Characterization of Diagnostic Biosignatures for Parkinson’s Disease and Renal Cell Carcinoma Through Quantitative Proteomics and Phosphoproteomics Analyses of Urinary Extracellular Vesicles
Abstract
Urine-based biomarkers offer numerous advantages for clinical analysis, including noninvasive collection, a suitable sample source for longitudinal disease monitoring, a better screenshot of disease heterogeneity, higher sample volumes, faster processing times, and lower rejection rates and costs. They will be extremely useful in a clinical trial context, which can be applied alone or in combination with other methods as long as they demonstrate clear reproducibility across cohorts. While biofluids such as urine present enormous challenges with a wide dynamic range and extreme complex typically dominated by a few highly abundant proteins, we have demonstrated that the analytical issue can be efficiently addressed by focusing on extracellular vesicles (EVs), tiny packages released by all kinds of cells. These tiny packages contain different kinds of molecules from inside the cells. Here, we established a robust EV isolation and characterization platform to screen and validate Parkinson’s Disease (PD) and Renal Cell Carcinoma (RCC) biomarkers from urine. PD is a progressive neurological disorder affecting body movement because some brain cells stop producing dopamine. PD is often not diagnosed until it has advanced, making early detection crucial. We investigated urinary EVs from 138 individuals to enable early detection and found several proteins involved in PD development that could be biological indicators for early disease detection. Several biochemical techniques were applied to verify our findings. In the second project, we attempted to develop a novel diagnostic technique for early intervention of RCC. Here, we made our efforts to develop a quantitative method based on data-independent acquisition (DIA) mass spectrometry to analyze urinary EV phosphoproteomics for non-invasive RCC biomarker screening. Combined with our in-house EVtrap method for EV isolation and PolyMAC enrichment of phosphopeptides, we quantified 2,584 unique phosphosites. We observed unique upregulated phosphosites and pathways differentiating healthy control (HC), chronic kidney disease (CKD), low-grade, and high-grade clear cell RCC. These applications have a significant promise for early PD and RCC diagnosis and monitoring based on actual functional proteins with urine as the source. These studies might provide a viable path to developing urinary EV-based disease diagnosis.
Degree
Ph.D.
Advisors
Tao, Purdue University.
Subject Area
Analytical chemistry|Bioinformatics|Cellular biology|Medicine|Neurosciences|Oncology|Pharmaceutical sciences
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