Chronic kidney disease (CKD) is a serious health issue that can lead to cardiovascular disease. Identifying individuals at high risk for CKD progression is important to prevent or delay its onset. The proposed research aims to examine metabolomic and proteomic changes in the kidney to identify biomarkers of the disease. These biomarkers can be used to help identify individuals at high risk of CKD progression and target them for early intervention. Human kidney transcriptomic studies highlighted key changes in lipid metabolism. Analyses of mouse models identified the key causal role of proximal tubule fatty acid oxidation in fibrosis development. The Metabolon platform allows for the quantitative measurement of over 1,500 analytes in human kidney tissue samples, allowing for the identification of metabolite changes in kidneys with DKD. It's important to analyze kidney metabolites as blood metabolite levels do not always reflect those in the kidney tissue. By integrating the analysis of metabolites and gene expression, alterations in multiple metabolic pathways in kidneys of patients with DKD and mouse disease models can be identified. Our initial proteomic analysis of human kidneys revealed changes in protein expression and only a moderate correlation between gene and protein expression. By using genotypes as instrumental variables, we can decipher the relationship between genetically determined epigenome and metabolite changes and pinpoint likely causal changes. The proposed research aims to define global changes in metabolite and protein levels in human kidney cortical tissue samples from patients with diabetic and hypertensive CKD and compare them to samples from healthy subjects, subjects with diabetes (DM), and hypertension (HTN) without kidney disease. The study will prioritize causal genes, proteins, and metabolites that contribute to the development of kidney dysfunction (eGFR) by using a hierarchical multi-staged integration approach and genotype information as an instrumental variable, followed by experimental validation. The ultimate goal is to identify specific dysregulated metabolic steps or pathways, discover biomarkers, and classify disease subtypes for patient stratification. The data and results will be made available to the community through an easy-to-use interactive website.