Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Plastid-localized amino acid metabolism coordinates rice ammonium tolerance and nitrogen use efficiency

Nature Plants volume 9, pages 1514–1529 (2023)Cite this article

Subjects

Abstract

Ammonium toxicity affecting plant metabolism and development is a worldwide problem impeding crop production. Remarkably, rice (Oryza sativa L.) favours ammonium as its major nitrogen source in paddy fields. We set up a forward-genetic screen to decipher the molecular mechanisms conferring rice ammonium tolerance and identified rohan showing root hypersensitivity to ammonium due to a missense mutation in an argininosuccinate lyase (ASL)-encoding gene. ASL localizes to plastids and its expression is induced by ammonium. ASL alleviates ammonium-inhibited root elongation by converting the excessive glutamine to arginine. Consequently, arginine leads to auxin accumulation in the root meristem, thereby stimulating root elongation under high ammonium. Furthermore, we identified natural variation in the ASL allele between japonica and indica subspecies explaining their different root sensitivity towards ammonium. Finally, we show that ASL expression positively correlates with root ammonium tolerance and that nitrogen use efficiency and yield can be improved through a gain-of-function approach.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: rohan root growth is hypersensitive to NH4+.
Fig. 2: ROHAN encodes a plastid-localized ASL.
Fig. 3: rohan is defective in the conversion of NH4+/Gln to Arg.
Fig. 4: The root response of rohan to NH4+ is mediated by auxin.
Fig. 5: Genetic variation in ASL is associated with its regulatory role in root response to NH4+.
Fig. 6: ASL expression determines rice tolerance to NH4+ toxicity and NUE.

Similar content being viewed by others

Data availability

The raw whole-genome sequencing (WGS) datasets and RNA-seq datasets have been deposited on NCBI BioProject (https://www.ncbi.nlm.nih.gov/bioproject) under the accession numbers PRJNA808438 and PRJNA808101. The datasets of widely targeted metabolomics are deposited in the OMIX, China Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences with accession number OMIX004476 (https://ngdc.cncb.ac.cn/omix), and are also available in Supplementary Table 1. Source data are provided with this paper. For any datasets unavailable through the links above, they can be requested from the corresponding author.

References

  1. Xu, G., Fan, X. & Miller, A. J. Plant nitrogen assimilation and use efficiency. Annu. Rev. Plant Biol. 63, 153–182 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Britto, D. T. & Kronzucker, H. J. NH4+ toxicity in higher plants: a critical review. J. Plant Physiol. 159, 567–584 (2002).

    Article  CAS  Google Scholar 

  3. Li, B., Li, G., Kronzucker, H. J., Baluška, F. & Shi, W. Ammonium stress in Arabidopsis: signaling, genetic loci, and physiological targets. Trends Plant Sci. 19, 107–114 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Barth, C., Gouzd, Z. A., Steele, H. P. & Imperio, R. M. A mutation in GDP-mannose pyrophosphorylase causes conditional hypersensitivity to ammonium, resulting in Arabidopsis root growth inhibition, altered ammonium metabolism, and hormone homeostasis. J. Exp. Bot. 61, 379–394 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Tanaka, H. et al. Identification and characterization of Arabidopsis AtNUDX9 as a GDP-d-mannose pyrophosphohydrolase: its involvement in root growth inhibition in response to ammonium. J. Exp. Bot. 66, 5797–5808 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li, B. et al. Arabidopsis plastid AMOS1/EGY1 integrates abscisic acid signaling to regulate global gene expression response to ammonium stress. Plant Physiol. 160, 2040–2051 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Britto, D. T. & Kronzucker, H. J. Nitrogen acquisition, PEP carboxylase, and cellular pH homeostasis: new views on old paradigms. Plant Cell Environ. 28, 1396–1409 (2005).

    Article  CAS  Google Scholar 

  8. Barker, A. V & Mills, H. A. in Horticultural Reviews Vol. 2 (ed. Janick, J.) 395–423 (Wiley, 1980).

  9. Li, B. et al. Shoot-supplied ammonium targets the root auxin influx carrier AUX1 and inhibits lateral root emergence in Arabidopsis. Plant Cell Environ. 34, 933–946 (2011).

