Genomic basis of geographical adaptation to soil nitrogen in rice

Abstract

The intensive application of inorganic nitrogen underlies marked increases in crop production, but imposes detrimental effects on ecosystems1,2: it is therefore crucial for future sustainable agriculture to improve the nitrogen-use efficiency of crop plants. Here we report the genetic basis of nitrogen-use efficiency associated with adaptation to local soils in rice (Oryza sativa L.). Using a panel of diverse rice germplasm collected from different ecogeographical regions, we performed a genome-wide association study on the tillering response to nitrogen—the trait that is most closely correlated with nitrogen-use efficiency in rice—and identified OsTCP19 as a modulator of this tillering response through its transcriptional response to nitrogen and its targeting to the tiller-promoting gene DWARF AND LOW-TILLERING (DLT)3,4. A 29-bp insertion and/or deletion in the OsTCP19 promoter confers a differential transcriptional response and variation in the tillering response to nitrogen among rice varieties. The allele of OsTCP19 associated with a high tillering response to nitrogen is prevalent in wild rice populations, but has largely been lost in modern cultivars: this loss correlates with increased local soil nitrogen content, which suggests that it might have contributed to geographical adaptation in rice. Introgression of the allele associated with a high tillering response into modern rice cultivars boosts grain yield and nitrogen-use efficiency under low or moderate levels of nitrogen, which demonstrates substantial potential for rice breeding and the amelioration of negative environment effects by reducing the application of nitrogen to crops.

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Fig. 1: GWAS and fine mapping of the major locus that underlies TRN variation.
Fig. 2: OsTCP19 negatively regulates rice tillering.
Fig. 3: Indel of 29 bp in the OsTCP19 promoter confers different nitrogen responses.
Fig. 4: DLT works downstream of OsTCP19.
Fig. 5: OsTCP19-H contributes to the geographical adaptation and significantly increases the NUE of modern cultivars.

Data availability

The genomic information of Rice Mini-Core Collection has previously been releasd13; the raw sequencing dataset is available on NCBI BioProject (https://www.ncbi.nlm.nih.gov/bioproject) under the accession number PRJNA301661. The RNA-sequencing data have been deposited in NCBI’s Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under accession number GSE161265. Data from the 3K Rice Genomes Project can be downloaded from Rice SNP-Seek Database (https://snp-seek.irri.org/). Soil nitrogen content data are available from Global Soil Data set (http://globalchange.bnu.edu.cn). The data for the rice-planting area of different countries are from the History Database of the Global Environment (HYDE 3.2.1) (https://doi.org/10.17026/dans-25g-gez3). Climate data are available from the Climatic Research Unit (CRU TS v.3.23) (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/cruts.1506241137.v3.23/). The base map in Fig. 1a was downloaded from https://www.R-project.org/32. The base map in Fig. 5a was downloaded from the ArcGIS Hub (https://hub.arcgis.com/datasets/a21fdb46d23e4ef896f31475217cbb08_1 (2020.11.01)). Uncropped data for gel is provided in Supplementary Fig. 1Source data are provided with this paper.

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Acknowledgements

The pYLCRISPR/Cas9-MH vector was provided by Y. Liu; the pGreenII 0800-LUC vector was provided by X. Chen; and the pJG4-5 and pLacZi2μ vectors were provided by R. Lin. This work was supported by the grants from the Strategic Priority Research Programme of the Chinese Academy of Sciences (XDA24020000), the National Natural Sciences Foundation of China (31922007), National Key Research and Development of China (2020YFE0202300) and the Major Programme of Guangdong Basic and Applied Research (2019B030302006).

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Authors

Contributions

Y. Liu performed experiments, analysed the data and wrote the manuscript. H.W. performed the GWA mapping and population genetic analysis. R.X. and H.L. performed the field tests of 110 Mini-Core accessions in Guangzhou, Q.W. and F.Z. collected and analysed the soil nitrogen data. Z.J., W.W., Z.Z., A.L., Y. Liang, S.O., X.L., S.C., H.T. and Y.W. conducted some of the experiments. B.H. and C.C. designed research, wrote the manuscript and supervised the project.

