Urinary proteome profiling in Parkinson’s disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive and non-invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive proteomics workflow for urinary proteome profiling by combining high-throughput sample preparation with state-of-the-art mass spectrometry (MS)-based proteomics. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent cohorts. The urinary proteome was significantly different between PD patients and healthy controls as well as between LRRK2 G2019S carriers and non-carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. Machine learning on the urinary proteome data alone classified mutation status and especially disease manifestation in mutation carriers remarkably well (ROC AUCs 0.87 and 0.94, respectively), identifying VGF, ENPEP and other PD-associated proteins as the most discriminating features. Our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.