    Article  CAS  PubMed  Google Scholar 

  10. Li, G. et al. The Arabidopsis AMOT1/EIN3 gene plays an important role in the amelioration of ammonium toxicity. J. Exp. Bot. 70, 1375–1388 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Coleto, I. et al. Arabidopsis thaliana transcription factors MYB28 and MYB29 shape ammonium stress responses by regulating Fe homeostasis. New Phytol. 229, 1021–1035 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Coskun, D., Britto, D. T., Li, M., Becker, A. & Kronzucker, H. J. Rapid ammonia gas transport accounts for futile transmembrane cycling under NH3/NH4+ toxicity in plant roots. Plant Physiol. 163, 1859–1867 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Britto, D. T., Siddiqi, M. Y., Glass, A. D. M. & Kronzucker, H. J. Futile transmembrane NH4+ cycling: a cellular hypothesis to explain ammonium toxicity in plants. Proc. Natl Acad. Sci. USA 98, 4255–4258 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Schjoerring, J. K., Husted, S., Mäck, G. & Mattsson, M. The regulation of ammonium translocation in plants. J. Exp. Bot. 53, 883–890 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Hachiya, T. et al. Excessive ammonium assimilation by plastidic glutamine synthetase causes ammonium toxicity in Arabidopsis thaliana. Nat. Commun. 12, 4944 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jia, L. et al. Rice plants respond to ammonium stress by adopting a helical root growth pattern. Plant J. 104, 1023–1037 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Jian, S. et al. NRT1.1-related NH4+ toxicity is associated with a disturbed balance between NH4+ uptake and assimilation. Plant Physiol. 178, 1473–1488 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hachiya, T. et al. Evidence for a nitrate-independent function of the nitrate sensor NRT1.1 in Arabidopsis thaliana. J. Plant Res. 124, 425–430 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Sun, S. K. et al. A molecular switch in sulfur metabolism to reduce arsenic and enrich selenium in rice grain. Nat. Commun. 12, 1392 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Beeckman, F., Motte, H. & Beeckman, T. Nitrification in agricultural soils: impact, actors and mitigation. Curr. Opin. Biotechnol. 50, 166–173 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Konishi, N. & Ma, J. F. Three polarly localized ammonium transporter 1 members are cooperatively responsible for ammonium uptake in rice under low ammonium condition. New Phytol. 232, 1178–1792 (2021).

    Article  Google Scholar 

  22. Hirano, T. et al. Inhibition of ammonium assimilation restores elongation of seminal rice roots repressed by high levels of exogenous ammonium. Physiol. Plant. 134, 183–190 (2008).

    Article  CAS  PubMed  Google Scholar 

  23. Xuan, Y. H. et al. Indeterminate domain 10 regulates ammonium-mediated gene expression in rice roots. New Phytol. 197, 791–804 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Jiao, X. et al. Promotion of BR biosynthesis by miR444 is required for ammonium-triggered inhibition of root growth. Plant Physiol. 182, 1454–1466 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Li, G. et al. OsEIL1 protects rice growth under NH4+ nutrition by regulating OsVTC1-3-dependent N-glycosylation and root NH4+ efflux. Plant. Cell Environ. 45, 1537–1553 (2022).

    Article  CAS  PubMed  Google Scholar 

  26. Xia, J. X., Yamaji, N., Che, J., Shen, R. F. & Ma, J. F. Normal root elongation requires arginine produced by argininosuccinate lyase in rice. Plant J. 78, 215–226 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Nacry, P., Bouguyon, E. & Gojon, A. Nitrogen acquisition by roots: physiological and developmental mechanisms ensuring plant adaptation to a fluctuating resource. Plant Soil 370, 1–29 (2013).

    Article  CAS  Google Scholar 

  28. Kotogany, E., Dudits, D., Horvath, G. V. & Ayaydin, F. A rapid and robust assay for detection of S-phase cell cycle progression in plant cells and tissues by using ethynyl deoxyuridine. Plant Methods 6, 5 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Slocum, R. D. Genes, enzymes and regulation of arginine biosynthesis in plants. Plant Physiol. Biochem. 43, 729–745 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Luo, L. et al. Ammonium transporters cooperatively regulate rice crown root formation responding to ammonium nitrogen. J. Exp. Bot. 73, 3671–3685 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Kusano, M. et al. Cytosolic GLUTAMINE SYNTHETASE1;1 modulates metabolism and chloroplast development in roots. Plant Physiol. 182, 1894–1909 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kusano, M. et al. Metabolomics data reveal a crucial role of cytosolic glutamine synthetase 1;1 in coordinating metabolic balance in rice. Plant J. 66, 456–466 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Gaufichon, L., Reisdorf-Cren, M., Rothstein, S. J., Chardon, F. & Suzuki, A. Biological functions of asparagine synthetase in plants. Plant Sci. 179, 141–153 (2010).

