Noninvasive MRI techniques to detect pathology in murine models of renal disease

项目总结/文摘

Project Summary/Abstract The broad, long-term goal of this multi-disciplinary collaborative of biomedical engineers and physician- scientists is to develop early, non-invasive methods to identify individuals at risk of developing chronic kidney disease (CKD). Current methods to detect kidney disease are only useful when more than half of the filtering units of the kidney, nephrons, are nonfunctional. Nephron number is determined at birth, declines over the lifespan of a human and is directly related to the development of chronic kidney and cardiovascular disease. Unfortunately, there are no techniques to count the total number of functioning nephrons in living individuals. Additionally, there is no way to integrate the major compartments of the kidney, such as glomerular changes to those in the vasculature or tubulointerstitial space. A diagnostic biomarker to assess renal microstructure, such as number and volume of glomeruli, could significantly benefit patients: earlier therapeutic intervention, novel endpoints for assessing renal safety in clinical trials for drug development, and for renal allografts allocation. This work has three Specific Aims: 1) We will assess the changes in the glomeruli by MRI during the development of CKD using two mouse models: a congenital reduction in nephron number and a glomerulosclerosis model of CKD. We will compare the changes in glomerular microstructure to the vascular and tubular compartments and to traditional biomarkers of renal disease. 2) We will determine the time course of tubulointerstitial pathology using MRI in mouse model representing the transition from acute kidney injury to chronic kidney disease. 3) We will determine the effect of ACE inhibition of the microstructure of the kidney in a mouse model of essential hypertension. At the conclusion of this project, we will have the first comprehensive, integrated MRI-based evaluation of the kidney, powerful data to inform the translation of these MRI-based biomarkers for future studies to predict kidney disease progression.