Plant Molecular Biology

, Volume 92, Issue 3, pp 293–312 | Cite as

Nitrogen assimilation system in maize is regulated by developmental and tissue-specific mechanisms

  • Darren Plett
  • Luke Holtham
  • Ute Baumann
  • Elena Kalashyan
  • Karen Francis
  • Akiko Enju
  • John Toubia
  • Ute Roessner
  • Antony Bacic
  • Antoni Rafalski
  • Kanwarpal S. Dhugga
  • Mark Tester
  • Trevor GarnettEmail author
  • Brent N. Kaiser


Key message

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.


Nitrogen 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.

Supplementary material

11103_2016_512_MOESM1_ESM.tif (897 kb)
Supplementary Figure 1: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding NR enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 896 KB)
11103_2016_512_MOESM2_ESM.tif (525 kb)
Supplementary Figure 2: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding NiR enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 524 KB)
11103_2016_512_MOESM3_ESM.tif (1.2 mb)
Supplementary Figure 3: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding GS enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 1185 KB)
11103_2016_512_MOESM4_ESM.tif (973 kb)
Supplementary Figure 4: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding GOGAT enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 972 KB)
11103_2016_512_MOESM5_ESM.tif (994 kb)
Supplementary Figure 5: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding AS enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 994 KB)
11103_2016_512_MOESM6_ESM.tif (1.1 mb)
Supplementary Figure 6: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding AspAT enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 1098 KB)
11103_2016_512_MOESM7_ESM.tif (975 kb)
Supplementary Figure 7: Unrooted Neighbour-joining tree of the Arabidopsis and maize genes encoding AlaAT enzymes. Sequence identifiers are found in Supplementary Table 1. Tree includes Arabidopsis genes in black, maize genes present on the microarray described previously (Plett et al. 2016) are in green and those not included are in red. (TIF 974 KB)
11103_2016_512_MOESM8_ESM.tif (6.8 mb)
Supplementary Figure 8: Hierarchical clustering analysis of the amino acids, enzyme activities and enzyme gene transcript abundance values. Values are the average transcript abundance value from each tissue/treatment data set subtracted from the individual transcript abundance value at each time point. Green squares represent positive values or greater than the average value for the tissue/treatment, red squares represent negative values or less than the average value for the tissue/treatment. All tissue/treatment data sets are included and the four colours represent: youngest fully emerged leaf blade (YEB) - 0.5 mM > 2.5 mM (light green); leaf - 2.5 mM > 0.5 mM (dark green); root – 0.5 mM > 2.5 mM (yellow); root – 2.5 mM > 0.5 mM (red). Analyses are presented separately for the YEB (top) and roots (bottom). (TIF 6956 KB)
11103_2016_512_MOESM9_ESM.xlsx (11 kb)
Supplementary Table 1: List of all Arabidopsis and maize genes (and identifiers) encoding NR, NiR, GS, GOGAT, AS, AspAT and AlaAT enzymes. Maize genes present in the microarray data are in green and those not present are in red. Predicted subcellular localisations of the proteins included by each gene are provided (M – mitochondria; C – cytoplasm). (XLSX 10 KB)
11103_2016_512_MOESM10_ESM.xlsx (5.4 mb)
Supplementary Table 2: Lists of all genes with transcript abundance profiles significantly positively (r > 0.95) or negatively (r > -0.95) correlated to enzyme activity profiles across the lifecycle. An image highlighting each profile and lists are provided for NR, NiR, GS, GOGAT, AS, AspAT and AlaAT in the YEB and roots for both 0.5 and 2.5 mM NO3 grown plants. Each list also contains results from Gene Ontology enrichment analyses. (XLSX 5516 KB)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Darren Plett
    • 1
    • 2
  • Luke Holtham
    • 1
    • 2
  • Ute Baumann
    • 1
    • 2
  • Elena Kalashyan
    • 1
    • 2
  • Karen Francis
    • 2
  • Akiko Enju
    • 1
    • 2
  • John Toubia
    • 1
    • 2
    • 3
    • 4
  • Ute Roessner
    • 5
    • 6
  • Antony Bacic
    • 6
    • 7
  • Antoni Rafalski
    • 8
  • Kanwarpal S. Dhugga
    • 9
    • 10
  • Mark Tester
    • 11
  • Trevor Garnett
    • 1
    • 2
    • 12
    Email author
  • Brent N. Kaiser
    • 2
    • 13
  1. 1.Australian Centre for Plant Functional Genomics, Waite Research InstituteUniversity of AdelaideAdelaideAustralia
  2. 2.School of Agriculture, Food and Wine, Waite Research InstituteUniversity of AdelaideAdelaideAustralia
  3. 3.ACRF South Australian Cancer Genomics FacilityCentre for Cancer Biology, SA PathologyAdelaideAustralia
  4. 4.School of Molecular and Biomedical ScienceThe University of AdelaideAdelaideAustralia
  5. 5.Australian Centre for Plant Functional Genomics, School of BioSciencesThe University of MelbourneParkvilleAustralia
  6. 6.Metabolomics Australia, School of BioSciencesThe University of MelbourneParkvilleAustralia
  7. 7.ARC Centre of Excellence in Plant Cell Walls, School of BioSciencesThe University of MelbourneParkvilleAustralia
  8. 8.DuPont PioneerWilmingtonUSA
  9. 9.DuPont PioneerJohnstonUSA
  10. 10.International Maize and Wheat Improvement Center (CIMMYT)TexcocoUSA
  11. 11.Center for Desert AgricultureKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  12. 12.The Plant Accelerator, Australian Plant Phenomics FacilityThe University of AdelaideGlen OsmondAustralia
  13. 13.Centre For Carbon Water and Food, The Faculty of Agriculture and EnvironmentThe University of SydneyCamdenAustralia

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