Recombinant expression and functional characterisation of regiospecific flavonoid glucosyltransferases from Hieracium pilosella L.

Five glucosyltransferases were cloned by RT-PCR amplification using total RNA from Hieracium pilosella L. (Asteraceae) inflorescences as template. Expression was accomplished in Escherichia coli, and three of the HIS-tagged enzymes, UGT90A7, UGT95A1, and UGT72B11 were partially purified and functionally characterised as UDP-glucose:flavonoid O-glucosyltransferases. Both UGT90A7 and UGT95A1 preferred luteolin as substrate, but possessed different regiospecificity profiles. UGT95A1 established a new subgroup within the UGT family showing high regiospecificity towards the C-3' hydroxyl group of luteolin, while UGT90A7 primarily yielded the 4'-O-glucoside, but concomitantly catalysed also the formation of the 7-O-glucoside, which could account for this flavones glucoside in H. pilosella flower heads. Semi quantitative expression profiles revealed that UGT95A1 was expressed at all stages of inflorescence development as well as in leaf and stem tissue, whereas UGT90A7 transcript abundance was nearly limited to flower tissue and started to develop with the pigmentation of closed buds. Other than these enzymes, UGT72B11 showed rather broad substrate acceptance, with highest activity towards flavones and flavonols which have not been reported from H. pilosella. As umbelliferone was also readily accepted, this enzyme could be involved in the glucosylation of coumarins and other metabolite

Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance

Nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LCMS) are frequently used as technological platforms for metabolomics applications. In this study, the metabolic profiles of ripe fruits from 50 different tomato cultivars, including beef, cherry and round types, were recorded by both 1H NMR and accurate mass LC-quadrupole time-of-flight (QTOF) MS. Different analytical selectivities were found for these both profiling techniques. In fact, NMR and LCMS provided complementary data, as the metabolites detected belong to essentially different metabolic pathways. Yet, upon unsupervised multivariate analysis, both NMR and LCMS datasets revealed a clear segregation of, on the one hand, the cherry tomatoes and, on the other hand, the beef and round tomatoes. Intra-method (NMR¿NMR, LCMS¿LCMS) and inter-method (NMR¿LCMS) correlation analyses were performed enabling the annotation of metabolites from highly correlating metabolite signals. Signals belonging to the same metabolite or to chemically related metabolites are among the highest correlations found. Inter-method correlation analysis produced highly informative and complementary information for the identification of metabolites, even in de case of low abundant NMR signals. The applied approach appears to be a promising strategy in extending the analytical capacities of these metabolomics techniques with regard to the discovery and identification of biomarkers and yet unknown metabolites

Metabolomics technologies applied to the identification of compounds in plants : a liquid chromatography-mass spectrometry - nuclear magnetic resonance perspective over the tomato fruit

