Chronic kidney disease (CKD) is responsible for premature death from cardiovascular disease, infections and cancer, considerable human suffering and social costs. A large proportion of CKD patients develop end stage renal disease and require dialysis or kidney transplantation. Identifying patients at risk for CKD progression may facilitate precision medicine and prevent attendant complications. Development of mechanistic biomarkers predictive of CKD progression may help develop new treatment(s). Non-coding RNA (ncRNA), specifically microRNA (miRNA) is increasingly recognized as biomolecules that affect multiple cellular processes via regulation of gene expression. It is known that tissue RNA expression conveys information on disease status. Recently, it has been recognized that extracellular RNA (exRNA) is protected from nucleases by their encompassing microvesicles, and therefore can be measured in biological fluids including urine, and convey pathophysiological knowledge. Our labs have extensive experience in high throughput analysis of RNA expression. We have developed a cDNA deep sequencing method to profile miRNA in multiple samples, in a cost-effective, transcriptome-wide and bias-reduced manner. By studying thousands of diverse human samples we have discovered and curated miRNA and other small RNA. Currently, we are developing a parallel method to screen for all ncRNA. We have also adapted quantitative reverse-transcription polymerase chain reaction (qRT-PCR) technology to allow non- invasive verification and validation in larger study cohorts of patients with native and transplant kidney disease. We propose to apply our methods to comprehensively profile miRNA and other ncRNA in urine specimens collected from CKD patients.
We aim to identify exRNA in the urine that can predict progression of CKD and develop prognostic biomarkers. We propose a 2-stage protocol;a discovery stage applying deep sequencing for transcriptome-wide profiling and identification of candidate markers, and a validation phase applying RT- qPCR to characterize additional samples and qualify the discovered findings. To allow rapid implementation of our research plan we established collaborations with investigators conducting longitudinal cohort studies of CKD. Urine specimens, linked with rich demographic, clinical and kidney outcome data are available from studies of both adult and pediatric cohorts;both glomerular and non-glomerular diseases;in both native kidneys and kidney transplant recipients. This permits discovery of biomarkers capable of predicting progression across the spectrum of kidney diseases as mechanisms of CKD progression are to a largely independent of CKD etiology. Nonetheless, our studies are powered for subgroup-specific biomarker discovery. Our preliminary findings confirm feasibility to profile exRNA in biofluids, and to detect associations with clinical relevant outcomes. We predict that our findings will inform basic scientist on RNA dysregulation in kidney disease progression and provide clinical scientist biomarker candidates for clinical trials.

Public Health Relevance

Chronic kidney disease is responsible for enormous human suffering and decreased life expectancy. Identifying patients with greater risk of worsening function of the kidneys may improve their care. We propose to examine urine of patients with kidney disease for the presence of small RNA molecules. We will follow patients to determine whether our approach can identify patients at risk for worsening disease, and will develop a laboratory test that can be used in the clinical setting to manage patients with chronic kidney disease.

Agency
National Institute of Health (NIH)
Institute
National Center for Advancing Translational Sciences (NCATS)
Type
Exploratory/Developmental Cooperative Agreement Phase I (UH2)
Project #
5UH2TR000933-02
Application #
8711593
Study Section
Special Emphasis Panel (ZRG1-GGG-R (51))
Program Officer
Tagle, Danilo A
Project Start
2013-08-01
Project End
2015-07-31
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
2
Fiscal Year
2014
Total Cost
$488,188
Indirect Cost
$93,254
Name
Rockefeller University
Department
Genetics
Type
Other Domestic Higher Education
DUNS #
071037113
City
New York
State
NY
Country
United States
Zip Code
10065