Pathway centrality in protein interaction networks identifies functional mediators of pulmonary disease

Park, Jisoo; Hescott, Benjamin; Slonim, Donna K.

doi:10.1101/171942

Cited by 2 publications

(1 citation statement)

References 68 publications

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“…Park et al [46] proposed a similar approach to identify functional pathways linking disease genes, with a disease-causing role, to differentially expressed genes, postulated to reflect more the downstream effects of a disease mechanism, in protein interaction networks. To find these central pathways, a variation of betweenness was also used, counting only shortest paths between disease genes and differentially expressed genes, and averaging the betweenness scores of a set of nodes to obtain the group centrality of the corresponding pathway.…”

Section: -Correlation Of S2b Score With Node Degree and Betweenness mentioning

confidence: 99%

Cross Disease Network Analysis

Ramos

García-Vaquero

Gama‐Carvalho

et al. 2019

2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

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Diseases are often complex, caused by a combination of several factors including genetic, environmental and lifestyle factors. The complexity makes it more challenging to uncover the pathomechanisms underlying genotype-phenotype relationships. Cellular networks offer a simple framework to represent the highly interlinked cellular systems, by reducing cellular components, such as metabolites, proteins, DNA molecules or RNA molecules, to nodes and physical, biochemical or functional interactions to links between them. Diseases can be viewed as perturbations of these cellular networks, that lead to faulty physiological functions. Different diseases can have common deregulated molecular pathways, represented in the network as an overlap of subnetworks that are affected in each disease, particularly if they partially share phenotypes. The discovery of genes associated with multiple diseases is especially interesting because it can shed light on the molecular mechanisms implicated in the commonly affected physiological functions and provide new polyvalent therapeutic targets. This dissertation builds upon a previously developed network-based method, called double specificbetweenness (S2B) method, to prioritize nodes with a higher probability of being simultaneously associated with two phenotypically similar diseases. The method was developed to use undirected networks of physical interactions between proteins and extract a network property, a modified version of betweenness centrality, to prioritize proteins specifically connected with two different diseases. The method was tested with artificial disease network modules and applied to two fatal motor neuron diseases: Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy. The present work aims to expand the S2B method enabling the analysis of networks with directed interactions. This expansion allows the analysis of signaling and transcriptional regulatory networks, providing new regulatory information that can't be captured with protein-protein interactions, contributing to richer mechanistic hypothesis to explain the common physiological deficiencies. The new extended version of the method was tested with several types of directed artificial disease modules, proving to be able to efficiently predict the network overlap between them and offer new insights into the role of the predicted candidates in the network. The directed S2B was also applied to the same motor neuron disease pair, demonstrating its ability to retrieve novel disease genes associated with regulatory mechanisms dysregulated in motor neuron degeneration.

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Section: -Correlation Of S2b Score With Node Degree and Betweenness mentioning

confidence: 99%

Cross Disease Network Analysis

Ramos

García-Vaquero

Gama‐Carvalho

et al. 2019

2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

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

García-Vaquero

Gama‐Carvalho

Rivas

et al. 2018

Sci Rep

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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.

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Pathway centrality in protein interaction networks identifies functional mediators of pulmonary disease

Cited by 2 publications

References 68 publications

Cross Disease Network Analysis

Cross Disease Network Analysis

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

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