Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data

Abstract

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications.

A Functional and Evolutionary Perspective on Transcription Factor Binding in Arabidopsis thaliana

“…In both cases, an exponential distribution was observed, which is distinct from the commonly described power law in biological networks (Barabási and Oltvai, 2004). However, the exponential distribution was also reported in the C. elegans gene regulatory network by Cheng et al (2011). We delineated hub genes and HOT regions, two proxies for complex gene regulation.…”

“…Some of the first integrative regulatory studies were in the context of the ModENCODE and ENCODE projects in C. elegans (Gerstein et al, 2010;Cheng et al, 2011;Van Nostrand and Kim, 2013), Drosophila melanogaster (Roy et al, 2010;Nègre et al, 2011), and Homo sapiens (Bernstein et al, 2012;Gerstein et al, 2012;Wang et al, 2012). Information on protein-protein interactions, microRNA (miRNA)-target interactions, and gene expression profiles has been harnessed for the identification of master regulators and network motifs (Cheng et al, 2011;Gerstein et al, 2012) and for inferring gene regulatory networks and predictive models of gene expression levels of target genes (Marbach et al, 2012;Van Nostrand and Kim, 2013).…”

“…Information on protein-protein interactions, microRNA (miRNA)-target interactions, and gene expression profiles has been harnessed for the identification of master regulators and network motifs (Cheng et al, 2011;Gerstein et al, 2012) and for inferring gene regulatory networks and predictive models of gene expression levels of target genes (Marbach et al, 2012;Van Nostrand and Kim, 2013). Ferrier et al (2011) and Mejia-Guerra et al (2012) have already generated an overview of the available TF profiling studies in Arabidopsis.…”

SND1 Transcription Factor-Directed Quantitative Functional Hierarchical Genetic Regulatory Network in Wood Formation in Populus trichocarpa

“…A combination of ChIP sequencing or ChIP-PCR and the TF-induced differential gene expression is needed to reveal the functional network and its regulatory effects. This combination has been used to discover specific functional regulatory networks in animal development, using cell cultures representing different developmental stages where specific sets of TFs are induced (Yu and Gerstein, 2006;Farnham, 2009;Bhardwaj et al, 2010;Gerstein et al, 2010;Roy et al, 2010;Cheng et al, 2011). These TF regulatory networks are arranged in well-organized hierarchies.…”

“…For tree species that are amenable to genetic transformation, methods are technically demanding and slow, requiring 12 to 18 months of tissue culture (Merkle and Dean, 2000). To reveal a functional hGRN for wood formation, an efficient transgenic system, such as those developed for the cell cultures of yeast (Saccharomyces cerevisiae) and animals (Horak et al, 2002;Yu and Gerstein, 2006;Gerstein et al, 2010;Cheng et al, 2011;Niu et al, 2011), is needed where immediate transcriptome responses to TF perturbation can be induced, characterized, and quantified.…”