    Article  CAS  Google Scholar 

  34. Hildebrandt, T. M., Nunes Nesi, A., Araújo, W. L. & Braun, H. P. Amino acid catabolism in plants. Mol. Plant 8, 1563–1579 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Griffith, O. W. & Meister, A. Differential inhibition of glutamine and γ-glutamylcysteine synthetases by α-alkyl analogs of methionine sulfoximine that induce convulsions. J. Biol. Chem. 253, 2333–2338 (1978).

    Article  CAS  PubMed  Google Scholar 

  36. Hu, B. et al. Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies. Nat. Genet. 47, 834–838 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Zhang, M. et al. Plasma membrane H+-ATPase overexpression increases rice yield via simultaneous enhancement of nutrient uptake and photosynthesis. Nat. Commun. 12, 735 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  38. van Gelderen, K., Kang, C. & Pierik, R. Light signaling, root development, and plasticity. Plant Physiol. 176, 1049–1060 (2018).

    Article  PubMed  Google Scholar 

  39. Vega, A., O’Brien, J. A. & Gutierrez, R. A. Nitrate and hormonal signaling crosstalk for plant growth and development. Curr. Opin. Plant Biol. 52, 155–163 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Hagen, G. & Guilfoyle, T. Auxin-responsive gene expression: genes, promoters and regulatory factors. Plant Mol. Biol. 49, 373–385 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. Yang, J. et al. Dynamic regulation of auxin response during rice development revealed by newly established hormone biosensor markers. Front. Plant Sci. 8, 256 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Otvos, K. et al. Modulation of plant root growth by nitrogen source-defined regulation of polar auxin transport. EMBO J. 40, e106862 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kim, J. Y. et al. Identification of an ABCB/P-glycoprotein-specific inhibitor of auxin transport by chemical genomics. J. Biol. Chem. 285, 23309–23317 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Teale, W. & Palme, K. Naphthylphthalamic acid and the mechanism of polar auxin transport. J. Exp. Bot. 69, 303–312 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Wang, R., Xing, X., Wang, Y., Tran, A. & Crawford, N. M. A genetic screen for nitrate regulatory mutants captures the nitrate transporter gene NRT1.1. Plant Physiol. 151, 472–478 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Xu, M., Zhu, L., Shou, H. & Wu, P. A PIN1 family gene, OsPIN1, involved in auxin-dependent adventitious root emergence and tillering in rice. Plant Cell Physiol. 46, 1674–1681 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Trovato, M., Funck, D., Forlani, G., Okumoto, S. & Amir, R. Editorial: Amino acids in plants: regulation and functions in development and stress defense. Front. Plant Sci. 12, 772810 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jia, Z., Giehl, R. F. H. & von Wirén, N. Local auxin biosynthesis acts downstream of brassinosteroids to trigger root foraging for nitrogen. Nat. Commun.12, 5437 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mazzoni-Putman, S. M., Brumos, J., Zhao, C., Alonso, J. M. & Stepanova, A. N. Auxin interactions with other hormones in plant development. Cold Spring Harb. Perspect. Biol. 13, a039990 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Grieneisen, V. A., Xu, J., Marée, A. F. M., Hogeweg, P. & Scheres, B. Auxin transport is sufficient to generate a maximum and gradient guiding root growth. Nature 449, 1008–1013 (2007).

    Article  CAS  PubMed  Google Scholar 

  51. Li, Y. et al. Functional divergence of PIN1 paralogous genes in rice. Plant Cell Physiol. 60, 2720–2732 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Takagi, H. et al. MutMap accelerates breeding of a salt-tolerant rice cultivar. Nat. Biotechnol. 33, 445–449 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Gujas, B., Alonso-Blanco, C. & Hardtke, C. S. Natural Arabidopsis brx loss-of-function alleles confer root adaptation to acidic soil. Curr. Biol. 22, 1962–1968 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Abe, A. et al. Genome sequencing reveals agronomically important loci in rice using MutMap. Nat. Biotechnol. 30, 174–178 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Pertea, M., Kim, D., Pertea, G. M., Leek, J. T. & Salzberg, S. L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650–1667 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Kovaka, S. et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 20, 278 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Ma, X. et al. PlantGSAD: a comprehensive gene set annotation database for plant species. Nucleic Acids Res. 50, D1456–D1467 (2021).

