title: Phenotypic robustness and the assortativity signature of human transcription factor networks.
abstract: Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs - such as their degree distribution - with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of the assortativity signature contributes to this robustness.
authors.affiliation: Computational Genetics Laboratory, Dartmouth College, Hanover, New Hampshire, United States of America.
authors.forename: Dov A
authors.initials: DA
authors.lastname: Pechenick
authors.affiliation: Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
authors.forename: Joshua L
authors.initials: JL
authors.lastname: Payne
authors.affiliation: Computational Genetics Laboratory, Dartmouth College, Hanover, New Hampshire, United States of America.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D019295:Computational Biology; D005786:Gene Expression Regulation / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D009928:Organ Specificity; D010641:Phenotype; D014157:Transcription Factors / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism
chemical_list: D014157:Transcription Factors