Urinary proteome profiling for stratifying patients with familial Parkinson’s disease
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Urinary proteome profiling for stratifying patients with familial Parkinson’s disease. / Virreira Winter, Sebastian; Karayel, Ozge; Strauss, Maximilian T.; Padmanabhan, Shalini; Surface, Matthew; Merchant, Kalpana; Alcalay, Roy N.; Mann, Matthias.
In: EMBO Molecular Medicine, Vol. 13, No. 3, e13257, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Urinary proteome profiling for stratifying patients with familial Parkinson’s disease
AU - Virreira Winter, Sebastian
AU - Karayel, Ozge
AU - Strauss, Maximilian T.
AU - Padmanabhan, Shalini
AU - Surface, Matthew
AU - Merchant, Kalpana
AU - Alcalay, Roy N.
AU - Mann, Matthias
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - biomarker
KW - DIA
KW - mass spectrometry
KW - Parkinson’s disease
KW - urinary proteome
U2 - 10.15252/emmm.202013257
DO - 10.15252/emmm.202013257
M3 - Journal article
C2 - 33481347
AN - SCOPUS:85099806738
VL - 13
JO - EMBO Molecular Medicine
JF - EMBO Molecular Medicine
SN - 1757-4676
IS - 3
M1 - e13257
ER -
ID: 257326238