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Open Access Highly Accessed Research article

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

Yu Liu1*, Mehmet Koyutürk123, Jill S Barnholtz-Sloan134 and Mark R Chance135

Author Affiliations

1 Center for Proteomics & Bioinformatics, Case Western Reserve University, Cleveland, OH, 44106, USA

2 Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH, 44106, USA

3 Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA

4 Department of Epidemiology and Biostatistics, Case Comprehensive Cancer Center, Cleveland, OH, 44106, USA

5 Department of Genetics, Case Western Reserve University, Cleveland, OH, 44106, USA

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BMC Systems Biology 2012, 6:65  doi:10.1186/1752-0509-6-65

Published: 13 June 2012

Abstract

Background

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). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).

Results

Here, 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.

Conclusions

We 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.

Keywords:
Gene-gene interaction; Dysregulated pathways; Enrichment analysis; BAD pathway