ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks

Sebastian Vlaic,Theresia Conrad,Reinhard Guthke,Stefan Schuster,Christian Tokarski-Schnelle,Mika Gustafsson,Uta Dahmen

doi:10.1038/s41598-017-18370-2

Abstract

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN’s community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Highlights

Structural analysis of intracellular molecular networks has attracted ample interest over several decades[1]
In contrast to the set of DEGs as well as the set of proteins captured by the modules identified using DEGAS, MATISSE or KeyPathwayMiner, we found that both non-alcoholic steatohepatitis (NASH)-modules are significantly enriched (p-value
We presented ModuleDiscoverer, a heuristic approach for the identification of regulatory modules in large-scale, whole-genome protein-protein interaction networks (PPINs)

Summary

Introduction

Structural analysis of intracellular molecular networks has attracted ample interest over several decades[1]. The identification of disease-associated modules has been applied mostly based on protein-protein interaction networks (PPINs) of Homo sapiens They have been successfully identified for, e.g., asthma[11], inflammatory and malignant diseases[12], obesity and type-2-diabetes (among others)[13] as well as different subtypes of breast cancer[14,15,16], providing new in-depth insights into the underlying molecular mechanisms of the respective disease. The approach presented by Barrenäs et al.[13] identifies protein communities by decomposition of the human PPIN into sub-graphs of maximal cliques. The idea of disease modules can obviously be generalized towards the detection of regulatory modules underlying an arbitrary phenotype of any organism This can be of high interest, e.g., for the molecular characterization of animal models of diverse human diseases. While heuristics were presented for DEGAS, KeyPathwayMiner and MATISSE, an efficient heuristic following the idea of the MCE-based approach is missing

Methods

Results

Discussion

Conclusion