Article | Published:

Modulating plant growth–metabolism coordination for sustainable agriculture

Naturevolume 560pages595600 (2018) | Download Citation

Abstract

Enhancing global food security by increasing the productivity of green revolution varieties of cereals risks increasing the collateral environmental damage produced by inorganic nitrogen fertilizers. Improvements in the efficiency of nitrogen use of crops are therefore essential; however, they require an in-depth understanding of the co-regulatory mechanisms that integrate growth, nitrogen assimilation and carbon fixation. Here we show that the balanced opposing activities and physical interactions of the rice GROWTH-REGULATING FACTOR 4 (GRF4) transcription factor and the growth inhibitor DELLA confer homeostatic co-regulation of growth and the metabolism of carbon and nitrogen. GRF4 promotes and integrates nitrogen assimilation, carbon fixation and growth, whereas DELLA inhibits these processes. As a consequence, the accumulation of DELLA that is characteristic of green revolution varieties confers not only yield-enhancing dwarfism, but also reduces the efficiency of nitrogen use. However, the nitrogen-use efficiency of green revolution varieties and grain yield are increased by tipping the GRF4–DELLA balance towards increased GRF4 abundance. Modulation of plant growth and metabolic co-regulation thus enables novel breeding strategies for future sustainable food security and a new green revolution.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Khush, G. S. Green revolution: preparing for the 21st century. Genome 42, 646–655 (1999).

  2. 2.

    Pingali, P. L. Green revolution: impacts, limits, and the path ahead. Proc. Natl Acad. Sci. USA 109, 12302–12308 (2012).

  3. 3.

    Evenson, R. E. & Gollin, D. Assessing the impact of the green revolution, 1960 to 2000. Science 300, 758–762 (2003).

  4. 4.

    Hedden, P. The genes of the green revolution. Trends Genet. 19, 5–9 (2003).

  5. 5.

    Peng, J. et al. ‘Green revolution’ genes encode mutant gibberellin response modulators. Nature 400, 256–261 (1999).

  6. 6.

    Zhang, C., Gao, L., Sun, J., Jia, J. & Ren, Z. Haplotype variation of green revolution gene Rht-D1 during wheat domestication and improvement. J. Integr. Plant Biol. 56, 774–780 (2014).

  7. 7.

    Sasaki, A. et al. Green revolution: a mutant gibberellin-synthesis gene in rice. Nature 416, 701–702 (2002).

  8. 8.

    Spielmeyer, W., Ellis, M. H. & Chandler, P. M. Semidwarf (sd-1), “green revolution” rice, contains a defective gibberellin 20-oxidase gene. Proc. Natl Acad. Sci. USA 99, 9043–9048 (2002).

  9. 9.

    Harberd, N. P., Belfield, E. & Yasumura, Y. The angiosperm gibberellin–GID1–DELLA growth regulatory mechanism: how an “inhibitor of an inhibitor” enables flexible response to fluctuating environments. Plant Cell 21, 1328–1339 (2009).

  10. 10.

    Xu, H., Liu, Q., Yao, T. & Fu, X. Shedding light on integrative GA signaling. Curr. Opin. Plant Biol. 21, 89–95 (2014).

  11. 11.

    Itoh, H., Ueguchi-Tanaka, M., Sato, Y., Ashikari, M. & Matsuoka, M. The gibberellin signaling pathway is regulated by the appearance and disappearance of SLENDER RICE1 in nuclei. Plant Cell 14, 57–70 (2002).

  12. 12.

    Asano, K. et al. Artificial selection for a green revolution gene during japonica rice domestication. Proc. Natl Acad. Sci. USA 108, 11034–11039 (2011).

  13. 13.

    Gooding, M. J., Addisu, M., Uppal, R. K., Snape, J. W. & Jones, H. E. Effect of wheat dwarfing genes on nitrogen-use efficiency. J. Agric. Sci. 150, 3–22 (2012).

  14. 14.

    Li, B.-Z. et al. Molecular basis and regulation of ammonium transporter in rice. Rice Sci. 16, 314–322 (2009).

  15. 15.

    Hawkesford, M. J. Reducing the reliance on nitrogen fertilizer for wheat production. J. Cereal Sci. 59, 276–283 (2014).

  16. 16.

    Zhao, X. et al. Nitrogen runoff dominates water nitrogen pollution from rice-wheat rotation in the Taihu Lake region of China. Agric. Ecosyst. Environ. 156, 1–11 (2012).

  17. 17.

