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Genomics, proteomics & bioinformatics
06, 2020;18 (3): 256-270.

Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis.

Jihong Hu, Tao Zeng, Qiongmei Xia, Liyu Huang, Yesheng Zhang, Chuanchao Zhang, Yan Zeng, Hui Liu, Shilai Zhang, Guangfu Huang, Wenting Wan, Yi Ding, Fengyi Hu, Congdang Yang, Luonan Chen, Wen Wang
Author Information
  1. Jihong Hu: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.
  2. Tao Zeng: CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China.
  3. Qiongmei Xia: Institute of Food Crop of Yunnan Academy of Agricultural Sciences, Kunming 650205, China.
  4. Liyu Huang: School of Agriculture, Yunnan University, Kunming 650500, China.
  5. Yesheng Zhang: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; BGI-Baoshan, Baoshan 678004, China.
  6. Chuanchao Zhang: CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
  7. Yan Zeng: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.
  8. Hui Liu: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.
  9. Shilai Zhang: School of Agriculture, Yunnan University, Kunming 650500, China.
  10. Guangfu Huang: School of Agriculture, Yunnan University, Kunming 650500, China.
  11. Wenting Wan: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Center for Ecological and Environmental Sciences, Northwestern Polytechnical University, Xi'an 710072, China.
  12. Yi Ding: State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, China.
  13. Fengyi Hu: School of Agriculture, Yunnan University, Kunming 650500, China. Electronic address: hfengyi@ynu.edu.cn.
  14. Congdang Yang: Institute of Food Crop of Yunnan Academy of Agricultural Sciences, Kunming 650205, China. Electronic address: yangcd2005@163.com.
  15. Luonan Chen: CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China. Electronic address: lnchen@sibs.ac.cn.
  16. Wen Wang: State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Center for Ecological and Environmental Sciences, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: wwang@mail.kiz.ac.cn.
PMID: 32736037 DOI: 10.1016/j.gpb.2019.11.007

Abstract

Significantly increasing crop yield is a major and worldwide challenge for food supply and security. It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide. Yet, the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery. Here, we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group. We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method, i.e., dynamic cross-tissue (DCT) network analysis. We used one of the candidate genes, OsSPL4, whose function was previously unknown, for gene editing experimental validation of the high yield, and confirmed that OsSPL4 significantly affects panicle branching and increases the rice yield. This study, which included extensive field phenotyping, cross-tissue systems biology analyses, and functional validation, uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice. The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample. DCT can be downloaded from https://github.com/ztpub/DCT.

Keywords

Dynamic cross-tissue (DCT) RNA-seq Rice Systems biology Ultrahigh yield

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MeSH Term

Chromosome Mapping
Gene Expression Profiling
Gene Expression Regulation, Plant
Gene Regulatory Networks
Genes, Plant
Oryza
Phenotype
Transcriptome
Journal Article Research Support, Non-U.S. Gov't

Links to CNCB-NGDC Resources

GSA: CRA002804 (RNA-seq of rice 9311 at Taoyuan for ultra-high yield)
BioProject: PRJCA002833 (RNA-seq of rice 9311 at Taoyuan)

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