Identification of RNAs in Carrier Protein and Exosomes Compared to RNA Found in the Whole Sample

Objective/Rationale:          
Isolation of RNA biomarkers from biofluids is a new promising approach to monitor health and disease. RNAs exist in different fractions of cell-free biofluids. Some are found in extracellular vesicles and others are in protein complexes. Whole sample RNA, RNA from extracellular microvesicles (exosomes) and RNA bound to the RNA-carrier protein AGO2 have never been directly compared with one another from the same patient sample. We hope to examine the RNAs associated with each of these RNA fractions to determine which one may reliably hold the RNAs of greatest relevance to PD pathology.

Project Description:             
We will collect plasma and cerebrospinal fluid (CSF) to evaluate the biomarker potential in different fractions of the samples. We will use specific isolation methods to capture all of the RNA within a sample and within exosomes, and those bound to the protein AGO2. We will evaluate the full micro RNA and RNA content of each of the isolated fractions using next generation sequencing. Once the most promising methods have been established based on quality, yield, reproducibility and interesting markers, we will expand the study to examine that RNA fraction in both plasma and CSF from 30 patients with PD and 30 normal controls.

Relevance to Diagnosis/Treatment of Parkinson’s Disease:                     
Identification of biomarkers is an important task for early diagnosis, as well as evaluation of potential new treatments for Parkinson’s disease (PD). In order to make discovery of RNA-based biomarkers more reliable and consistent, examination of different RNA fractions is warranted. Information regarding population-specific RNA signals will allow further characterization of RNA markers of PD.

Anticipated Outcome:          
Information on population-specific RNA signals would help reduce the variability of biofluid RNA samples and potentially provide us with a more reliable signal to monitor the disease. This could hopefully help in diagnosis as well as speed up evaluation of new therapeutics for the disease.