Objective: Identification of probable drug targets.

Background: Parkinson’s disease, a motor system disorder, which manifests due to the loss of brain cells that produces dopamine. Gene network analysis is a good tool to understand the system level properties of the cell and could be useful to understand the disease pathology and development and therapeutics.

Methods: The DisGeNET – a database of gene-disease associations, was used to retrieve the genes associated with Parkinson’s disease. Gene-gene interaction network was constructed by using Cytoscape Reactome FI plugin. Further, the network properties was computed by using NetworkAnalyzer plugin and used to identify probable drug targets.

Results: A total of 546 genes were found to be associated with Parkinsion’s disease. The interactome analysis reveals various physiological interactions (edges; n = 2106) related to both structural and functional associations. The properties of the network viz. degree, shortest path length, Betweenness Centrality, determine the functional importance of each node in the network. We have found six genes showing lowest connectivity in the network. Therefore, the nodes with lower value may show least effect on the network and could be potential drug target for the disease phenotype.

Conclusions: Our data suggest six genes GAD1, CD200R1, GAD2, MAOB, MAOA, CD200 as probable drug targets in the Parkinsion’s disease phenotype as they showed lowest connectivity. However, further experimental validation is essential.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/gene-network-driven-probable-drug-target-identification-an-in-silico-study-on-parkinsons-disease/