Showing papers on "Metabolome published in 2005"
TL;DR: Progress made on several aspects of elicitor signal transduction leading to production of plant secondary metabolites are summarized, including the integration of multiple signaling pathways into or by transcription factors, as well as the linkage of the above signal components in eliciting network through protein phosphorylation and dephosphorylation.
1,649 citations
TL;DR: GD, The Golm Metabolome Database is presented, an open access metabolome database, which provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds.
Abstract: Summary: Metabolomics, in particular gas chromatography--mass spectrometry (GC--MS) based metabolite profiling of biological extracts, is rapidly becoming one of the cornerstones of functional genomics and systems biology. Metabolite profiling has profound applications in discovering the mode of action of drugs or herbicides, and in unravelling the effect of altered gene expression on metabolism and organism performance in biotechnological applications. As such the technology needs to be available to many laboratories. For this, an open exchange of information is required, like that already achieved for transcript and protein data. One of the key-steps in metabolite profiling is the unambiguous identification of metabolites in highly complex metabolite preparations from biological samples. Collections of mass spectra, which comprise frequently observed metabolites of either known or unknown exact chemical structure, represent the most effective means to pool the identification efforts currently performed in many laboratories around the world. Here we present GMD, The Golm Metabolome Database, an open access metabolome database, which should enable these processes. GMD provides public access to custom mass spectral libraries, metabolite profiling experiments as well as additional information and tools, e.g. with regard to methods, spectral information or compounds. The main goal will be the representation of an exchange platform for experimental research activities and bioinformatics to develop and improve metabolomics by multidisciplinary cooperation.
Availability: http://csbdb.mpimp-golm.mpg.de/gmd.html
Contact: Steinhauser@mpimp-golm.mpg.de
Supplementary information: http://csbdb.mpimp-golm.mpg.de/
1,198 citations
TL;DR: A review of mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques for the study of variations within the metabolome in many animal, plant and microbial systems discusses the advantages and disadvantages of each technique.
Abstract: The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.
858 citations
TL;DR: In this paper, a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over-expressing the PAP1 gene encoding an MYB transcription factor was performed for the identification of novel gene functions involved in flavonoid biosynthesis.
Abstract: The integration of metabolomics and transcriptomics can provide precise information on gene-to-metabolite networks for identifying the function of unknown genes unless there has been a post-transcriptional modification. Here, we report a comprehensive analysis of the metabolome and transcriptome of Arabidopsis thaliana over-expressing the PAP1 gene encoding an MYB transcription factor, for the identification of novel gene functions involved in flavonoid biosynthesis. For metabolome analysis, we performed flavonoid-targeted analysis by high-performance liquid chromatography-mass spectrometry and non-targeted analysis by Fourier-transform ion-cyclotron mass spectrometry with an ultrahigh-resolution capacity. This combined analysis revealed the specific accumulation of cyanidin and quercetin derivatives, and identified eight novel anthocyanins from an array of putative 1800 metabolites in PAP1 over-expressing plants. The transcriptome analysis of 22,810 genes on a DNA microarray revealed the induction of 38 genes by ectopic PAP1 over-expression. In addition to well-known genes involved in anthocyanin production, several genes with unidentified functions or annotated with putative functions, encoding putative glycosyltransferase, acyltransferase, glutathione S-transferase, sugar transporters and transcription factors, were induced by PAP1. Two putative glycosyltransferase genes (At5g17050 and At4g14090) induced by PAP1 expression were confirmed to encode flavonoid 3-O-glucosyltransferase and anthocyanin 5-O-glucosyltransferase, respectively, from the enzymatic activity of their recombinant proteins in vitro and results of the analysis of anthocyanins in the respective T-DNA-inserted mutants. The functional genomics approach through the integration of metabolomics and transcriptomics presented here provides an innovative means of identifying novel gene functions involved in plant metabolism.
