Multi-tissue high-throughput proteomic and genomic study in Parkinson's Disease

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Project Summary/Abstract Parkinson's disease (PD) is the most common neurodegenerative movement disorder, affecting more than 6 million people worldwide, with the prevalence projected to double in the next few decades. Despite the improvements in high-throughput genomics and proteomics that have significantly facilitated the advancement of biomarker discovery in other neurodegenerative diseases, there are no reliable biomarkers for PD. Currently, the PD diagnosis relies almost entirely on clinical examination. There are several reasons for the lack of reliable biomarkers in PD including most studies have been focused on single molecules in one tissue, small samples sizes and a lack of independent replication cohorts. To overcome these limitations, we propose leveraging a unique resource that includes quantitative proteomic analysis of ~1,300 proteins from CSF and plasma of clinically diagnosed PD patients coupled with validation in brain samples from autopsy-confirmed PD cases. We will pair the proteomic data with novel and powerful unbiased (hypothesis-free) genomic approaches to select the most plausible candidates for targeted replication studies. This large-scale screening of ~3,110 samples could identify biomarkers of known molecular pathways involving PD or with a clear genetic connection to PD risk. To achieve these goals, we propose three aims: Specific Aim 1: To identify proteins differentially expressed in PD patients in plasma, CSF or brain tissue. We plan to carry out a quantitative proteomic analysis using Somalogic SOMAscan® assay of plasma (n=600) and CSF (n=200) from clinically diagnosed PD patients and of brain tissue (n=200) from autopsy-confirmed PD patients. We will also evaluate CSF (n=740), plasma (n=410) and brain tissue (n=114) from an independent cohort of healthy individuals and CSF (n=275), plasma (n=234) and brain tissue (n=345) from AD patients. We expect to obtain precise and accurate levels of a large number of proteins across multiple tissues in the analyzed samples. Specific Aim 2: To prioritize candidate biomarkers based on an integrative analysis of proteomic and genome-wide genotyping data using Mendelian Randomization (MR). We plan to integrate proteomic and GWAS data to identify protein quantitative loci (pQTLs) and apply MR approaches to determine proteins involved in the causal pathway of PD. Using this approach, we will be able to select reliable PD biomarker candidates for validation. Specific Aim 3: To determine whether genetic and proteomic data improves biomarker specificity. We will ascertain whether combining proteomic and genomic data could increase biomarker accuracy. We expect to uncover a genome-proteome network that may provide a basis for novel approaches to diagnostic and pharmacotherapeutic applications in PD.