Parkinson's
Definition
Parkinson's disease is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor symptoms including tremor, rigidity, bradykinesia, and postural instability. The pathological hallmark is the accumulation of misfolded alpha-synuclein protein in Lewy bodies. Beyond motor dysfunction, patients experience non-motor symptoms such as cognitive impairment, sleep disorders, and autonomic dysfunction. The disease involves complex interactions between genetic factors (SNCA, LRRK2, PARK genes), environmental triggers, mitochondrial dysfunction, oxidative stress, and neuroinflammation. Understanding these multifactorial pathways is critical for developing disease-modifying therapies, as current treatments primarily address symptomatic relief rather than halting neurodegeneration.
Visualize Parkinson's in Nodes Bio
Researchers can map Parkinson's disease networks by connecting genetic risk factors, protein interactions, affected pathways, and phenotypic outcomes. Visualize how LRRK2 mutations cascade through cellular processes, explore alpha-synuclein aggregation networks, or analyze dopaminergic signaling disruptions. Network analysis reveals disease modules, identifies therapeutic targets, and uncovers relationships between genetic variants and clinical manifestations across patient populations.
Visualization Ideas:
- Protein-protein interaction networks centered on alpha-synuclein aggregation and propagation
- Gene regulatory networks showing transcriptional changes in dopaminergic neuron degeneration
- Multi-omics integration networks linking genetic variants, protein expression, and clinical phenotypes in patient cohorts
Example Use Case
A pharmaceutical team investigating LRRK2 kinase inhibitors uses network visualization to map how LRRK2 mutations affect downstream signaling cascades in dopaminergic neurons. They integrate protein-protein interaction data, phosphorylation networks, and gene expression profiles from patient-derived neurons. By visualizing the network, they identify RAB proteins as key nodes connecting LRRK2 to autophagy dysfunction and vesicular trafficking defects. This reveals combination therapy opportunities targeting both LRRK2 and autophagy pathways, potentially slowing disease progression in genetically defined patient subgroups.