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Bioinformatics, Computational and Systems Biology

Bioinformatics, Computational and Systems Biology - Poster Session B

Poster A9 - Epigenetic Insights into Parkinson's Disease Progression: DNA Methylation Profiling across Disease Subtypes

Thursday, October 24, 2024

2:45 PM – 3:45 PM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Nadine Grant

Student
Rensselaer Polytechnic Institute
Freehold, United States

Co-Author(s)

Diego Machado Reyes

PhD student
Rensselaer Polytechnic Institute
Troy, New York, United States

Last Author(s)

Pingkun Yan, PhD

Associate Professor
Rensselaer Polytechnic Institute
Troy, New York, United States

Introduction: Parkinson's disease (PD) is a heterogeneous neurodegenerative disease with a varied etiology. Epigenetic factors such as DNA methylation have been previously identified for disease onset and progression for many diseases, including PD. Understanding the role of DNA methylation in PD is crucial for elucidating the underlying pathophysiology of disease subtypes and trajectories.

Early-stage PD and advanced-stage PD have been shown to display distinct methylation patterns in genes associated with neuroinflammation, oxidative stress, and mitochondrial dysfunction, reflecting the progressive nature of the disease. Additionally, differential methylation patterns are linked to cognitive decline, with specific epigenetic modifications being observed in patients as they progress from mild cognitive impairment to Parkinson’s disease dementia. Identifying genes regulated through DNA methylation could lead not only to potential biomarkers for disease progression analysis but also offer insights into therapeutic targets. Characterizing these epigenetic modifications is essential for developing personalized treatment strategies, improving patient outcomes, and identifying new therapeutic avenues. In this research, DNA methylation profiles from 96 healthy controls and 219 PD patients were analyzed to identify differentially regulated genes across three distinctly progressing patient subgroups. Gene ontology and pathway analysis were performed to identify the gene regulation differences between the subgroups, and between PD and healthy controls.

Materials and

Methods: Data description:
Patient clinical information and DNA methylation profiles from the Parkinson’s Progression Markers Initiative (PPMI) were utilized. Following the methodology from Dadu et al. in the paper titled “Identification and prediction of Parkinson’s disease subtypes and progression using machine learning in two cohorts,” patients were stratified into distinct subgroups based on their disease progression trajectories using a Gaussian Mixture Model applied to patient clinical data. The clusters were characterized by various clinical features along three dimensions: motor, cognitive, and sleep disturbance. The subgroups reflect fast, intermediate and slow progressing trajectories. This stratification enabled a focused analysis of DNA methylation patterns corresponding to different PD progression subtypes.

DNA methylation analysis:
DNA methylation data was obtained from PPMI. DNA was extracted from participant blood samples, subjected to bisulfite conversion, and loaded onto Infinium MethylationEPIC BeadChip arrays. The arrays were read via the Illumina iScan System, resulting in IDAT files for over 860,000 CpG sites. Differential methylation analysis was conducted to identify CpG sites using functions from the R package minfi to identify significantly different methylated regions between patient clusters.

Gene Ontology and KEGG:
Identified differentially methylated regions (DMRs) were mapped to their respective genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify potential biological processes and pathways implicated in PD and its subgroups. The Enrichr system was used to analyze the gene sets and obtain the enriched GO biological processes and KEGG pathways.

Results, Conclusions, and Discussions: A commonly affected gene across all groups was APP, involved in neuron and dendrite morphogenesis. The APP gene is related to the formation of a precursor to amyloid-β peptides, which are a key biomarker in neurogenerative diseases such as Alzheimer’s, Dementia, and Parkinson’s.

The high progression group displayed pathways related to neurogenesis, axon guidance, and synaptic transmission, with key genes including CLSTN and SEMA5A suggesting differences in neural activity and development. CLSTN encodes for a transmembrane protein thought to mediate the transport of APP, and SEMA5A is involved in the guidance of axons during neuronal development. These genes indicate that accelerated progression of PD may be linked to alterations in neuronal connectivity and signaling.

In the intermediate progression group, significant pathways included regulation of ion transport, biosynthetic processes, and protein modification processes. Key genes such as RPTOR and PRKDC were identified. PRKDC encodes a subunit involved in DNA double-strand break repair, while RPTOR regulates cell growth in response to nutrients or growth factors and is involved in the recruitment of MTOR, which mediates cellular responses to damage. These findings suggest intermediate progression is influenced by both metabolic and reparative processes.

The slow progression group was characterized by pathways involved in immune system processes, inflammatory response, response to cytokines, and cell activation, with significant genes such CTNND2 and CSF1. CTNND2 has a critical role in forming and maintaining dendritic spines and may be involved in neuronal cell adhesion and tissue morphogenesis, while CSF1 plays a role in actin cytoskeleton reorganization. This suggests that neuronal development and immune regulation processes may play a role in slower progression of PD.

Parkinsons disease heterogeneity in its expression through cognitive and motor function decline, is also reflected in its varied epigenetic regulation. DNA methylation identified a diverse set of shared and distinct regulated genes and pathways across PD subgroups. Methylated genes shared across PD groups indicate their role in PD pathogenesis as diminished expression of these can lead to dementia onset, as the amyloid precursor protein (APP) proteolysis gives rise to amyloid-β (Aβ) peptides.

Acknowledgements (Optional):