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THP9 enhances seed protein content and nitrogen-use efficiency in maize

Abstract

Teosinte, the wild ancestor of maize (Zea mays subsp. mays), has three times the seed protein content of most modern inbreds and hybrids, but the mechanisms that are responsible for this trait are unknown1,2. Here we use trio binning to create a contiguous haplotype DNA sequence of a teosinte (Zea mays subsp. parviglumis) and, through map-based cloning, identify a major high-protein quantitative trait locus, TEOSINTE HIGH PROTEIN 9 (THP9), on chromosome 9. THP9 encodes an asparagine synthetase 4 enzyme that is highly expressed in teosinte, but not in the B73 inbred, in which a deletion in the tenth intron of THP9-B73 causes incorrect splicing of THP9-B73 transcripts. Transgenic expression of THP9-teosinte in B73 significantly increased the seed protein content. Introgression of THP9-teosinte into modern maize inbreds and hybrids greatly enhanced the accumulation of free amino acids, especially asparagine, throughout the plant, and increased seed protein content without affecting yield. THP9-teosinte seems to increase nitrogen-use efficiency, which is important for promoting a high yield under low-nitrogen conditions.

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Fig. 1: Comparison of the protein content and genome sequence of teosinte and modern maize.
Fig. 2: Map-based cloning and expression of THP9.
Fig. 3: Genetic confirmation and natural variation of THP9.
Fig. 4: NUE of NILTeo and NILB73.
Fig. 5: Hybrids that contain THP9-T have an improved nitrogen content.

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Data availability

All sequencing data for creation of the Ames 21814 haplotype have been deposited at the National Genomics Data Center (NGDC; https://ngdc.cncb.ac.cn/) under the BioProject number PRJCA011706, in which the PacBio HiFi data are under the accession number SAMC874385, the Hi-C data are under the accession number SAMC873392, the PacBio isoform sequencing data are under the accession number SAMC873393 and the Illumina WGS data are under the accession numbers SAMC874386 and SAMC874387. The final assembled genome sequence data reported in this paper have been deposited under accession number GWHBKHM00000000 that is publicly accessible at https://ngdc.cncb.ac.cn/gwh. The RNA-sequencing data of Ames 21814, B73 × Ames 21814 and B73 (roots and leaves at flowering stage) are under the accession numbers SAMC874334SAMC874357Source data are provided with this paper.

References

  1. Karn, A., Gillman, J. D. & Flint-Garcia, S. A. Genetic analysis of teosinte alleles for kernel composition traits in maize. G3 7, 1157–1164 (2017).

    Article  CAS  Google Scholar 

  2. Flint-Garcia, S. A., Bodnar, A. L. & Scott, M. P. Wide variability in kernel composition, seed characteristics, and zein profiles among diverse maize inbreds, landraces, and teosinte. Theor. Appl. Genet. 119, 1129–1142 (2009).

    Article  Google Scholar 

  3. Han, C., Zhen, S., Zhu, G., Bian, Y. & Yan, Y. Comparative metabolome analysis of wheat embryo and endosperm reveals the dynamic changes of metabolites during seed germination. Plant Physiol. Biochem. 115, 320–327 (2017).

    Article  CAS  Google Scholar 

  4. De Lumen, B. O. Molecular approaches to improving the nutritional and functional properties of plant seeds as food sources: developments and comments. J. Agric. Food Chem. 38, 1779–1788 (1990).

    Article  Google Scholar 

  5. Palacios‐Rojas, N. et al. Mining maize diversity and improving its nutritional aspects within agro‐food systems. Comp. Rev. Food Sci. Food Saf. 19, 1809–1834 (2020).

    Article  Google Scholar 

  6. Doebley, J. F., Gaut, B. S. & Smith, B. D. The molecular genetics of crop domestication. Cell 127, 1309–1321 (2006).

    Article  CAS  Google Scholar 

  7. Matsuoka, Y. et al. A single domestication for maize shown by multilocus microsatellite genotyping. Proc. Natl Acad. Sci. USA 99, 6080–6084 (2002).

    Article  ADS  CAS  Google Scholar 

  8. Whitt, S. R., Wilson, L. M., Tenaillon, M. I., Gaut, B. S. & Buckler, E. S. Genetic diversity and selection in the maize starch pathway. Proc. Natl Acad. Sci. USA 99, 12959–12962 (2002).

