Proteomic analysis of urinary extracellular vesicles reveal biomarkers for neurologic disease.

Published

Journal Article

BACKGROUND: Extracellular vesicles (EVs) harbor thousands of proteins that hold promise for biomarker development. Usually difficult to purify, EVs in urine are relatively easily obtained and have demonstrated efficacy for kidney disease prediction. Herein, we further characterize the proteome of urinary EVs to explore the potential for biomarkers unrelated to kidney dysfunction, focusing on Parkinson's disease (PD). METHODS: Using a quantitative mass spectrometry approach, we measured urinary EV proteins from a discovery cohort of 50 subjects. EVs in urine were classified into subgroups and EV proteins were ranked by abundance and variability over time. Enriched pathways and ontologies in stable EV proteins were identified and proteins that predict PD were further measured in a cohort of 108 subjects. FINDINGS: Hundreds of commonly expressed urinary EV proteins with stable expression over time were distinguished from proteins with high variability. Bioinformatic analyses reveal a striking enrichment of endolysosomal proteins linked to Parkinson's, Alzheimer's, and Huntington's disease. Tissue and biofluid enrichment analyses show broad representation of EVs from across the body without bias towards kidney or urine proteins. Among the proteins linked to neurological diseases, SNAP23 and calbindin were the most elevated in PD cases with 86% prediction success for disease diagnosis in the discovery cohort and 76% prediction success in the replication cohort. INTERPRETATION: Urinary EVs are an underutilized but highly accessible resource for biomarker discovery with particular promise for neurological diseases like PD.

Full Text

Duke Authors

Cited Authors

  • Wang, S; Kojima, K; Mobley, JA; West, AB

Published Date

  • July 2019

Published In

Volume / Issue

  • 45 /

Start / End Page

  • 351 - 361

PubMed ID

  • 31229437

Pubmed Central ID

  • 31229437

Electronic International Standard Serial Number (EISSN)

  • 2352-3964

Digital Object Identifier (DOI)

  • 10.1016/j.ebiom.2019.06.021

Language

  • eng

Conference Location

  • Netherlands