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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- NUE:
-
Nitrogen use efficiency
- IE:
-
Internal efficiency
- NHI:
-
Nitrogen Harvest Index
- PNUE:
-
Physiological nitrogen use efficiency
- DET:
-
Differentially expressed transcripts
References
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
Broadbent FE, De DSK, Laureles EV (1987) Measurement of nitrogen utilization efficiency in rice genotypes. Agron J 791:786–791
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
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
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
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
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
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
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
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
FAO (2018) Food Security and Nutrition in the World.
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
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
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
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
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
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
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. Bioinformatics. https://doi.org/10.1186/1471-2105-9-559
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
The AROF, Panel I, Climate ON (1995) IPCC Second Assessment Climate Change 1995.
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
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
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
Vinod KK, Heuer S (2012) Approaches towards nitrogen- and phosphorus-efficient rice. AoB Plants. https://doi.org/10.1093/aobpla/pls028
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
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
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
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
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
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
Yoshida S (1981) Fundamental of rice crop science. International Rice Research Institute, Los Baños, Laguna, Philippines, pp 269
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
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
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
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
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].
Author information
Authors and Affiliations
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
Ethics declarations
Conflict of interest
All the authors declare that they have no conflict of interest in the publication.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13205-020-02631-5