Location: Plant, Soil and Nutrition ResearchTitle: Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors
|TU, XIAOYU - Shandong Agricultural University|
|MAJIA-GUERRA, MARIA KATHERINE - Cornell University|
|VALDES FRANCO, JOSE - Cornell University|
|TZENG, DAVID - The Chinese University Of Hong Kong (CUHK)|
|CHU, PO-YU - The Chinese University Of Hong Kong (CUHK)|
|SHEN, WEI - The Chinese University Of Hong Kong (CUHK)|
|WEI, YINGYING - The Chinese University Of Hong Kong (CUHK)|
|DAI, XIURU - Shandong Agricultural University|
|LI, PINGHUA - Shandong Agricultural University|
|Buckler, Edward - Ed|
|ZHONG, SILIN - The Chinese University Of Hong Kong (CUHK)|
Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/17/2020
Publication Date: 10/9/2020
Citation: Tu, X., Majia-Guerra, M., Valdes Franco, J.A., Tzeng, D., Chu, P., Shen, W., Wei, Y., Dai, X., Li, P., Buckler IV, E.S., Zhong, S. 2020. Reconstructing the maize leaf regulatory network using ChIP-seq data of 104 transcription factors. Nature Communications. (11):5089. https://doi.org/10.1038/s41467-020-18832-8.
Interpretive Summary: It's become increasingly clear that to identify what controls the myriad of ways a plant grows in a given environment, it’s not enough to simply sequence its genes. What is becoming more important to understand is what regulates the expression of those genes. For this purpose, we set out to analyze a large set of the proteins known as transcription factors (TF), which are responsible of regulating gene expression by binding the DNA surrounding gene regions. With these experiments, and by implementing machine-learning methods that look at where these TF proteins are located in the genome, we were able to identify that these proteins do not simply act by themselves, but actually require the specific co-localization of multiple sets of them, effectively working as a network of TF interactions in order to properly carry their regulatory function. Having identified where these TF proteins bind, along with the interactions between them, we expect new models will shed new light in the overall regulatory machine of plants, which in return will help in developing better and more accurate genomic prediction models.
Technical 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.