Abstract

Parkinson’s disease is highly heterogeneous across disease symptoms, clinical manifestations and progression trajectories, hampering the identification of therapeutic targets. Despite knowledge gleaned from genetics analysis, dysregulated proteome mechanisms stemming from genetic aberrations remain underexplored. In this study, we develop a three-phase system-level proteogenomic analytical framework to characterize disease-associated proteins and dysregulated mechanisms. Proteogenomic analysis identified 577 proteins that enrich for Parkinson’s disease-related pathways, such as cytokine receptor interactions and lysosomal function. Converging lines of evidence identified nine proteins, including LGALS3, CSNK2A1, SMPD3, STX4, APOA2, PAFAH1B3, LDLR, HSPB1, BRK1, with potential roles in disease pathogenesis. This study leverages the largest population-scale proteomics dataset, the UK Biobank Pharma Proteomics Project, to characterize genetically-driven protein disturbances associated with Parkinson’s disease. Taken together, our work contributes to better understanding of genome-proteome dynamics in Parkinson’s disease and sets a paradigm to identify potential indirect mediators connected to GWAS signals for complex neurodegenerative disorders.

Here, the authors leverage large-scale population data to identify genetic variants associated with the risk of Parkinson’s disease and examine their impact on plasma proteins that can potentially contribute to the disease.

Details

Title
Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson’s disease
Author
Doostparast Torshizi, Abolfazl 1   VIAFID ORCID Logo  ; Truong, Dongnhu T. 1 ; Hou, Liping 1 ; Smets, Bart 2 ; Whelan, Christopher D. 3 ; Li, Shuwei 1 

 LLC, Population Analytics & Insights, AI/ML, Data Science & Digital Health, Janssen Research & Development, Spring House, USA (GRID:grid.497530.c) (ISNI:0000 0004 0389 4927) 
 Janssen Pharmaceutica NV, Neuroscience Data Science, Beerse, Belgium (GRID:grid.419619.2) (ISNI:0000 0004 0623 0341) 
 LLC, Neuroscience Data Science, Janssen Research & Development, Cambridge, USA (GRID:grid.419619.2) 
Pages
6430
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3086184949
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.