Down regulation of transcripts involved in selective metabolic pathways as an acclimation strategy in nitrogen use efficient genotypes of rice under low nitrogen

Abstract

To understand the molecular mechanism of nitrogen use efficiency (NUE) in rice, two nitrogen (N) use efficient genotypes and two non-efficient genotypes were characterized using transcriptome analyses. The four genotypes were evaluated for 3 years under low and recommended N field conditions for 12 traits/parameters of yield, straw, nitrogen content along with NUE indices and 2 promising donors for rice NUE were identified. Using the transcriptome data generated from GS FLX 454 Roche and Illumina HiSeq 2000 of two efficient and two non-efficient genotypes grown under field conditions of low N and recommended N and their de novo assembly, differentially expressed transcripts and pathways during the panicle development were identified. Down regulation was observed in 30% of metabolic pathways in efficient genotypes and is being proposed as an acclimation strategy to low N. Ten sub metabolic pathways significantly enriched with additional transcripts either in the direction of the common expression or contra-regulated to the common expression were found to be critical for NUE in rice. Among the up-regulated transcripts in efficient genotypes, a hypothetical protein OsI_17904 with 2 alternative forms suggested the role of alternative splicing in NUE of rice and a potassium channel SKOR transcript (LOC_Os06g14030) has shown a positive correlation (0.62) with single plant yield under low N in a set of 16 rice genotypes. From the present study, we propose that the efficient genotypes appear to down regulate several not so critical metabolic pathways and divert the thus conserved energy to produce seed/yield under long-term N starvation.

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Abbreviations

NUE:

Nitrogen use efficiency

IE:

Internal efficiency

NHI:

Nitrogen Harvest Index

PNUE:

Physiological nitrogen use efficiency

DET:

Differentially expressed transcripts

References

  1. Beatty PH, Shrawat AK, Carroll RT et al (2009) Transcriptome analysis of nitrogen-efficient rice over-expressing alanine aminotransferase. Plant Biotechnol J 7:562–576. https://doi.org/10.1111/j.1467-7652.2009.00424.x

    CAS  Article  PubMed  Google Scholar 

  2. Broadbent FE, De DSK, Laureles EV (1987) Measurement of nitrogen utilization efficiency in rice genotypes. Agron J 791:786–791

    Article  Google Scholar 

  3. Cai H, Lu Y, Xie W et al (2012) Transcriptome response to nitrogen starvation in rice. J Biosci 37:731–747. https://doi.org/10.1007/s12038-012-9242-2

    CAS  Article  PubMed  Google Scholar 

  4. Cai H, Chen H, Yi T et al (2013) VennPlex-A novel Venn diagram program for comparing and visualizing datasets with differentially regulated datapoints. PLoS ONE. https://doi.org/10.1371/journal.pone.0053388

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chandran AKN, Priatama RA, Kumar V et al (2016) Genome-wide transcriptome analysis of expression in rice seedling roots in response to supplemental nitrogen. J Plant Physiol 200:62–75. https://doi.org/10.1016/j.jplph.2016.06.005

    CAS  Article  PubMed  Google Scholar 

  6. Chen Q, Liu Z, Wang B et al (2015) Transcriptome sequencing reveals the roles of transcription factors in modulating genotype by nitrogen interaction in maize. Plant Cell Rep 34:1761–1771. https://doi.org/10.1007/s00299-015-1822-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Coneva V, Simopoulos C, Casaretto JA et al (2014) Metabolic and co-expression network-based analyses associated with nitrate response in rice. BMC Genomics 15:1–14. https://doi.org/10.1186/1471-2164-15-1056

    CAS  Article  Google Scholar 

  8. Curci PL, Aiese Cigliano R, Zuluaga DL et al (2017) Transcriptomic response of durum wheat to nitrogen starvation. Sci Rep 7:1–14. https://doi.org/10.1038/s41598-017-01377-0

    CAS  Article  Google Scholar 

  9. de Araujo Junior AT, da Rosa Farias D (2015) The quest for more tolerant rice: how high concentrations of iron affect alternative splicing? Transcr Open Access 03:3–7. https://doi.org/10.4172/2329-8936.1000122

