Fine-Tuning Florigen Increases Field Yield Through Improving Photosynthesis in Soybean

被引:6
|
作者
Xu, Kun [1 ,2 ]
Zhang, Xiao-Mei [1 ]
Chen, Haifeng [3 ]
Zhang, Chanjuan [3 ]
Zhu, Jinlong [1 ,2 ]
Cheng, Zhiyuan [1 ]
Huang, Penghui [1 ]
Zhou, Xinan [3 ]
Miao, Yuchen [4 ]
Feng, Xianzhong [5 ]
Fu, Yong-Fu [1 ]
机构
[1] Chinese Acad Agr Sci, Inst Crop Sci, Natl Key Facil Crop Gene Resource & Genet Improve, MOA Key Lab Soybean Biol, Beijing, Peoples R China
[2] Chinese Acad Sci, Innovat Acad Seed Design, Northeast Inst Geog & Agroecol, Key Lab Soybean Mol Design Breeding, Harbin, Peoples R China
[3] Chinese Acad Agr Sci, Key Lab Biol & Genet Improvement Oil Crops, Minist Agr, Oil Crops Res Inst, Wuhan, Peoples R China
[4] Henan Univ, Sch Life Sci, State Key Lab Cotton Biol, Key Lab Plant Stress Biol, Kaifeng, Peoples R China
[5] Chinese Acad Sci, Northeast Inst Geog & Agroecol, CAS Key Lab Soybean Mol Design Breeding, Changchun, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
high yield; florigen; FT; photosynthesis; soybean; vegetative growth; FLOWERING-LOCUS-T; GLOBAL FOOD DEMAND; EXPRESSION; TIME; GENE; HETEROSIS; HOMOLOGS; DYNAMICS; PROTEIN; FUTURE;
D O I
10.3389/fpls.2021.710754
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Crop yield has been maintaining its attraction for researchers because of the demand of global population growth. Mutation of flowering activators, such as florigen, increases plant biomass at the expense of later flowering, which prevents crop maturity in the field. As a result, it is difficult to apply flowering activators in agriculture production. Here, we developed a strategy to utilize florigen to significantly improve soybean yield in the field. Through the screening of transgenic lines of RNAi-silenced florigen homologs in soybean (Glycine-max-Flowering Locus T Like, GmFTL), we identified a line, GmFTL-RNAi#1, with minor changes in both GmFTL expression and flowering time but with notable increase in soybean yield. As expected, GmFTL-RNAi#1 matured normally in the field and exhibited markedly high yield over multiple locations and years, indicating that it is possible to reach a trade-off between flowering time and high yield through the fine-tuning expression of flowering activators. Further studies uncovered an unknown mechanism by which GmFTL negatively regulates photosynthesis, a substantial source of crop yield, demonstrating a novel function of florigen. Thus, because of the highly conserved functions of florigen in plants and the classical RNAi approach, the findings provide a promising strategy to harness early flowering genes to improve crop yield.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Fine-Tuning Photosynthesis
    Rochaix, Jean-David
    SCIENCE, 2013, 342 (6154) : 50 - 51
  • [2] Improving CLIP Fine-tuning Performance
    Wei, Yixuan
    Hu, Han
    Xie, Zhenda
    Liu, Ze
    Zhang, Zheng
    Cao, Yue
    Bao, Jianmin
    Chen, Dong
    Guo, Baining
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 5416 - 5426
  • [3] Improving optimization of convolutional neural networks through parameter fine-tuning
    Becherer, Nicholas
    Pecarina, John
    Nykl, Scott
    Hopkinson, Kenneth
    NEURAL COMPUTING & APPLICATIONS, 2019, 31 (08): : 3469 - 3479
  • [4] Improving optimization of convolutional neural networks through parameter fine-tuning
    Nicholas Becherer
    John Pecarina
    Scott Nykl
    Kenneth Hopkinson
    Neural Computing and Applications, 2019, 31 : 3469 - 3479
  • [5] FINE-TUNING FIELD MECHANIZATION IN AUSTRALIA
    不详
    SUGAR Y AZUCAR, 1982, 77 (10): : 29 - &
  • [6] Improving fine-tuning in composite Higgs models
    Banerjee, Avik
    Bhattacharyya, Gautam
    Ray, Tirtha Sankar
    PHYSICAL REVIEW D, 2017, 96 (03)
  • [7] Improving unbalanced image classification through fine-tuning method of reinforcement learning
    Wang, Jin-Qiang
    Guo, Lan
    Jiang, Yuanbo
    Zhang, Shengjie
    Zhou, Qingguo
    APPLIED SOFT COMPUTING, 2024, 163
  • [8] Fine-Tuning Photosynthesis: Structural Basis of Photoprotective Energy Dissipation
    Eckardt, Nancy A.
    PLANT CELL, 2011, 23 (04): : 1189 - 1189
  • [9] Noise Stability Regularization for Improving BERT Fine-tuning
    Hua, Hang
    Li, Xingjian
    Dou, Dejing
    Xu, Chengzhong
    Luo, Jiebo
    2021 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL-HLT 2021), 2021, : 3229 - 3241
  • [10] Improving Pre-Trained Weights through Meta-Heuristics Fine-Tuning
    de Rosa, Gustavo H.
    Roder, Mateus
    Papa, Joao Paulo
    dos Santos, Claudio F. G.
    2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021), 2021,