Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases.

Mark R Chance,Jill S Barnholtz-Sloan,Yu Liu,Mehmet Koyutürk

doi:10.1186/1752-0509-6-65

Abstract

BackgroundThe molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).ResultsHere, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.ConclusionsWe showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.

Highlights

The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions
In order to further explore the dysregulation of gene interactions in disease, we have developed Gene Interaction Enrichment and Network Analysis (GIENA), which implements four mathematically simple, yet powerful interaction profile functions to model gene interactions
Overview of GIENA Using the four profiles described above for each pair of genes, we identify gene sets that are enriched in dysregulated gene interactions, i.e., pathways in which these profiles are significantly altered between disease and control samples for a large number of gene pairs in the pathway

Summary

Introduction

The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). While being quite useful for system-level analyses, GSEA and similar methods, such as gene set analysis (GSA, [5]) have a limitation: they focus only on the molecules (i.e., genes) that comprise a pathway and may neglect the changing interactions among genes within a pathway. Several studies have demonstrated that the changes in the dynamics of interaction are associated with cancer and other diseases [7,8,9]

Methods

Results

Conclusion