    Conway, G. One Billion Hungry. Can We Feed the World? (Cornell Univ. Press, Ithaca, 2012).

  18. 18.

    Che, R. et al. Control of grain size and rice yield by GL2-mediated brassinosteroid responses. Nat. Plants 2, 15195 (2015).

  19. 19.

    Duan, P. et al. Regulation of OsGRF4 by OsmiR396 controls grain size and yield in rice. Nat. Plants 2, 15203 (2015).

  20. 20.

    Hu, J. et al. A rare allele of GS2 enhances grain size and grain yield in rice. Mol. Plant 8, 1455–1465 (2015).

  21. 21.

    Ma, X. et al. A robust CRISPR/Cas9 system for convenient, high-efficiency multiplex genome editing in monocot and dicot plants. Mol. Plant 8, 1274–1284 (2015).

  22. 22.

    Sun, H. et al. Heterotrimeric G proteins regulate nitrogen-use efficiency in rice. Nat. Genet. 46, 652–656 (2014).

  23. 23.

    Somers, D. A., Kuo, T. M., Kleinhofs, A., Warner, R. L. & Oaks, A. Synthesis and degradation of barley nitrate reductase. Plant Physiol. 72, 949–952 (1983).

  24. 24.

    Tabuchi, M., Abiko, T. & Yamaya, T. Assimilation of ammonium ions and reutilization of nitrogen in rice (Oryza sativa L.). J. Exp. Bot. 58, 2319–2327 (2007).

  25. 25.

    Peng, J. et al. The Arabidopsis GAI gene defines a signaling pathway that negatively regulates gibberellin responses. Genes Dev. 11, 3194–3205 (1997).

  26. 26.

    Nunes-Nesi, A., Fernie, A. R. & Stitt, M. Metabolic and signaling aspects underpinning the regulation of plant carbon nitrogen interactions. Mol. Plant 3, 973–996 (2010).

  27. 27.

    LeCain, D. R., Morgan, J. A. & Zerbi, G. Leaf anatomy and gas exchange in nearly isogenic semidwarf and tall winter wheat. Crop Sci. 29, 1246–1251 (1989).

  28. 28.

    Morgan, J. A., LeCain, D. R. & Wells, R. Semidwarfing genes concentrate photosynthetic machinery and affect leaf gas exchange of wheat. Crop Sci. 30, 602–608 (1990).

  29. 29.

    Fabian, T., Lorbiecke, R., Umeda, M. & Sauter, M. The cell cycle genes cycA1;1 and cdc2Os-3 are coordinately regulated by gibberellin in planta. Planta 211, 376–383 (2000).

  30. 30.

    Sauter, M. Differential expression of a CAK (cdc2-activating kinase)-like protein kinase, cyclins and cdc2 genes from rice during the cell cycle and in response to gibberellin. Plant J. 11, 181–190 (1997).

  31. 31.

    Yu, J. et al. OsLG3 contributing to rice grain length and yield was mined by Ho-LAMap. BMC Biol. 15, 28 (2017).

  32. 32.

    Huang, X. et al. Natural variation at the DEP1 locus enhances grain yield in rice. Nat. Genet. 41, 494–497 (2009).

  33. 33.

    Serrano-Mislata, A. et al. DELLA genes restrict inflorescence meristem function independently of plant height. Nat. Plants 3, 749–754 (2017).

  34. 34.

    Wang, S. et al. Non-canonical regulation of SPL transcription factors by a human OTUB1-like deubiquitinase defines a new plant type rice associated with higher grain yield. Cell Res. 27, 1142–1156 (2017).

  35. 35.

    Liu, W.-J., Zhu, Y.-G., Smith, F. A. & Smith, S. E. Do phosphorus nutrition and iron plaque alter arsenate (As) uptake by rice seedlings in hydroponic culture? New Phytol. 162, 481–488 (2004).

  36. 36.

    Wang, S. et al. Control of grain size, shape and quality by OsSPL16 in rice. Nat. Genet. 44, 950–954 (2012).

  37. 37.

    Bracha-Drori, K. et al. Detection of protein–protein interactions in plants using bimolecular fluorescence complementation. Plant J. 40, 419–427 (2004).

  38. 38.

    Chen, H. et al. Firefly luciferase complementation imaging assay for protein–protein interactions in plants. Plant Physiol. 146, 368–376 (2008).

  39. 39.

    Chen, L. et al. OsMADS57 together with OsTB1 coordinates transcription of its target OsWRKY94 and D14 to switch its organogenesis to defense for cold adaptation in rice. New Phytol. 218, 219–231 (2018).

