Multi-tissue High-throughput Proteomic and Genomic Study in Parkinson's Disease

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.

Grant Number: 5R01NS118146-06
NIH Institute/Center: NIH
Principal Investigator: Bruno Benitez