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An understanding of the balance between carbon and nitrogen assimilation in plants is key to future bioengineering for a range of applications. Metabolomic analysis of the model plant, Arabidopsis thaliana, using combined NMR-MS revealed the presence of two hemiterpenoid glycosides that accumulated in leaf tissue, to ~1% dry weight under repeated nitrate-deficient conditions. The formation of these isoprenoids was correlated with leaf nitrate concentrations that could also be assayed in the metabolomic data using a unique flavonoid-nitrate mass spectral adduct. Analysis of leaf and root tissue from plants grown in hydroponics with a variety of root stressors identified the conditions under which the isoprenoid pathway in leaves was diverted to the hemiterpenoids. These compounds were strongly induced by root wounding or oxidative stress and weakly induced by potassium deficiency. Other stresses such as cold, saline, and osmotic stress did not induce the compounds. Replacement of nitrate with ammonia failed to suppress the formation of the hemiterpenoids, indicating that nitrate sensing was a key factor. Feeding of intermediates was used to study aspects of 2-C-methyl-d-erythritol-4-phosphate pathway regulation leading to hemiterpenoid formation. The formation of the hemiterpenoids in leaves was strongly correlated with the induction of the phenylpropanoids scopolin and coniferin in roots of the same plants. These shunts of photosynthetic carbon flow are discussed in terms of overflow mechanisms that have some parallels with isoprene production in tree species.

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Modern 'metabolomic' methods allow us to compare levels of many structurally diverse compounds in an automated fashion across a large number of samples. This technology is ideally suited to screening of populations of plants, including trials where the aim is the determination of unintended effects introduced by GM. A number of metabolomic methods have been devised for the determination of substantial equivalence. We have developed a methodology, using [1H]-NMR fingerprinting, for metabolomic screening of plants and have applied it to the study of substantial equivalence of field-grown GM wheat. We describe here the principles and detail of that protocol as applied to the analysis of flour generated from field plots of wheat. Particular emphasis is given to the downstream data processing and comparison of spectra by multivariate analysis, from which conclusions regarding metabolome changes due to the GM can be assessed against the background of natural variation due to environment.

Black-grass (Alopecurus myosuroides) is one of the most problematic agricultural weeds of Western Europe, causing significant yield losses in winter wheat (Triticum aestivum) and other crops through competition for space and resources. Previous studies link black-grass patches to water-retaining soils, yet its specific adaptations to these conditions remain unclear. We designed pot-based waterlogging experiments to compare 13 biotypes of black-grass and six cultivars of wheat. These showed that wheat roots induced aerenchyma when waterlogged whereas aerenchyma-like structures were constitutively present in black-grass. Aerial biomass of waterlogged wheat was smaller, whereas waterlogged black-grass was similar or larger. Variability in waterlogging responses within and between these species was correlated with transcriptomic and metabolomic changes in leaves of control or waterlogged plants. In wheat, transcripts associated with regulation and utilisation of phosphate compounds were upregulated and sugars and amino acids concentrations were increased. Black-grass biotypes showed limited molecular responses to waterlogging. Some black-grass amino acids were decreased and one transcript commonly upregulated was previously identified in screens for genes underpinning metabolism-based resistance to herbicides. Our findings provide insights into the different waterlogging tolerances of these species and may help to explain the previously observed patchiness of this weed's distribution in wheat fields. Black-grass is a problematic weed of winter wheat. To better understand if waterlogging tolerance increases its weediness, we conducted controlled waterlogging studies comparing the physiology and molecular responses of black-grass and wheat. These revealed differences in roots and aerial tissues within and between species. Molecular analyses showed that wheat mounts a significant molecular response, altering gene expression and metabolites with waterlogging, whereas black-grass showed minimal responses to waterlogging despite its apparent tolerance. This article belongs to the Collection Flooding Stress and Hypoxic Responses in Plants. [ABSTRACT FROM AUTHOR]

