Principal Investigator:

Contributors: Manikkam Suthanthiran

DESCRIPTION (provided by applicant): 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.

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