Decoding the regulatory architecture of the maize leaf

Transcription factors (TF) binding is key to understanding and characterizing the effect of genetic variability on phenotypic differences. Here, we used a novel scalable ChIP-seq approach to annotate the regulatory landscape of the maize genome with binding data from 104 leaf TFs. TF binding regions co-localized with open chromatin regions, with ~70% of TF binding nearby genes. TF binding sites are evolutionarily conserved and show enrichment for GWAS-hits, cis-expression QTLs. Furthermore, the regulatory network shows characteristics of real-word networks such as scale-free topology and larger modularity than random graphs. Finally, machine-learning analyses reveal that sequence preferences are alike within TF families, and that TF co-localization is key for TF binding specificity. Our comprehensive TF-DNA interaction approach provides the starting point to decipher the gene regulatory system in plant leaves. ChIP-Seq for 104 maize leaf transcription factors. BioProject: PRJNA518749, SRA Study accession: SRP183225