    Article  ADS  CAS  Google Scholar 

  9. Duvick, D. N. in Advances in Agronomy Vol. 86 (ed. Sparks, D.) 83–145 (Academic, 2005).

  10. Wani, S. H. et al. Nitrogen use efficiency (NUE): elucidated mechanisms, mapped genes and gene networks in maize (Zea mays L.). Physiol. Mol. Biol. Plants 27, 2875–2891 (2021).

    Article  CAS  Google Scholar 

  11. Ciampitti, I. A. & Lemaire, G. From use efficiency to effective use of nitrogen: a dilemma for maize breeding improvement. Sci. Total Environ. 826, 154125 (2022).

    Article  ADS  CAS  Google Scholar 

  12. Day, L. Proteins from land plants—potential resources for human nutrition and food security. Trends Food Sci. Technol. 32, 25–42 (2013).

    Article  CAS  Google Scholar 

  13. Lee, S. et al. OsASN1 overexpression in rice increases grain protein content and yield under nitrogen-limiting conditions. Plant Cell Physiol. 61, 1309–1320 (2020).

    Article  CAS  Google Scholar 

  14. Wu, Y. & Messing, J. in Achieving Sustainable Cultivation of Maize Vol 1 (ed. Watson, D.) Ch. 4 (Burleigh Dodds, 2017).

  15. Esen, A. A proposed nomenclature for the alcohol-soluble proteins (zeins) of maize (Zea mays L.). J. Cereal Sci. 5, 117–128 (1987).

    Article  CAS  Google Scholar 

  16. Thompson, G. & Larkins, B. in The Maize Handbook (eds Freeling, M. & Walbot, V.) 639–647 (Springer, 1994).

  17. Koren, S. et al. De novo assembly of haplotype-resolved genomes with trio binning. Nat. Biotechnol. 36, 1174–1182 (2018).

    Article  CAS  Google Scholar 

  18. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).

    Article  CAS  Google Scholar 

  19. Hufford, M. B. et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science 373, 655–662 (2021).

    Article  ADS  CAS  Google Scholar 

  20. Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).

    Article  CAS  Google Scholar 

  21. Albert, P. S., Gao, Z., Danilova, T. V. & Birchler, J. A. Diversity of chromosomal karyotypes in maize and its relatives. Cytogenet. Genome Res. 129, 6–16 (2010).

    Article  CAS  Google Scholar 

  22. Ou, S., Chen, J. & Jiang, N. Assessing genome assembly quality using the LTR Assembly Index (LAI). Nucleic Acids Res. 46, e126 (2018).

    Google Scholar 

  23. Chen, J. et al. Dynamic transcriptome landscape of maize embryo and endosperm development. Plant Physiol. 166, 252–264 (2014).

    Article  Google Scholar 

  24. Moose, S. P., Dudley, J. W. & Rocheford, T. R. Maize selection passes the century mark: a unique resource for 21st century genomics. Trends Plant Sci. 9, 358–364 (2004).

    Article  CAS  Google Scholar 

  25. Goldman, I., Rocheford, T. & Dudley, J. Quantitative trait loci influencing protein and starch concentration in the Illinois long term selection maize strains. Theor. Appl. Genet. 87, 217–224 (1993).

    Article  CAS  Google Scholar 

  26. Lea, P. J., Sodek, L., Parry, M. A. J., Shewry, P. R. & Halford, N. G. Asparagine in plants. Ann. Appl. Biol. 150, 1–26 (2007).

    Article  CAS  Google Scholar 

  27. Jiang, L. et al. Analysis of gene regulatory networks of maize in response to nitrogen. Genes 9, 151 (2018).

    Article  Google Scholar 

  28. Gaufichon, L. et al. Arabidopsis thaliana ASN2 encoding asparagine synthetase is involved in the control of nitrogen assimilation and export during vegetative growth. Plant Cell Environ. 36, 328–342 (2013).

    Article  CAS  Google Scholar 

  29. Gaufichon, L. et al. ASN1-encoded asparagine synthetase in floral organs contributes to nitrogen filling in Arabidopsis seeds. Plant J. 91, 371–393 (2017).

    Article  CAS  Google Scholar 

  30. Lam, H.-M. et al. Overexpression of the ASN1 gene enhances nitrogen status in seeds of Arabidopsis. Plant Physiol. 132, 926–935 (2003).

    Article  CAS  Google Scholar 

  31. Luo, L. et al. OsASN1 plays a critical role in asparagine-dependent rice development. Int. J. Mol. Sci. 20, 130 (2018).