Corresponding authors

Correspondence to Bin Hu or Chengcai Chu.

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The authors declare no competing interests.

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Peer review information Nature thanks Ando Radanielson, Nicolaus von Wiren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Nitrogen response of agronomic traits and expression analysis of three candidate genes.

a, PCA with genotype likelihoods in 110 accessions. Principal component 1 and principal component 2 divided 110 accessions into 4 groups: japonica, indica, aus and aromatic. b, Genome-wide linkage disequilibrium analysis of 110 Mini-Core accessions. c, d, Grain number per panicle (c) and 1,000-grain weight (d) of 110 accessions under low, moderate and high nitrogen conditions in the field. Dots represent individual data points of 110 accessions and lines connect the data points of the same accession under low, moderate and high nitrogen. Black diamonds represent means. For each individual data point, data are average (n = 5 plants). e, Nitrogen response of tiller number, grain number per panicle, and 1,000-grain weight from low to moderate nitrogen. f, TRN among different subgroups. g, The position of three candidate genes and the 15 most-significant SNPs that underlie the significant locus on rice chromosome 6. x axis, position; y axis, P value of the SNPs. Red dots, 15 SNPs. h, Expression analysis of three candidate genes by qRT–PCR in the roots of ZH11 plants grown in different nitrogen concentrations (NH4NO3). Data are mean ± s.d. (n = 3 biologically independent samples). i, Expression analysis of OsTCP19 under different nitrate and ammonium concentrations. Data are mean ± s.d. (n = 3 biologically independent samples). e, f, The bars in the violin plots represent 25th percentiles, medians and 75th percentiles. In c, d, f, h, i, letters indicate significant differences (P < 0.05, one-way ANOVA, Tukey’s HSD test). For P values, see Source Data. Source data

Extended Data Fig. 2 Phenotype of low-TRN and high-TRN varieties.

a, b, Phenotype of 8 low-TRN varieties (a) and 8 high-TRN varieties (b) grown in low nitrogen (50 kg ha−1) and moderate nitrogen (150 kg ha−1) field conditions. Low-TRN varieties are H4078, X4206, S4162, W4199, B4013, K4092, L4104 and D4037; high-TRN varieties are R4160, B4011, H4072, D4039, Q4150, R4157, R4153 and G4070. Scale bars, 24 cm.

Extended Data Fig. 3 Transcript expression of three candidate genes in response to nitrogen in the shoot base.

Expression analysis of LOC_Os06g12210, LOC_Os06g12220 and OsTCP19 by qRT–PCR in shoot bases of low-TRN varieties and high-TRN varieties grown in different nitrogen concentrations (NH4NO3). Data are mean ± s.d. (n = 3 biologically independent samples). Source data

Extended Data Fig. 4 OsTCP19 acts as a negative modulator in rice tillering and TRN.

a, The neighbour-joining tree was constructed in MEGA 5.0. The numbers represent bootstrap (1,000 replicates). OsTCP19 and its orthologues in Arabidopsis are shown in red text. b, Phenotype of ZH11 and cTO lines (cTO1 and cTO2) under low nitrogen (50 kg ha−1) and moderate nitrogen (150 kg ha−1) conditions. Scale bar, 24 cm. c, Expression analysis of OsTCP19 in ZH11, cTO1 and cTO2 lines by qRT–PCR. Data are mean ± s.d. (n = 3 biologically independent samples). d, Statistical analysis of tiller number per plant of ZH11, cTO1 and cTO2 lines under low and moderate nitrogen conditions. Data are mean ± s.e.m. (n = 24 plants). e, TRN of ZH11, cTO1 and cTO2 lines generated from d. Data are mean ± s.e.m. (n = 24 plants). f, Phenotype of ZH11, T-Ri1 and T-Ri2. Scale bar, 24 cm. g, qRT–PCR analysis of OsTCP19 expression level in ZH11, T-Ri1 and T-Ri2 lines. Data are mean ± s.d. (n = 3 biologically independent samples). h, Statistical analysis of tiller number per plant of ZH11, T-Ri1 and T-Ri2 lines. Data are mean ± s.e.m. (n = 18 plants). i, Diagram of OsTCP19 CRISPR knockout lines (T-cr1 and T-cr2). The length and the position of the mutations are indicated on the frame. In c, d, e, g, h, different letters indicate significant differences (P < 0.05, one-way ANOVA, Tukey’s HSD test). For P values, see Source Data. Source data