    Article  PubMed Central  Google Scholar 

  65. Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb.) 2, 100141 (2021).

    CAS  PubMed  Google Scholar 

  66. Yang, C. et al. Rice metabolic regulatory network spanning the entire life cycle. Mol. Plant 15, 258–275 (2022).

    Article  CAS  PubMed  Google Scholar 

  67. Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  PubMed  Google Scholar 

  71. Hiei, Y., Komari, T. & Kubo, T. Transformation of rice mediated by Agrabacterium tumefaciens. Plant Mol. Biol. 35, 205–218 (1997).

    Article  CAS  PubMed  Google Scholar 

  72. De Smet, I. et al. An easy and versatile embedding method for transverse sections. J. Microsc. 213, 76–80 (2004).

    Article  PubMed  Google Scholar 

  73. Kurihara, D., Mizuta, Y., Sato, Y. & Higashiyama, T. ClearSee: A rapid optical clearing reagent for whole-plant fluorescence imaging. Development 142, 4168–4179 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Tang, Z. et al. Knockdown of a rice stelar nitrate transporter alters long-distance translocation but not root influx. Plant Physiol. 160, 2052–2063 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zhang, S. et al. Nitrogen mediates flowering time and nitrogen use efficiency via floral regulators in rice. Curr. Biol. 31, 671–683 (2021).

    Article  CAS  PubMed  Google Scholar 

  76. Novák, O., Pěnčík, A., Blahoušek, O. & Ljung, K. Quantitative auxin metabolite profiling using stable isotope dilution UHPLC-MS/MS. Curr. Protoc. Plant Biol. 1, 419–430 (2016).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank H. Qu, X. Dai and K. Qian for their technical help with the 15N uptake assays and confocal imaging. This work was supported by China National Key Programme for Research and Development (grant no. 2021YFF1000403), the Project of Sanya Yazhou Bay Science and Technology City (grant no. SCKJ-JYRC-2022-21), National Natural Science Foundation (grant nos 32072658 and 31822047), Key Research and Development Programme of Jiangsu Province (grant no. BE2020339), Jiangsu Seed Industry Revitalization Project (grant no. JBGS [2021] 011) and the Research Foundation – Flanders: Bilateral Scientific Cooperation fund with China (grant no. G002817N). Y.X. is supported by a grant from the Chinese Scholarship Council (grant no. 201806850032).

Author information

Author notes

  1. These authors contributed equally: Yuanming Xie, Yuanda Lv.

Authors and Affiliations

  1. State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization and MOA Key Laboratory of Plant Nutrition and Fertilization in Lower-Middle Reaches of the Yangtze River, Sanya Institute of Nanjing Agricultural University, Nanjing Agricultural University, Nanjing, China

    Yuanming Xie, Letian Jia, Lulu Zheng, Yonghui Li, Ming Zhu, Long Luo, Shaoyan Lin, Le Luo, Guohua Xu & Wei Xuan

  2. Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium

    Yuanming Xie, Hugues De Gernier, Pierre-Mathieu Pélissier, Hans Motte & Tom Beeckman

  3. VIB-UGent Center for Plant Systems Biology, Ghent, Belgium

    Yuanming Xie, Hugues De Gernier, Pierre-Mathieu Pélissier, Hans Motte & Tom Beeckman

  4. Excellence and Innovation Center, Jiangsu Academy of Agricultural Sciences, Nanjing, China

    Yuanda Lv & Weicong Qi

  5. Department of Plant Pathology, Nanjing Agricultural University, Nanjing, China

    Mengjun Tian & Ming Wang

Authors
  1. Yuanming Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanda Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Letian Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lulu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yonghui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ming Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mengjun Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Weicong Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Long Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hugues De Gernier
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Pierre-Mathieu Pélissier
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Hans Motte
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Shaoyan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Le Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Guohua Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Tom Beeckman
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Wei Xuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.X. and T.B. directed the experiments. Y.X. and Y. Lv performed most of the experiments and analysis. L.J., L.Z., M.Z. and Long Luo helped with vector construction. Y. Li helped with root section. M.T and M.W. helped the indole-3-acetic acid content analysis. W.Q. helped the phenotyping analysis. H.D.G. P.-M.P. and H.M. helped the imaging. S.L. and Le Luo helped the amino acid content analysis. All authors discussed the results and contributed to the finalization of the manuscript.

