Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Abstract

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

Revealing Shared and Distinct Gene Network Organization in Arabidopsis Immune Responses by Integrative Analysis

“…S1), including (1) experimental protein-protein interactions (PPIs), (2) confirmed protein-DNA binding data, (3) TF and target coexpression relationships inferred from expression profiles under two physiological conditions (development and abiotic stress), and (4) TF and target chromatin comodification (Marbach et al, 2012) information inferred from genome-wide chromatin immunoprecipitation (ChIP)-chip experiments of 13 modification types (Supplemental Table S1). We selected these data because they are representative of and highly complementary to gene regulation relationships (Walhout, 2006;Marbach et al, 2012). To reduce false positives from these high-throughput data, we adopted a series of scoring strategies to remove unreliable gene association data (for more details, see "Materials and Methods" and Supplemental Figs.…”

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

“…In a network context, hub genes are attributed the important function of providing crosstalk between different processes (Barabási and Oltvai, 2004). To delineate the hub genes in the ChIP gene regulatory network, a random TF-gene target distribution was built ( Figure 2A) by randomizing the relationships between TFs and potential target genes while preserving the number of potential target genes per TF (Marbach et al, 2012). Based on the 99th percentile values of the randomized distributions, we defined the 1174 potential target genes that are bound by eight TFs or more as target hubs.…”

“…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.…”

Modulation of Gene Expression Regulated by the Transcription Factor NF-κB/RelA

“…These methods include conditional information (13,14), correlation coefficient, machine learning (15), and statistical models (16). Recently, heterogeneous data integration of promoter sequence and gene expression profiles has also been used to infer gene regulation patterns (17,18).…”