The Lecythidaceae family tree, Couroupita guianensis Aubl, popularly known as Nagpushpa, is a widely cultivated ornamental tree with several uses in traditional medicine. The tree is revered as highly sacred in Indian traditional culture due to its uniquely shaped, fragrant flowers. Considering the significance, we were prompted to carry out the metabolite and transcriptome analysis of Nagapushpa. The flower, petals, stamen, stem and leaf of C. guianensis were metabolically profiled, and it was discovered that the flower tissue contained the highest terpenoid reservoir. A number of terpenoid pathway transcripts were also found in the flower tissue after transcriptome profiling. KEGG pathway mapping was carried out to correlate transcript sequences with the biosynthesis of different types of terpenes. We were able to clone three full-length terpene synthase gene candidates, i.e. monoterpene ocimene synthase, diterpene ent-kaurene synthase and sesquiterpene farnesene synthase. The transcript expression of selected terpene synthase genes was also verified in flower tissue. These cloned sequences were used for in silico structural investigations and protein function prediction at the level of 3D structure. The data presented in this study provide a comprehensive resource for the metabolic and transcriptomic profiles of C. guianensis. The study paves the way towards the elucidation of terpene biosynthetic pathway in C. guianensis and heterologous production of useful terpenoids in the future.

Arctoparmelia collatolica is described as a species new to science based on morphological, chemical and molecular data. The species is similar to the usnic acid-deficient chemotype of A. centrifuga but differs in the grey-brown to brown upper surface in the central part of the thallus and ivory white to pale brown rhizines. The species contains collatolic acid and its derivatives. Seven secondary lichen substances are reported as new for the genus Arctoparmelia. A key to Arctoparmelia taxa is provided.

Saflufenacil (Kixor™) is a new herbicide of the pyrimidinedione chemical class for preplant burndown and selective preemergence dicot weed control in multiple crops, including corn. In this study, the mode of action of saflufenacil was investigated. For initial characterization, a series of biotests was used in a physionomics approach for comprehensive physiological profiling of saflufenacil effects. With the use of treated duckweed plants, metabolite profiling was performed based on quantification of metabolite changes, relative to untreated controls. Physiological and metabolite profiling suggested a mode of action similar to inhibitors of protoporphyrinogen IX oxidase (PPO) in tetrapyrrole biosynthetic pathway. Saflufenacil inhibited PPO enzyme activity in vitro with 50% inhibition of 0.4 nM for the enzymes isolated from black nightshade, velvetleaf, and corn. PPO inhibition by saflufenacil caused accumulations of protoporphyrin IX (Proto) and hydrogen peroxide (H2O2) in leaf tissue of black nightshade and velvetleaf. In corn, only slight increases in Proto and H2O2 were found, which reflects in planta tolerance of this crop. The results show that saflufenacil is a new PPO-inhibiting, peroxidizing herbicide.

New trends in crop breeding include analytical approaches to identify metabolic fingerprints that can be used for associations to the genetic background. The biochemical phenotype, as a result of plant endogenous factors and interaction with the environment, has the potential to increase the accuracy of forecasting regarding agronomical quality factors. In this study a metabolite profile analysis by gas chromatography–mass spectrometry (GC–MS) was conducted on sets of seed material from sugar beet. One set represented high-performing varieties with a close genetic background and with a similar quality in terms of germination capacity. The second set contained seed lots from different genotypes comprising different germination capacities. By multivariate statistical analyses high variance in both sample sets was revealed. These data were further allocated to corresponding metabolite classes. It could be shown that an untargeted GC–MS approach has the power to resolve differences in the molecular phenotypes of related offspring lines. Metabolic profiles were found to correlate more to genotypic differences than to differences in the germination capacity.