    Article  Google Scholar 

  32. Ohashi, M. et al. Asparagine synthetase1, but not asparagine synthetase 2, is responsible for the biosynthesis of asparagine following the supply of ammonium to rice roots. Plant Cell Physiol. 56, 769–778 (2015).

    Article  CAS  Google Scholar 

  33. Curtis, T. Y., Bo, V., Tucker, A. & Halford, N. G. Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress. Food Energy Secur. 7, e00126 (2018).

    Article  Google Scholar 

  34. Raffan, S. et al. Wheat with greatly reduced accumulation of free asparagine in the grain, produced by CRISPR/Cas9 editing of asparagine synthetase gene TaASN2. Plant Biotechnol. J. 19, 1602–1613 (2021).

    Article  CAS  Google Scholar 

  35. Avila-Ospina, L., Marmagne, A., Talbotec, J., Krupinska, K. & Masclaux-Daubresse, C. The identification of new cytosolic glutamine synthetase and asparagine synthetase genes in barley (Hordeum vulgare L.), and their expression during leaf senescence. J. Exp. Bot. 66, 2013–2026 (2015).

    Article  CAS  Google Scholar 

  36. Seebauer, J. R., Moose, S. P., Fabbri, B. J., Crossland, L. D. & Below, F. E. Amino acid metabolism in maize earshoots. Implications for assimilate preconditioning and nitrogen signaling. Plant Physiol. 136, 4326–4334 (2004).

    Article  CAS  Google Scholar 

  37. Todd, J. et al. Identification and characterization of four distinct asparagine synthetase (AsnS) genes in maize (Zea mays L.). Plant Sci. 175, 799–808 (2008).

    Article  CAS  Google Scholar 

  38. Raffan, S. & Halford, N. G. Cereal asparagine synthetase genes. Ann. Appl. Biol. 178, 6–22 (2021).

    Article  CAS  Google Scholar 

  39. The, S. V., Snyder, R. & Tegeder, M. Targeting nitrogen metabolism and transport processes to improve plant nitrogen use efficiency. Front. Plant Sci. 11, 628366 (2021).

    Article  Google Scholar 

  40. Sieciechowicz, K. A., Joy, K. W. & Ireland, R. J. The metabolism of asparagine in plants. Phytochemistry 27, 663–671 (1988).

    Article  CAS  Google Scholar 

  41. Moison, M. et al. Three cytosolic glutamine synthetase isoforms localized in different-order veins act together for N remobilization and seed filling in Arabidopsis. J. Exp. Bot. 69, 4379–4393 (2018).

    Article  CAS  Google Scholar 

  42. Liu, X., Hu, B. & Chu, C. Nitrogen assimilation in plants: current status and future prospects. J. Genet. Genomics 49, 394–404 (2021).

    Article  Google Scholar 

  43. Congreves, K. A. et al. Nitrogen use efficiency definitions of today and tomorrow. Front. Plant Sci. 12, 637108 (2021).

    Article  Google Scholar 

  44. Silva, I. T. et al. Biochemical and genetic analyses of N metabolism in maize testcross seedlings: 2. Roots. Theor. Appl. Genet. 131, 1191–1205 (2018).

    Article  CAS  Google Scholar 

  45. Gallais, A. & Hirel, B. An approach to the genetics of nitrogen use efficiency in maize. J. Exp. Bot. 55, 295–306 (2004).

    Article  CAS  Google Scholar 

  46. Hirel, B. et al. Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant Physiol. 125, 1258–1270 (2001).

    Article  CAS  Google Scholar 

  47. Zhang, N. et al. Genome-wide association of carbon and nitrogen metabolism in the maize nested association mapping population. Plant Physiol. 168, 575–583 (2015).

    Article  CAS  Google Scholar 

  48. Liu, Q. et al. Improving crop nitrogen use efficiency toward sustainable green revolution. Annu. Rev. Plant Biol. 73, 523–551 (2022).

    Article  Google Scholar 

  49. Hou, M., Yu, M., Li, Z., Ai, Z. & Chen, J. Molecular regulatory networks for improving nitrogen use efficiency in rice. Int. J. Mol. Sci. 22, 9040 (2021).

    Article  CAS  Google Scholar 

  50. Xuan, W., Beeckman, T. & Xu, G. Plant nitrogen nutrition: sensing and signaling. Curr. Opin. Plant Biol. 39, 57–65 (2017).

