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

Abstract

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.

Transcriptional factors (TFs) bind in a combinatorial fashion to specify the on-and-off states of genes in a complex and redundant regulatory network. Here, the authors construct the transcription regulatory network in maize leaf using 104 TFs ChIP-seq data and train machine learning models to predict TF binding and colocalization.