Genomic basis of geographical adaptation to soil nitrogen in rice.

Yongqiang Liu, Hongru Wang, Zhimin Jiang, Wei Wang, Ruineng Xu, Qihui Wang, Zhihua Zhang, Aifu Li, Yan Liang, Shujun Ou, Xiujie Liu, Shouyun Cao, Hongning Tong, Yonghong Wang, Feng Zhou, Hong Liao, Bin Hu, Chengcai Chu
Author Information
  1. Yongqiang Liu: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  2. Hongru Wang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China. ORCID
  3. Zhimin Jiang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  4. Wei Wang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  5. Ruineng Xu: State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China.
  6. Qihui Wang: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China.
  7. Zhihua Zhang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  8. Aifu Li: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  9. Yan Liang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China. ORCID
  10. Shujun Ou: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  11. Xiujie Liu: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  12. Shouyun Cao: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China.
  13. Hongning Tong: National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China. ORCID
  14. Yonghong Wang: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China. ORCID
  15. Feng Zhou: Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China. ORCID
  16. Hong Liao: State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, China.
  17. Bin Hu: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China. bhu@genetics.ac.cn. ORCID
  18. Chengcai Chu: State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy for Seed Design, Chinese Academy of Sciences, Beijing, China. ccchu@genetics.ac.cn. ORCID

Abstract

The intensive application of inorganic nitrogen underlies marked increases in crop production, but imposes detrimental effects on ecosystems: 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). 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.