A new era of plant biochemistry at the systems level is emerging in which the detailed description of biochemical phenomena, at the cellular level, is important for a better understanding of physiological, developmental, and biomolecular processes in plants. This emerging field is oriented towards the characterisation of small molecules (metabolites) that act as substrates, products, ligands or signalling entities in cells. This thesis concerns the development and establishment of such metabolomics strategies for screening and identifying metabolites in biological systems. Most technological strategies were applied to the assignment of metabolites from tomato (Solanum lycopersicum) fruit. Tomato was chosen for being a widely consumed crop with nutritional attributes, representing a model for the Solanaceae family. In order to achieve both high coverage of detected metabolites and valuable information for identification purposes, liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic resonances (NMR) technologies were used. In addition, metabolite databases, based on experimental data (mass-based, in the case of LC-MS and chemical shift-based, in the case of NMR) were initiated, in order to systemize the extensive metabolite information. The chapters in this thesis describe method developments and their applications in plant metabolomics that are also feasible to be implemented on other biological systems. A review on the technologies used for metabolomics with a perspective on compound identification is presented in Chapter 1. In Chapter 2, a robust large scale LC-MS method for the analysis of metabolites in plants is described in detail. It presents a step-by-step protocol with thorough information about the reagents used, sample preparation, instrument setup, methods of analysis and data processing strategies. The described analytical method combines LC with photo diode array (PDA) and MS detection, and allows the analysis of mostly semi-polar secondary metabolites present in plants, such as phenolic acids, flavonoids, glucosinolates, saponins, alkaloids and derivatives thereof. Chapter 3 presents an application of the LC-PDA-MS method for the profiling of metabolites present in tomato fruit. The metabolites putatively identified in this fruit were included in a tomato dedicated-database (the MoTo DB) that is available for public search on the web (see: http://appliedbioinformatics.wur.nl). A comparison between two tomato fruit tissues, peel and flesh, for their metabolite content was made using this MoTo DB. Using the same LC-PDA-MS setup, several different tomato fruit tissues were compared in more detail, along the fruit ripening timeline, in Chapter 4. The presence of tissue-specific metabolites, at determined ripening stages, suggests developmental control of metabolite biosynthesis. Such tissue-specific metabolomics approach may give rise to a biological view over metabolite compartmentalisation. Chapters 5 and 6 describe the implementation of a NMR database for secondary metabolites, mostly including flavonoids, the Flavonoid Database (see: Flavonoid Database under http://www.wnmrc.nl). The acquisition of a large data set of related standard compounds allowed the analysis of shifts in NMR characteristics by the presence of certain functional groups or substituents in the flavonoid backbone. In addition, a 1H NMR-based prediction model was iteratively trained from the acquired experimental data and can be used for the prediction of unknown related molecules. This approach greatly increases the efficiency in the identification of (flavonoid) metabolites. Chapter 7 describes correlations of metabolomics data derived from LC-MS and NMR analyses of a large number of different tomato cultivars. The identification of metabolites is obtained among other available sources, the MoTo DB and the Flavonoid Database. This approach illustrates the complementariness and coincidence of NMR and MS as analytical techniques, applied to the detection of metabolites in tomato fruit. The summarizing discussion and conclusions, sets the work presented in this thesis into a biochemical perspective, and prospects suggestions for the future

Chemical Identification Strategies Using Liquid Chromatography-Photodiode Array-Solid-Phase Extraction-Nuclear Magnetic Resonance/Mass Spectroscopy.

The identification of metabolites in biochemical studies is a major bottleneck in the proliferating field of metabolomics. In particular in plant metabolomics, given the diversity and abundance of endogenous secondary metabolites in plants, the identification of these is not only challenging but also essential to understanding their biological role in the plant, and their value to quality and nutritional attributes as food crops. With the new generation of analytical technologies, in which liquid chromatography (LC)-mass spectrometry (MS) and nuclear magnetic resonance (NMR) play a pioneering role, profiling metabolites in complex extracts is feasible at high throughput. However, the identification of key metabolites remains a limitation given the analytical effort necessary for traditional structural elucidation strategies. The hyphenation of LC-solid phase extraction (SPE)-NMR is a powerful analytical platform for isolating and concentrating metabolites for unequivocal identification by NMR measurements. The combination with LC-MS is a relatively straightforward approach to obtaining all necessary information for structural elucidation. Using this set-up, we could, as an example, readily identify five related glycosylated phenolic acids present in broccoli (Brassica oleracea, group Italica, cv Monaco): 1,2-di-O-E-sinapoyl-ß-gentiobiose, 1-O-E-sinapoyl-2-O-E-feruloyl-ß-gentiobiose, 1,2-di-O-E-feruloyl-ß-gentiobiose, 1,2,2'-tri-O-E-sinapoyl-ß-gentiobiose, and 1,2'-di-O-E-sinapoyl-2-O-E-feruloyl-ß-gentiobiose

Plant Micrometabolomics: The Analysis of Endogenous Metabolites Present in a Plant Cell or Tissue.