  40. 40.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

  41. 41.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

  42. 42.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  43. 43.

    Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N. & Golani, I. Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).

  44. 44.

    O’Geen, H., Frietze, S. & Farnham, P. J. Using ChIP–seq technology to identify targets of zinc finger transcription factors. Methods Mol. Biol. 649, 437–455 (2010).

  45. 45.

    Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).

  46. 46.

    Lu, Z. et al. Genome-wide binding analysis of the transcription activator ideal plant architecture1 reveals a complex network regulating rice plant architecture. Plant Cell 25, 3743–3759 (2013).

  47. 47.

    Wang, S. et al. The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality. Nat. Genet. 47, 949–954 (2015).

  48. 48.

    Ho, C. H., Lin, S. H., Hu, H. C. & Tsay, Y. F. CHL1 functions as a nitrate sensor in plants. Cell 138, 1184–1194 (2009).

  49. 49.

    Loqué, D. et al. Additive contribution of AMT1;1 and AMT1;3 to high-affinity ammonium uptake across the plasma membrane of nitrogen-deficient Arabidopsis roots. Plant J. 48, 522–534 (2006).

Download references

Acknowledgements

We thank J. F. Ma for comments on this manuscript. This research was supported by grants from the National Key Research and Development Program of China (2016YFD0100401, 2016YFD0100706 and 2016YFD0100901), National Natural Science Foundation of China (91635302), Chinese Academy of Sciences (XDA08010101) and by the Biological and Biotechnological Sciences Research Council (UK) ‘Newton Fund’ Rice Research Initiative grant BB/M011224/1.

Reviewer information

Nature thanks B. Hirel, M. Matsuoka and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Affiliations

  1. State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    • Shan Li
    • , Yonghang Tian
    • , Kun Wu
    • , Yafeng Ye
    • , Jianping Yu
    • , Jianqing Zhang
    • , Qian Liu
    • , Yiping Tong
    •  & Xiangdong Fu
  2. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    • Shan Li
    •  & Xiangdong Fu
  3. Hebei Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China

    • Mengyun Hu
    •  & Hui Li
  4. Department of Plant Sciences, University of Oxford, Oxford, UK

    • Nicholas P. Harberd

Authors

  1. Search for Shan Li in:

  2. Search for Yonghang Tian in:

  3. Search for Kun Wu in:

  4. Search for Yafeng Ye in:

  5. Search for Jianping Yu in:

  6. Search for Jianqing Zhang in:

  7. Search for Qian Liu in:

  8. Search for Mengyun Hu in:

  9. Search for Hui Li in:

  10. Search for Yiping Tong in:

  11. Search for Nicholas P. Harberd in:

  12. Search for Xiangdong Fu in:

Contributions

S.L. performed most of the experiments; Y.Ti. and S.L. conducted QTL analysis; S.L., J.Z. and K.W. constructed near isogenic lines; S.L., Y.Y. and Q.L. performed field experiments; Y.To., M.H. and H.L. characterized the phenotypes of transgenic wheat plants; J.Y. performed haplotype analysis; N.P.H. and X.F. designed experiments; N.P.H. and X.F. wrote the manuscript. All authors discussed the results and contributed to the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Xiangdong Fu.

Extended data figures and tables

  1. Extended Data Fig. 1 Allelic variation at the GRF4 locus affects GRF4 mRNA abundance and root \({}^{{\bf{15}}}{{\bf{NH}}}_{{\bf{4}}}^{{\boldsymbol{+}}}\) uptake.

