Showing papers on "Metabolome published in 2002"

Nontargeted metabolome analysis by use of Fourier Transform Ion Cyclotron Mass Spectrometry

Abstract: Advanced functional genomic tools now allow the parallel and high-throughput analyses of gene and protein expression. Although this information is crucial to our understanding of gene function, it offers insufficient insight into phenotypic changes associated with metabolism. Here we introduce a high-capacity Fourier Transform Ion Cyclotron Mass Spectrometry (FTMS)–based method, capable of nontargeted metabolic analysis and suitable for rapid screening of similarities and dissimilarities in large collections of biological samples (e.g., plant mutant populations). Separation of the metabolites was achieved solely by ultra-high mass resolution; Identification of the putative metabolite or class of metabolites to which it belongs was achieved by determining the elemental composition of the metabolite based upon the accurate mass determination; and relative quantitation was achieved by comparing the absolute intensities of each mass using internal calibration. Crude plant extracts were introduced via direct (...

Nmr-based metabolomics

Abstract: Similar to genomics and proteomics which yield vast amounts of data about the expression of genes and proteins, metabolomics refers to the whole metabolic profile of the cell. The focus of this report concerns the use of nuclear magnetic resonance (NMR) spectroscopy for metabolic analyses and, in particular, its use in toxicology for examining the metabolic profile of biofluids. Examples from the literature will demonstrate how 1H NMR and pattern recognition methods are used to obtain the urinary metabolic profile, and how this profile is affected by exposure to various toxicants. These particular studies which focus on the metabolic profiles of biofluids, specifically urine, are referred to as metabonomics. NMR-based metabonomics provides a means to categorize organ-specific toxicity, monitor the onset and progression of toxicological effects, and identify biomarkers of toxicity. A future challenge, however, is to describe the cellular metabolome for purposes of understanding cellular functions (i.e., metabolomics). Thus the capabilities and advantages of multinuclear NMR to provide metabolic information in cells and tissues will also be discussed. Such information is essential if metabolomics is to provide a complementary dataset which together with genomics and proteomics can be used to construct computer network models to describe cellular functions.

A functional genomics approach using metabolomics and in silico pathway analysis

Abstract: In the field of functional genomics increasing effort is being undertaken to analyze the function of orphan genes using metabolome data. Improved analytical equipment allows screening simultaneously for a high number of metabolites. Such metabolite profiles are analyzed using multivariate data analysis techniques and changes in the genotype will in many cases lead to different metabolite profiles. Here, a theoretical framework that may be applied to identify the function of orphan genes is presented. The approach is based on a combination of metabolome analysis combined with in silico pathway analysis. Pathway analysis may be carried out using convex analysis and a change in the active pathway structure of deletion mutants expressed in a different metabolite profile may disclose the function or the functional class of an orphan gene. The concept is illustrated using a simplified model for growth of Saccharomyces cerevisiae.

Metabolomics and Machine Learning: Explanatory Analysis of Complex Metabolome Data Using Genetic Programming to Produce Simple, Robust Rules

Abstract: Kell, D. B. (2002). Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Molecular Biology Reports, 29, (1-2), 237-241.

Discovery of sulfated metabolites in mycobacteria with a genetic and mass spectrometric approach

Abstract: The study of the metabolome presents numerous challenges, first among them being the cataloging of its constituents. A step in this direction will be the development of tools to identify metabolites that share common structural features. The importance of sulfated molecules in cell–cell communication motivated us to develop a rapid two-step method for identifying these metabolites in microorganisms, particularly in pathogenic mycobacteria. Sulfurcontaining molecules were initially identified by mass spectral analysis of cell extracts from bacteria labeled metabolically with a stable sulfur isotope (34SO). To differentiate sulfated from reduced-sulfur-containing molecules, we employed a mutant lacking the reductive branch of the sulfate assimilation pathway. In these sulfur auxotrophs, heavy sulfate is channeled exclusively into sulfated metabolites. The method was applied to the discovery of several new sulfated molecules in Mycobacterium tuberculosis and Mycobacterium smegmatis. Because a sulfur auxotrophic strain is the only requirement of the approach, many microorganisms can be studied in this manner. Such genetic engineering in combination with stable isotopic labeling can be applied to various metabolic pathways and their products.

Characterization of Diet-Dependent Metabolic Serotypes: Proof of Principle in Female and Male Rats

Abstract: Our research seeks to identify a serum profile, or serotype, that reflects substantial changes in food intake. Earlier studies demonstrated that a number of low-molecular-weight, redox-active compounds of metabolome were sufficiently stable analytically and biologically to identify biomarkers of dietary restriction (DR, restriction of total food intake) in rats. A second initial requirement is to demonstrate feasibility, i.e., that concentration changes in selected serum metabolites can contain sufficient information to classify rats by diet. The current study distinguished 101 (female) and 112 (male) chromatographically identifiable compounds that differ between ad libitum (AL) consumption and DR 6-mo-old rats. In a cohort of female rats, both hierarchical cluster analysis (HCA) and principal component analyses (PCA) could distinguish dietary groups with 100% efficiency (101 metabolites). Repeating the classification studies using the 63 biologically and analytically most robust metabolites decreased noise without affecting categorical separation. In a cohort of male rats, PCA, but not HCA, distinguished the original dietary groups with 100% accuracy (112 metabolites). A subset of 52 of the 112 metabolites enabled both HCA and PCA to group the male rats with 100% accuracy. These data demonstrate that quantitative analysis of selected serum metabolites can yield sufficient information by which to classify the dietary intake of a group of rats, identify such markers chromatographically and set the stage for validation of these metabolic serotypes in independent datasets.

