Nitrogen assimilation system in maize is regulated by developmental and tissue-specific mechanisms
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We found metabolites, enzyme activities and enzyme transcript abundances vary significantly across the maize lifecycle, but weak correlation exists between the three groups. We identified putative genes regulating nitrate assimilation.
Progress in improving nitrogen (N) use efficiency (NUE) of crop plants has been hampered by the complexity of the N uptake and utilisation systems. To understand this complexity we measured the activities of seven enzymes and ten metabolites related to N metabolism in the leaf and root tissues of Gaspe Flint maize plants grown in 0.5 or 2.5 mM NO3 − throughout the lifecycle. The amino acids had remarkably similar profiles across the lifecycle except for transient responses, which only appeared in the leaves for aspartate or in the roots for asparagine, serine and glycine. The activities of the enzymes for N assimilation were also coordinated to a certain degree, most noticeably with a peak in root activity late in the lifecycle, but with wide variation in the activity levels over the course of development. We analysed the transcriptional data for gene sets encoding the measured enzymes and found that, unlike the enzyme activities, transcript levels of the corresponding genes did not exhibit the same coordination across the lifecycle and were only weakly correlated with the levels of various amino acids or individual enzyme activities. We identified gene sets which were correlated with the enzyme activity profiles, including seven genes located within previously known quantitative trait loci for enzyme activities and hypothesise that these genes are important for the regulation of enzyme activities. This work provides insights into the complexity of the N assimilation system throughout development and identifies candidate regulatory genes, which warrant further investigation in efforts to improve NUE in crop plants.
KeywordsNitrogen use efficiency NUE Nitrogen metabolism Amino acids Enzyme activity Transcript abundance
The project was funded by the Australian Centre for Plant Functional Genomics, DuPont Pioneer, Australian Council Linkage Grant (LP0776635) to BNK, MT (University of Adelaide) and AR, KSD (DuPont Pioneer). The authors gratefully acknowledge the assistance of Lynne Fallis, Hari Kishan Rao Abbaraju, Vanessa Conn, Stephanie Feakin, Jaskaranbir Kaur, Simon Conn, Mary Beatty, and Kevin Hays. The authors also thank Ms Priyanka Reddy and Ms Chia Ng, Metabolomics Australia, School of BioSciences, The University of Melbourne, for sample preparation and amino acid analysis. UR and AB are also grateful to Victorian Node of Metabolomics Australia, which is funded through Bioplatforms Australia Pty Ltd, a National Collaborative Research Infrastructure Strategy (NCRIS), 5.1 biomolecular platforms and informatics investment, and co-investment from the Victorian State government and The University of Melbourne.
- Beevers L, Hageman RH (1980) Nitrate and Nitrite Reduction. In: Miflin BJ (ed) The Biochemistry of Plants, vol 3. Academic Press, New York, pp 115–168Google Scholar
- Cho B-K, Federowicz S, Park Y-S, Zengler K, Palsson BØ (2012) Deciphering the transcriptional regulatory logic of amino acid metabolism Nat Chem Biol 8:65–71 http://www.nature.com/nchembio/journal/v8/n1/abs/nchembio.710.html#supplementary-information CrossRefGoogle Scholar
- Cohen SA, Michaud DP (1993) Synthesis of a fluorescent derivatizing reagent, 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate, and its application for the analysis of hydrolysate amino acids via high-performance liquid chromatography. Anal Biochem 211:279–287. doi: 10.1006/abio.1993.1270 CrossRefPubMedGoogle Scholar
- FAO (2013) FAO statistical yearbook 2013: world food and agriculture. Food and Agriculture Organization of the United Nations, Rome, ItalyGoogle Scholar
- Gibon Y et al (2004) A Robot-based platform to measure multiple enzyme activities in arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness. Plant Cell 16:3304–3325. doi: 10.1105/tpc.104.025973 CrossRefPubMedPubMedCentralGoogle Scholar
- Hawkesford MJ (2011) An overview of nutrient use efficiency and strategies for crop improvement. In: The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops. Wiley-Blackwell, pp 3–19. doi: 10.1002/9780470960707.ch1
- Joy KW, Ireland RJ (1990) 17-Enzymes of Asparagine Metabolism. In: P. J LEA (ed) Methods in Plant Biochemistry, vol 3. Academic Press, pp 287–296. doi: 10.1016/B978-0-12-461013-2.50024-1
- Lea US, Leydecker M-T, Quillere I, Meyer C, Lillo C (2006) Posttranslational regulation of nitrate reductase strongly affects the levels of free amino acids and nitrate, whereas transcriptional regulation has only minor influence. Plant Physiol 140:1085–1094. doi: 10.1104/pp.105.074633 CrossRefPubMedPubMedCentralGoogle Scholar
- Masclaux-Daubresse C et al (2014) Stitching together the multiple dimensions of autophagy using metabolomics and transcriptomics reveals impacts on metabolism, development, and plant responses to the environment in arabidopsis. Plant Cell Online 26:1857–1877 doi: 10.1105/tpc.114.124677 CrossRefGoogle Scholar
- Ober E, Parry MAJ (2011) Drought and implications for nutrition. In: The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops. Wiley-Blackwell, pp 429–441. doi: 10.1002/9780470960707.ch18
- Ranathunge K, El-kereamy A, Gidda S, Bi Y-M, Rothstein SJ (2014) AMT1;1 transgenic rice plants with enhanced NH4 + permeability show superior growth and higher yield under optimal and suboptimal NH4 + conditions. J Exp Bot 65:965–979. doi: 10.1093/jxb/ert458 CrossRefPubMedPubMedCentralGoogle Scholar
- Schlüter U, Mascher M, Colmsee C, Scholz U, Bräutigam A, Fahnenstich H, Sonnewald U (2012) Maize source leaf adaptation to nitrogen deficiency affects not only nitrogen and carbon metabolism but also control of phosphate homeostasis. Plant Physiol 160:1384–1406. doi: 10.1104/pp.112.204420 CrossRefPubMedPubMedCentralGoogle Scholar
- Smyth GK (2005) limma: linear models for microarray data. In: Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S (eds) Bioinformatics and computational biology solutions using r and bioconductor statistics for biology and health. Springer New York, pp 397–420. doi: 10.1007/0-387-29362-0/23
- Wolt J (1994) Soil solution chemistry:applications to environmental science and agriculture. Wiley, New YorkGoogle Scholar