    Article  Google Scholar 

  10. Dobermann A, Fairhurst T (2000) Rice: Nutrient Disorders & Nutrient Management. Potash & Phosphate Institute (PPI), Potash & Phosphate Institute of Canada (PPIC) and International Rice Research Institute, Philippine

  11. FAO (2018) Food Security and Nutrition in the World.

  12. Gelli M, Duo Y, Konda AR et al (2014) Identification of differentially expressed transcripts between sorghum genotypes with contrasting nitrogen stress tolerance by genome-wide transcriptional profiling. BMC Genomics 15:179. https://doi.org/10.1186/1471-2164-15-179

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Hsieh PH, Kan CC, Wu HY et al (2018) Early molecular events associated with nitrogen deficiency in rice seedling roots. Sci Rep 8:1–23. https://doi.org/10.1038/s41598-018-30632-1

    CAS  Article  Google Scholar 

  14. Huang A, Sang Y, Sun W et al (2016) Transcriptomic analysis of responses to imbalanced carbon: nitrogen availabilities in rice seedlings. PLoS ONE 11:1–26. https://doi.org/10.1371/journal.pone.0165732

    CAS  Article  Google Scholar 

  15. Kawahara Y, de la Bastide M, Hamilton JP et al (2013) Improvement of the oryza sativa nipponbare reference genome using next generation sequence and optical map data. Rice 6:3–10. https://doi.org/10.1186/1939-8433-6-1

    Article  Google Scholar 

  16. Krapp A, Berthome R, Mathilde O et al (2011) Arabidopsis roots and shoots show distinct temporal adaptation patterns toward nitrogen starvation. Plant Physiol. 157:1255–1282. https://doi.org/10.1104/pp.111.179838

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Ladha JK, Pathak H, Krupnik TJ et al (2005) Efficiency of fertilizer nitrogen in cereal production: retrospects and prospects. Adv Agron 87:85–156. https://doi.org/10.1016/S0065-2113(05)87003-8

    CAS  Article  Google Scholar 

  18. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. Bioinformatics. https://doi.org/10.1186/1471-2105-9-559

    Article  PubMed  Google Scholar 

  19. Li H, Hu B, Chu C (2017) Nitrogen use efficiency in crops: lessons from Arabidopsis and rice. J Exp Bot 68:2477–2488. https://doi.org/10.1093/jxb/erx101

    CAS  Article  PubMed  Google Scholar 

  20. Lian X, Wang S, Zhang J et al (2006) Expression profiles of 10,422 transcripts at early stage of low nitrogen stress in rice assayed using a cDNA microarray. Plant Mol Biol 60:617–631. https://doi.org/10.1007/s11103-005-5441-7

    CAS  Article  PubMed  Google Scholar 

  21. Naoumkina MA, Zhao Q, Gallego-Giraldo L et al (2010) Genome-wide analysis of phenylpropanoid defence pathways. Mol Plant Pathol 11:829–846. https://doi.org/10.1111/j.1364-3703.2010.00648.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Oliveros JC (2016) Venny 2.1.0. Venny. An Interactive Tool for Comparing Lists with Venn's Diagrams. http://bioinfogp.cnb.csic.es/tools/venny/index.html

  23. Phule AS, Barbadikar KM, Madhav MS et al (2018) Transcripts encoding membrane proteins showed stable expression in rice under aerobic condition: novel set of reference transcripts for expression studies. 3 Biotech 8:383. https://doi.org/10.1007/s13205-018-1406-9

    Article  PubMed  PubMed Central  Google Scholar 

  24. Quan X, Zeng J, Ye L et al (2016) Transcriptome profiling analysis for two Tibetan wild barley genotypes in responses to low nitrogen. BMC Plant Biol 16:1–16. https://doi.org/10.1186/s12870-016-0721-8

    CAS  Article  Google Scholar 

  25. Rao IS, Neeraja CN, Srikanth B et al (2018) Identification of rice landraces with promising yield and the associated genomic regions under low nitrogen. Sci Rep 8:1–13. https://doi.org/10.1038/s41598-018-27484-0

