The underlying cause of Duchenne muscular dystrophy (DMD) – mutations in the dystrophin gene – is known for 25 years. Still many details are to be elucidated to reconstruct the complete picture of DMD pathogenesis explaining how the lack of dystrophin leads to the disease symptoms. Dissecting the complex disease into a set of disturbed pathways helps organizing already known facts and discovering new nodes important for disease progression.
We suggest three approaches to characterize DMD through pathways. First, we manually built DMD pathways based on the literature evidence to show how intersecting disease-specific pathways allows identification of common regulators in DMD which might be considered as potential drug targets. Second, we used algorithmically generated subnetworks and a set of curated expression targets pathways to analyze genes that change expression in DMD. Using collection of the predefined pathways or automatically generated subnetworks for data analysis reveals new nodes (e.g. ESRRA and SREBF1) and pathways (e.g. IL6 and IGF1 signaling) crucial for the disease but not yet covered in literature.
Keywords: Bioinformatics, calcium, data analysis, disease models, disease networks, disease pathways, DMD, drug target discovery, duchenne muscular dystrophy, dystrophin, dystrophin glycan complex, expression analysis, mechanotransduction, meta-analysis, mitochondria biogenesis, muscle remodeling, nitric oxide, oxidative stress, pathways, signaling.