827 citations
TL;DR: A platform for mass spectral and retention time index libraries that will enable metabolite profiling and should ameliorate many of the problems that each laboratory will face both for the initial establishment of metabolome analysis and for its maintenance at a constant sample throughput.
622 citations
TL;DR: This review is mainly focused on the status of MS in the metabolome field, trying to direct the reader to the main approaches for analysis of metabolites, reviewing basic methodologies in sample preparation, and the most recent MS techniques introduced.
Abstract: In the post-genomic era, increasing efforts have been made to describe the relationship between the genome and the phenotype in cells and organisms. It has become clear that even a complete understanding of the state of the genes, messages, and proteins in a living system does not reveal its phenotype. Therefore, researchers have started to study the metabolome (or the metabolic complement of functional genomics). Within this context, mass spectrometry (MS) has increasingly occupied a central position in the methodologies developed for determination of the metabolic state. This review is mainly focused on the status of MS in the metabolome field, trying to direct the reader to the main approaches for analysis of metabolites, reviewing basic methodologies in sample preparation, and the most recent MS techniques introduced. Apart from the description of the different methods, this review will try to state a general comparison between the several different techniques that involve MS and metabolite analysis, and will highlight their limitations and preferred applicability.
571 citations
TL;DR: An extraction and derivatization protocol, developed using experimental design theory, for analyzing the human blood plasma metabolome by GC/MS, suggests that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.
Abstract: Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, w ...
478 citations
TL;DR: A novel method was developed for the quantitative analysis of the microbial metabolome using a mixture of fully uniformly (U) (13)C-labeled metabolites as internal standard (IS) in the metabolite extraction procedure the subsequent liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) analysis.
411 citations
TL;DR: The leakage of intracellular metabolites observed during quenching yeast cells with cold methanol solution, the efficacy of six different methods for the extraction of intrACEllular metabolites, and the losses noticed during sample concentration by lyophilization and solvent evaporation are reported.
Abstract: Sample preparation is considered one of the limiting steps in microbial metabolome analysis. Eukaryotes and prokaryotes behave very differently during the several steps of classical sample preparation methods for analysis of metabolites. Even within the eukaryote kingdom there is a vast diversity of cell structures that make it imprudent to blindly adopt protocols that were designed for a specific group of microorganisms. We have therefore reviewed and evaluated the whole sample preparation procedures for analysis of yeast metabolites. Our focus has been on the current needs in metabolome analysis, which is the analysis of a large number of metabolites with very diverse chemical and physical properties. This work reports the leakage of intracellular metabolites observed during quenching yeast cells with cold methanol solution, the efficacy of six different methods for the extraction of intracellular metabolites, and the losses noticed during sample concentration by lyophilization and solvent evaporation. A more reliable procedure is suggested for quenching yeast cells with cold methanol solution, followed by extraction of intracellular metabolites by pure methanol. The method can be combined with reduced pressure solvent evaporation and therefore represents an attractive sample preparation procedure for high-throughput metabolome analysis of yeasts.
390 citations
TL;DR: Mutual influences between sulfur assimilation, nitrogen imbalance, lipid breakdown, purine metabolism, and enhanced photorespiration associated with sulfur-deficiency stress are revealed in this study and may be assembled into a global scheme of metabolic regulation induced by sulfur nutritional stress, which optimizes resources for seed production.
Abstract: Sulfur is an essential macroelement in plant and animal nutrition. Plants assimilate inorganic sulfate into two sulfur-containing amino acids, cysteine and methionine. Low supply of sulfate leads to decreased sulfur pools within plant tissues. As sulfur-related metabolites represent an integral part of plant metabolism with multiple interactions, sulfur deficiency stress induces a number of adaptive responses, which must be coordinated. To reveal the coordinating network of adaptations to sulfur deficiency, metabolite profiling of Arabidopsis has been undertaken. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry techniques revealed the response patterns of 6,023 peaks of nonredundant ion traces and relative concentration levels of 134 nonredundant compounds of known chemical structure. Here, we provide a catalogue of the detected metabolic changes and reconstruct the coordinating network of their mutual influences. The observed decrease in biomass, as well as in levels of proteins, chlorophylls, and total RNA, gives evidence for a general reduction of metabolic activity under conditions of depleted sulfur supply. This is achieved by a systemic adjustment of metabolism involving the major metabolic pathways. Sulfur/carbon/nitrogen are partitioned by accumulation of metabolites along the pathway O-acetylserine to serine to glycine, and are further channeled together with the nitrogen-rich compound glutamine into allantoin. Mutual influences between sulfur assimilation, nitrogen imbalance, lipid breakdown, purine metabolism, and enhanced photorespiration associated with sulfur-deficiency stress are revealed in this study. These responses may be assembled into a global scheme of metabolic regulation induced by sulfur nutritional stress, which optimizes resources for seed production.
373 citations
TL;DR: A comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome " fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected.
Abstract: There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome “fingerprinting” to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.
TL;DR: The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.
TL;DR: This work has shown that metabolite data can be complemented by protein, transcript and external (environmental) data, thereby leading to the identification of multiple physiological biomarkers embedded in correlative molecular networks that are not approachable with targeted studies.
TL;DR: The research community must develop databases of metabolite concentrations in cells that are grown in several well-defined conditions if metabolomic data are to be integrated meaningfully with data from the other levels of functional-genomic analysis and to make a significant contribution to systems biology.
TL;DR: Results demonstrate that insertion of high-flux pathways directing synthesis and intracellular storage of high amounts of a cyanogenic glucoside or a glucosinolate is achievable in transgenic A. thaliana plants with marginal inadvertent effects on the transcriptome and metabolome.
Abstract: Focused and nontargeted approaches were used to assess the impact associated with introduction of new high-flux pathways in Arabidopsis thaliana by genetic engineering. Transgenic A. thaliana plants expressing the entire biosynthetic pathway for the tyrosine-derived cyanogenic glucoside dhurrin as accomplished by insertion of CYP79A1, CYP71E1, and UGT85B1 from Sorghum bicolor were shown to accumulate 4% dry-weight dhurrin with marginal inadvertent effects on plant morphology, free amino acid pools, transcriptome, and metabolome. In a similar manner, plants expressing only CYP79A1 accumulated 3% dry weight of the tyrosine-derived glucosinolate, p-hydroxybenzylglucosinolate with no morphological pleitropic effects. In contrast, insertion of CYP79A1 plus CYP71E1 resulted in stunted plants, transcriptome alterations, accumulation of numerous glucosides derived from detoxification of intermediates in the dhurrin pathway, and in loss of the brassicaceae-specific UV protectants sinapoyl glucose and sinapoyl malate and kaempferol glucosides. The accumulation of glucosides in the plants expressing CYP79A1 and CYP71E1 was not accompanied by induction of glycosyltransferases, demonstrating that plants are constantly prepared to detoxify xenobiotics. The pleiotrophic effects observed in plants expressing sorghum CYP79A1 and CYP71E1 were complemented by retransformation with S. bicolor UGT85B. These results demonstrate that insertion of high-flux pathways directing synthesis and intracellular storage of high amounts of a cyanogenic glucoside or a glucosinolate is achievable in transgenic A. thaliana plants with marginal inadvertent effects on the transcriptome and metabolome.
TL;DR: The similarity in metabolic alterations in fruits of hp-1 and hp-2 mutant plants helps to understand how hp mutations affect cellular processes and may be more closely implicated as resources recruited by plants to respond to and manage light stress.
Abstract: Overall metabolic modifications between fruit of light-hyperresponsive high-pigment (hp) tomato (Lycopersicon esculentum) mutant plants and isogenic nonmutant (wt) control plants were compared. Targeted metabolite analyses, as well as large-scale nontargeted mass spectrometry (MS)-based metabolite profiling, were used to phenotype the differences in fruit metabolite composition. Targeted high-performance liquid chromatography with photodiode array detection (HPLC-PDA) metabolite analyses showed higher levels of isoprenoids and phenolic compounds in hp-2dg fruit. Nontargeted GC-MS profiling of red fruits produced 25 volatile compounds that showed a 1.5-fold difference between the genotypes. Analyses of red fruits using HPLC coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in both ESI-positive and ESI-negative mode generated, respectively, 6168 and 5401 mass signals, of which 142 and 303 showed a twofold difference between the genotypes. hp-2dg fruits are characterized by overproduction of many metabolites, several of which are known for their antioxidant or photoprotective activities. These metabolites may now be more closely implicated as resources recruited by plants to respond to and manage light stress. The similarity in metabolic alterations in fruits of hp-1 and hp-2 mutant plants helps us to understand how hp mutations affect cellular processes.
TL;DR: The use of metabolite profiling is discussed for the identification and classification of yeasts and filamentous fungi, functional analysis or discovery by integration of high performance analytical methodology, efficient data handling techniques and core concepts of species, and intelligent screening.
Abstract: Filamentous fungi and yeast from the genera Saccharomyces, Penicillium, Aspergillus, and Fusarium are well known for their impact on our life as pathogens, involved in food spoilage by degradation or toxin contamination, and also for their wide use in biotechnology for the production of beverages, chemicals, pharmaceuticals, and enzymes. The genomes of these eukaryotic micro-organisms range from about 6000 genes in yeasts (S. cerevisiae) to more than 10,000 genes in filamentous fungi (Aspergillus sp.). Yeast and filamentous fungi are expected to share much of their primary metabolism; therefore much understanding of the central metabolism and regulation in less-studied filamentous fungi can be learned from comparative metabolite profiling and metabolomics of yeast and filamentous fungi. Filamentous fungi also have a very active and diverse secondary metabolism in which many of the additional genes present in fungi, compared with yeast, are likely to be involved. Although the 'blueprint' of a given organism is represented by the genome, its behaviour is expressed as its phenotype, i.e. growth characteristics, cell differentiation, response to the environment, the production of secondary metabolites and enzymes. Therefore the profile of (secondary) metabolites--fungal chemodiversity--is important for functional genomics and in the search for new compounds that may serve as biotechnology products. Fungal chemodiversity is, however, equally efficient for identification and classification of fungi, and hence a powerful tool in fungal taxonomy. In this paper, the use of metabolite profiling is discussed for the identification and classification of yeasts and filamentous fungi, functional analysis or discovery by integration of high performance analytical methodology, efficient data handling techniques and core concepts of species, and intelligent screening. One very efficient approach is direct infusion Mass Spectrometry (diMS) integrated with automated data handling, but a full metabolic picture requires the combination of several different analytical techniques.
TL;DR: The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays.
Abstract: The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription–metabolism studies of yeasts.
TL;DR: Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.
TL;DR: Multivariate data analyses indicate that, in Arabidopsis, a global reprogramming of metabolism occurs as a result of cold acclimation, and it appears that some metabolic networks are modulated by the environment, others require development under low-temperature conditions for adjustment.
Abstract: Many plants, including Arabidopsis, increase their freezing tolerance in response to low, non-freezing temperatures. This process is known as cold acclimation and involves many complex biochemical changes at the level of the metabolome. Our goal was to examine the effects of cold acclimation on the metabolome using a non-targeted metabolic fingerprinting approach. Multivariate data analyses indicate that, in Arabidopsis, a global reprogramming of metabolism occurs as a result of cold acclimation. By measuring an entire spectrum of putative metabolites based on mass-to-charge (m/z) ratios, vs. an individual or group of metabolite(s), a comprehensive, unbiased assessment of metabolic processes relative to cold acclimation was determined. Whereas leaves shifted to low temperature present metabolic profiles that are constantly changing, leaves developed at low temperature demonstrate a stable complement of components. Although it appears that some metabolic networks are modulated by the environment, others require development under low-temperature conditions for adjustment. Understanding how metabolism as a whole is regulated allows the integration of cellular, physiological and ecological attributes in a biological system, a necessity if complex traits, such as freezing tolerance, are to be modified by breeding or genetic manipulation.
TL;DR: The results clearly indicate that the metabolic state of a cell cannot be safely predicted based solely on proteomic and/or gene expression data and combined metabolome and proteome studies are necessary to draw a comprehensive and integrated view of cell metabolism.
TL;DR: NMR spectroscopy contributes to this objective by providing a versatile suite of analytical techniques for the detection of metabolites and the fluxes between them.
Abstract: 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.
TL;DR: The results support the continued development of NMR-based metabolomics as a rapid and reproducible tool for biomarker discovery and environmental risk assessment.
Abstract: Fish embryo toxicity tests for chemical risk assessment have traditionally been based upon non-specific endpoints including morphological abnormalities, hatching success, and mortality. Here we extend the application of 1H NMR-based metabolomics in environmental toxicology by adding a suite of metabolic endpoints to the Japanese medaka (Oryzias latipes) embryo assay, with the goal to provide more sensitive, specific and unbiased biomarkers of toxicity. Medaka were exposed throughout embryogenesis to five concentrations of trichloroethylene (TCE; 0, 8.76, 21.9, 43.8, 87.6, 175 mg/L) and the relative sensitivities of the traditional and metabolomic endpoints compared. While the no-observable-adverse-effect-level for hatching success, the most sensitive traditional indicator, was 164 mg/L TCE, metabolic perturbations were detected at all exposure concentrations. Principal components analysis (PCA) highlighted a dose-response relationship between the NMR spectra of medaka extracts. In addition, 12 metabolites that exhibited highly significant dose-response relationships were identified, which indicated an energetic cost to TCE exposure. Next, embryos were exposed to 0, 0.88, 8.76 mg/L TCE and sampled on each of the 8 days of development. Projections of 66 two-dimensional J-resolved NMR spectra were obtained, and PCA revealed developmental metabolic trajectories that characterized the basal and TCE-perturbed changes in the entire NMR-visible metabolome throughout embryogenesis. Although no significant increases in mortality, gross deformity or developmental retardation were observed relative to the control group, TCE-induced metabolic perturbations were observed on day 8. In conclusion, these results support the continued development of NMR-based metabolomics as a rapid and reproducible tool for biomarker discovery and environmental risk assessment.
TL;DR: Ion analysis using Fourier transformed ion cyclotron resonance mass spectrometry (FT-ICR-MS) may provide a high-throughput approach to measure genotype dependency of the inferred metabolome if reproducible techniques can be established.
Abstract: Withabout200000phytochemicalsinexistence,identifyingthoseofbiomedicalsignificanceisamammothtask.Inthepostgenomic era, relating metabolitefingerprints, abundances, and profiles to genotype is also a large task. Ion analysis using Fourier transformed ion cyclotron resonance mass spectrometry (FT-ICR-MS) may provide a high-throughput approach to measure genotype dependency of the inferred metabolome if reproducible techniques can be established. Ion profile inferred metabolite fingerprints are coproducts. We used FT-ICR-MS-derived ion analysis to examine gdhA (glutamate dehydrogenase (GDH; EC 1.4.1.1)) transgenic Nicotiana tabacum (tobacco) carrying out altered glutamate, amino acid, and carbon metabolisms, that fundamentally alter plant productivity. Cause and effect between gdhA expression, glutamate metabolism, and plant phenotypes was analyzed by 13 NH + labeling of amino acid fractions, and by FT-ICR-MS analysis of metabolites. The gdhA transgenic plants increased 13 N labeling of glutamate and glutamine significantly. FT-ICR-MS detected 2012 ions reproducible in 2 to 4 ionization protocols. There were 283 ions in roots and 98 ions in leaves that appeared to significantly change abundance due to the measured GDH activity. About 58% percent of ions could not be used to infer a corresponding metabolite. From the 42% of ions that inferred known metabolites we found that certain amino acids, organic acids, and sugars increased and some fatty acids decreased. The transgene caused increased ammonium assimilation and detectable ion variation. Thirty-two compounds with biomedical significance were altered in abundance by GDH including 9 known carcinogens and 14 potential drugs. Therefore, the GDH transgene may lead to new uses for crops like tobacco.
TL;DR: If the urine metabolome approach can accurately observe quantitative abnormality for hundreds of metabolites, reflecting 100 different disease-causing reactions in a body, then it is possible to simultaneously detect different mutant genotypes of far more than tens of thousands.
Abstract: Urine contains numerous metabolites, and can provide evidence for the screening or molecular diagnosis of many inborn errors of metabolism (IEMs). The metabolomic analysis of urine by the combined use of urease pretreatment, stable-isotope dilution, and capillary gas chromatography/mass spectrometry offers reliable and quantitative data for the simultaneous screening or molecular diagnosis of more than 130 IEMs. Those IEMs include hyperammonemias and lactic acidemias, and the IEMs of amino acids, pyrimidines, purines, carbohydrates, and others including primary hyperoxalurias, hereditary fructose intolerance, propionic acidemia, and methylmalonic acidemia. Metabolite analysis is comprehensive for mutant genotypes. Enzyme dysfunction-either by the abnormal structure of an enzyme/apoenzyme, the reduced quantity of a normal enzyme/apoenzyme, or the lack of a coenzyme-is involved. Enzyme dysfunction-either by an abnormal regulatory gene, abnormal sub-cellular localization, or by abnormal post-transcriptional or post-translational modification-is included. Mutations-either known or unknown, common or uncommon-are involved. If the urine metabolome approach can accurately observe quantitative abnormality for hundreds of metabolites, reflecting 100 different disease-causing reactions in a body, then it is possible to simultaneously detect different mutant genotypes of far more than tens of thousands.
TL;DR: A new method based on liquid chromatography-mass spectrometry and (15)N uniform metabolic labeling of Saccharomyces cerevisiae is described for accurate and absolute quantitation of nitrogen-containing cell metabolites in metabolic profiling experiments.
Abstract: Metabolomics, i.e., the global analysis of cellular metabolites, is becoming a powerful tool for gaining insights into biological functions in the postgenomic context. However, absolute quantitation of endogenous metabolites in biological media remains an issue, and available technologies for the analysis of metabolome still lack robustness and accuracy. We describe here a new method based on liquid chromatography-mass spectrometry and (15)N uniform metabolic labeling of Saccharomyces cerevisiae for accurate and absolute quantitation of nitrogen-containing cell metabolites in metabolic profiling experiments. As a proof of concept study, eight sulfur metabolites involved in the glutathione biosynthesis pathway (i.e., cysteine, homocysteine, methionine, gamma-glutamylcysteine, cystathionine, reduced and oxidized forms of glutathione, and S-adenosylhomocysteine) were simultaneously quantified. The analytical method has been validated by studies of stability, selectivity, precision, and linearity and by the determination of the limits of detection and quantification. It was then applied to the analysis of extracts from cadmium-treated yeasts. In these conditions, the intracellular concentrations of most of the metabolites involved in the glutathione biosynthesis pathway were increased when compared to control extracts. These data correlate with previous proteomic results and also underline the importance of glutathione in cadmium detoxication.
TL;DR: This work reviews emerging technologies that aim to assign endogenous biochemical functions to enzymes by profiling the metabolome by analyzing the metabolites of eukaryotic and prokaryotic genomes.
TL;DR: Despite evidence for quantitative hybrid novelty in this system, NMR profiling did not detect any novel compounds among the plant groups studied and metabolomic profiling is a useful technique for identifying qualitative changes in major metabolites according to plant species and/or genotype, but is less useful for identifying small differences between plant groups, or differences in compounds expressed in low concentrations.
Abstract: Summary
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Hybridization may lead to unique phytochemical expression in plant individuals. Hybrids may express novel combinations or extreme concentrations of secondary metabolites or, in some cases, produce metabolites novel to both parental species.
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Here we test whether there is evidence for extreme metabolite expression or novelty in F1 hybrids between Senecio aquaticus and Senecio jacobaea. Hybridization is thought to occur frequently within Senecio, and hybridization might facilitate secondary metabolite diversification within this genus.
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Parental species express different quantities of several classes of compounds known to be involved in antiherbivore defence, including pyrrolizidine alkaloids, chlorogenic acid, flavonoids and benzoquinoids. Hybrids demonstrate differential expression of some metabolites, producing lower concentrations of amino acids, and perhaps flavonoids, than either parental species. Despite evidence for quantitative hybrid novelty in this system, NMR profiling did not detect any novel compounds among the plant groups studied.
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Metabolomic profiling is a useful technique for identifying qualitative changes in major metabolites according to plant species and/or genotype, but is less useful for identifying small differences between plant groups, or differences in compounds expressed in low concentrations.
TL;DR: Results indicate that stimulus-response experiments should also be applied to analyse pathway dynamics in anabolic routes, and an LC-MS/MS technique is presented that allows the quantification of intracellular pools of central metabolism as well as of the aromatic amino acid pathway.
Abstract: So far it is mainly transcriptome and proteome analysis that has been applied to elucidate the correlation between genotype and phenotype although thorough metabolome studies can provide substantial information on the control of the metabolism at the biochemical level. Stimulus-response experiments, i.e. the investigation of metabolism dynamics after a glucose pulse (pulse experiment), can be used to study the in vivo enzyme kinetics offering insight into underlying reaction mechanisms. Usually, this requires rapid cell quenching combined with cell inactivation to'freeze' the microbial metabolism response at a definite time-lag after pulse stimulation. To access the 'frozen' metabolic reply, adequate analytical methods are needed to measure intracellular metabolite concentrations in the cell extract. As shown in the introductory review part, stimulus-response experiments were usually applied to study central metabolism dynamics in wildtype strains. Our own results, presented in the second part of the contribution, indicate that stimulus-response experiments should also be applied to analyse pathway dynamics in anabolic routes. Using the example of the aromatic amino acid pathway, an LC-MS/MS technique is presented that allows the quantification of intracellular pools of central metabolism as well as of the aromatic amino acid pathway. Based on the analytical approach metabolic profiling is performed to monitor the metabolism dynamics after a glucose pulse experiment allowing the conclusion that pulse stimulation is transmitted to the anabolic pathway of interest.
TL;DR: The ability to control Escherichia coli metabolism systematically through the mutagenesis of genes that code for primary metabolic enzymes has enabled us to elucidate the biosynthetic origin of the isocyanide functional group found in 1 (Scheme 1).
Abstract: In vivo studies on the biosynthesis of natural products are often hampered by the inability to predictably control the metabolome of the producing organism. In contrast, when biosynthetic pathways are expressed in well-understood model organisms, the ability to predictably control metabolism through the use of mutant strains allows pathways to be examined with a rigor that cannot be achieved in the wildtype-producing organism. Recently, we have described the isolation and characterization of the isocyanide-containing antibiotic 1 along with its biosynthetic genes (isnA and isnB) from an environmental DNA (eDNA) clone. The ability to control Escherichia coli metabolism systematically through the mutagenesis of genes that code for primary metabolic enzymes has enabled us to elucidate the biosynthetic origin of the isocyanide functional group found in 1 (Scheme 1).