References

  1. Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015). [DOI: 10.1038/nature15743]
  2. Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010). [DOI: 10.1126/science.1182570]
  3. Tong, H. et al. DWARF AND LOW-TILLERING, a new member of the GRAS family, plays positive roles in brassinosteroid signaling in rice. Plant J. 58, 803–816 (2009). [DOI: 10.1111/j.1365-313X.2009.03825.x]
  4. Tong, H. et al. DWARF AND LOW-TILLERING acts as a direct downstream target of a GSK3/SHAGGY-like kinase to mediate brassinosteroid responses in rice. Plant Cell 24, 2562–2577 (2012). [DOI: 10.1105/tpc.112.097394]
  5. Hakeem, K. R., Ahmad, A., Iqbal, M., Gucel, S. & Ozturk, M. Nitrogen-efficient rice cultivars can reduce nitrate pollution. Environ. Sci. Pollut. Res. 18, 1184–1193 (2011). [DOI: 10.1007/s11356-010-0434-8]
  6. Wang, F. & Peng, S. Yield potential and nitrogen use efficiency of China’s super rice. J. Integr. Agric. 16, 1000–1008 (2017). [DOI: 10.1016/S2095-3119(16)61561-7]
  7. Savolainen, O. The genomic basis of local climatic adaptation. Science 334, 49–50 (2011). [DOI: 10.1126/science.1213788]
  8. Russell, J. et al. Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat. Genet. 48, 1024–1030 (2016). [DOI: 10.1038/ng.3612]
  9. Navarro, J. A. R. et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat. Genet. 49, 476–480 (2017). [DOI: 10.1038/ng.3784]
  10. Hu, B. et al. Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies. Nat. Genet. 47, 834–838 (2015). [DOI: 10.1038/ng.3337]
  11. Li, S. et al. Modulating plant growth-metabolism coordination for sustainable agriculture. Nature 560, 595–600 (2018). [DOI: 10.1038/s41586-018-0415-5]
  12. Wu, K. et al. Enhanced sustainable green revolution yield via nitrogen-responsive chromatin modulation in rice. Science 367, eaaz2046 (2020). [DOI: 10.1126/science.aaz2046]
  13. Wang, H. et al. The power of inbreeding: NGS-based GWAS of rice reveals convergent evolution during rice domestication. Mol. Plant 9, 975–985 (2016). [DOI: 10.1016/j.molp.2016.04.018]
  14. Xu, G., Fan, X. & Miller, A. J. Plant nitrogen assimilation and use efficiency. Annu. Rev. Plant Biol. 63, 153–182 (2012). [DOI: 10.1146/annurev-arplant-042811-105532]
  15. Mukhopadhyay, P. & Tyagi, A. K. OsTCP19 influences developmental and abiotic stress signaling by modulating ABI4-mediated pathways. Sci. Rep. 5, 9998 (2015). [DOI: 10.1038/srep09998]
  16. Nicolas, M. & Cubas, P. TCP factors: new kids on the signaling block. Curr. Opin. Plant Biol. 33, 33–41 (2016). [DOI: 10.1016/j.pbi.2016.05.006]
  17. Rubin, G., Tohge, T., Matsuda, F., Saito, K. & Scheible, W. R. Members of the LBD family of transcription factors repress anthocyanin synthesis and affect additional nitrogen responses in Arabidopsis. Plant Cell 21, 3567–3584 (2009). [DOI: 10.1105/tpc.109.067041]
  18. Li, C. et al. OsLBD37 and OsLBD38, two class II type LBD proteins, are involved in the regulation of heading date by controlling the expression of Ehd1 in rice. Biochem. Biophys. Res. Commun. 486, 720–725 (2017). [DOI: 10.1016/j.bbrc.2017.03.104]
  19. Endo, M. et al. CDKB2 is involved in mitosis and DNA damage response in rice. Plant J. 69, 967–977 (2012). [DOI: 10.1111/j.1365-313X.2011.04847.x]
  20. Hu, Y. & Lai, Y. Identification and expression analysis of rice histone genes. Plant Physiol. Biochem. 86, 55–65 (2015). [DOI: 10.1016/j.plaphy.2014.11.012]
  21. Luo, L. et al. OsASN1 plays a critical role in asparagine-dependent rice development. Int. J. Mol. Sci. 20, 130 (2018). [DOI: 10.3390/ijms20010130]
  22. Fang, Z. et al. Strigolactones and brassinosteroids antagonistically regulate the stability of the D53–OsBZR1 complex to determine FC1 expression in rice tillering. Mol. Plant 13, 586–597 (2020). [DOI: 10.1016/j.molp.2019.12.005]
  23. Alexandrov, N. et al. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 43, D1023–D1027 (2015). [DOI: 10.1093/nar/gku1039]
  24. Xie, W. et al. Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection. Proc. Natl Acad. Sci. USA 112, E5411–E5419 (2015). [DOI: 10.1073/pnas.1515919112]
  25. Glaszmann, J. C. Isozymes and classification of Asian rice varieties. Theor. Appl. Genet. 74, 21–30 (1987). [DOI: 10.1007/BF00290078]
  26. Huang, X. et al. A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501 (2012). [DOI: 10.1038/nature11532]
  27. Muthayya, S., Sugimoto, J. D., Montgomery, S. & Maberly, G. F. An overview of global rice production, supply, trade, and consumption. Ann. N. Y. Acad. Sci. 1324, 7–14 (2014). [DOI: 10.1111/nyas.12540]
  28. Guerrero, J., Andrello, M., Burgarella, C. & Manel, S. Soil environment is a key driver of adaptation in Medicago truncatula: new insights from landscape genomics. New Phytol. 219, 378–390 (2018). [DOI: 10.1111/nph.15171]
  29. Khush, G. S. Breaking the yield frontier of rice. GeoJournal 35, 329–332 (1995). [DOI: 10.1007/BF00989140]
  30. Yang, W., Peng, S., Laza, R. C., Visperas, R. M. & Dionisiosese, M. L. Grain yield and yield attributes of new plant type and hybrid rice. Crop Sci. 47, 1393–1400 (2007). [DOI: 10.2135/cropsci2006.07.0457]
  31. Peng, S., Khush, G. S., Virk, P. S., Tang, Q. & Zou, Y. Progress in ideotype breeding to increase rice yield potential. Field Crops Res. 108, 32–38 (2008). [DOI: 10.1016/j.fcr.2008.04.001]
  32. R Core Team. R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (R Foundation for Statistical Computing, 2017).
  33. Moll, R., Kamprath, E. & Jackson, W. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron. J. 74, 562–564 (1982). [DOI: 10.2134/agronj1982.00021962007400030037x]
  34. Zhou, X. & Stephens, M. Genome-wide efficient mixed-model analysis for association studies. Nat. Genet. 44, 821–824 (2012). [DOI: 10.1038/ng.2310]
  35. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007). [DOI: 10.1086/519795]
  36. Fumagalli, M. et al. Quantifying population genetic differentiation from next-generation sequencing data. Genetics 195, 979–992 (2013). [DOI: 10.1534/genetics.113.154740]
  37. Fumagalli, M., Vieira, F. G., Linderoth, T. & Nielsen, R. ngsTools: methods for population genetics analyses from next-generation sequencing data. Bioinformatics 30, 1486–1487 (2014). [DOI: 10.1093/bioinformatics/btu041]
  38. Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics15, 356 (2014). [DOI: 10.1186/s12859-014-0356-4]
  39. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).
  40. Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557, 43–49 (2018).
  41. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).
  42. Wang, H., Vieira, F. G., Crawford, J. E., Chu, C. & Nielsen, R. Asian wild rice is a hybrid swarm with extensive gene flow and feralization from domesticated rice. Genome Res. 27, 1029–1038 (2017).

MeSH Term

Adaptation, Physiological
Alleles
Crops, Agricultural
Epistasis, Genetic
Gene Expression Regulation, Plant
Genetic Introgression
Genetic Variation
Genome-Wide Association Study
INDEL Mutation
Nitrogen
Oryza
Plant Proteins
Promoter Regions, Genetic
Soil

Chemicals

Plant Proteins
Soil
Nitrogen

Word Cloud

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