    a, Positional cloning indicates the equivalence of GRF4 with qNGR2 (nitrogen-mediated growth response 2). Successive maps show progressive narrowing of focus of qNGR2 (red dot, using recombination break points and linked DNA markers) to an approximately 2.7-kb region on chromosome 2 flanked by molecular markers L17 and L18 and overlapping candidate gene LOC_02g47280 (also known as GRF4). The start ATG (nucleotide 1) and close TGA (nucleotide 3385) of GRF4 are shown, together with the protein-coding DNA sequence (thick black bars). The target site for miR396 is indicated by an asterisk. The structure of a CRISPR–Cas9-generated grf4 mutant 91-bp deletion allele spanning parts of exon 1 and intron 1 is shown. b, \({}^{15}{{\rm{NH}}}_{4}^{+}\) uptake rates of roots of BC2F2 progeny (derived from a NJ6 × NM73 cross) homozygous or heterozygous for GRF4NGR2 or GRF4ngr2 grown in high nitrogen supply (1.25 mM NH4NO3). Data are mean ± s.e.m. (n = 9). Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test. c, GRF4 mRNA abundance in plants (genotypes as shown) relative to the abundance in NJ6 (set to one). Data are mean ± s.e.m. (n = 3). Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test. d, Natural varietal GRF4 allelic variation. Nucleotide position relative to the GRF4 start ATG is shown in a. SNPs shared between varieties NM73, RD23 and TZZL1 are highlighted. Sequences representative of GRF4 promoter haplotypes A, B and C (see main text) are shown. e, GRF4 mRNA abundance in various rice varieties under the high nitrogen conditions (1.25 mM NH4NO3), GRF4 promoter haplotypes are indicated. Abundance data are all relative to the abundance of rice Actin2 mRNA. Data are mean ± s.e.m. (n = 3). Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test. f, Comparisons of GRF4 mRNA abundance in selected rice varieties grown in between high (HN, 1.25 mM NH4NO3) and low (LN, 0.375 mM NH4NO3) nitrogen conditions. Data are mean ± s.e.m. (n = 3). Abundance data are all relative to the high nitrogen condition (set to one). **P < 0.05 compared to high nitrogen in a two-sided Student’s t-test. g, Relative abundances of rice miR396 family members in NJ6 plants grown at different levels of nitrogen supply (0.15N, 0.1875 mM NH4NO3; 0.3N, 0.375 mM NH4NO3; 0.6N, 0.75 mM NH4NO3; 1N, 1.25 mM NH4NO3), shown relative to the abundance in plants grown in 1N conditions (set to one). Data are mean ± s.e.m. (n = 3). Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test.

  2. Extended Data Fig. 2 Comparisons NJ6, NJ6-sd1 and NJ6-sd1-GRF4ngr2 isogenic line traits reveals that GRF4 regulates expression of \({{\bf{NH}}}_{{\bf{4}}}^{{\boldsymbol{+}}}\)-metabolism genes.

    a, Mature plant height. Data are mean ± s.e.m. (n = 16). b, The number of tillers per plant. Data are mean ± s.e.m. (n = 16). c, The number of grains per panicle. Data are mean ± s.e.m. (n = 16). d, Flag-leaf width. Data are mean ± s.e.m. (n = 16). e, Culm (stem) width expressed as diameter of the uppermost internode. Data are mean ± s.e.m. (n = 16). f, Grain yield per plant. Data are mean ± s.e.m. (n = 220). g, Relative root abundance of AMT1.2 mRNA in NILs, genotypes as indicated. Abundances shown are relative to NJ6 plants (set to 1). Data are mean ± s.e.m. (n = 3). h, Root glutamine synthase (GS) activities. Data are mean ± s.e.m. (n = 3). i, Relative shoot abundance of Fd-GOGAT mRNA. Abundances shown are relative to NJ6 plants (set to 1). Data are mean ± s.e.m. (n = 3). j, Shoot glutamine synthase (GS) activities. Data are mean ± s.e.m. (n = 3). k–n, Flag–GRF4-mediated ChIP–PCR enrichment (relative to input) of GCGG-containing promoter fragments (marked with an asterisk) from AMT1.2, GS2, NADH-GOGAT2 and Fd-GOGAT promoters. Diagrams depict putative AMT1.2, GS2, NADH-GOGAT2 and Fd-GOGAT promoters and fragments (1–6). Data are mean ± s.e.m. (n = 3; panels k–n). an, Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test. o, GRF4 activates AMT1.2, GS2, NADH-GOGAT2 and Fd-GOGAT promoter–luciferase fusion constructs in transient transactivation assays. Data are mean ± s.e.m. (n = 3). **P < 0.05 compared to control group by two-sided Student’s t-tests.

  3. Extended Data Fig. 3 GRF4 regulates expression of multiple \({{\bf{NO}}}_{{\bf{3}}}^{{\boldsymbol{-}}}\) metabolism genes.