Identification and quantification of metabolites occurring within specific cell types or single cells of plants and other organisms is of particular interest for natural product chemistry, chemical ecology, and biochemistry in general. The integration of studies at the gene, transcript, protein and metabolite levels in localized regions will provide useful information for the understanding of biology as a system. In this review, we summarize the latest developments in the analysis of metabolites present in small samples, micrometabolomics, dealing with sample preparation methods, with focus on laser-assisted microdissection, and the analytical technologies used. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) are among the most emergent technologies in metabolomics, enabling the shortest route toward metabolite identification

LC-MS-SPE-NMR for the Isolation and Characterization of neo-Clerodane Diterpenoids from Teucrium luteum subsp. flavovirens

neo-Clerodane diterpenes of plant origin are molecules difficult to monitor due to their nonspecific UV/vis absorption. The present work describes for the first time the application of the LC-MS-SPE-NMR technique for the isolation and characterization of three new neo-clerodane diterpenes, 3ß-hydroxyteucroxylepin and teuluteumin A and teuluteumin B, from Teucrium luteum subsp. flavovirens, harvested from two different location

Metabolomics technologies and metabolite identification

Metabolomics studies rely on the analysis of the multitude of small molecules (metabolites) present in a biological system. Most commonly, metabolomics is heavily supported by mass spectrometry (MS) and nuclear magnetic resonance (NMR) as parallel technologies that provide an overview of the metabolome and high-power compound elucidation. Over and above large-scale analysis, a major effort is needed for unequivocal identification of metabolites. The combination of liquid chromatography (LC)-MS and NMR is a powerful methodology for identifying metabolites. Better chemical characterization of the metabolome will undoubtedly enlarge knowledge of any biological system

Building-Up a comprehensive database of flavonoids based on nuclear magnetic resonance data.

The improvements in separation and analysis of complex mixtures by LC-NMR during the last decade have shifted its emphasis from data acquisition to data analysis. For correct data analysis, not only high quality datasets are necessary, but adequate software and adequate databases for semi (or fully)-automated assignments of complex molecules are needed. Only by using NMR, when necessary in combination with MS, the identification of molecules, as present for example in natural products, can be achieved. Here we report on the ongoing efforts required for the construction of an NMR database of flavonoids, implemented for automated assignments of flavonoids. The procedure is demonstrated for a series of flavonoid

Isolation and identification of glycinol from Glycine max [L.] Merri

As one of the main phytoalexins and phytoestrogens, glyceollin is an important prenylflavonoid in Glycine max [L.] Merri. (soybean). Many kinds of elicitors can be used to induce its accumulation. Its biosynthesis pathway is commonly used to study the characteristics of prenyltransferase, which catalyzes the prenylated reaction happening in a very few plant families in nature. Glycinol, the direct precursor of glyceollin, is necessary to study the prenylated reaction in soybean. In comparing with the other elicitors to elicit the glycinol accumulation in soybean cotyledons, AgNO3 is the most effective elicitor. Exposure of 6-8 days old cotyledons to 0.01 mol/L AgNO3 and incubation for 24 h result in the accumulation of 256 microg (glycinol)/g (fresh weight). The glycinol was extracted by methanol. Then the isolation and purification were conducted by preparative high performance liquid chromatography. Instead of 100% acetonitrile-0.1% formic acid as the elution system, the extract was eluted by 100% methanol-0.1% formic acid. Glycinol eluted earlier than daidzin under this system and decreased the disturbance from the large amount of daidzin. Identification was performed by comparing the mass spectrum (liquid chromatography/quadrupole-time of flight) and ultraviolet spectrum with those of the standard. At last, 100 mg purified glycinol was obtained from 390 g of fresh materia

Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry

Untargeted metabolomics aims to gather information on as many metabolites as possible in biological systems by taking into account all information present in the data sets. Here we describe a detailed protocol for large-scale untargeted metabolomics of plant tissues, based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (LC-QTOF MS) of aqueous methanol extracts. Dedicated software, MetAlign, is used for automated baseline correction and alignment of all extracted mass peaks across all samples, producing detailed information on the relative abundance of thousands of mass signals representing hundreds of metabolites. Subsequent statistics and bioinformatics tools can be used to provide a detailed view on the differences and similarities between (groups of) samples or to link metabolomics data to other systems biology information, genetic markers and/or specific quality parameters. The complete procedure from metabolite extraction to assembly of a data matrix with aligned mass signal intensities takes about 6 days for 50 sample