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.
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| Format: | Doctoral thesis biblioteca |
| Language: | English |
| Subjects: | liquid chromatography-mass spectrometry, metabolites, metabolomics, nuclear magnetic resonance spectroscopy, phytochemicals, solanum lycopersicum, tomatoes, fytochemicaliën, kernmagnetische resonantiespectroscopie, lc-ms, metabolieten, metabolomica, tomaten, |
| Online Access: | https://research.wur.nl/en/publications/metabolomics-technologies-applied-to-the-identification-of-compou |
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| Summary: | 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. |
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