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

Xiaoyu Tu, María Katherine Mejía-Guerra (), Jose A. Valdes Franco, David Tzeng, Po-Yu Chu, Wei Shen, Yingying Wei, Xiuru Dai, Pinghua Li (), Edward S. Buckler and Silin Zhong ()
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Xiaoyu Tu: Shandong Agricultural University
María Katherine Mejía-Guerra: Cornell University
Jose A. Valdes Franco: Cornell University
David Tzeng: State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
Po-Yu Chu: State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
Wei Shen: State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
Yingying Wei: The Chinese University of Hong Kong
Xiuru Dai: Shandong Agricultural University
Pinghua Li: Shandong Agricultural University
Edward S. Buckler: Cornell University
Silin Zhong: State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18832-8

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