Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis

Abstract

Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B.

Cross Disease Network Analysis

“…The project of this dissertation is based on the novel prioritization algorithm developed by Garcia-Vaquero et al [9] that predicts and classifies proteins according to their probability of being simultaneously associated with two phenotypically similar diseases. The method relies on the notion that diseases with similar phenotypes are associated by alterations in similar cellular processes, and consequently, are expected to share more disease genes that appear in the overlap between the two disease modules.…”

“…The work developed in this dissertation is the extension of a network-based disease gene prediction method proposed by Garcia-Vaquero et al [9], called S2B (double specific-betweenness), that aims to predict and prioritize proteins in PPI networks (networks with undirected links representing physical interactions between proteins) simultaneously associated with two phenotypically similar diseases, shedding light on the molecular mechanisms implicated in the commonly affected biological functions and potentially providing new polyvalent therapeutic targets.…”

“…Many common diseases are complex or multifactorial, caused by a combination of genetic, environmental and lifestyle factors, involving several genes that can be classified as causal, as modifiers of the disease phenotype, or as phenotypic, genes affected by the perturbations but unable to influence the disease course [9]. A complex disease phenotype is the result of multiple pathomechanisms and therefore, a drug targeting only one mechanism may not be effective in all clinical cases.…”

“…Both methods are also susceptible to select new genes only because they are central in the PPI network structure and therefore will mediate the connection between several modules, but don't have a specific connection to both diseases' genes. With a solution to these shortcomings, a method was proposed by Garcia-Vaquero et al [9], on which this dissertation is based. The method called S2B (double specific-betweenness) aims to directly predict disease genes in simultaneously associated with two related diseases, by using a variant of the betweenness measure.…”

“…proteins with specificity scores higher than a set high threshold, and black dots proteins with lower specificity to the method's seeds. Adapted from [9].…”