B-cell lymphoma gene regulatory networks: biological consistency among inference methods

Ricardo De Matos Simoes,Matthias Dehmer,Frank Emmert-Streib

doi:10.3389/fgene.2013.00281

Abstract

Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene expression data in routine practice. In our study, we conduct a structural and a functional analysis of B-cell lymphoma GRNs that were inferred using 3 mutual information-based GRN inference methods: C3Net, BC3Net and Aracne. From a comparative analysis on the global level, we find that the inferred B-cell lymphoma GRNs show major differences. However, on the edge-level and the functional-level—that are more important for our biological understanding—the B-cell lymphoma GRNs were highly similar among each other. Also, the ranks of the degree centrality values and major hub genes in the inferred networks are highly conserved as well. Interestingly, the major hub genes of all GRNs are associated with the G-protein-coupled receptor pathway, cell-cell signaling and cell cycle. This implies that hub genes of the GRNs can be highly consistently inferred with C3Net, BC3Net, and Aracne, representing prominent targets for signaling pathways. Finally, we describe the functional and structural relationship between C3Net, BC3Net and Aracne gene regulatory networks. Our study shows that these GRNs that are inferred from large-scale gene expression data are promising for the identification of novel candidate interactions and pathways that play a key role in the underlying mechanisms driving cancer hallmarks. Overall, our comparative analysis reveals that these GRNs inferred with considerably different inference methods contain large amounts of consistent, method independent, biological information.

Highlights

To date, a vast amount of gene regulatory network (GRN) inference methods are being developed with the future goal to establish qualitative and quantitative procedures for a structural, biological and experimental validation of the inferred networks (Friedman, 2004; Wille et al, 2004; Werhli et al, 2006; Margolin and Califano, 2007; Yip et al, 2010; Zhang et al, 2011; Emmert-Streib et al, 2012)
We provide a structural and a functional comparison between the sparse, modular network structure inferred by C3Net and the more densely connected BC3Net and Aracne GRNs
The C3Net GRN has the lowest edge density (1.9 × 10−5) and it is composed of 463 separated network components

Summary

Introduction

A vast amount of gene regulatory network (GRN) inference methods are being developed with the future goal to establish qualitative and quantitative procedures for a structural, biological and experimental validation of the inferred networks (Friedman, 2004; Wille et al, 2004; Werhli et al, 2006; Margolin and Califano, 2007; Yip et al, 2010; Zhang et al, 2011; Emmert-Streib et al, 2012). This study demonstrated that interactions (edges) of genes with a low number of direct neighbors (low degree) are more likely to be inferred correctly compared to interactions of genes with a large number of direct neighbors From this observation one can presume that the interaction periphery of the unknown gene network is more prominently represented in an inferred GRN due to the lower complexities of the gene expression dependencies between the genes. In de Matos Simoes et al (2012) it was shown that the giant connected component (GCC) of the GRN using C3Net is highly enriched with membrane associated proteins This observation suggested that the periphery of a gene network represents, to some extend, the physical periphery of the biological cell that www.frontiersin.org de Matos Simoes et al

Methods

Results

Conclusion