Methods of using quantitative lipid metabolome data

Abstract: Described herein in various embodiments are methods for using quantitative and/or comparative lipid metabolite data, particularly for identifying and interpreting individual metabolomic profiles as indicative of metabolic status. The provided methods, for instance, allow analysis of the likelihood or progression of weight gain or weight loss, growth or wasting, obesity, diabetes, and aging in an individual based on measurements of the measurement of the quantity of one or more lipid biomarkers, profiles of such markers, or ratios of such markers.

Metabolome profiling methods using chromatographic and spectroscopic data in pattern recognition analysis

Abstract: Methods are provided that apply neural network technology to recognize small metabolic changes in microorganisms, plants or animals to detect changes induced by pesticide (herbicide, insecticide, fungicide) treatment, genetic modification, environmental stress, and other external or internal factors that have influence on metabolite concentrations. The method implements recognition of nuclear magnetic resonance spectra, mass spectra, and/or chromatograms of crude plant extracts and association of such spectra or chromatograms with the treatment of tissue before harvest. The spectra and chromatograms have information of all the metabolites above a concentration threshold contained in the plant tissue extract. The method applies mathematical models to the very complex plant tissue extract and allows the detection of treatments with bioregulators such as pesticides, or genetic modifications such as gene insertions or deletions.

Lipid metabolome-wide effects of the peroxisome proliferator-activated receptor agonist rosiglitazone

Abstract: Successful therapy for chronic diseases must normalize a targeted aspect of metabolism without disrupting the regulation of other metabolic pathways essential for maintaining health. Use of a limited number of single molecule surrogates for disease, or biomarkers, to monitor the efficacy of a therapy may fail to predict undesirable side effects. In this study, a comprehensive metabolomic assessment of lipid metabolites was employed to determine the specific effects of the peroxisome proliferator-activated receptor (PPAR agonist rosiglitazone on structural lipid metabolism in a new mouse model of Type 2 diabetes. Dietary supplementation with rosiglitazone (200 mg/kg diet) suppressed Type 2 diabetes in obese (NZO x NON)F1 male mice, but chronic treatment markedly exacerbated hepatic steatosis. The metabolomic data revealed that rosiglitazone (i) induced hypolipidemia (by dysregulating liver–plasma lipid exchange), (ii) induced de novo fatty acid synthesis, (iii) decreased the biosynthesis of lipids within the peroxisome, (iv) substantially altered free fatty acid and cardiolipin metabolism in heart, and (v) elicited an unusual accumulation of polyunsaturated fatty acids within adipose tissue. These observations suggest that the phenotypes induced by rosiglitazone are mediated by multiple tissue-specific metabolic variables. Because many of the effects of rosiglitazone on tissue metabolism were reflected in the plasma lipid metabolome, metabolomics has excellent potential for developing clinical assessments of metabolic response to drug therapy. Supplementary key words diabetes lipid metabolism metabolic profiling metabolomics steatosis PPARsrosiglitazone mouse

Vzyme: A Template-Based Method to Predict Reactions between Two Chemical Compounds

Abstract: Metabolomics plays an important role for linking genomes and cellular functions, since metabolites are components of cellular regulatory processes and their levels can be related to responses of biological systems to genetic or enviromental changes. Compared to genome, proteome, and transcriptome, however, metabolome is just beginning to be unraveled because of the difficulties of experimental measurements. Especially, secondary metabolites are chemically instable and have complicated structures and their pathways are less well understood. We have developed a virtual enzyme system (Vzyme), which constructs metabolite networks by predicting whether each chemical compound has a reactive connection with each other. Vzyme is a template-based search program utilizing chemical knowledge and consisting of two steps. In the first step, empirical knowledge of reactant-product relationships is acquired from the REACTION section of the LIGAND database and organized in the form of a template library. In the second step, a query compound pair is checked with the template library and if any template is found an edge is made between the pair. We applied this method to infer reaction pathways of phytosterols which are plant secondary metabolites.

Genomics, proteomics: what's next?

Abstract: The Genome: DNA, the genomic material of all life’s forms, is the archival information of life, the blue print for the architecture and the engines of the cell. The Proteome: Proteins, the gene products defined within the cell’s blueprint, are the cell’s architecture and engines. The Metabolome: Small, usually monomeric, molecular metabolites of the cell that comprise the fundamental materials for macromolecular assemblage, energy exchange and signal transduction. Within this set of molecules reside the system controls of the cell and the foundations of cellular homeostasis; ‘The cell’s Software.’

Combined Metabolome and Transcriptome Analysis of Bacillus subtilis Cells Cultured on Different Carbon Sources

Abstract: Keiko Matsuda1 mkeiko@is.aist-nara.ac.jp Tomoyoshi Soga3 soga@sfc.keio.ac. jp Hirotake Yamaguchi2 hyamaguchi@bt.fubt.fukuyama-u.ac.jp Yasutaro Fujita2 yfujita@bt.fubt.fukuyama-u.ac.jp Yuki Ueno3 yuki-u@ttck.keio.ac.jp Takaaki Nishioka3,4 nishioka@scl.kyoto-u.ac.jp 1 Graduate School of Inform ation Science, Nara Institute of Science and Technology, Ikoma 630-0101, Japan 2 Department of Biotechnology , Fukuyama University, Fukuyama 729-0292, Japan 3 Institute for Advanced Biosciences , Keio University, Tsuruoka 997-0035, Japan 4 Graduate School of Agriculture , Kyoto University, Kitashirakawa, Sakyo-ku, Kyoto 606-8502, Japan