Acute and chronic glomerular damage is associated with reduced CD133 expression in urinary extracellular vesicles.

Extracellular vesicles released into urine (uEVs) can represent interesting biomarkers of renal cell damage. CD133, a stem/progenitor cell marker expressed by renal progenitor cells, is highly expressed in uEVs of healthy individuals. In the present study, we evaluated the level of CD133 in the uEVs of patients with acute and chronic glomerular damage by cytofluorimetric analysis. The level of CD133+ uEVs was significantly decreased in pediatric patients with acute glomerulonephritis during the acute phase of renal damage, while it was restored after the subsequent recovery. A similar decrease was also observed in patients with chronic glomerulonephritis. Moreover, CD133+ uEVs significantly declined in patients with type 2 diabetes, used as validation group, with the lowest levels in patients with albuminuria with diabetic nephropathy. Indeed, receiver-operating characteristic curve analysis indicates the ability of CD133+ uEV values to discriminate the health condition from that of glomerular disease. In parallel, a significant decrease of CD133 in renal progenitor cells and in their derived EVs was observed in vitro after cell treatment with a combination of glucose and albumin overload, mimicking the diabetic condition. These data indicate that the level of CD133+ uEVs may represent an easily accessible marker of renal normal physiology and could provide information on the "reservoir" of regenerating cells within tubules.

Proteomic Analysis of Urinary Microvesicles and Exosomes in Medullary Sponge Kidney Disease and Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND AND OBJECTIVES: Microvesicles and exosomes are involved in the pathogenesis of autosomal dominant polycystic kidney disease. However, it is unclear whether they also contribute to medullary sponge kidney, a sporadic kidney malformation featuring cysts, nephrocalcinosis, and recurrent kidney stones. We addressed this knowledge gap by comparative proteomic analysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The protein content of microvesicles and exosomes isolated from the urine of 15 patients with medullary sponge kidney and 15 patients with autosomal dominant polycystic kidney disease was determined by mass spectrometry followed by weighted gene coexpression network analysis, support vector machine learning, and partial least squares discriminant analysis to compare the profiles and select the most discriminative proteins. The proteomic data were verified by ELISA. RESULTS: A total of 2950 proteins were isolated from microvesicles and exosomes, including 1579 (54%) identified in all samples but only 178 (6%) and 88 (3%) specific for medullary sponge kidney microvesicles and exosomes, and 183 (6%) and 98 (3%) specific for autosomal dominant polycystic kidney disease microvesicles and exosomes, respectively. The weighted gene coexpression network analysis revealed ten modules comprising proteins with similar expression profiles. Support vector machine learning and partial least squares discriminant analysis identified 34 proteins that were highly discriminative between the diseases. Among these, CD133 was upregulated in exosomes from autosomal dominant polycystic kidney disease and validated by ELISA. CONCLUSIONS: Our data indicate a different proteomic profile of urinary microvesicles and exosomes in patients with medullary sponge kidney compared with patients with autosomal dominant polycystic kidney disease. The urine proteomic profile of patients with autosomal dominant polycystic kidney disease was enriched of proteins involved in cell proliferation and matrix remodeling. Instead, proteins identified in patients with medullary sponge kidney were associated with parenchymal calcium deposition/nephrolithiasis and systemic metabolic derangements associated with stones formation and bone mineralization defects. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_04_24_CJASNPodcast_19_06_.mp3.