Investigation of Parkinson's disease heterogeneity and driving factors based on DIA proteomics analysis of patient PBMCs

Introduction:

Parkinson's disease (PD) is the second most common and the fastest growing neurodegenerative disease. The disease is characterized by loss of dopaminergic neurons in mid brain and debilitating motor symptoms. Non-motor symptoms such as loss of smell, constipation and sleep disturbances are also present, and their appearance precede the motor symptoms by years. Cause of the disease is unknown, but it is thought that environmental factors, risk factors such as head injury, as well as disease causing genetic variants and proteins work together in pathogenesis of PD. Roughly 10% of individuals with PD carry a mutation and ~15% have a PD family history. The most common mutations are in genes coding for Leucine-rich repeat kinase 2 (LRRK2) and Glucocerebrosidase (GBA). Significant heterogeneity exists in PD progression, motor and non-motor symptoms and biomarkers. To better understand the heterogeneity and how proteins contribute to disease, progression we seek to find biomarkers and biology behind the PD using peripheral blood mononuclear cells (PBMC) from two independent PD patient cohorts. These contain samples from sporadic and genetic PD patients, motor disease patients without PD diagnosis (nonPD), prodromal individuals +/- disease-associated mutations and healthy individuals.

Methods:

PBMC samples in the first cohort were obtained from South-Western Finland catchment area (32 PD, 25 nonPD and 32 healthy). Second cohort is from Parkinson's Progression Marker Initiative consortium (PPMI) from 180 sporadic PD, 72 GBA+PD, 91 LRRK2+PD and 79 healthy individuals. Additionally, PPMI cohort includes 53 prodromal individuals without a mutation, 151 GBA and 139 LRRK2 mutation carriers without PD diagnosis. Total number of samples from PPMI cohort is 1181 with up to three time points per individual. High throughput digestion with S-trap™ was used to generate tryptic peptides. Each digestion batch contained two digestion controls from a standard sample. Resulting tryptic peptides were analysed with Evosep 30 sample per day method, with a daily standard sample on Oribitrap Lumos interfaced with FAIMS-pro with a single compensation voltage, in data independent acquisition mode.

Results:

MS data analysis is performed with Spectronaut® 18 yielding information on ~6700 protein groups after filtration and batch correction. PTMs that are quantifiable without PTM enrichment are also analysed. Statistical analysis is done with an in house developed MS data analysis tool PhosPiR. Weighted correlation network analysis is used to identify patient subgroups and protein modules, with clinical feature association. Preliminary findings will be shown at the poster.

Conclusions:

Insight into PD disease progression and what are the disease driving changes at the prodromal stage were obtained with associated clinical features. The results are also useful for multiomic analysis of the same individuals from PPMI cohort.