Extended Data Fig. 5 The 29-bp indel of the OsTCP19 promoter contributes to TRN variation.

a, Diagram of NILOsTCP19-H. M1 to M10 represent the molecular markers used for NILOsTCP19-H construction. Green bar, genomic region from Kasa. The double-headed arrow shows the length of the substitution segment. b, Diagram of the OsTCP19 promoter sequence of ZH11 and TGE line. Red box indicates the position of the 29-bp indel. c, Phenotype of ZH11 and TGE plants grown in a greenhouse under low nitrogen (0.5 kg m−2) and moderate nitrogen (1.5 kg m−2) conditions. d, qRT–PCR-based transcript abundance analysis of OsTCP19 in ZH11 and TGE plants grown under 0.15 mM and 1.25 mM NH4NO3. Data are mean ± s.d. (n = 3 biologically independent samples). e, ERN of OsTCP19 generated from d. Data are mean ± s.d. (n = 3 biologically independent samples). f, Statistical analysis of tiller number per plant of ZH11 and TGE plants of c. Data are mean ± s.e.m. (n = 18 plants). g, TRN of ZH11 and TGE plants generated from f. Data are mean ± s.e.m. (n = 18 plants). h, Diagram of the predicted LBD binding sites in OsTCP19 promoter of Kos and Kasa. Blue bars indicate predicted LBD binding sites. i, Phylogenetic analysis of AtLBD37, AtLBD38 and AtLBD39 and their orthologues in rice. The neighbour-joining tree was constructed in MEGA 5.0 and the numbers represent bootstrap of 1,000 replicates. j, Expression analysis of OsLBD37 and OsLBD39 under different nitrogen (NH4NO3) concentrations by qRT–PCR. Data are mean ± s.d. (n = 3 biologically independent samples). k, Expression analysis of OsLBD37 and OsTCP19 in OsLBD37 genomic overexpression lines (OsLBD37-gOE1, -gOE2 and -gOE3). Data are mean ± s.d. (n = 3 biologically independent samples). l, Expression analysis of OsLBD37 and OsTCP19 in OsLBD37 constitutive overexpression lines (OsLBD37-OE1, -OE2 and -OE3). Data are mean ± s.d. (n = 3 biologically independent samples). m, Expression analysis of OsLBD39 and OsTCP19 in OsLBD39 genomic overexpression lines (OsLBD39-gOE1, -gOE2 and -gOE3). Data are mean ± s.d. (n = 3 biologically independent samples). n, Expression analysis of OsLBD39 and OsTCP19 in OsLBD39 constitutive overexpression lines (OsLBD39-OE1, -OE2 and -OE3). Data are mean ± s.d. (n = 3 biologically independent samples). T0 generation of OsLBD37 and OsLBD39 overexpression lines were used for qRT–PCR analysis and the negative lines were selected as control. In d, f, j–n, different letters indicate significant differences at P < 0.05 according to one-way ANOVA and Tukey’s HSD test. For P values, see Source Data. In e, g, significant difference was determined by the two-sided Student’s t-test. Source data

Extended Data Fig. 6 Two variants of the OsTCP19 coding region showed no difference in regulating rice tillering.

a, Subcellular localization of OsTCP19-L–GFP and OsTCP19-H–GFP fusion proteins in rice protoplasts. Rice transcription factor OsbZIP52 fused with RFP was used as nucleus marker. Scale bars, 10 μm. b, Phenotype of ZH11 and OsTCP19 overexpression lines driven by the CaMV 35S promoter. TL-OE1 and TL-OE2 represent overexpression lines of OsTCP19-L, and TH-OE1 and TH-OE2 represent OsTCP19-H overexpression lines. Scale bar, 24 cm. c, Expression analysis of OsTCP19 in ZH11 and OsTCP19 overexpression lines by qRT–PCR. Data are mean ± s.d. (n = 3 biologically independent samples). d, Statistical analysis of tiller number per plant of ZH11 and OsTCP19-L or OsTCP19-H overexpression lines. Data are mean ± s.e.m. (n = 15 plants). In c, d, different letters indicate significant differences at P < 0.05 according to one-way ANOVA and Tukey’s HSD test. For P values, see Source Data. Source data