The present report discusses targeted and non-targeted approaches to monitor single nutrients and global metabolite profiles in nutritional research. Non-targeted approaches such as metabolomics allow for the global description of metabolites in a biological sample and combine an analytical platform with multivariate data analysis to visualise patterns between sample groups. In nutritional research metabolomics has generated much interest as it has the potential to identify changes to metabolic pathways induced by diet or single nutrients, to explore relationships between diet and disease and to discover biomarkers of diet and disease. Although still in its infancy, a number of studies applying this technology have been performed; for example, the first study in 2003 investigated isoflavone metabolism in females, while the most recent study has demonstrated changes to various metabolic pathways during a glucose tolerance test. As a relatively new technology metabolomics is faced with a number of limitations and challenges including the standardisation of study design and methodology and the need for careful consideration of data analysis, interpretation and identification. Targeted approaches are used to monitor single or multiple nutrient and/or metabolite status to obtain information on concentration, absorption, distribution, metabolism and elimination. Such applications are currently widespread in nutritional research and one example, using stable isotopes to monitor nutrient status, is discussed in more detail. These applications represent innovative approaches in nutritional research to investigate the role of both single nutrients and diet in health and disease.

Assessing the performance of the plant metabolic network, with its varied biosynthetic capacity and its characteristic subcellular compartmentation, remains a considerable challenge. The complexity of the network is such that it is not yet possible to build large-scale predictive models of the fluxes it supports, whether on the basis of genomic and gene expression analysis or on the basis of more traditional measurements of metabolites and their interconversions. This limits the agronomic and biotechnological exploitation of plant metabolism, and it undermines the important objective of establishing a rational metabolic engineering strategy. Metabolic analysis is central to removing this obstacle and currently there is particular interest in harnessing high-throughput and/or large-scale analyses to the task of defining metabolic phenotypes. Nuclear magnetic resonance (NMR) spectroscopy contributes to this objective by providing a versatile suite of analytical techniques for the detection of metabolites and the fluxes between them.

The principles that underpin the analysis of plant metabolism by NMR are described, including a discussion of the measurement options for the detection of metabolites in vivo and in vitro, and a description of the stable isotope labelling experiments that provide the basis for metabolic flux analysis. Despite a relatively low sensitivity, NMR is suitable for high-throughput system-wide analyses of the metabolome, providing methods for both metabolite fingerprinting and metabolite profiling, and in these areas NMR can contribute to the definition of plant metabolic phenotypes that are based on metabolic composition. NMR can also be used to investigate the operation of plant metabolic networks. Labelling experiments provide information on the operation of specific pathways within the network, and the quantitative analysis of steady-state labelling experiments leads to the definition of large-scale flux maps for heterotrophic carbon metabolism. These maps define multiple unidirectional fluxes between branch-points in the metabolic network, highlighting the existence of substrate cycles and discriminating in favourable cases between fluxes in the cytosol and plastid. Flux maps can be used to define a functionally relevant metabolic phenotype and the extensive application of such maps in microbial systems suggests that they could have important applications in characterising the genotypes produced by plant genetic engineering.

The post-genomic era has been driven by the development of technologies that allow the function of cells and whole organisms to be explored at the molecular level. Metabolomics is concerned with the measurement of global sets of low-molecular-weight metabolites. Metabolite profiles of body fluids or tissues can be regarded as important indicators of physiological or pathological states. Such profiles may provide a more comprehensive view of cellular control mechanisms in man and animals, and raise the possibility of identifying surrogate markers of disease. Metabolomic approaches use analytical techniques such as NMR spectroscopy and MS to measure populations of low-molecular-weight metabolites in biological samples. Advanced statistical and bioinformatic tools are then employed to maximise the recovery of information and interpret the large datasets that are generated. Metabolomics has already been used to study toxicological mechanisms and disease processes and offers enormous potential as a means of investigating the complex relationship between nutrition and metabolism. Examples include the metabolism of dietary substrates, drug-induced disturbances of lipid metabolites in type 2 diabetes mellitus and the therapeutic effects of vitamin supplementation in the treatment of chronic metabolic disorders.