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.