Willow ( Salix sp.) is a historically well-known herbal medicine that provided the lead compound (salicin) for the discovery of aspirin, one of the most successful plant derived drugs in human medicine. During a metabolomics screen of 86 Salix species contained in the UK National Willow Collection, we have discovered, isolated and fully characterised a new natural salicinoid – salicin-7-sulfate. This molecule may have important human pharmacological actions that need to be considered in determining the efficacy and safety of willow herbal medicines. [ABSTRACT FROM AUTHOR]

Background: Genomic prediction of agronomic traits as targets for selection in plant breeding programmes is increasingly common. The methods employed can also be applied to predict traits from other sources of covariates, such as metabolomics. However, prediction combining sets of covariates can be less accurate than using the best of the individual sets. Results: We describe a method, termed Differentially Penalized Regression (DiPR), which uses standard ridge regression software to combine sets of covariates while applying independent penalties to each. In a dataset of wheat varieties, field traits are better predicted, on average, by seed metabolites than by genetic markers, but DiPR using both sets of predictors is best. Conclusion: DiPR is a simple and accessible method of using existing software to combine multiple sets of covariates in trait prediction when there are more predictors than observations and the contribution to accuracy from each set differs. [ABSTRACT FROM AUTHOR]

Melon ( Cucumis melo L.) is a global crop in terms of economic importance and nutritional quality. The aim of this study was to explore the variability in metabolite and elemental composition of several commercial varieties of melon in various environmental conditions. Volatile and non-volatile metabolites as well as mineral elements were profiled in the flesh of mature fruit, employing a range of complementary analytical technologies. More than 1,000 metabolite signatures and 19 mineral elements were determined. Data analyses revealed variations related to factors such as variety, growing season, contrasting agricultural management practices (greenhouse vs. field with or without fruit thinning) and planting date. Two hundred and ninety-one analytes discriminated two contrasting varieties, one from the var. inodorous group and the other from the var. cantaloupensis group. Two hundred and eighty analytes discriminated a short shelf-life from a mid-shelf-life variety within the var. cantaloupensis group. Three hundred and twenty-seven analytes discriminated two seasons, and two hundred and fifty-two analytes discriminated two contrasting agricultural management practices. The affected compound families greatly depended on the factor studied. The compositional variability of identified or partially identified compounds was used to study metabolite and mineral element co-regulation using correlation networks. The results confirm that metabolome and mineral element profiling are useful diagnostic tools to characterize the quality of fruits cultivated under commercial conditions. They can also provide knowledge on fruit metabolism and the mechanisms of plant response to environmental modifications, thereby paving the way for metabolomics-guided improvement of cultural practices for better fruit quality. [ABSTRACT FROM AUTHOR]

Increasing demands for productivity together with environmental concerns about fertilizer use dictate that the future sustainability of agricultural systems will depend on improving fertilizer use efficiency. Characterization of the biological processes responsible for efficient fertilizer use will provide tools for crop improvement under reduced inputs. Transcriptomic and metabolomic approaches were used to study the impact of nitrogen (N) and sulphur (S) deficiency on N and S remobilization from senescing canopy tissues during grain filling in winter wheat (Triticum aestivum). Canopy tissue N was remobilized effectively to the grain after anthesis. S was less readily remobilized. Nuclear magnetic resonance (NMR) metabolite profiling revealed significant effects of suboptimal N or S supply in leaves but not in developing grain. Analysis of amino acid pools in the grain and leaves revealed a strategy whereby amino acid biosynthesis switches to the production of glutamine during grain filling. Glutamine accumulated in the first 7 d of grain development, prior to conversion to other amino acids and protein in the subsequent 21 d. Transcriptome analysis indicated that a down-regulation of the terminal steps in many amino acid biosynthetic pathways occurs to control pools of amino acids during leaf senescence. Grain N and S contents increased in parallel after anthesis and were not significantly affected by S deficiency, despite a suboptimal N:S ratio at final harvest. N deficiency resulted in much slower accumulation of grain N and S and lower final concentrations, indicating that vegetative tissue N has a greater control of the timing and extent of nutrient remobilization than S. [ABSTRACT FROM PUBLISHER]