    a, Relative abundance of NRT1.1B, NRT2.3a and NPF2.4 mRNAs that encode \({{\rm{NO}}}_{3}^{-}\) uptake transporters. Abundances shown are relative to NJ6 (set to 1). Data are mean ± s.e.m. (n = 3). b, Relative abundances of NIA1, NIA3 and NiR1 mRNAs that encode \({{\rm{NO}}}_{3}^{-}\)-assimilation enzymes. Abundances shown are relative to NJ6 (set to 1). Data are mean ± s.e.m. (n = 3). c–h, Flag–GRF4-mediated ChIP–PCR enrichment (relative to input) of GCGG-containing fragments (marked with asterisks) from promoters of NRT1.1B (c), NRT2.3a (d) and NPF2.4 (e) genes that encode \({{\rm{NO}}}_{3}^{-}\)-uptake transporters and NIA1 (f), NIA3 (g) and NiR1 (h) genes that encode \({{\rm{NO}}}_{3}^{-}\)-assimilation enzymes. Data are mean ± s.e.m. (n = 3). ah, Different letters denote significant differences (P < 0.05) from a Duncan’s multiple range test. i, GRF4 activates NRT1.1B, NRT2.3a, NPF2.4, NIA1, NIA3 and NiR1 promoter–luciferase fusion constructs in transient transactivation assays. Data are mean ± s.e.m. (n = 3) in all panels. P values are from a two-sided Student’s t-test.

  4. Extended Data Fig. 4 GA promotes glutamine synthase and nitrate reductase activities.

    a, Glutamine synthase activities in roots of two-week-old rice plants treated with 100 μM GA (GA3) and/or 2 μM PAC, genotypes as indicated. b, Glutamine synthase activities in shoots of plants treated with GA and/or PAC, genotypes and treatments as indicated in a. c, Nitrate reductase activities in shoots of plants treated with GA and/or PAC, genotypes and treatments as indicated in a. ac, Data are mean ± s.e.m. (n = 3); P values are from two-sided Student’s t-tests.

  5. Extended Data Fig. 5 BiFC visualization of SLR1–GIF1–GRF4 interactions.

    a, Details of constructs expressing GRF4 and variants deleted for specific domains. GRF4 contains the QLQ and WRC domains, positions as indicated. b, BiFC assays. Constructs expressing GRF4 or deletion variants (shown as in a) tagged with the N terminus of YFP were co-transformed into tobacco leaf epidermal cells, together with constructs expressing GIF1 or SLR1 tagged with the C terminus of YFP, respectively. Scale bars, 60 μm. c, BiFC assays. Constructs expressing GRF1 or related GRFs and GIFs family proteins tagged with the N terminus of YFP were co-transformed into tobacco leaf epidermal cells together with a construct expressing SLR1 tagged with the C terminus of YFP. Scale bar, 60 μm. b, c, Images of BiFC assays are representative of three experiments performed independently with similar results.

  6. Extended Data Fig. 6 SLR1 inhibits GRF4–GIF1 self-promotion of GRF4 mRNA and GRF4 protein abundance.

    a, GFR4 mRNA abundance, plant genotypes as indicated. Abundances shown are relative to NJ6 (set to 1). b, The effects of GA and PAC on GRF4 mRNA abundance in two-week-old NJ6 plants. Abundances shown are relative to the water treatment control (set to 1). c, ChIP–PCR GRF4-mediated enrichment (relative to input) of GCGG-containing GRF4 promoter fragments (marked with asterisks). d, GRF4-activated promotion of transcription from the GRF4 gene promoter-luciferase reporter construct is enhanced by GIF1 and inhibited by SLR1. Luciferase/renilla activity shown relative to the empty vector control (set to 1). ad, Data are mean ± s.e.m. (n = 3). Different letters denote significant differences (P < 0.05) from Duncan’s multiple range tests. e, GRF4 abundance (as detected by an anti-GRF4 antibody), plant genotypes as indicated. HSP90 serves as loading control. Blots are representative of three experiments performed independently with similar results.

