Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.

The transcription regulatory code of a plant leaf

The transcription regulatory network underlying essential and complex functionalities inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex regulatory netowrk. Here, we used ChIP-seq to determine the binding profiles of 104 TF expressed in the maize leaf. With this large dataset, we could reconstruct a transcription regulatory network that covers over 77% of the expressed genes, and reveal its scale-free topology and functional modularity like a real-world network. We found that TF binding occurs in clusters covering ∼2% of the genome, and shows enrichment for sequence variations associated with eQTLs and GWAS hits of complex agronomic traits. Machine-learning analyses were used to identify TF sequence preferences, and showed that co-binding is key for TF specificity. The trained models were used to predict and compare the regulatory networks in other species and showed that the core network is evolutionarily conserved. This study provided an extensive description of the architecture, organizing principle and evolution of the transcription regulatory network inside the plant leaf.

Identification of consistent functional genetic modules

It is often of scientific interest to find a set of genes that may represent an independent functional module or network, such as a functional gene expression module causing a biological response, a transcription regulatory network, or a constellation of mutations jointly causing a disease. In this paper we are specifically interested in identifying modules that control a particular outcome variable such as a disease biomarker. We discuss the statistical properties that functional networks should possess and introduce the concept of network consistency which should be satisfied by real functional networks of cooperating genes, and directly use the concept in the pathway discovery method we present. Our method gives superior performance for all but the simplest functional networks.