Urinary proteome profiling for stratifying patients with familial Parkinson's disease. Issue 3 (22nd January 2021)

• Record Type: Journal Article
• Authors: Virreira Winter, Sebastian; Karayel, Ozge; Strauss, Maximilian T; Padmanabhan, Shalini; Surface, Matthew; Merchant, Kalpana; Alcalay, Roy N; Mann, Matthias
• Is Part Of: EMBO molecular medicine. Volume 13:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-22
• Subjects: biomarker -- DIA -- mass spectrometry -- Parkinson's disease -- urinary proteome; Molecular biology -- Periodicals; Medical genetics -- Periodicals; Pathology, Molecular -- Periodicals; 616.04205
• DOI: 10.15252/emmm.202013257
• Languages: English
• ISSNs: 1757-4676

Abstract

Abstract: 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 mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2, 000 proteins in more than 200 urine samples using minimal volumes from two independent patient 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. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD. Synopsis: This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson's disease (PD). The presented workflow allows quantification of more than 2, 000 proteins in urine. Lysosomal dysregulation is reflected in the urinary proteomes of individuals with the pathogenic LRRK2 G2019S mutation. Machine learning on the urinary proteome classifies LRRK2 mutation and PD disease states with sensitivities of 78% and 74% and specificities of 73% and 84%, respectively. The neurotrophic factor VGF was identified as the most important feature to discriminate manifesting from non‐manifesting LRRK2 G2019S carriers. Abstract : This study presents a scalable, sensitive and reproducible mass spectrometry‐based proteomics workflow for urinary proteome profiling, and demonstrates it as a promising strategy for urine biomarker discovery for Parkinson's disease (PD).