    CAS  Article  Google Scholar 

  26. Richard-Molard C, Krapp A, Francxois B et al (2008) Plant response to nitrate starvation is determined by N storage capacity matched by nitrate uptake capacity in two Arabidopsis genotypes. J Exp Botany 59:779–791. https://doi.org/10.1093/jxb/erm363

    CAS  Article  Google Scholar 

  27. Saeed AI, Sharov V, White J et al (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34:374–378. https://doi.org/10.2144/03342mt01

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C T method. Nat Protoc 3:1101–1108. https://doi.org/10.1038/nprot.2008.73

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Shin SY, Jeong JS, Lim JY et al (2018) Transcriptomic analyses of rice (Oryza sativa) transcripts and non-coding RNAs under nitrogen starvation using multiple omics technologies. BMC Genomics 19:1–20. https://doi.org/10.1186/s12864-018-4897-1

    CAS  Article  Google Scholar 

  30. Singh U, Ladha JK, Castillo EG et al (1998) Genotypic variation in nitrogen use ef ® ciency in medium- and long-duration rice. Field Crops Res 58:35–53

    Article  Google Scholar 

  31. Sinha SK, Amitha Mithra SV, Chaudhary S et al (2018) Transcriptome analysis of two rice varieties contrasting for nitrogen use efficiency under chronic N starvation reveals differences in chloroplast and starch metabolism-related transcripts. Transcripts (Basel) 9:1–22. https://doi.org/10.3390/transcripts9040206

    CAS  Article  Google Scholar 

  32. Staiger D, Brown JWS (2013) Alternative splicing at the intersection of biological timing, development, and stress responses. Plant Cell 25:3640–3656. https://doi.org/10.1105/tpc.113.113803

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Sun L, Di D, Li G et al (2017) Spatio-temporal dynamics in global rice gene expression (Oryza sativa L.) in response to high ammonium stress. J Plant Physiol 212:94–104. https://doi.org/10.1016/j.jplph.2017.02.006

    CAS  Article  PubMed  Google Scholar 

  34. Surekha K, Satishkumar YS (2014) Productivity, nutrient balance, soil quality, and sustainability of rice (Oryza sativa L.) under organic and conventional production systems. Commun Soil Sci Plant Anal 245:415–428. https://doi.org/10.1080/00103624.2013.872250

    CAS  Article  Google Scholar 

  35. Takehisa H, Sato Y, Antonio BA, Nagamur Y (2013) Global transcriptome profile of rice root in response to essential macronutrient deficiency. Plant Signal Behav. https://doi.org/10.4161/psb.24409

    Article  PubMed  PubMed Central  Google Scholar 

  36. Tegeder M, Masclaux-Daubresse C (2018) Source and sink mechanisms of nitrogen transport and use. New Phytol 217:35–53. https://doi.org/10.1111/nph.14876

    Article  PubMed  PubMed Central  Google Scholar 

  37. The AROF, Panel I, Climate ON (1995) IPCC Second Assessment Climate Change 1995.

  38. Thimm O, Bläsing O, Gibon Y et al (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37:914–939. https://doi.org/10.1111/j.1365-313X.2004.02016.x

    CAS  Article  PubMed  Google Scholar 

  39. Tirol-Padre A, Ladha JK, Singh U et al (1996) Grain yield performance of rice genotypes at suboptimal levels of soil N as affected by N uptake and utilization efficiency. Field Crops Res 46:127–143. https://doi.org/10.1016/0378-4290(95)00095-x

    Article  Google Scholar 

  40. Vijayalakshmi P, Vishnukiran T, Ramana Kumari B et al (2015) Biochemical and physiological characterization for nitrogen use efficiency in aromatic rice genotypes. Field Crops Res 179:132–143. https://doi.org/10.1016/j.fcr.2015.04.012

    Article  Google Scholar 

  41. Vinod KK, Heuer S (2012) Approaches towards nitrogen- and phosphorus-efficient rice. AoB Plants. https://doi.org/10.1093/aobpla/pls028