    Article  CAS  Google Scholar 

  51. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    Article  CAS  Google Scholar 

  52. Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).

    Article  CAS  Google Scholar 

  53. Ou, S. et al. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol. 20, 275 (2019).

    Article  CAS  Google Scholar 

  54. Bruna, T., Hoff, K. J., Lomsadze, A., Stanke, M. & Borodovsky, M. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genom. Bioinform. 3, lqaa108 (2021).

    Article  Google Scholar 

  55. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  Google Scholar 

  56. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Article  CAS  Google Scholar 

  57. Zhang, Z., Yang, J. & Wu, Y. Transcriptional regulation of zein gene expression in maize through the additive and synergistic action of opaque2, prolamine-box binding factor, and O2 heterodimerizing proteins. Plant Cell 27, 1162–1172 (2015).

    Article  CAS  Google Scholar 

  58. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

  59. Bukowski, R. et al. Construction of the third-generation Zea mays haplotype map. Gigascience 7, gix134 (2018).

    Article  Google Scholar 

  60. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  63. 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  Google Scholar 

  64. Mansfeld, B. N. & Grumet, R. QTLseqr: an R package for bulk segregant analysis with next-generation sequencing. Plant Genome 11, 180006 (2018).

  65. Dong, J. et al. Analysis of tandem gene copies in maize chromosomal regions reconstructed from long sequence reads. Proc. Natl Acad. Sci. USA 113, 7949–7956 (2016).

    Article  ADS  CAS  Google Scholar 

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Acknowledgements

We thank Y. Xiao, H. Zhan, B. Lin, Y. Gu, T. Zhang, X. Song and D. An for help with BSA sequencing and related analysis; K. Liu for help with the high-protein QTL analysis and gene mapping; C. Li from Anhui Agricultural University for help with the bioinformatics analysis; J. Shi and W. Li for help with the analysis of teosinte domestication and sequence variation; and Z. Wang, L. Zhang, X. Zeng and Y. Zhou for help with material planting in Harbin. This research was supported by the Chinese Academy of Sciences (XDB27010201 to Y.W.), the National Natural Science Foundation of China (31830063 and 31925030 to Y.W.; 32072008 to W.W.), the China Postdoctoral Science Foundation (2020M681412 to Y.H.) and the Shanghai ‘Super Postdoctoral’ Incentive Program (2020456 to Y.H.).

Author information

Authors and Affiliations

Authors

Contributions

Y.W., W.W., Y.H. and H.W. designed the research, analysed the data and supervised the project. Y.W., Y.H., H.W., Y. Zhu, X.H. and H.L. created genetic populations and materials. Y.H., H.W., Y. Zhu, X.H., Y. Zhao, L.Q., Y.J., Y.C., Q.X., Q.W., J.W., H.L. and X.L. performed experiments. W.W., S.L., X.W., Z.B. and Y.H. performed teosinte Ames 21814 haplotype sequencing, assembly and annotation. G.M. and X.Y. performed the GWAS analysis. Y.W., X.L., Y.H., H.W. and Y. Zhu performed the field NUE tests in Sanya. Y.W., W.W., Y.H., H.W. and B.A.L. explained the data, drafted and edited the manuscript.

Corresponding authors

Correspondence to Wenqin Wang or Yongrui Wu.

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The authors declare no competing interests.

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Nature thanks Shujun Ou, Jianming Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Content of total nitrogen and free amino acids in roots, stems and leaves of Ames 21814 and B73 and SDS–PAGE of zein and non-zein proteins in B73 and 10 teosinte lines.

a, Nitrogen content in roots, stems, leaves and seeds of B73 determined by acid hydrolysis and the Dumas method. Data are presented as mean ± s.d. (n = 6 biologically independent samples for each method). b, Total nitrogen content in roots, stems, and leaves of Ames 21814 and B73. Data are presented as mean ± s.d. (n = 10 biologically independent samples). c, Content of individual free amino acids in roots (the top panel), stems (the middle panel), and leaves (the bottom panel) of Ames 21814 and B73. Data are presented as mean ± s.d. (n = 6 biologically independent samples). d, SDS–PAGE of zein and non-zein proteins in B73 and 10 teosinte lines. The apparent size in kDa of each protein band is indicated on the left. M, protein markers. γ27, 27-kDa γ-zein; α22, 22-kDa α-zein; α19, 19-kDa α-zein; γ16, 16-kDa γ-zein; γ15, 15-kDa γ-zein; δ10, 10-kDa δ-zein. 3 times the SDS–PAGE analysis experiment were repeated independently with similar results. In a and b, a two-tailed Student’s t-test was used to determine P values, see Source Data.

