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

Author

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  • Xiaoyu Tu

    (Shandong Agricultural University
    State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong)

  • 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
    Cornell University
    Agricultural Research Service, United States Department of Agriculture)

  • Silin Zhong

    (State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong)

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.

Suggested Citation

  • 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 & Silin Zhong, 2020. "Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18832-8
    DOI: 10.1038/s41467-020-18832-8
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    Cited by:

    1. Yue Yuan & Qiang Huo & Ziru Zhang & Qun Wang & Juanxia Wang & Shuaikang Chang & Peng Cai & Karen M. Song & David W. Galbraith & Weixiao Zhang & Long Huang & Rentao Song & Zeyang Ma, 2024. "Decoding the gene regulatory network of endosperm differentiation in maize," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Xiaoyu Tu & Sibo Ren & Wei Shen & Jianjian Li & Yuxiang Li & Chuanshun Li & Yangmeihui Li & Zhanxiang Zong & Weibo Xie & Donald Grierson & Zhangjun Fei & Jim Giovannoni & Pinghua Li & Silin Zhong, 2022. "Limited conservation in cross-species comparison of GLK transcription factor binding suggested wide-spread cistrome divergence," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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