Chronic Kidney Disease affects 20 million people in the US and is associated with an approximately 3-5-fold increase in cardiovascular mortality. ESRD is the 9th leading cause of death in the US;it is associated with an approximately 20% yearly mortality rate, which is worse than most solid (colon, breast lung) cancers. Diabetic and hypertensive renal disease (DNP, HN) and FSGS -so called non- immune mediated degenerative glomerular diseases- are responsible for >75% of ESRD cases in the US. Currently, we have very few therapeutic options to offer to people with renal disease. In addition we have very limited tools to identify patients who are increased risk for the development of renal disease. In order to answer these questions we performed large scale gene expression studies using expression microarrays on human control and diseased kidney samples. We identified new gene expression patterns (potential biomarker patterns) that are associated with progression of renal disease. However, we found that the variation of gene expression levels is much higher in human samples than we previously observed in mouse. Thereby currently the expression levels of large number of genes are needed to identify patients with progressive renal disease. We propose that gene expression studies performed together with epigenomics analysis would facilitate the identification of new diagnostic and prognostic markers for progressive renal disease. 1.Generate genome scale cytosine methylation maps in control healthy and diseased kidney samples. We propose to use the HELP assay to determine DNA methylation patterns of microdissected glomerular and tubulointerstitial samples. 2. Compare DNA methylation profiles in the glomeruli and tubuli in control (""""""""healthy"""""""") and diseased kidney tissue samples and identify candidate methylation changes in kidney disease 3. Integrate results of mRNA expression and DNA methylation studies in order to identify a) new pathways, b) new biomarkers of progressive renal disease. The results of gene expression data that we already generated using Affymetrix expression arrays will be cross referenced and integrated with results of DNA methylation assays in order to identify new pathways and new biomarkers. In summary these studies would describe for the first time epigenetic modification in the kidney and help us to understand the complex regulatory network that leads to progressive loss of renal function. The availability of large number of well characterized human tissue samples with the corresponding gene expression data puts us into a unique position to achieve these goals.

Public Health Relevance

Chronic kidney disease affects 20 million people in the US and is associated with an approximately 3-5-fold increase in mortality. ESRD (end stage renal disease) is the 9th leading cause of death in the US;it is associated with an approximately 20% yearly mortality rate, which is worse than most solid (prostate, colon and breast) cancers. Currently there are approximately 500,000 patients that require renal replacement therapy (dialysis or transplant) in the US, which is associated with an annual cost of about 30 billion dollars. The epigenomic landscape of chronic kidney disease has not been studied. This would be the first study to perform genome scale DNA methylation studies on human kidney tissue. Based on our preliminary results we propose that there are significant differences in DNA methylation patterns, we also propose that these methylation differences can drive gene expression differences that govern the progression of chronic kidney disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK087635-05
Application #
8326102
Study Section
Special Emphasis Panel (ZRG1-GGG-M (53))
Program Officer
Rasooly, Rebekah S
Project Start
2009-09-01
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2014-08-31
Support Year
5
Fiscal Year
2012
Total Cost
$336,633
Indirect Cost
$127,570
Name
University of Pennsylvania
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Gluck, Caroline; Ko, Yi-An; Susztak, Katalin (2017) Precision Medicine Approaches to Diabetic Kidney Disease: Tissue as an Issue. Curr Diab Rep 17:30
Li, Man; Carey, Jacob; Cristiano, Stephen et al. (2017) Genome-Wide Association of Copy Number Polymorphisms and Kidney Function. PLoS One 12:e0170815
Li, Szu-Yuan; Park, Jihwan; Qiu, Chengxiang et al. (2017) Increasing the level of peroxisome proliferator-activated receptor ? coactivator-1? in podocytes results in collapsing glomerulopathy. JCI Insight 2:
Bhagat, Tushar D; Zou, Yiyu; Huang, Shizheng et al. (2017) Notch Pathway Is Activated via Genetic and Epigenetic Alterations and Is a Therapeutic Target in Clear Cell Renal Cancer. J Biol Chem 292:837-846
Beckerman, Pazit; Qiu, Chengxiang; Park, Jihwan et al. (2017) Human Kidney Tubule-Specific Gene Expression Based Dissection of Chronic Kidney Disease Traits. EBioMedicine 24:267-276
Beckerman, Pazit; Bi-Karchin, Jing; Park, Ae Seo Deok et al. (2017) Transgenic expression of human APOL1 risk variants in podocytes induces kidney disease in mice. Nat Med 23:429-438
Kruse, Michael; Fiallo, Ariana; Tao, Jianling et al. (2017) A High Fat Diet During Pregnancy and Lactation Induces Cardiac and Renal Abnormalities in GLUT4 +/- Male Mice. Kidney Blood Press Res 42:468-482
Chu, Audrey Y; Tin, Adrienne; Schlosser, Pascal et al. (2017) Epigenome-wide association studies identify DNA methylation associated with kidney function. Nat Commun 8:1286
Scerbo, Diego; Son, Ni-Huiping; Sirwi, Alaa et al. (2017) Kidney triglyceride accumulation in the fasted mouse is dependent upon serum free fatty acids. J Lipid Res 58:1132-1142
Ko, Yi-An; Yi, Huiguang; Qiu, Chengxiang et al. (2017) Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease. Am J Hum Genet 100:940-953

Showing the most recent 10 out of 43 publications