Source data

Extended Data Fig. 2 Ames 21814 haplotype assembled by trio binning.

a, Phenotypes of teosinte Zea mays subsp. parviglumis (accession number: Ames 21814), B73 x Ames 21814 and B73. Scale bar, 35 cm. b, Teosinte haplotype genome assembly flow chart. To perform a de novo assembly of the teosinte haplotype, we sequenced and assembled its haplotype by integrating three technologies: HiFi long reads with the PacBio Sequel platform, paired-end sequencing with the Illumina HiSeq platform, high-throughput chromatin conformation capture (Hi-C). We completed assembly of the teosinte haplotype based on the trio-binning strategy because of the characteristics of high heterozygosity of Ames 21814. c, Whole genome Hi-C interaction heat map of 2.5 Mb windows. Each blue number indicates the corresponding chromosome. Each cluster represents a chromosome in the haplotype. In a set of chromosomes, the bottom cluster represents the hap1 (teosinte Ames 21814) chromosome, the top cluster represents the hap2 (B73) chromosome. d, Each chromosomes Hi-C contact map, Ames 21814 (Teo). e, Dot plot of B73 genome assembly (hap2, this study) and B73_v5 genome assembly. Alignment less than 20 kb was filtered out. f, Dot plot of Ames 21814 haplotype (hap1) and B73 haplotype (hap2). Alignment less than 20 kb was filtered out. g, Haplotype-specific inversions supported by the Hi-C contact map. Fourteen inversions larger than 1 Mb were selected for Hi-C zoom-in inspection by excluding those caused by tandem repeat arrays (CentC or knob). Thirteen inversions were correctly verified, while the Chr1: 235 Mb inversion is a misscaffold contig.

Extended Data Fig. 3 Variation in α-zein gene copies between Ames 21814 and maize inbreds.

a, Copy numbers of α-zein genes in Ames 21814, B73 and W22. Gene numbers of α19 (z1A1, z1A2, z1B and z1D) and α22 (z1C1 and z1C2) are indicated beside each locus. b, Statistical analysis of copy number of α19 (z1A1, z1A2, z1B and z1D) and α22 (z1C1 and z1C2) in Ames 21814, B73 and W22.

Source data

Extended Data Fig. 4 Diagram showing how the genetic populations were created and measurement of protein content in backcrossing populations.

a, The population size for each generation is indicated. b, SDS–PAGE showing zein accumulation in B73, teosinte (Ames 2184), and B73 x Ames 21814 seeds. c, Nitrogen content in roots of B73 and F1 plants of B73 x Ames 21814. Data are presented as mean ± s.d. (n = 6 biologically independent samples for each method). d, Nitrogen content in stems of B73 and F1 plants of B73 x Ames 21814. Data are presented as mean ± s.d. (n = 6 biologically independent samples for each method). e, Nitrogen content in leaves of B73 and F1 plants of B73 x Ames 21814. Data are presented as mean ± s.d. (n = 6 biologically independent samples for each method). f, SDS–PAGE of zein accumulation in 10 F2 seeds of B73 x Ames 21814. B73 seed was used as the control. g, SDS–PAGE of zein accumulation in seeds from 12 different BC2 eras. B73 seed was used as the control. h, SDS–PAGE of zein accumulations in 12 BC2 seeds from a single high-protein ear. B73 seed was used as the control. i, Protein content in seeds from 30 different BC3 ears. j, Protein content in 8 high-protein ears in the BC3 population. Seven single seeds for each ear were measured. B73 was used as the control. k, The protein content in seeds from 30 different BC4 ears. l, The protein content in 8 high-protein ears in the BC4 population. Seven single seeds for each ear were measured. B73 was used as the control. In b, f-h, more than three times the SDS–PAGE analysis experiment were repeated independently with similar results. In c-e, a two-tailed Student’s t-test was used to determine P values, see Source Data.