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wang K, Singh D, Zeng Z et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38:1–14. https://doi.org/10.1093/nar/gkq622

    CAS  Article  Google Scholar 

  43. Wei H, Lou Q, Xu K et al (2017) Alternative splicing complexity contributes to genetic improvement of drought resistance in the rice maintainer HuHan2B. Sci Rep. https://doi.org/10.1038/s41598-017-12020-3

    Article  PubMed  PubMed Central  Google Scholar 

  44. Xu G, Fan X, Miller AJ (2012) Plant nitrogen assimilation and use efficiency. Annu Rev Plant Biol 63:153–182. https://doi.org/10.1146/annurev-arplant-042811-105532

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Yang SY, Hao DL, Song ZZ et al (2015a) RNA-Seq analysis of differentially expressed transcripts in rice under varied nitrogen supplies. Gene 555:305–317. https://doi.org/10.1016/j.gene.2014.11.021

    CAS  Article  PubMed  Google Scholar 

  46. Yang W, Yoon J, Choi H et al (2015b) Transcriptome analysis of nitrogen-starvation-responsive transcripts in rice. BMC Plant Biol 15:1–12. https://doi.org/10.1186/s12870-015-0425-5

    CAS  Article  Google Scholar 

  47. Ye J, Fang L, Zheng H et al (2006) WEGO: a web tool for plotting GO annotations. Nucleic Acids Res 34:293–297. https://doi.org/10.1093/nar/gkl031

    Article  Google Scholar 

  48. Yoshida S (1981) Fundamental of rice crop science. International Rice Research Institute, Los Baños, Laguna, Philippines, pp 269

  49. Young MD, Wakefield MJ, Smyth GK, Oshlack A (2010) Gene ontology analysis for RNA-seq: accounting for selection bias GOseq GOseq is a method for GO analysis of RNA-seq data that takes into account the length bias inherent in RNA-seq. Genome Biol. https://doi.org/10.1186/gb-2010-11-2-r14

    Article  PubMed  PubMed Central  Google Scholar 

  50. Zhao SP, Zhao XQ, Shi WM (2012) Genotype variation in grain yield response to basal N fertilizer supply among different rice cultivars. Afr J Biotechnol 11:12298–12304

    CAS  Google Scholar 

  51. Zhao X, Wang W, Xie Z et al (2018) ScienceDirect comparative analysis of metabolite changes in two contrasting rice genotypes in response to low- nitrogen stress. Crop J 6:464–474. https://doi.org/10.1016/j.cj.2018.05.006

    Article  Google Scholar 

  52. Zhu GH, Zhuang CH, Wang YQ et al (2006) Differential expression of rice transcripts under different nitrogen forms and their relationship with sulfur metabolism. J Integr Plant Biol 48:1177–1184

    CAS  Article  Google Scholar 

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Acknowledgements

This work was supported by National Initiative on Climate Resilient Agriculture (NICRA), Indian Council of Agricultural Research (ICAR), Ministry of Agriculture, Govt. of India [F. No. Phy/ NICRA/2011-2012].

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Contributions

CNN*—planning of the experiments, writing the manuscript, analyses, logistics. KMB—planning of some experiments, data analyses, writing the manuscript. TKK—executed the experiments. SB—executed the experiments. ISR—data collection. BS—executed the experiments. DSR—analyses. DS—analyses. PRR—support of the logistics. SRV—planning of the experiments, support of the logistics.

Corresponding author

Correspondence to C. N. Neeraja.

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All the authors declare that they have no conflict of interest in the publication.

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Neeraja, C.N., Barbadikar, K.M., Krishnakanth, T. et al. Down regulation of transcripts involved in selective metabolic pathways as an acclimation strategy in nitrogen use efficient genotypes of rice under low nitrogen. 3 Biotech 11, 80 (2021). https://doi.org/10.1007/s13205-020-02631-5

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Keywords

  • Nitrogen use efficiency
  • Landraces
  • Metabolic pathways
  • Transcriptomics
  • Differential gene expression
  • SKOR transporter