Extended Data Fig. 7 DLT was identified as the downstream target of OsTCP19.

a, Six Gene Ontology (GO) terms were selected by GO analysis. x axis, GO terms; y axis, gene number of each GO term. Blue, upregulated genes; orange, downregulated genes. b, A heat map of 304 TCP-binding DEGs. The colour key (blue to red) represents gene expression (FPKM) as log2-transformed fold changes of (TO1/WT) or (TO2/WT). c, Expression analysis of DLT in ZH11, T-cr1, T-cr2 and OsTCP19-overexpression lines by qRT–PCR. Data are mean ± s.d. (n = 3 biologically independent samples). d, Binding assays of OsTCP19-L or OsTCP19-H to DLT promoter using yeast one-hybrid assay. e, Enrichment of OsTCP19-L and OsTCP19-H to the promoter of DLT by ChIP–qPCR analysis. The samples were immunoprecipitated with anti-Flag or no antibody (NA) and anti-Flag/NA represents the enrichment fold. Red triangles indicate the position of P1 to P4 on DLT genomic sequence. Data are mean ± s.d. (n = 3 biologically independent samples). f, qRT–PCR-based transcript abundance analysis of DLT in various tissues. R, roots; SB, shoot bases; C, culms; L, leaves; LS, leaf sheaths; P, panicles. Data are mean ± s.d. (n = 3 biologically independent samples). g, qRT–PCR-based transcript abundance analysis of OsTCP19 and DLT in different nitrogen concentrations (NH4NO3). Data are mean ± s.d. (n = 3 biologically independent samples). h, i, Statistical analysis of plant height (h) and panicle length (i) of ZH11, TO2 and dlt plants. Data are mean ± s.d. (n = 13 plants). j, Brassinosteroid response of ZH11, TO1 and TO2 plants with lamina inclination assay. Scale bars, 1 cm. k, Statistical analysis of the lamina inclination in j. Data are mean ± s.d. (n = 20 seedlings). l, m, Statistical analysis of plant height (l) and panicle length (m) of ZH11, TO2, TO2/DLT-OE1, and TO2/DLT-OE2. Data are mean ± s.d. (n = 17 plants). n, ERN of DLT in low-TRN and high-TRN varieties under different nitrogen concentrations (0.15 mM and 1.25 mM NH4NO3). Data are mean ± s.d. (n = 3 biologically independent samples). o, Pearson correlation coefficient analysis of ERN of DLT with ERN of OsTCP19 or TRN in low-TRN and high-TRN varieties. Orange, low-TRN varieties; green, high-TRN varieties. p, qRT–PCR-based transcript abundance analysis of DLT by qRT–PCR in Kos and NILOsTCP19-H plants under different nitrogen concentrations (NH4NO3). Data are mean ± s.d. (n = 3 biologically independent samples). In c, e, h, i, l, m, p, different letters indicate significant differences at P < 0.05 according to one-way ANOVA and Tukey’s HSD test. For P values, see Source Data. Source data

Extended Data Fig. 8 OsTCP19–DLT module modulates rice tillering by regulating tiller bud outgrowth.

