Identification of Candidate Transcription Factors that Bind to the ASCN Gene, Associated with Parkinson's Disease, Using Bioinformatics Analysis

Abstract

Parkinson's disease (PD) is a neurodegenerative illness marked by progressive damage of dopaminergic neurons in the substantia nigra. Synuclein-α protein plays a key role in this term by aggregating in clumps of Lewy bodies causing PD. Despite unclear etiology of PD, growing indications show that PD pathogenesis is associated with gene expression dysregulation. Transcription factors (TFs) are the key players in regulating gene expression. In this study, we employed a bioinformatics tool to predict TF binding to Synuclein-α (SNCA)gene utilizing DNA sequences, epigenetic modifications, TF binding motifs, and creating machine learning algorithms. PROMO database was utilized to identify candidate TFs. Here we found TFs that act as regulators of neuronal function and dopaminergic signaling pathways, including members of the Forkhead box family, and nuclear factor-kappa B family members such as c-Jun, and STATs family. These findings provide a better understanding of the molecular mechanisms underlying PD disease and determine potential therapeutic targets.