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Metabolite Identification Project summaries

Metabolite Identification Project summaries

Systematic metabolite identification strategies using LC-SPE-NMR-MSⁿThis project aims to establish novel methods and protocols for the unambiguous identification of unknown metabolites by NMR and high resolution LC-MSⁿ., starting with plant-derived semi-polar and apolar compounds. These classes of compounds comprise economically and nutritionally important metabolites including health-related phytonutrients present in fruits, vegetables and products derived thereof. For differently accumulating semi-polar and apolar LC-MS or NMR features, identified in plants and human body fluids within associated projects, identification techniques will be developed based on high resolution LC-MSⁿtrees and LCxLC-SPE-NMR-MS. As for many metabolites only limited amounts will be available, we will focus on lowering the sensitivity limits for NMR to concentrations of 100 or, preferably, 10 nanograms of purified compound, for both plant and human body fluid samples. The LC-MSⁿ and NMR data generated will be used for algorithm development and tools for automated metabolite detection.

Identification of metabolites using high resolution MSⁿ: novel tools for data acquisition and spectral tree interpretation
Identification of (differentially expressed) metabolites is a major bottleneck in the outcome of metabolomics studies. In this project a novel identification strategy is followed based on the acquisition of high resolution mass spectral trees. Novel algorithms, novel data acquisition procedures and a mass spectral tree database will allow the unequivocal identification of metabolites in complex biological samples as human plasma.

Development of algorithms or rule-based tools for identification of human metabolites from high resolution mass spectral trees
Main bottleneck in metabolomics-based biomarker research is the identification of the human metabolome present in various body fluids and various tissues. The strategy followed in this proposal is based on the creation of a database containing high resolution mass spectral information of all metabolites detected as mass spectral trees and to develop and apply rule-based and algorithm-based strategies to identify the metabolites. Complete and partial spectral trees will be compared for structure and substructure analysis using different search algorithms.

NMR/MS based prediction
Identification of molecules often relies on a combination of MS and NMR spectral information. MS can provide detailed information on the nature of the molecule but may fail in unique identification. In these cases ¹H NMR can be of great virtue, especially when both the ¹H chemical shifts and the ¹H NMR fingerprint (the splitting patterns obtained from extended spin systems) are analysed in detail. In this project we will develop ¹H NMR adaptive databases for specific natural classes of molecules, phenolics (and derivatives), carotenoids (and derivatives) and amino acid derived metabolites. The database will be complemented with peak analysis and fitting software, which will help researchers to identify molecules based on ¹H NMR data (chemical shifts and splitting patterns). High accurate MS data will help in pre-selecting possible structures to be matched onto the measured ¹H NMR datasets.

Top down identification of unknown metabolites: structure generation and candidate rejection
Metabolite identification is one the most important issues in metabolomics research for which no general methodology exists at the moment. Successful identification of metabolites will have a major impact on biomarker and omics-research. In this project a new strategy is proposed for metabolite identification by developing and applying rule- and algorithm-based methodologies together with biology- and chemistry-based constraints during structure generation and candidate rejection.This project integrates the various tools developed in the other core projects within the theme Metabolite Identification to obtain methods for generation of candidate metabolite structures for metabolites detected by MS-based methods, using information about substructures, metabolite class and biochemical and physicochemical parameters. A top down approach will be used starting from the possible elemental compositions obtained from high resolution MS data. Further confirmation/elimination of candidates is carried out using metabolite databases, substructure information from MSⁿ data, NMR information, physical and (bio)chemical constraints.


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Metabolite Identification Project summaries Metabolite Identification Project summaries Systematic metabolite identification strategies using LC-SPE-NMR-MSⁿThis project aims to establish novel methods and protocols for the unambiguous identification of unknown metabolites by NMR and high resolution LC-MSⁿ., starting with plant-derived semi-polar and apolar compounds. These classes of compounds comprise economically and nutritionally important metabolites including health-related phytonutrients present in fruits, vegetables and products derived thereof. For differently accumulating semi-polar and apolar LC-MS or NMR features, identified in plants and human body fluids within associated projects, identification techniques will be developed based on high resolution LC-MSⁿtrees and LCxLC-SPE-NMR-MS. As for many metabolites only limited amounts will be available, we will focus on lowering the sensitivity limits for NMR to concentrations of 100 or, preferably, 10 nanograms of purified compound, for both plant and human body fluid samples. The LC-MSⁿ and NMR data generated will be used for algorithm development and tools for automated metabolite detection. Identification of metabolites using high resolution MSⁿ: novel tools for data acquisition and spectral tree interpretation Identification of (differentially expressed) metabolites is a major bottleneck in the outcome of metabolomics studies. In this project a novel identification strategy is followed based on the acquisition of high resolution mass spectral trees. Novel algorithms, novel data acquisition procedures and a mass spectral tree database will allow the unequivocal identification of metabolites in complex biological samples as human plasma. Development of algorithms or rule-based tools for identification of human metabolites from high resolution mass spectral trees Main bottleneck in metabolomics-based biomarker research is the identification of the human metabolome present in various body fluids and various tissues. The strategy followed in this proposal is based on the creation of a database containing high resolution mass spectral information of all metabolites detected as mass spectral trees and to develop and apply rule-based and algorithm-based strategies to identify the metabolites. Complete and partial spectral trees will be compared for structure and substructure analysis using different search algorithms. NMR/MS based prediction Identification of molecules often relies on a combination of MS and NMR spectral information. MS can provide detailed information on the nature of the molecule but may fail in unique identification. In these cases ¹H NMR can be of great virtue, especially when both the ¹H chemical shifts and the ¹H NMR fingerprint (the splitting patterns obtained from extended spin systems) are analysed in detail. In this project we will develop ¹H NMR adaptive databases for specific natural classes of molecules, phenolics (and derivatives), carotenoids (and derivatives) and amino acid derived metabolites. The database will be complemented with peak analysis and fitting software, which will help researchers to identify molecules based on ¹H NMR data (chemical shifts and splitting patterns). High accurate MS data will help in pre-selecting possible structures to be matched onto the measured ¹H NMR datasets. Top down identification of unknown metabolites: structure generation and candidate rejection Metabolite identification is one the most important issues in metabolomics research for which no general methodology exists at the moment. Successful identification of metabolites will have a major impact on biomarker and omics-research. In this project a new strategy is proposed for metabolite identification by developing and applying rule- and algorithm-based methodologies together with biology- and chemistry-based constraints during structure generation and candidate rejection.This project integrates the various tools developed in the other core projects within the theme Metabolite Identification to obtain methods for generation of candidate metabolite structures for metabolites detected by MS-based methods, using information about substructures, metabolite class and biochemical and physicochemical parameters. A top down approach will be used starting from the possible elemental compositions obtained from high resolution MS data. Further confirmation/elimination of candidates is carried out using metabolite databases, substructure information from MSⁿ data, NMR information, physical and (bio)chemical constraints.		Life Sciences Momentum NGI The Hague, 23 November 2010 Johan Westerhuis receives EAS Award Amsterdam, March 2010 30th ISPPP - Call for Papers Bologna, Italy, 6-8 September 2010 NGI sponsors Postdoc Retreat The Hague, 21-23 April 2010 Pre-Seed Grant NGI The Hague, January 2010 Metabolomics in Nature's vision 2020 Nature Magazine 7 January 2010 Metabolomics receives first ever Impact Factor Rating UK 17 August 2009 NMC makes the difference NMC, Leiden Puzzling with metabolites NMC, NBIC, May 2009 First NMC paper published Amsterdam, Utrecht, January 2009 Robert Hall elected as secretary Metabolomics Society 11 December 2008

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