The ‘substantial equivalence’ of three transgenic wheats expressing additional high-molecular-weight subunit genes and the corresponding parental lines (two lines plus a null transformant) was examined using metabolite profiling of samples grown in replicate field trials on two UK sites (Rothamsted, Hertfordshire and Long Ashton, near Bristol) for 3 years. Multivariate comparison of the proton nuclear magnetic resonance spectra of polar metabolites extracted with deuterated methanol–water showed a stronger influence of site and year than of genotype. Nevertheless, some separation between the transgenic and parental lines was observed, notably between the transgenic line B73-6-1 (which had the highest level of transgene expression) and its parental line L88-6. Comparison of the spectra showed that this separation resulted from increased levels of maltose and/or sucrose in this transgenic line, and that differences in free amino acids were also apparent. More detailed studies of the amino acid composition of material grown in 2000 were carried out using gas chromatography-mass spectrometry. The most noticeable difference was that the samples grown at Rothamsted consistently contained larger amounts of acidic amino acids (glutamic, aspartic) and their amides (glutamine, asparagine). In addition, the related lines, L88-6 and B73-6-1, both contained larger amounts of proline and γ-aminobutyric acid when grown at Long Ashton than at Rothamsted. The results clearly demonstrate that the environment affects the metabolome and that any differences between the control and transgenic lines are generally within the same range as the differences observed between the control lines grown on different sites and in different years. [ABSTRACT FROM AUTHOR]

An approach to metabolite fingerprinting of crude plant extracts that utilizes 1H nuclear magnetic resonance (NMR) spectroscopy and multivariate statistics has been tested. Using ecotypes of Arabidopsis thaliana as experimental material, a method has been developed for the rapid analysis of unfractionated polar plant extracts, enabling the creation of reproducible metabolite fingerprints. These fingerprints could be readily stored and compared by a variety of chemometric methods. Comparison by principal component analysis using SIMCA-P allowed the generation of residual NMR spectra of the compounds that contributed significantly to the differences between samples. From these plots, conclusions were drawn with respect to the identity and relative levels of metabolites differing between samples. [Copyright &y& Elsevier]

[Display omitted] • Three cultivars each of tritordeum, bread wheat and durum wheat were compared. • Samples were grown in replicate randomised field trials on two sites for two years. • Protein, minerals, phenolics, metabolites and fibre were determined on wholemeals. • Tritordeum had higher contents of protein, minerals, phenolics and betaine. • Durum wheat had lower contents of arabinoxylan and β-glucan fibre. Three genotypes each of bread wheat, durum wheat and tritordeum were grown in randomized replicated field trials in Andalusia (Spain) for two years and wholemeal flours analysed for a range of components to identify differences in composition. The contents of all components that were determined varied widely between grain samples of the individual species and in most cases also overlapped between the three species. Nevertheless, statistically significant differences between the compositions of the three species were observed. Notably, tritordeum had significantly higher contents of protein, some minerals (magnesium and iron), total phenolics and methyl donors. Tritordeum also had higher levels of total amino acids (but not asparagine) and total sugars, including raffinose. By contrast, bread wheat and tritordeum had similar contents of the two major dietary fibre components in white flour, arabinoxylan and β-glucan, with significantly lower contents in durum wheat. [ABSTRACT FROM AUTHOR]

Abstract: We have carried out a detailed study of grain development of wheat cv. Hereward in order to provide a reference source for researchers and to obtain new information on aspects of development that relate to end use quality. This has included published reports of transcriptome analysis using Affymetrix wheat GeneChip oligonucleotide arrays, the expression profiles of transcription factors, the deposition of starch and storage proteins and the deposition and metabolism of endosperm cell walls. The present review brings together data from these studies with new analyses of grain structure and metabolic profiling to provide the most detailed description that is currently available of wheat grain development using a single series of samples. [Copyright &y& Elsevier]