Source data

Extended Data Fig. 5 Mapping THP9 by BSA sequencing of three populations of BC4, BC6 and BC8 and deep resequencing of five high-protein and five low-protein BC6F4 lines.

a, Phenotyping high- and low-protein ears by SDS–PAGE of zein (upper panel) and non-zein protein (lower panel) accumulation in the BC4 population. M, protein mol wt markers. γ27, 27-kDa γ-zein; α22, 22-kDa α-zein; α19, 19-kDa α-zein; γ16, 16-kDa γ-zein; γ15, 15-kDa γ-zein; δ10, 10-kDa δ-zein. b, Gene introgression analysis based on BSA sequencing of the BC4 population. c, Frequency distribution analysis of seed protein content in the BC6 population. A group of 1,314 ears were phenotyped and classified. d, Gene introgression analysis based on BSA sequencing of the BC6 population. e, Frequency distribution analysis of protein content in the BC8 population. A group of 1,344 ears was phenotyped and classified. f, Gene introgression analysis by BSA sequencing of the BC8 population. Bottom is the differential expression of introgressed genes based on RNA-Seq analysis of BC8 leaves. g, A total of 10 lines were analysed for gene introgression by resequencing. The high-protein lines are S2528, S2590, S2591, S2592 and S2596, and the low-protein lines are S2468, S2513, S2529, S2605 and S2609. The peak of introgressed teosinte DNA fragments in these lines was based on the B73 genome (B73_v4) coordinates are 13 Mb-143 Mb in S2528, 22.7 Mb-144.4 Mb in S2590, S2591, S2592 and S2596, 13 Mb-99 Mb in S2529, 22.7 Mb-135.5 Mb in S2605 and S2609. The smallest common region of the candidate interval is located between 135.5 and 143 Mb as indicated by the dotted box.

Source data

Extended Data Fig. 6 Three genes annotated in the 150-kb fine-mapping region and ASN4 transcript analysis.

a, Gene structure of Zm00001d047732 and SNPs in the coding sequences in B73 and Ames 21814. b, Expression fold change of the three genes in roots and leaves of NILB73 and NILTeo based on our RNA-Seq data. c, Schematic representation of ASN4 transcripts in B73 and Ames 21814. d,TPM mean analysis of ASN4 transcripts in B73 and Ames 21814 leaves and roots based on the RNA-seq data. TPM, Transcripts Per Million. Data are mean ± s.d. (n = 3 biologically independent samples). e, RNA-seq reads of THP9 in NILB73 (upper panel) and NILTeo (lower panel) leaves. The number refers to the number of reads across the junction. f, Expression of the ASN4 promoter sequences amplified from Ames 21814 and B73 compared by dual-luciferase (LUC) assay. Data are presented as mean ± s.d. P values, a two-tailed Student’s t-test was used to determine P values, see Source Data.

Source data

Extended Data Fig. 7 Linkage of THP9-T with high asparagine and high protein contents in the BC7F3 population and phenotypic comparison of NILTeo and NILB73.

a, Phenotypes of three representative BC7F3 ears with maternal genotypes that are homozygous THP9-B, heterozygous THP9-H (T/B) and homozygous THP9-T, respectively. b, Free asparagine content in THP9-B, THP9-H and THP9-T roots in the BC7F3 population. Data are mean ± s.d. (n = 10 biologically independent samples). c, Protein content in seeds from self-pollinated THP9-B, THP9-H and THP9-T plants in the BC7F3 population. Each ear was used as one unit. Data are mean ± s.d. (n = more than 20 biologically independent samples). d, Plant phenotypes of NILTeo and NILB73. Scale bar, 30cm. e, Plant height of NILTeo and NILB73. The plants were grown in Sanya in 2021. Data are mean ± s.d. (n = 18 biologically independent samples). f, Plant fresh weight (root and aboveground mass) of NILTeo and NILB73. Data are mean ± s.d. (n = 6 biologically independent samples). g, Association analysis of seed protein content with total stem nitrogen content in the BC8 population. The corresponding seed protein content and stem nitrogen content of the same plant are connected by a solid grey line (n = 1334 biologically independent samples). h, The total nitrogen content in NILTeo and NILB73 roots, stems, and leaves. Data are mean ± s.d. (n = 33, 41 and 20 biologically independent samples, respectively). i, Total free amino acid content in NILTeo and NILB73 roots and leaves. Data are mean ± s.d. (n = 20 biologically independent samples, respectively). In b-c, different letters indicate significant differences (P < 0.01, one-way ANOVA and further Tukey’s test, see Source Data). In e, f and h, i, a two-tailed Student’s t-test was used to determine P values, see Source Data.

