Cross Disease Network Analysis

Diseases are caused by the deregulation of cellular networks, leading to faulty physiological functions. Different diseases can have common deregulated molecular pathways, particularly if they partially share phenotypes. These commonalities are frequently represented as an overlap of disease-related cellular subnetworks. The discovery of the overlaps and thus genes simultaneously associated with multiple diseases can shed light on the molecular pathomechanisms and provide new polyvalent therapeutic targets. This work builds upon a previously developed double specific-betweenness (S2B) network-based method, to prioritize proteins with a higher probability of being simultaneously associated with two phenotypically similar diseases. This method was designed to use undirected networks of protein physical interactions. The present work aims to expand the S2B method, enabling the analysis of networks with directed interactions, such as signaling and transcriptional regulatory cellular networks, providing new regulatory information and contributing to richer mechanistic hypothesis to explain the common physiological deficiencies. The new extended version of the method was tested with artificial disease modules, which enabled to test the S2B method ability to predict the overlap between the disease-related subnetworks. As in the undirected version of S2B, this novel version was applied to a pair of motor neuron diseases - Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) -demonstrating once again its potential to bring new insights into the common pathological mechanisms and particularly, its ability to retrieve novel disease genes associated with the disturbance of regulatory mechanisms involved in motor neuron degeneration.