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.

In plants, the basic helix-loop-helix (bHLH) transcription factors play key roles in diverse biological processes. Genome-wide comprehensive and systematic analyses of bHLH proteins have been well conducted in Arabidopsis, rice, tomato and other plant species. However, only few of bHLH family genes have been functional characterized in maize. In this study, our genome-wide analysis identified 208 putative bHLH family proteins (ZmbHLH proteins) in maize (Zea mays). We classified these proteins into 18 subfamilies by comparing the ZmbHLHs with Arabidopsis thaliana bHLH proteins. Phylogenetic analysis, conserved protein motifs, and exon-intron patterns further supported the evolutionary relationships among these bHLH proteins. Genome distribution analysis found that the 208 ZmbHLH loci were located non-randomly on the ten maize chromosomes. Further, analysis of conserved cis-elements in the promoter regions, protein interaction networks, and expression patterns in roots, leaves, and seeds across developmental stages, suggested that bHLH family proteins in maize are probably involved in multiple physiological processes in plant growth and development. We performed a genome-wide, systematic analysis of bHLH proteins in maize. This comprehensive analysis provides a useful resource that enables further investigation of the physiological roles and molecular functions of the ZmbHLH transcription factors.

Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C₄ metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites’ regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.