Corresponding authors

Correspondence to Tom Beeckman or Wei Xuan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Plants thanks Takushi Hachiya and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 The seminal root growth of rohan is hypersensitive to external NH4+.

a, Relative seminal roots (SR) of wild-type (WT) and rohan seedlings treated with varying concentrations of NH4+ and NO3−for 6 days. b, Relative SR length of WT and rohan plants treated without or with 2.5 mM NH4+ over one day to five days. c,d, Root phenotype (c) and SR length (d) of WT and rohan seedlings grown in modified Kimura B solution supplied with or without indicated nutrient elements. Scale bars, 1 cm. Data represent the means ± s.d. (numbers in columns represent the number of individual seedlings treated). e,f, Root phenotype (e) and SR length (f) of WT and rohan seedlings grown in H2O supplied with or without indicated nutrients. the concentrations of chemicals were used as follows: 0.3 mM Gln, 1.25 mM (NH4)2SO4, 0.3 mM NaH2PO4, 0.65 mM K2SO4, 1 mM CaCl2, 1 mM MgSO4, 20 μM EDTA-Fe, and 0.5 mM Na2SiO3. Scale bars, 1 cm. Data represent the means ± s.d. (numbers in columns represent the number of individual seedlings treated). h,i, Confocal images (h) and cortex cell length (i) in root maturation zone of WT and rohan seedlings treated with or without NH4+ for 4 days. Ep, epidermis. Ex, exodermis. Sc, Sclerenchyma. Co, cortex. Scale bars, 100 μm. Data represent the means ± s.d. (numbers in columns represent the number of individual seedlings treated). a,b, Boxplot, median (centerline), upper/lower quartiles (box), minimum/maximum (whiskers) (n indicates the number of individual seedlings treated), and the asterisks indicate significant differences between WT and rohan under each concentration of NH4+ or NO3− (a) or each day (b) (two-sided student’s t-test, ****P < 0.0001). c,e, The yellow dotted lines indicate the positions of root tip when the seedings were transferred to media supplied with or without indicated nutrients. f,i, The asterisks indicate significant differences (two-sided student’s t-test); ns, not significant.

Source data

Extended Data Fig. 2 ASL-mediated seminal root elongation is independent of NH4+ uptake and low pH.

a, Relative expression level of the ammonium transporters AMT1.1, AMT1.2, AMT1.3 and AMT2.1 in the root tips of wild-type (WT) and rohan seedlings treated with or without 2.5 mM NH4+ for 4 days. Data represent the means ± s.e. of three biological replicates, and the asterisks indicate significant differences (two-sided student’s t-test). b, Quantification of the net NH4+ and proton fluxes in the root tip of WT and rohan seedlings treated with or without 2.5 mM NH4+, Data represent the means ± s.d. (n = 3 individual seedlings of each line), and the asterisks indicate significant differences between WT and rohan at each tested position along the root (two-sided student’s t-test, ****P < 0.0001). c, BCP staining of grinded root tissues showing the root acidification of WT, rohan and rohan-C1 seedlings grown under either N-free or 2.5 mM NH4+ condition (three biological replicates). d,e, Root phenotype (d) and seminal roots (SR) length (e) of WT and rohan seedlings treated with or without 2.5 mM NH4+, under the conditions of pH 5.5 and pH 4.0. The white dotted line indicates the position of the root tips when the seedlings were transferred to N-free or NH4+ media with different pHs. Scale bar, 1 cm. Data represent the means ± s.d. (numbers in columns represent the number of individual seedlings treated). The asterisks indicate significant differences between pH 5.5 and pH 4.0 treatments (two-sided student’s t-test).

Source data

Extended Data Fig. 3 Map-based cloning of ASL gene.

a, Root phenotypes of the wild-type (WT), rohan, and the F1 progeny of the rohan x wild-type backcross treated with 2.5 mM NH4+ for 6 days. 12 independent F1 progeny seedlings showed identical root phenotype. Scale bar, 1 cm. b, Segregation statistic of the wild-type and rohan root phenotypes in the backcross F2 progeny. c, Euclidean distance association analysis of the ASL candidate interval in the rice genome. ED4 was calculated based on the SNP and InDel frequency. The red arrow indicates the chromosomal location of the ASL gene. ED, Euclidean Distance. d, Sketch map of mutation sites in the CRISPR knockout lines of ASL. Red triangles indicate the mutation target sites.

