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Urinary proteome profiling for stratifying patients with familial Parkinson’s disease

View ORCID ProfileSebastian Virreira Winter, View ORCID ProfileOzge Karayel, View ORCID ProfileMaximilian T Strauss, View ORCID ProfileShalini Padmanabhan, Matthew Surface, View ORCID ProfileKalpana Merchant, View ORCID ProfileRoy N. Alcalay, View ORCID ProfileMatthias Mann
doi: https://doi.org/10.1101/2020.08.09.243584
Sebastian Virreira Winter
1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
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Ozge Karayel
1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
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Maximilian T Strauss
1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
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Shalini Padmanabhan
2The Michael J. Fox Foundation for Parkinson’s Research, NY, USA
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Matthew Surface
3Department of Neurology, Columbia University, NY, USA
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Kalpana Merchant
4Northwestern University Feinberg School of Medicine, IL, USA
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Roy N. Alcalay
3Department of Neurology, Columbia University, NY, USA
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Matthias Mann
1Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
5Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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  • For correspondence: mmann@biochem.mpg.de
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SUMMARY

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 was 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 10, 2020.
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Urinary proteome profiling for stratifying patients with familial Parkinson’s disease
Sebastian Virreira Winter, Ozge Karayel, Maximilian T Strauss, Shalini Padmanabhan, Matthew Surface, Kalpana Merchant, Roy N. Alcalay, Matthias Mann
bioRxiv 2020.08.09.243584; doi: https://doi.org/10.1101/2020.08.09.243584
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Urinary proteome profiling for stratifying patients with familial Parkinson’s disease
Sebastian Virreira Winter, Ozge Karayel, Maximilian T Strauss, Shalini Padmanabhan, Matthew Surface, Kalpana Merchant, Roy N. Alcalay, Matthias Mann
bioRxiv 2020.08.09.243584; doi: https://doi.org/10.1101/2020.08.09.243584

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