a, Longitudinal sections show no difference in tiller bud initiation in ZH11, TO2 and dlt plants. Arrows indicate tiller buds and numbers 1–3 represent the first to third tiller buds. Scale bar, 1 mm. The results are representative of three independent experiments. b, Tiller buds in ZH11, TO2 and dlt plants at 21, 28 and 35 days after germination. White arrows indicate the third tiller bud (at the axil of the third complete leaf). Scale bars, 0.6 cm. c, Statistical analysis of the third tiller bud length of ZH11, TO2 and dlt plants. Data are mean ± s.d. (n = 5 seedlings). d, Tiller bud outgrowth is repressed in TO2 and dlt plants at 90 days after germination. Six independent seedlings were collected and the 1st to 12th tiller buds of each seedling were analysed. e, Expression analysis of cell-cycle marker genes in ZH11, TO2 and dlt plants by qRT–PCR. Data are mean ± s.d. (n = 3 biologically independent samples). Different letters indicate significant differences at P < 0.05 according to one-way ANOVA and Tukey’s HSD test. For P values, see Source Data. f, Phenotype of ZH11, TO2 and dlt seedlings with or without 1 μM brassinolide treatment. Scale bars, 8 cm. g, Statistical analysis of tiller number per plant of ZH11, TO2 and dlt plants. Data are mean ± s.e.m. (n = 6 plants). Significant difference was determined by the two-sided Student’s t-test. NS, not significant. Source data

Extended Data Fig. 9 OsTCP19-H negatively correlates with soil nitrogen content under the similar atmospheric temperature or precipitation conditions.

a, Soil total nitrogen content variation in 29 countries or regions with similar mean annual temperature (>25 °C). b, OsTCP19-H negatively correlates with soil nitrogen content in the 29 countries or regions in a. c, Soil total nitrogen content variation in 37 countries or regions with similar annual precipitation (>800 mm). d, OsTCP19-H negatively correlates with soil nitrogen content in the 37 countries or regions in c. In b, d, data are average ± s.d. (n = 4 soil layers), and P values are determined by the two-sided Pearson correlation coefficient analysis. Source data

Extended Data Fig. 10 OsTCP19-H increases crop yield and NUE of modern cultivars.

a, b, Statistical analysis of tiller number (a) (n = 22 plants) and grain yield per plant (b) (n = 20 plants) of Kos and NILOsTCP19-H plants under two nitrogen conditions in Beijing in 2017. c, Phenotype of Kos and NILOsTCP19-H plants grown under two nitrogen conditions in Beijing in 2018. Scale bar, 24 cm. d–g, Statistical analysis of Kos and NILOsTCP19-H plants grown under two nitrogen conditions in field tests in Beijing in 2018. Tiller number (d) (n = 54 plants), grain yield per plant (e) (n = 20 plants), plot yield (f) (n = 4 plots) and NUE (g) (n = 4 plots) of Kos and NILOsTCP19-H plants under two nitrogen conditions. h, Phenotype of Kongyu131 (KY131) and KY131OsTCP19-H plants under two nitrogen conditions. Scale bar, 24 cm. i, Statistical analysis of tiller number shown in h (n = 50 plants). j, Phenotype of Xiushui134 (XS134) and XS134OsTCP19-H plants under two nitrogen conditions. Scale bar, 24 cm. k, Statistical analysis of tiller number shown in j (n = 50 plants). l, A heat map of DEGs involved in nitrogen metabolism pathway (Kyoto Encyclopedia of Genes and Genomes, ko00910). The colour key (red to yellow) represents gene expression (FPKM) as log2-transformed fold changes of (TO1/WT) or (TO2/WT). The gene-encoding proteins are shown on the right. m, qRT–PCR-based transcript abundance analysis of OsNRT2.1, OsNRT2.2, OsAMT1.3, OsNIR1 and OsGS1.2 in Kos and NILOsTCP19-H plants. Data are mean ± s.d. (n = 3 biologically independent samples). n, 15N accumulation in roots of Kos and NILOsTCP19-H plants. 15N-nitrate and 15N-ammonium were used for 15N labelling in Kos and NILOsTCP19-H plants, respectively. DW, dry weight. Data are mean ± s.d. (n = 4 biologically independent samples). In a, b, d, e, i, k, the bars in the violin plots represent 25th percentiles, medians, and 75th percentiles. f, g, The horizontal bars of boxes represent minima, 25th percentiles, medians, 75th percentiles and maxima. In a, b, d–g, i, k, different letters indicate significant differences at P < 0.05 according to one-way ANOVA and Tukey’s HSD test. For P values, see Source Data. In m, n, significant difference was determined by the two-sided Student’s t-test. Source data

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Liu, Y., Wang, H., Jiang, Z. et al. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature (2021). https://doi.org/10.1038/s41586-020-03091-w

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