Extended Data Fig. 4 Phylogenetic and protein sequence analysis of ASL.

a, Phylogenetic tree of ASL established with MEGA 7.0 software by Neighbor-jointing method with 1000 bootstrapping trials. The numbers on the branch indicate bootstrapping values. Escherichia coli (Ec), Triticum aestivum (TAE), Oryza sativa (Os), Medicago truncatula (Mt), Selaginella moellendorffii (Sm), Arabidopsis thaliana (At), Sorghum bicolor (Sb), Zea mays (Zm), Hordeum vulgare (HV), Brachypodium distachyon (Bd). b, Expression pattern of proASL:GUS in the root tissues of wild-type (Oryza sativa cv. Wuyungeng7) seedlings. LR, lateral root; LRP, lateral root primordium; DZ, differentiation zone; MZ, meristem zone. Similar results were obtained from root sections of three independent seedlings. Scale bars, 100 μm. T-LRP, transverse section of lateral root primordium. T-DZ, transverse section of root differentiation zone. T-MZ, transverse section of root meristem zone. Scale bars, 100 μm. c, Alignment of ASL amino acid sequence in different plant species using ESPript 3 software. Red arrowhead indicates the location of mutation site. Accession numbers for aligned sequences: EcASL, WP_109536061.1; AtASL, NP_196653.1; OsASL, XP_015632553.1; BdASL, XP_010228716.1; HvASL, BAJ96216.1; SmASL, EFJ24721.1; MtASL, XP_003602904.2; TaeASL, XP_044394196.1.

Extended Data Fig. 5 The effects of NH4+ on the Gln content in gs1;1 mutants, and ASL expression in gs1;1 and qko mutants.

a, Sketch map of mutation sites in the CRISPR knockout lines of Glutamine Synthestase (GS1;1). b, Quantification of Gln content in the root tips of wild-type (Oryza sativa cv. Nipponbare) and two gs1;1 mutant lines treated with or without 2.5 mM NH4+ for 6 days. Data represent the means ± s.d. of three biological repeats, and the asterisks indicate significant differences relative to wild-type (two-sided student’s t-test). ns, not significant. c, Relative expression level of ASL at the root tip of wild-type, gs1;1-1, gs1;1-2, qko-1 and qko-2 seedlings under treatments with or without 2.5 mM NH4+ for 3 days. Data represent the means ± s.e. of three biological replicates, and asterisks indicate significant differences relative to N-free (two-sided student’s t-test). Similar results were obtained in three independent experiments.

Source data

Extended Data Fig. 6 Metabolomic analysis of wild-type and rohan.

a, Principal component analysis (PCA) between wild-type (WT) and rohan. b, Metabolites abundant ranking profile based on log2 (Fold change) and p-value between wild-type and rohan. c, Distribution of differential metabolites. Red represents up-regulating in rohan while blue represents down-regulating. d, KEGG classification of differential metabolites between WT and rohan. e, KEGG enrichment analysis of differential metabolites between WT and rohan.

Extended Data Fig. 7 ASL acts on the conversion of NH4+/Gln to Arg.

a, Contents of free amino acids in the roots of wild-type (WT), rohan, and rohan-C1 treated with or without 2.5 mM NH4+ for 6 days. rohan-C1 is a complementary line of the rohan. Data represent the means ± s.d. of four biological repeats, and the asterisks indicate significant differences relative to WT (two-sided student’s t-test), ND, not detected. b,c, Root phenotype (b) and relative seminal roots (SR) length (c) of WT and rohan seedlings grown under indicated treatments for 6 days. The white dotted line indicates the position of the root tip when the seedlings were transferred to media supplied with indicated chemicals. Scale bars, 1 cm. Data represent the means ± s.d. (numbers in columns represent the number of individual seedlings treated), and the letters denote significant differences (P < 0.05, by one-way ANOVA followed by Duncan’s test). d, Relative seminal roots (SR) length of WT and rohan seedlings treated with varying concentrations of Gln, MSO, and Arg, under the indicated N condition for 6 days. Boxplot, median (centerline), upper/lower quartiles (box), minimum/maximum (whiskers) (n indicates the number of individual seedlings treated), and the asterisks indicate significant differences relative to WT or rohan under control treatment (0 mM Gln, MSO, or Arg) (two-sided student’s t-test, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001). Similar results were obtained in three independent experiments.

