In this project, we develop statistical data integration approaches for omics datasets from various biological samples to identify genes and proteins involved in Parkinson’s Disease. The genes and proteins will pinpoint to potential drug-targets.
Previously generated datasets
The aggregation of the protein alpha-synuclein in neurons and oligodendrocytes in the brain leads to a group of neurodegenerative diseases known as synucleinopathies, which include Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. In preparatory projects, the team has already generated extensive datasets from patient-derived materials (genome-wide association studies, epigenome-wide DNA methylation studies, RNA sequencing in MSA and PD) and cell models (DNA methylome, miRnome, transcriptome, proteome).
SynOD project
In the SynOD project, we will integrate these unique large omics datasets and analyze them using advanced computational methods to create a comprehensive map of the molecular pathways involved in synucleinopathies, with a particular focus on identifying drug targets.
At Radboud
At Radboud, we are developing partial least squares (PLS) models to integrate various datasets across molecular levels and systems. These PLS models consider latent components representing joint, dataset-specific, and biological sample specific subspaces.
The genes that contribute most to the joint components will be further investigated using functional networks to identify potential drug targets, paving the way for new therapeutic strategies.
