Profiling of spatial metabolite distributions in wheat leaves under normal and nitrate limiting conditions

J William Allwood, Surya Chandra, Yun Xu, Warwick B Dunn, Elon Correa, Laura Hopkins, Royston Goodacre, Alyson K Tobin, Caroline G Bowsher

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
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The control and interaction between nitrogen and carbon assimilatory pathways is essential in both photosynthetic and non-photosynthetic tissue in order to support metabolic processes without compromising growth. Physiological differences between the basal and mature region of wheat (Triticum aestivum) primary leaves confirmed that there was a change from heterotrophic to autotrophic metabolism. Fourier Transform Infrared (FT-IR) spectroscopy confirmed the suitability and phenotypic reproducibility of the leaf growth conditions. Principal Component-Discriminant Function Analysis (PC-DFA) revealed distinct clustering between base, and tip sections of the developing wheat leaf, and from plants grown in the presence or absence of nitrate. Gas Chromatography-Time of Flight/Mass Spectrometry (GC-TOF/MS) combined with multivariate and univariate analyses, and Bayesian network (BN) analysis, distinguished different tissues and confirmed the physiological switch from high rates of respiration to photosynthesis along the leaf. The operation of nitrogen metabolism impacted on the levels and distribution of amino acids, organic acids and carbohydrates within the wheat leaf. In plants grown in the presence of nitrate there was reduced levels of a number of sugar metabolites in the leaf base and an increase in maltose levels, possibly reflecting an increase in starch turnover. The value of using this combined metabolomics analysis for further functional investigations in the future are discussed.

Original languageEnglish
Pages (from-to)99-111
Number of pages13
Early online date10 Feb 2015
Publication statusPublished - Jul 2015


  • Triticum aestivum
  • wheat
  • Leaves
  • Metabolite fingerprinting
  • Metabolite profiling
  • Bayesian network analysis
  • Nitrate


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