Source data

Extended Data Fig. 8 ASL regulates the nitrogen metabolism pathway at the transcriptional level.

a, Venn diagram showing the number of differentially expressed genes affected by 2.5 mM NH4+ treatment in the root tips of wild-type (WT) and rohan. b, Gene set enrichment analysis of differentially expressed genes based on GO and KEGG databases between the wild-type and rohan. c, GSEA results of nitrogen metabolism related gene sets in rohan comparing with WT under one-day and three -day treatment of 2.5 mM NH4+, Permutation test, two-side. d, Heatmap of Z-score values from expression abundance of genes associated with nitrogen metabolism. Each treatment is a column, while each row is a gene. The colour ranged from red for positive Z-score values to blue for negative values. e, Relative expression level of indicated genes in the root tips of WT and rohan seedlings treated with or without 2.5 mM NH4+ for 3 days. Data represent the means ± s.e. of three biological repeats, and the asterisks indicate significant differences (two-sided student’s t-test).

Source data

Extended Data Fig. 9 The NH4+-induced expression of proton flux-associated genes is suppressed in rohan.

Relative expression levels of indicated proton flux-associated genes in the root tips of wild-type (WT) and rohan seedlings treated with or without 2.5 mM NH4+ for 3 days. Data represent the means ± s.e. of three biological repeats, and the asterisks indicate significant differences (two-sided student’s t-test).

Source data

Extended Data Fig. 10 rohan root response to NH4+ is mediated by auxin metabolism.

a, GSEA results of plant hormone signal transduction related gene sets in rohan comparing with wild-type (WT) under one-day and three-day treatment of high NH4+, Permutation test, two-side. b, Quantification of the free IAA content in the roots of WT, rohan and rohan-C1 (complementary line of the rohan) seedlings treated with or without 2.5 mM NH4+, 0.3 mM Gln, 0.3 mM Arg and their combination for 6 days. Data represent the means ± s.d. of 5 biological replicates,and the asterisks indicate significant differences relative to WT (two-sided student’s t-test). ND, not detected. c, Expression pattern of DR5rev:3xVENUS-N7 in the root tips of WT and rohan seedlings grown under N-free condition for 48 h (related to Fig. 4d). The white and green arrowheads indicate St (stele) and Ep (epidermis), respectively. Similar results were obtained in three independent experiments. Scale bar, 100 μm. d, Relative SR length of seminal roots (SR) length of WT and rohan seedlings treated with NPA or BUM, under N-free condition for 6 days (related to Fig. 4g). Boxplot, median (centerline), upper/lower quartiles (box), minimum/maximum (whiskers) (n indicates the number of individual seedlings treated), and the asterisks indicate significant differences between WT and rohan under each concentration of BUM or NPA (two-sided student’s t-test). e, Relative expression levels of PINs at the root tip of WT and rohan seedlings under 2.5 mM NH4+ treatments for 3 days. Data represent the means ± s.e. of three biological replicates, and the asterisks indicate significant differences (two-sided student’s t-test). f, Sketch map of mutation of CRISPR knockout lines of PIN1a and PIN1b. Red triangles indicate the mutation target sites. g, Quantification of free IAA content in the root of WT and pin1apin1b seedlings treated with or without 2.5 mM NH4+ for 6 days. Data represent the means ± s.d. of 5 biological replicates, and the asterisks indicate significant differences (two-sided student’s t-test).

Source data

Supplementary information

Supplementary Information

Supplementary Figs 1–5.

Reporting Summary

Supplementary Tables

Supplementary Table 1 Metabolite profiling of wild-type and rohan seedlings. Supplementary Table 2 Sequences of primers used in this study.

Supplementary Data

Table 1 Statistical source data for Supplementary Fig. 2. Table 2 Statistical source data for Supplementary Fig. 3. Table 3 Statistical source data for Supplementary Fig. 4. Table 4 Statistical source data for Supplementary Fig. 5.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Y., Lv, Y., Jia, L. et al. Plastid-localized amino acid metabolism coordinates rice ammonium tolerance and nitrogen use efficiency. Nat. Plants 9, 1514–1529 (2023). https://doi.org/10.1038/s41477-023-01494-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-023-01494-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing