Showing papers on "Metabolome published in 2007"
TL;DR: The prominent role of central carbohydrate metabolism seems to be a major feature of the reprogramming of the metabolome during temperature stress, and future metabolomic studies of plant temperature-stress responses should reveal additional metabolic pathways that have important functions in temperature- stress tolerance mechanisms.
Abstract: Plants possess inducible tolerance mechanisms that extend the temperature range for survival during acute temperature stress. The inducible mechanisms of cold acclimation and acquired thermotolerance involve highly complex processes. These include perception and signal transduction of non-optimal temperatures or their physical consequences on cellular components that program extensive modification of the transcriptome, proteome, metabolome and composition and physical structure of the cytoplasm, membranes and cell walls. Therefore, a systems biology approach will be necessary to advance the understanding of plant stress responses and tolerance mechanisms. One promise of systems biology is that it will greatly enhance our understanding of individual and collective functions and thereby provide a more holistic view of plant stress responses. Past studies have found that several metabolites that could functionally contribute to induced stress tolerance have been associated with stress responses. Recent metabolite-profiling studies have refocused attention on these and other potentially important components found in the 'temperature-stress metabolome'. These metabolomic studies have demonstrated that active reconfiguration of the metabolome is regulated in part by changes in gene expression initiated by temperature-stress-activated signaling and stress-related transcription factors. One aspect of metabolism that is consistent across all of the temperature-stress metabolomic studies to date is the prominent role of central carbohydrate metabolism, which seems to be a major feature of the reprogramming of the metabolome during temperature stress. Future metabolomic studies of plant temperature-stress responses should reveal additional metabolic pathways that have important functions in temperature-stress tolerance mechanisms.
476 citations
TL;DR: Data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level, impacting directly on the host's ability to metabolize lipids.
Abstract: Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography–mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.
463 citations
TL;DR: It is demonstrated that sample pretreatment with urease dramatically alters the metabolome composition apart from removal of urea, which will likely lead to clinically applicable assays for earlier diagnosis of RCC, as well as other malignancies, and thereby improved patient prognosis.
444 citations
TL;DR: Comparison of metabolite levels in the culture supernatant and the cell interior revealed that the common assumption of whole broth quenching protocols attributing the metabolites found exclusively to the intracellular pools may not be valid in many cases.
Abstract: In the present work we investigated the most commonly applied methods used for sampling of microorganisms in the field of metabolomics in order to unravel potential sources of error previously ignored but of utmost importance for accurate metabolome analysis. To broaden the significance of our study, we investigated different Gram-negative and Gram-positive bacteria, i.e., Bacillus subtilis, Corynebacterium glutamicum, Escherichia coli, Gluconobacter oxydans, Pseudomonas putida, and Zymononas mobilis, and analyzed metabolites from different catabolic and anabolic intracellular pathways. Quenching of cells with cold methanol prior to cell separation and extraction led to drastic loss (>60%) of all metabolites tested due to unspecific leakage. Using fast filtration, Gram-negative bacteria also revealed a significant loss (>80%) when inappropriate washing solutions with low ionic strength were applied. Adapting the ionic strength of the washing solution to that of the cultivation medium could almost completely avoid this problem. Gram-positive strains did not show significant leakage independent of the washing solution. Fast filtration with sampling times of several seconds prior to extraction appears to be a suitable approach for metabolites with relatively high intracellular level and low turnover such as amino acids or TCA cycle intermediates. Comparison of metabolite levels in the culture supernatant and the cell interior revealed that the common assumption of whole broth quenching protocols attributing the metabolites found exclusively to the intracellular pools may not be valid in many cases. In such cases a differential approach correcting for medium-contained metabolites is required.
370 citations
TL;DR: The combination of liquid chromatography (LC)-MS and NMR is a powerful methodology for identifying metabolites and will undoubtedly enlarge knowledge of any biological system.
Abstract: 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.
349 citations
TL;DR: Experiments involving isotopic tracers revealed that the improved triph phosphate yields in the acidic acetonitrile were in part due to reduced triphosphate decomposition, which is a major problem when extracting with other solvent systems such as methanol/water.
Abstract: Cellular metabolome analysis by chromatography-mass spectrometry (MS) requires prior metabolite extraction. We examined a diversity of solvent systems for extraction of water-soluble metabolites from Escherichia coli. Quantitative yields of approximately 100 different metabolites were measured by liquid chromatography-tandem MS and displayed in clustered heat map format. Many metabolites, including most amino acids and components of central carbon metabolism, were adequately extracted by a broad spectrum of solvent mixtures. For nucleotide triphosphates, however, mixtures of acidic (0.1 M formic acid-containing) acetonitrile/water (80:20) or acetonitrile/methanol/water (40:40:20) gave superior triphosphate yields. Experiments involving isotopic tracers revealed that the improved triphosphate yields in the acidic acetonitrile were in part due to reduced triphosphate decomposition, which is a major problem when extracting with other solvent systems such as methanol/water. We recommend acidic solvent mixtures containing acetonitrile for extraction of the E. coli metabolome.
318 citations
TL;DR: This work presents a method and software implementation that can systematically detect components that are conserved across samples without the need for a reference library or manual curation, and demonstrates an application with a brief analysis of the Escherichia coli metabolome.
Abstract: Analysis of metabolomic profiling data from gas chromatography-mass spectrometry (GC/MS) measurements usually relies upon reference libraries of metabolite mass spectra to structurally identify and track metabolites. In general, techniques to enumerate and track unidentified metabolites are nonsystematic and require manual curation. We present a method and software implementation, freely available at http://spectconnect.mit.edu, that can systematically detect components that are conserved across samples without the need for a reference library or manual curation. We validate this approach by correctly identifying the components in a known mixture and the discriminating components in a spiked mixture. Finally, we demonstrate an application of this approach with a brief analysis of the Escherichia coli metabolome. By systematically cataloguing conserved metabolite peaks prior to data analysis methods, our approach broadens the scope of metabolomics and facilitates biomarker discovery.
241 citations
TL;DR: A strategy for the development of an advanced analytical platform that allows the comprehensive analysis of microbial metabolomes and its application to the analysis of the metabolome composition of mid-logarithmic E. coli cells grown on a mineral salts medium using glucose as the carbon source is presented.
208 citations
01 Jan 2007
TL;DR: This review of H2O2 physiology broadly, both as a stress and as a developmentally and physiologically important metabolite, including its sources and mobility, and the vexing question of tissue level concentrations is considered.
Abstract: The relationship between plants and hydrogen peroxide is a challenging one: H2O2 has many essential roles in plant metabolism but at the same time, accumulation related to virtually any environmental stress is potentially damaging. In this review, I consider H2O2 physiology broadly, both as a stress and as a developmentally and physiologically important metabolite, including its sources and mobility, and the vexing question of tissue level concentrations. I then consider problems associated with H2O2 as a signaling molecule, including mechanisms of H2O2 sensing, signaling, and response networks. Finally, I discuss recent advances in transcript network modeling, and complex systems approaches to understanding the interactions between the transcriptome, proteome and metabolome in responses to H2O2.
204 citations
TL;DR: The blood metabolome of rats treated with HPPD inhibitors, a novel class of herbicides, was analyzed and it was demonstrated that a single metabolite, tyrosine, can be used as a biomarker and a specific pattern of change was found that involved nine metabolites.
192 citations
TL;DR: In this paper, the effects of Cinnamoyl CoA reductase (CCR) and cinnamyl alcohol dehydrogenase (CAD) on the transcriptome and metabolome of tobacco were investigated.
Abstract: Lignin is an important component of secondarily thickened cell walls. Cinnamoyl CoA reductase (CCR) and cinnamyl alcohol dehydrogenase (CAD) are two key enzymes that catalyse the penultimate and last steps in the biosynthesis of the monolignols. Downregulation of CCR in tobacco (Nicotiana tabacum) has been shown to reduce lignin content, whereas lignin in tobacco downregulated for CAD incorporates more aldehydes. We show that altering the expression of either or both genes in tobacco has far-reaching consequences on the transcriptome and metabolome. cDNA-amplified fragment length polymorphism-based transcript profiling, combined with HPLC and GC-MS-based metabolite profiling, revealed differential transcripts and metabolites within monolignol biosynthesis, as well as a substantial network of interactions between monolignol and other metabolic pathways. In general, in all transgenic lines, the phenylpropanoid biosynthetic pathway was downregulated, whereas starch mobilization was upregulated. CCR-downregulated lines were characterized by changes at the level of detoxification and carbohydrate metabolism, whereas the molecular phenotype of CAD-downregulated tobacco was enriched in transcript of light- and cell-wall-related genes. In addition, the transcript and metabolite data suggested photo-oxidative stress and increased photorespiration, mainly in the CCR-downregulated lines. These predicted effects on the photosynthetic apparatus were subsequently confirmed physiologically by fluorescence and gas-exchange measurements. Our data provide a molecular picture of a plant's response to altered monolignol biosynthesis.
Book•
16 Feb 2007
TL;DR: The author revealed that metabolites are arranged in networks that are part of a cellular interactome and the dynamics of the metabolism-the mass analyzer-the ion-trap are very different from that of conventionalomics, which focuses on the role of riboflavin.
Abstract: PREFACE. LIST OF CONTRIBUTORS. PART I: CONCEPTS AND METHODOLOGY. 1 Metabolomics in Functional Genomics and Systems Biology. 1.1 From genomic sequencing to functional genomics. 1.2 Systems biology and metabolic models. 1.3 Metabolomics. 1.4 Future perspectives. 2 The Chemical Challenge of the Metabolome. 2.1 Metabolites and metabolism. 2.2 The structural diversity of metabolites. 2.2.1 The chemical and physical properties. 2.2.2 Metabolite abundance. 2.2.3 Primary and secondary metabolism. 2.3 The number of metabolites in a biological system. 2.4 Controlling rates and levels. 2.4.1 Control by substrate level. 2.4.2 Feedback and feedforward control. 2.4.3 Control by "pathway independent" regulatory molecules. 2.4.4 Allosteric control. 2.4.5 Control by compartmentalization. 2.4.6 The dynamics of the metabolism-the mass fl ow. 2.4.7 Control by hormones. 2.5 Metabolic channeling or metabolons. 2.6 Metabolites are arranged in networks that are part of a cellular interactome. 3 Sampling and Sample Preparation. 3.1 Introduction. 3.2 Quenching-the fi rst step. 3.2.1 Overview on metabolite turnover. 3.2.2 Different methods for quenching. 3.2.3 Quenching microbial and cell cultures. 3.2.4 Quenching plant and animal tissues. 3.3 Obtaining metabolites from biological samples. 3.3.1 Release of intracellular metabolites. 3.3.2 Structure of the cell envelopes-the main barrier to be broken. 3.3.3 Cell disruption methods. 3.3.4 Nonmechanical disruption of cell envelopes. 3.3.5 Mechanical disruption of cell envelopes. 3.4 Metabolites in the extracellular medium. 3.4.1 Metabolites in solution. 3.4.2 Metabolites in the gas phase. 3.5 Improving detection via sample concentration. 4 Analytical Tools. 4.1 Introduction. 4.2 Choosing a methodology. 4.3 Starting point-samples. 4.4 Principles of chromatography. 4.4.1 Basics of chromatography. 4.4.2 The chromatogram and terms in chromatography. 4.5 Chromatographic systems. 4.5.1 Gas chromatography. 4.5.2 HPLC systems. 4.6 Mass spectrometry. 4.6.1 The mass spectrometer-an overview. 4.6.2 GC-MS-the EI ion source. 4.6.3 LC-MS-the ESI ion source. 4.6.4 Mass analyzer-the quadrupole. 4.6.5 Mass analyzer-the ion-trap. 4.6.6 Mass analyzer-the time-of-fl ight. 4.6.7 Detection and computing in MS. 4.7 The analytical work-fl ow. 4.7.1 Separation by chromatography. 4.7.2 Mass spectrometry. 4.7.3 General analytical considerations. 4.8 Data evaluation. 4.8.1 Structure of data. 4.8.2 The chromatographic separation. 4.8.3 Mass spectral data. 4.8.4 Exporting data for processing. 4.9 Beyond the core methods. 4.9.1 Developments in chromatography. 4.9.2 Capillary electrophoresis. 4.9.3 Tandem MS and advanced scanning techniques. 4.9.4 NMR spectrometry. 4.10 Further reading. 5 Data Analysis. 5.1 Organizing the data. 5.2 Scales of measurement. 5.2.1 Qualitative data. 5.2.2 Quantitative data. 5.3 Data structures. 5.4 Preprocessing of data. 5.4.1 Calibration of data. 5.4.2 Combining profi le scans. 5.4.3 Filtering. 5.4.4 Centroid calculation. 5.4.5 Internal mass scale correction. 5.4.6 Binning. 5.4.7 Baseline correction. 5.4.8 Chromatographic profi le matching. 5.5 Deconvolution of spectroscopic data. 5.6 Data standardization (normalization). 5.7 Data transformations. 5.7.1 Principal component analysis. 5.7.2 Fisher discriminant analysis. 5.8 Similarities and distances between data. 5.8.1 Continuous functions. 5.8.2 Binary functions. 5.9 Clustering techniques. 5.9.1 Hierarchical clustering. 5.9.2 k-means clustering. 5.10 Classifi cation techniques. 5.10.1 Decision theory. 5.10.2 k-nearest neighbor. 5.10.3 Tree-based classifi cation. 5.11 Integrated tools for automation, libraries, and data evaluation. PART II: CASE STUDIES AND REVIEWS. 6 Yeast Metabolomics: The Discovery of New Metabolic Pathways in Saccharomyces cerevisiae. 6.1 Introduction. 6.2 Brief description of the methodology used. 6.2.1 Sample preparation. 6.2.2 The analysis. 6.3 Early discoveries. 6.4 Yeast stress response gives evidence of alternative pathway for glyoxylate biosynthesis in S. cerevisiae. 6.5 Biosynthesis of glyoxylate from glycine in S. cerevisiae. 6.5.1 Stable isotope labeling experiment to investigate glycine catabolism in S. cerevisiae. 6.5.2 Data leveraged for speculation. 7 Microbial Metabolomics: Rapid Sampling Techniques to Investigate Intracellular Metabolite Dynamics-An Overview. 7.1 Introduction. 7.2 Starting with a simple sampling device proposed by Theobald et al. (1993). 7.3 An improved device reported by Lange et al. (2001). 7.4 Sampling tube device by Weuster-Botz (1997). 7.5 Fully automated device by Schaefer et al. (1999). 7.6 The stopped-fl ow technique by Buziol et al. (2002). 7.7 The BioScope: a system for continuous-pulse experiments. 7.8 Conclusions and perspectives. 8 Plant Metabolomics. 8.1 Introduction. 8.2 History of plant metabolomics. 8.3 Plants, their metabolism and metabolomics. 8.3.1 Plant structures. 8.3.2 Plant metabolism. 8.4 Specifi c challenges in plant metabolomics. 8.4.1 Light dependency of plant metabolism. 8.4.2 Extraction of plant metabolites. 8.4.3 Many cell types in one tissue. 8.4.4 The dynamical range of plant metabolites. 8.4.5 Complexity of the plant metabolome. 8.4.6 Development of databases for metabolomics-derived data in plant science. 8.5 Applications of metabolomics approaches in plant research. 8.5.1 Phenotyping. 8.5.2 Functional genomics. 8.5.3 Fluxomics. 8.5.4 Metabolic trait analysis. 8.5.5 Systems biology. 8.6 Future perspectives. 9 Mass Profi ling of Fungal Extract from Penicillium Species. 9.1 Introduction. 9.2 Methodology for screening of fungi by DiMS. 9.2.1 Cultures. 9.2.2 Extraction. 9.2.3 Analysis by direct infusion mass spectrometry. 9.3 Discussion. 9.3.1 Initial data processing. 9.3.2 Metabolite prediction. 9.3.3 Chemical diversity and similarity. 9.4 Conclusion. 10 Metabolomics in Humans and Other Mammals. 10.1 Introduction. 10.2 A brief history of mammalian metabolomics. 10.3 Sample preparation for mammalian metabolomics studies. 10.3.1 Working with blood. 10.3.2 Working with urine. 10.3.3 Working with cerebrospinal fl uid. 10.3.4 Working with cells and tissues. 10.4 Sample analysis. 10.4.1 GC-MS analysis of urine, plasma, and CSF. 10.4.2 LC-MS analysis of urine, blood, and CFS. 10.4.3 NMR analysis of CSF, urine, and blood. 10.5 Applications. 10.5.1 Identifi cation and classifi cation of metabolic disorders. 10.6 Future outlook. INDEX.
TL;DR: The isotopically labeled 35 amine-containing analogues were found to be stable and proved to be effective in overcoming matrix effects in both relative and absolute quantification of these analytes present in a complicated sample, human urine.
Abstract: One of the challenges associated with metabolome profiling in complex biological samples is to generate quantitative information on the metabolites of interest. In this work, a targeted metabolome analysis strategy is presented for the quantification of amine-containing metabolites. A dimethylation reaction is used to introduce a stable isotopic tag onto amine-containing metabolites followed by LC−ESI MS analysis. This labeling reaction employs a common reagent, formaldehyde, to label globally the amine groups through reductive amination. The performance of this strategy was investigated in the analysis of 20 amino acids and 15 amines by LC−ESI MS. It is shown that the labeling chemistry is simple, fast (<10-min reaction time), specific, and provides high yields under mild reaction conditions. The issue of isotopic effects of the labeled amines on reversed-phase (RP) and hydrophilic interaction (HILIC) LC separations was examined. It was found that deuterium labeling causes an isotope effect on the elutio...
TL;DR: Evidence is presented that superoxide anion (and hydrogen peroxide) and nitric oxide (and peroxynitrite) constitute regulated prooxidant second messenger systems, with specific sub-cellular locales of production and are essential for normal metabolome and physiological function.
Abstract: The production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) has long been proposed as leading to random deleterious modification of macromolecules with an associated progressive development of age associated systemic disease. ROS and RNS formation has been posited as a major contributor to the aging process. On the contrary, this review presents evidence that superoxide anion (and hydrogen peroxide) and nitric oxide (and peroxynitrite) constitute regulated prooxidant second messenger systems, with specific sub-cellular locales of production and are essential for normal metabolome and physiological function. The role of these second messengers in the regulation of the metabolome is discussed in terms of radical formation as an essential contributor to the physiologically normal regulation of sub-cellular bioenergy systems; proteolysis regulation; transcription activation; enzyme activation; mitochondrial DNA changes; redox regulation of metabolism and cell differentiation; the concept that orally administered small molecule antioxidant therapy is a chimera. The formation of superoxide anion/hydrogen peroxide and nitric oxide do not conditionally lead to random macromolecular damage; under normal physiological conditions their production is actually regulated consistent with their second messenger roles.
01 Jan 2007
TL;DR: Systems-oriented approaches aiming at investigating the link between the different components of cellular physiology such as transcriptome, fluxome and metabolome provide a novel powerful platform that will surely drive future research towards holistic understanding of lysine over-producing microorganisms as well as the creation of superior production strains.
Abstract: l-lysine is an essential amino acid required for nutrition of animals and humans. It has to be present in food and feed, which, in many cases, is realized by supplementation of the feed-stuffs with pure lysine. The high importance of lysine in nutrition has stimulated intensive research on the lysine biosynthetic pathways and their regulation and the search for microorganisms capable of over-producing this amino acid. As an important milestone, the glutamate producing soil bacterium Corynebacterium glutamicum was isolated in 1956 and soon received interest to be used for production of another amino acid stemming from the TCA cycle: lysine. Within a few years the first lysine producing strains were obtained. The past 50 years following the discovery of C. glutamicum were characterized by a huge progress towards understanding the physiology of this organism and developing and optimizing industrial production strains. This has resulted in effective biotechnological processes currently used for producing about 750 000 tons of lysine per year. Today, systems-oriented approaches aiming at investigating the link between the different components of cellular physiology such as transcriptome, fluxome and metabolome, provide a novel powerful platform that will surely drive future research towards holistic understanding of lysine over-producing microorganisms as well as the creation of superior production strains.
TL;DR: Two Arabidopsis mutants, mto1 and tt4, exhibited the overall loss of metabolic stability or the generation of a metabolic network of a backup pathway for the lost physiological functions (tt4).
Abstract: Metabolites are not only the catalytic products of enzymatic reactions but also the active regulators or the ultimate phenotype of metabolic homeostasis in highly complex cellular processes. The modes of regulation at the metabolome level can be revealed by metabolic networks. We investigated the metabolic network between wild-type and 2 mutant (methionine-over accumulation 1 [mto1] and transparent testa4 [tt4]) plants regarding the alteration of metabolite accumulation in Arabidopsis thaliana. In the GC-TOF/MS analysis, we acquired quantitative information regarding over 170 metabolites, which has been analyzed by a novel score (ZMC, z-score of metabolite correlation) describing a characteristic metabolite in terms of correlation. Although the 2 mutants revealed no apparent morphological abnormalities, the overall correlation values in mto1 were much lower than those of the wild-type and tt4 plants, indicating the loss of overall network stability due to the uncontrolled accumulation of methionine. In the tt4 mutant, a new correlation between malate and sinapate was observed although the levels of malate, sinapate, and sinapoylmalate remain unchanged, suggesting an adaptive reconfiguration of the network. Gene-expression correlations presumably responsible for these metabolic networks were determined using the metabolite correlations as clues. Two Arabidopsis mutants, mto1 and tt4, exhibited the following changes in entire metabolome networks: the overall loss of metabolic stability (mto1) or the generation of a metabolic network of a backup pathway for the lost physiological functions (tt4). The expansion of metabolite correlation to gene-expression correlation provides detailed insights into the systemic understanding of the plant cellular process regarding metabolome and transcriptome.
TL;DR: Current innovations in small-molecule analysis have created the ability to rapidly identify and quantify numerous compounds, and these data are creating new opportunities for understanding plant metabolism and for plant metabolic engineering.
Abstract: Plant metabolomics — the high-throughput analysis of plant compounds — is an invaluable tool for understanding plant metabolism. Recent innovations in mass-spectrometry-based analyses are shedding light on the structure and regulation of biosynthetic pathways and the temporal and spatial dynamics of the plant metabolome. Methods for network-wide analysis are increasingly showing that the textbook view of the regulation of plant metabolism is often incomplete and misleading. Recent innovations in small-molecule analysis have created the ability to rapidly identify and quantify numerous compounds, and these data are creating new opportunities for understanding plant metabolism and for plant metabolic engineering.
TL;DR: Elicitor-induced sanguinarine accumulation in opium poppy cell cultures provides a responsive model system to profile modulations in gene transcripts and metabolites related to alkaloid biosynthesis.
Abstract: Elicitor-induced sanguinarine accumulation in opium poppy (Papaver somniferum) cell cultures provides a responsive model system to profile modulations in gene transcripts and metabolites related to alkaloid biosynthesis. An annotated expressed sequence tag (EST) database was assembled from 10,224 random clones isolated from an elicitor-treated opium poppy cell culture cDNA library. The most abundant ESTs encoded defense proteins, and enzymes involved in alkaloid metabolism and S-adenosylmethionine-dependent methyl transfer. ESTs corresponding to 40 enzymes involved in the conversion of sucrose to sanguinarine were identified. A corresponding DNA microarray was probed with RNA from cell cultures collected at various time-points after elicitor treatment, and compared with RNA from control cells. Several diverse transcript populations were coordinately induced, with alkaloid biosynthetic enzyme and defense protein transcripts displaying the most rapid and substantial increases. In addition to all known sanguinarine biosynthetic gene transcripts, mRNAs encoding several upstream primary metabolic enzymes were coordinately induced. Fourier transform-ion cyclotron resonance-mass spectrometry was used to characterize the metabolite profiles of control and elicitor-treated cell cultures. Principle component analysis revealed a significant and dynamic separation in the metabolome, represented by 992 independent detected analytes, in response to elicitor treatment. Identified metabolites included sanguinarine, dihydrosanguinarine, and the methoxylated derivatives dihydrochelirubine and chelirubine, and the alkaloid pathway intermediates N-methylcoclaurine, N-methylstylopine, and protopine. Some of the detected analytes showed temporal changes in abundance consistent with modulations in the profiles of alkaloid biosynthetic gene transcripts.
TL;DR: In this paper, the authors used gas chromatography-mass spectrometry to obtain metabolic profiles from methanol/water extracts of four samples of different age down the stem of sugarcane.
Abstract: Sucrose content increases with internode development down the stem of sugarcane. In an attempt to determine which other changes in metabolites may be linked to sucrose accumulation gas chromatography-mass spectrometry was used to obtain metabolic profiles from methanol/water extracts of four samples of different age down the stem of cultivar Q117. Extracts were derivatized with either N-methyl-N-(trimethylsilyl) trifluoracetamide (TMS) or N-methyl N-(tert-butyldimethylsilyl) trifluoroacetamide (TBS) separately in order to increase the number of metabolites that could be detected. This resulted in the measurement of 121 and 71 metabolites from the TMS and TBS derivatization, respectively. Fifty-five metabolites were identified using commercial and publicly available libraries. Statistical analysis of the metabolite profiles resulted in clustering of tissue types. Particular metabolites were correlated with the level of sucrose accumulation, which as expected increased down the stem. Metabolites, such as tricarboxylic acid cycle intermediates and amino acids, were more abundant in the M2 sample (meristem to internode 2) that was actively growing and decreased in an apparently coordinated developmentally programmed manner in more mature internodes down the stem. However, other metabolites such as trehalose and raffinose showed positive correlations with sucrose concentration. Here we discuss the technique used to measure metabolites in sugarcane and the changes in metabolite abundance down the sugarcane stem.
TL;DR: An endocannabinoid profile specifically for the frontal cortex of the rat brain is obtained and anandamide level differences following the administration of the fatty acid amide hydrolase inhibitor AM374 are determined.
Abstract: The endocannabinoid system's biological significance continues to grow as novel endocannabinoid metabolites are discovered. Accordingly, a myopic view of the system that focuses solely on one or two endocannabinoids, such as anandamide or 2-arachidonoyl glycerol, is insufficient to describe the biological responses to perturbations of the system. Rather, the endocannabinoid metabolome as a whole must be analyzed. The work described here is based on liquid chromatography coupled with atmospheric pressure chemical ionization mass spectrometry. This method has been validated to quantify, in a single chromatographic run, the levels of 15 known or suspected metabolites of the endocannabinoid system in the rat brain and is applicable to other biological matrixes. We have obtained an endocannabinoid profile specifically for the frontal cortex of the rat brain and have determined anandamide level differences following the administration of the fatty acid amide hydrolase inhibitor AM374.
TL;DR: The concept that superoxide anion/H2O2 cause random macromolecular damage is rebutted and the administration of antioxidants to quench the inferred toxicity of these compounds as a therapy for age associated diseases is unsupported by extant mammalian clinical trials and should be subject to serious re-evaluation.
TL;DR: In this paper, the authors applied the principle of chemoselective probes to the metabolome, creating a general strategy to tag, enrich and profile large classes of small molecules from biological systems.
Abstract: Chemical probes that target classes of proteins based on shared functional properties have emerged as powerful tools for proteomics. The metabolome rivals, if not surpasses, the proteome in terms of size and complexity, suggesting that efforts to profile metabolites would also benefit from targeted technologies. Here we apply the principle of chemoselective probes to the metabolome, creating a general strategy to tag, enrich and profile large classes of small molecules from biological systems. Key to success was incorporation of a protease-cleavage step to release captured metabolites in a format compatible with liquid chromatography‐mass spectrometry (LC-MS) analysis. This technology, termed metabolite enrichment by tagging and proteolytic release (METPR), is applicable to small molecules of any physicochemical class, including polar, labile and low-mass (o100 Da) compounds. We applied METPR to profile changes in the thiol metabolome of human cancer cells treated with the antioxidant N-acetyl-L-cysteine. Understanding the metabolic and signaling networks that regulate health and disease is a principal goal of post-genomic research. The remarkable complexity of these biochemical pathways necessitates the integrated application of multiple molecular profiling methods to facilitate their discovery and annotation. Although a wealth of information has been gained from genomic 1 and proteomic 2 studies, they provide an incomplete picture of the molecular pathways that regulate cell physiology and pathology. To fully understand the composition and function of biochemical networks, analysis of the small-molecule complement of cells and tissues, commonly referred to as the metabolome, is essential 3,4 .
TL;DR: For the first time, small molecules secreted from undifferentiated hES cells and hES cell-derived neural precursors (hNPs) are measured and identified using metabolomics and these compounds are altered in response to known disruptors of human development.
Abstract: Metabolomics enables the discovery of small molecules that may serve as candidate biomarkers of pharmacological efficacy or toxicity. Biochemical pathways of human development are likely active in human embryonic stem (hES) cells and derivatives, since they recapitulate organogenesis in vitro. We hypothesized that small molecules could be measured from undifferentiated hES cells and hES cell-derived neural precursors (hNPs) using metabolomics and that these compounds are altered in response to known disruptors of human development. Metabolite profiling was performed in hES cells and hNPs after exposure to valproate, an inducer of neurodevelopmental disorders. Kynurenine, an intermediate in tryptophan metabolism, and other small molecules in glutamate metabolism were significantly upregulated in response to valproate. Thus, for the first time, we have been able to measure and identify small molecules secreted from hES cells and cells derived from hES cells. The hES cell metabolome may thus serve as a sourc...
01 Jan 2007
TL;DR: This chapter attempts to exemplify the potential of metabolome analysis for the screening of genetic diversity selected by breeding, by focusing on GC-TOF-MS, the focus technology of this chapter.
Abstract: Metabolomics aims for comprehensive analysis of the metabolic complement. The metabolic phenotype is typically described by changes in metabolic pool sizes. Today investigations are technologically limited to a few hundred metabolites. Metabolomics studies are typically restricted to a single analytical technology, such as GC-TOF-MS which will be the focus technology of this chapter. Two strategies for data analysis are applied. Metabolite fingerprinting investigates all analytical signals. Metabolite profiling considers only information which represents known metabolites. In the last 8–10 years functional metabolome analysis has passed from concept discussion, method development and feasibility assessment into a phase of method automation and increased scope of applications for enhanced hypothesis generation. It is, however, still an early time for lessons to be learned from high-throughput metabolome analyses. This chapter attempts to exemplify the potential of metabolome analysis for the screening of genetic diversity selected by breeding. This diversity is a widely recognized but also a hard to investigate biological resource. In land races, selection has lead to successful adaptation, for example towards environmental stress tolerance. However, the underlying genomic changes remain elusive. Metabolic phenotyping analysis may circumvent the problem by identifying metabolic markers for a targeted selection. Ultimately metabolic profiling may allow an initial functional insight into metabolic modes of tolerance acquisition without prior knowledge of genomic modifications
01 Jan 2007
TL;DR: The present work presents a meta-analysis of metabolome analysis using gaschromatography coupled to mass spectrometry (GC/MS) and its applications in metabolomics and structural elucidation, and discusses the role of NMR in this research.
Abstract: PART I. Metabolome analysis using gaschromatography coupled to mass spectrometry (GC/MS) 1 Metabolite profiling in blood plasma Oliver Fiehn and Tobias Kind 2 Non-Supervised Construction and Application of Mass Spectral and Retention Time Index Libraries from Time-of-Flight GC-MS Metabolite Profiles Alexander Erban, Nicolas Schauer, Alisdair R. Fernie, Joachim Kopka 3 Metabolomic profiling of natural volatiles: head space trapping - GC/MS Yury M. Tikunov, Francel W.A. Verstappen & Robert D Hall PART II. Metabolomics - Data integration and data mining 4 Integrative profiling of metabolites and proteins: improving pattern recognition and biomarker selection for systems level approaches Katja Morgenthal, Stefanie Wienkoop, Florian Wolschin, and Wolfram Weckwerth 5 Integrating profiling data: using linear correlation to reveal co-regulation of transcript and metabolites Ewa Urbanczyk-Wochniak, Lothar Willmitzer and Alisdair Fernie 6 Visualization and Analysis of Molecular Data Matthias Scholz and Joachim Selbig 7 A gentle guide to the analysis of metabolomic data Ralf Steuer, Katja Morgenthal, Wolfram Weckwerth, and Joachim Selbig PART III. Capillary electrophoresis coupled to mass spectrometry (CE/MS) 8 CE-MS for Metabolomics Tomoyoshi Soga PART IV. Liquid chromatography coupled to mass spectrometry (LC/MS) for metabolomics and structural elucidation 9 Application of LC-MS analysis in Metabolomics: Reversed-Phase Monolithic Capillary Chromatography and Hydrophilic Chromatography coupled to Electrospray Ionization Mass Spectrometry Vladimir V. Tolstikov, Oliver Fiehn, and Nobuo Tanaka PART V. Electrochemical detection of metabolites 10 HPLC-Separations Coupled with Coulometric-Electrode Array Detectors A Unique Approach to Metabolomics Bruce S. Kristal, Yevgeniya I. Shurubor, Rima Kaddurah-Daouk, and Wayne R. Matson PART VI. Metabolic Fluxes 11 Determination of metabolic flux ratios from 13C-experiments and GC MS data: protocol and principles Annik Nanchen, Tobias Fuhrer and Uwe Sauer 12 Understanding the Roadmap of Metabolism by Pathway Analysis Stefan Schuster, Axel von Kamp and Mikhail Pachkov PART VII. NMR metabolome analysis 13 Revealing the metabolome of animal tissues using 1H NMR spectroscopy Mark R. Viant 14 NMR Metabonomics - Methods for Drug Discovery and Development Karl-Heinz Ott and Nelly Aranibar
TL;DR: Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry.
Abstract: Escherichia coli is among the simplest and best-understood free-living organisms. It has served as a valuable model for numerous biological processes, including cellular metabolism. Just as E. coli stood at the front of the genomic revolution, it is playing a leading role in the development of cellular metabolomics: the study of the complete metabolic contents of cells, including their dynamic concentration changes and fluxes. This review briefly describes the essentials of cellular metabolomics and its fundamental differentiation from biomarker metabolomics and lipidomics. Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry. In particular emphasis is given to the cell handling and sample preparation steps required for collecting data of high biological reliability, such as fast metabolome quenching. Future challenges, both in terms of data collection and application of the data to obtain a comprehensive understanding of metabolic dynamics, are discussed.
TL;DR: The relatively large pool of sugars and phenolic acids in the vacuole compartment implies high levels of starch metabolism and phenylpropanoid biosynthesis and the low amino acids pool, on the other hand, suggests low nitrogen accumulation in the leaves of soybean.
Abstract: In the present study, non-aqueous fractionation (NAQF) and GC-MS were used to obtain a spatially resolved view of metabolism in mature leaves of soybean (Glycine
max Merr.). NAQF of lyophilized soybean leaves was performed using CCl4-n-heptane and ultracentrifugation that yielded a gradient comprised of six fractions. Chlorophyll content, and marker enzyme activities, phosphoenolpyruvate carboxylase (PEPC) and α-mannosidase, were utilized as stroma, cytosol and vacuole markers, respectively. GC-MS analyses of each fraction resulted in the identification of around 100 different metabolites. The distribution of these identified compounds showed a decreasing order from the vacuole to cytosol to chloroplast stroma. In other words, a greater number of identified compounds were found in the vacuole when compared to the cytosol or stroma. Levels of sugars, organic acids and fatty acids showed greater relative abundances in the vacuole with 50, 55, and 50% of the respective pools. A greater relative abundance of amino acids was observed in the cytosol where 45% of the total of amino acids content was recorded. The relatively large pool of sugars and phenolic acids in the vacuole compartment implies high levels of starch metabolism and phenylpropanoid biosynthesis. The low amino acids pool, on the other hand, suggests low nitrogen accumulation in the leaves of soybean. Hierarchical cluster analysis on the most abundant metabolites revealed three clusters containing 10, 20, and 2 of the 32 selected metabolites. The data were discussed in term of NAQF and GC-MS analysis of soybean mature leaves, and also in term of distribution and compartmentation of metabolites at subcellular levels.
TL;DR: Supporters of this burgeoning branch of molecular medicine are gung-ho about their chances of success, and believe that metabolomics — the study of all the body’s metabolites — will finally come up with the goods.
Abstract: Imagine that at a routine medical check-up your doctor takes a urine sample, then reports a few days later that your risk of type 2 diabetes is normal, but there are hints that your arteries are furring up. A similar scenario has been promised for the past 20 years by those working in genomics and proteomics, but has not yet materialized. Now, however, an increasing number of researchers are claiming that metabolomics — the study of all the body’s metabolites — will finally come up with the goods. Supporters of this burgeoning branch of molecular medicine are gung-ho about their chances of success. “In retrospect, we wonder why we spent millions on the genome,” says Bruce German, who studies lipid metabolism at the University of California, Davis. With the knowledge we have today, he reckons, scientists should have gone straight for the metabolome. But can it deliver? Metabolomics is the study of the raw materials and products of the body’s biochemical reactions, molecules that are smaller than most proteins, DNA and other macromolecules. The aim is to be able to take urine, blood or some other body fluid, scan it in a machine and find a profile of tens or hundreds of chemicals that can predict whether an individual is on the road to a disease, say, or likely to experience side-effects from a particular drug. Researchers are already trying to flag impending disease by measuring levels of gene expression or proteins, but supporters of metabolomics say they should be able to do it better. Small changes in the activity of a gene or protein (which may have an unknown impact on the workings of a cell) often create a much larger change in metabolite levels. The approach has already proved its worth: cholesterol and glucose have long been chemical canaries for heart disease and diabetes.
TL;DR: Integrated analysis of the metabolome and transcriptome of these OASA1D transgenic lines indicates that the over-accumulation of free Trp may be partly due to the low activity of Trp decarboxylase or other metabolic genes that directly utilize Trp as a substrate.
Abstract: Transgenic rice plants overexpressing a mutant rice gene for anthranilate synthase alpha subunit (OASA1D) accumulate large amounts of free tryptophan (Trp) with few adverse effects on the phenotype, except for poor germination and weak seedling growth. Metabolic profiling of 8-d-old seedlings of Nipponbare and two high-Trp lines, HW1 and HW5, by high performance liquid chromatography-photo diode array (HPLC-PDA) confirmed that, relative to Nipponbare, only the peak attributed to Trp was significantly changed in the profiles of the OASA1D lines. More detailed and targeted analysis using HPLC coupled with tandem mass spectrometry revealed that the OASA1D lines had higher levels of anthranilate, tryptamine, and serotonin than Nipponbare, but these metabolites were at much lower levels than free Trp. The levels of phenylalanine (Phe) and tyrosine (Tyr) were not affected by the overproduction of Trp. Transcriptomic analysis by microarray validated by quantitative Real-Time PCR (qRT-PCR) revealed that at least 12 out of 21 500 genes showed significant differential expression among genotypes. Except for the OASA1D transgene and a putative IAA b-glucosyltransferase, these were not related to Trp metabolism. Most importantly, the overexpression of the OASA1D and the consequent accumulation of Trp in these lines had little effect on the overall transcriptome, consistent with the minimal effects on growth and the metabolome. Integrated analysis of the metabolome and transcriptome of these OASA1D transgenic lines indicates that the over-accumulation of free Trp may be partly due to the low activity of Trp decarboxylase or other metabolic genes that directly utilize Trp as a substrate.
TL;DR: The potential for the development of a database strategy for large scale, long-term projects requiring comparison of chemical composition in plant breeding, mutant population analysis in functional genomics experiments, or food raw material analysis is described.
Abstract: Understanding attributes of crop varieties and food raw materials underlying desirable characteristics is a significant challenge Metabolomics technology based on flow infusion electrospray ionization mass spectrometry (FIE-MS) has been used to investigate the chemical composition of potato cultivars associated with quality traits in harvested tubers Through the combination of metabolite fingerprinting with random forest data modeling, a subset of metabolome signals explanatory of compositional differences between individual genotypes were ranked for importance Interpretative analysis of highlighted signals based on ranking behavior, intensity correlations, and mathematical relationships of ion masses correctly predicted metabolites associated with flavor and pigmentation traits in potato tubers GC-MS profiling was used to further validate proposed compositional differences The potential for the development of a database strategy for large scale, long-term projects requiring comparison of chemical co