  7. Extended Data Fig. 7 The GRF4–SLR1 antagonism regulates carbon assimilation and plant growth.

    a, b, Relative shoot abundances of carbon-fixation gene mRNAs. Abundances of transcripts of genes regulating photosynthesis (a), sucrose metabolism and transport/phloem loading (b) in NJ6, NJ6-sd1 and NJ6-sd1-GRF4ngr2 plants. Abundances in NJ6 and NJ6-sd1-GRF4ngr2 are expressed relative to NJ6-sd1 (set to 1). c, ChIP–PCR assays. Diagrams depict the PsbS1, TPS1 and SWEET11 promoters and regions used for ChIP–PCR, and GCGG-containing promoter fragment (marked with asterisks) enrichment (relative to input). ac, Data are mean ± s.e.m. (n = 3). d, Transactivation assays. The luciferase/renilla activity obtained from a co-transfection with an empty effector construct and indicated reporter constructs was set to 1. Data are mean ± s.e.m. (n = 9). e, Immunoblot detection of Lhca1, Lhca3, Lhca4, Lhcb2, PsaD and PsaE using antibodies as shown in genotypes as indicated. HSP90 serves as loading control. Blots are representative of three experiments performed independently with similar results. f–i, Comparisons of photosynthetic rates (f), biomass (g), carbon content (h) and C:N ratio (i) in NJ6, NJ6-sd1 and NJ6-sd1-GRF4ngr2 plants. Data are mean ± s.e.m. (n = 30). j, Relative shoot abundances of mRNAs transcribed from cell-cycle regulatory genes in NJ6, NJ6-sd1 and NJ6-sd1-GFR4ngr2 plants. Transcription is relative to NJ6-sd1 plants (set to 1). Data are mean ± s.e.m. (n = 3). k, ChIP–PCR assays. Diagrams depict the cycA1.1 and cdc2Os-3 promoters and regions (GCGG-containing fragment marked with asterisks) used for ChIP–PCR. Data are mean ± s.e.m. (n = 3). l, Transactivation assays from the cycA1.1and cdc2Os-3 promoters. Data are mean ± s.e.m. (n = 12). ad, fl, Different letters denote significant differences (P < 0.05) from Duncan’s multiple range tests.

  8. Extended Data Fig. 8 Natural allelic variation at GRF4 is associated with variation in plant and grain morphology and grain yield performance.

    a, DNA polymorphisms in the promoter region of GRF4. Green-shaded regions indicate the three unique SNP variations associated with phenotypic variation in NM73 and RD23. b–f, Box plots for plant height (b), grain length (c), grain width (d), the number of grains per panicle (e) and grain yield performance (f) of rice varieties carrying different GRF4 promoter haplotypes (Hap. A, B or C). All data are from plants grown under normal paddy-field fertilization conditions22. Data are mean ± s.e.m. (Hap. A, n = 74; Hap. B, n = 28; Hap. C, n = 123). The violin plot was constructed in R. bf, Different letters indicate statistically significant differences between groups (P < 0.05) from a Tukey’s honestly significant difference (HSD) test.

  9. Extended Data Fig. 9 Agronomic traits displayed by 9311 and 9311-GRF4ngr2 plants grown at varying nitrogen fertilization levels.

    a, b, Flag leaf width (a) and culm width of the uppermost internode (b) at varying levels of nitrogen fertilization. c, d, \({}^{15}{{\rm{NH}}}_{4}^{+}\) (c) and \({}^{15}{{\rm{NO}}}_{3}^{-}\) (d) uptake rates of four-week-old plants grown with varying nitrogen supply (0.15N, 0.1875 mM NH4NO3; 0.3N, 0.375 mM NH4NO3; 0.6N, 0.75 mM NH4NO3; 1N, 1.25 mM NH4NO3). eh, The number of grains per panicle (e), 1,000-grain weight (f), harvest index (g) and dry biomass per plant (h) at varying levels of nitrogen fertilization. ah, Data are mean ± s.e.m. (n = 30); P values are from two-sided Student’s t-tests.

  10. Extended Data Fig. 10 Growth, nitrogen uptake and grain yield performance of WYJ7-dep1-1 and transgenic WYJ7-dep1-1 plants carrying the 35S::GRF4ngr2–GFP construct at varying levels of nitrogen fertilization.

    a, Mature plant heights. Scale bar, 15 cm. The picture is representative of three experiments performed independently with similar results. bd, Root uptake rates for \({}^{15}{{\rm{NH}}}_{4}^{+}\) (b), \({}^{15}{{\rm{NO}}}_{3}^{-}\) (c), and \({}^{15}{{\rm{NH}}}_{4}^{+}\) and \({}^{15}{{\rm{NO}}}_{3}^{-}\) combined (d) of four-week-old rice plants grown in low nitrogen (0.3N, 0.375 mM NH4NO3) and high nitrogen (1N, 1.25 mM NH4NO3) conditions. Data are mean ± s.e.m. (n = 9). ei, Mature plant height (e), heading date (f), the number of tillers per plant (g), the number of grains per panicle (h) and grain yield per plant (i) at varying levels of nitrogen fertilization. ei, Data shown as mean ± s.e.m. (n = 30). bi, P values are from two-sided Student’s t-tests.

Supplementary information

  1. Supplementary Figure

    This file contains the uncropped scans with size marker indications.

  2. Reporting Summary

  3. Supplementary Tables

    This file contains Supplementary Tables 1-9 and a Supplementary Guide.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41586-018-0415-5

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.