Source data

Extended Data Fig. 8 NILB73 and NILTeo under normal and low-nitrogen conditions.

a-c, Construction of four aboveground concrete containers with plastic film covering the containers. d, NILB73 and NILTeo grown in container without nitrogen fertilizer application. e, The nitrogen content of soil in containers with and without nitrogen application. Data are mean ± s.d. (n = 16 biologically independent samples). f, Plant phenotypes of NILB73 and NILTeo with and without nitrogen application. Scale bar, 20 cm. g, Root phenotypes of NILB73 and NILTeo with and without nitrogen application. Scale bar, 5 cm. h, Plant height of NILTeo and NILB73. Data are mean ± s.d. (n = 8 biologically independent samples). i, qRT–PCR analysis of THP9 expression in NILB73 and NILTeo roots with and without nitrogen application. Data are mean ± s.d. (n = 3 biologically independent samples). j, Root fresh weight of NILB73 and NILTeo with and without nitrogen application. Data are mean ± s.d. (n = 8 biologically independent samples). k, Aboveground biomass of NILB73 and NILTeo with and without nitrogen application. Data are mean ± s.d. (n = 14 biologically independent samples). l. Total nitrogen content in NILB73 and NILTeo roots with and without nitrogen application. Data are mean ± s.d. (n = 6 biologically independent samples). m, Total nitrogen content in NILB73 and NILTeo stems with and without nitrogen application. Data are mean ± s.d. (n = 10 biologically independent samples). n, Total nitrogen content in NILB73, and NILTeo leaves with and without nitrogen application. Data are mean ± s.d. (n = 6 biologically independent samples). o, Protein content in NILB73 and NILTeo seeds with and without nitrogen application. Data are mean ± s.d. (n = 20 biologically independent samples). In e and h-o, letters indicate significant differences (P < 0.01, one-way ANOVA and further Tukey’s test).

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Extended Data Fig. 9 Wild-type B73 and THP9-OE2 under normal and low-nitrogen conditions.

a, Plant phenotypes of WT (B73), NILTeo and THP9-OE2 with and without nitrogen application. Scale bar, 20 cm. b, Root phenotypes of WT, NILTeo and THP9-OE2 with and without nitrogen application. Scale bar, 5 cm. c, The nitrogen content of soil in containers with and without nitrogen application. Data are mean ± s.d. (n = 8 biologically independent samples). d, Plant height of WT and THP9-OE2. Data are mean ± s.d. (n = 8 biologically independent samples). e, The root fresh weight of WT and THP9-OE2 with and without nitrogen application. Data are mean ± s.d. (n = 14 biologically independent samples). f, The aboveground biomass of WT and THP9-OE2 with and without nitrogen application. Data are mean ± s.d. (n = 14 biologically independent samples). g. Total nitrogen content in WT and THP9-OE2 roots with and without nitrogen application. Data are mean ± s.d. (n = 12 biologically independent samples). h, Total nitrogen content in WT and THP9-OE2 stems with and without nitrogen application. Data are mean ± s.d. (n = 12 biologically independent samples). i, Total nitrogen content in WT and THP9-OE2 leaves with and without nitrogen application. Data are mean ± s.d. (n = 12 biologically independent samples). j, Protein content in WT and THP9-OE2 seeds with and without nitrogen application. Data are mean ± s.d. (n = 20 biologically independent samples). In c-g, letters indicate significant differences (P < 0.01, one-way ANOVA and further Tukey’s test).

Source data

Extended Data Fig. 10 Seed protein content of NILTeo × Mo17 and Zhengdan 958-T hybrids.

a, Seed protein content in Zhengdan 958-B and Zhengdan 958-T seeds. Data are presented as mean ± s.d. (n = 20 biologically independent samples). Protein content in F2 seeds of NILB73 x Mo17 and NILTeo x Mo17. Data are presented as mean ± s.d. (n = 30 biologically independent samples). In a,b, a two-tailed Student’s t-test was used to determine P values, see Source Data.

Source data

Supplementary information

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This file contains Supplementary Figs. 1–5, the extended SDS–PAGE analysis of protein and the original source images for the western blots, and Supplementary Tables 1–7.

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Huang, Y., Wang, H., Zhu, Y. et al. THP9 enhances seed protein content and nitrogen-use efficiency in maize. Nature 612, 292–300 (2022). https://doi.org/10.1038/s41586-022-05441-2

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