Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties.

Katja Pogacar,Wilberforce Zachary Ouma,Ilya Ioshikhes,Erich Grotewold

doi:10.1371/journal.pcbi.1006098

Katja Pogacar, Wilberforce Zachary Ouma + Show 2 more

Open Access

https://doi.org/10.1371/journal.pcbi.1006098

Copy DOI

Abstract

Understanding complexity in physical, biological, social and information systems is predicated on describing interactions amongst different components. Advances in genomics are facilitating the high-throughput identification of molecular interactions, and graphs are emerging as indispensable tools in explaining how the connections in the network drive organismal phenotypic plasticity. Here, we describe the architectural organization and associated emergent topological properties of gene regulatory networks (GRNs) that describe protein-DNA interactions (PDIs) in several model eukaryotes. By analyzing GRN connectivity, our results show that the anticipated scale-free network architectures are characterized by organism-specific power law scaling exponents. These exponents are independent of the fraction of the GRN experimentally sampled, enabling prediction of properties of the complete GRN for an organism. We further demonstrate that the exponents describe inequalities in transcription factor (TF)-target gene recognition across GRNs. These observations have the important biological implication that they predict the existence of an intrinsic organism-specific trans and/or cis regulatory landscape that constrains GRN topologies. Consequently, architectural GRN organization drives not only phenotypic plasticity within a species, but is also likely implicated in species-specific phenotype.

Highlights

Complex systems are formed by large numbers of components organized into networks, and modelled by graphs in which nodes are connected by edges
Central to this is the transcription of genes, a process regulated by proteins that bind DNA, including the transcription factors (TFs)
Using protein-DNA interaction data derived from the budding yeast, the fruit fly, Caenorhabditis elegans, and Arabidopsis, we determine that Gene regulatory networks (GRNs) are scalefree, wherein a majority of TFs bind comparatively fewer target genes, while a small

Summary

Introduction

Complex systems are formed by large numbers of components organized into networks, and modelled by graphs in which nodes are connected by edges. In scale-free networks, most nodes have comparatively few interactions manifested as a lower degree, while a small number of nodes, the ‘hubs’, have a higher degree [11, 12]. This scale-free connectivity distribution is observed at different levels of biological organization ranging from the cellular and molecular, to the ecological level. Gene regulatory networks (GRNs), characterized by the interaction of a specific type of proteins, the transcription factors (TFs) with the regulatory DNA regions in the genes that the TFs control, provide excellent examples of molecular-level scale-free networks [2, 6, 10, 13,14,15]. We anticipate that the advent of new experimental approaches to map PDIs and place them in a biological context will permit to explore the convergence of incoming and outgoing connectivity in many organisms

Methods

Results

Discussion

Conclusion