Showing papers on "Metabolome published in 2001"

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

Abstract: A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are "silent," that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing "metabolic snapshots," can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY—an abbreviation for functional analysis by co-responses in yeast.

Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks

Abstract: Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms – the phenotypes – are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed. Copyright # 2001 John Wiley & Sons, Ltd.

Networking proteins in yeast

Abstract: The advent of genome sequencing projects—culminating in the recent reports of the human sequence (1, 2)—has resulted in both the identification of novel genes and proteins as well as the proliferation of the “omes” that come from their analyses: the proteome (the complement of proteins), transcriptome (the complement of mRNA transcripts), metabolome (the complement of metabolites), and so on. These end products of global assays are needed to interpret the large fraction (typically close to half) of predicted proteins for which no proteins of similar structure exist or have been functionally characterized. The report by Ito et al. (3) is the largest contribution to date in the effort to generate the protein interactome, or map of protein–protein interactions, for the yeast Saccharomyces cerevisiae.

Characterization of Diet-Dependent Metabolic Serotypes: Analytical and Biological Variability Issues in Rats

Abstract: This report, the first in a series on diet-dependent changes in the serum metabolome (metabolic serotype), describes validation of the use of high performance liquid chromatography (HPLC) separations coupled with Coulometric array detectors to characterize changes in the metabolome. The long-term aim of these studies is to improve understanding of the effects of significant variation in nutritive status on physiology and on disease processes. Initial studies focus on identifying the effects of dietary (or caloric) restriction on the redox-active components of rat serum. Identification of compounds of interest is being carried out using HPLC separations coupled with coulometric array analysis, an approach allowing simultaneous examination of nearly 1200 serum compounds. The technical and practical issues discussed in this report are related to both analytical validity (HPLC running conditions, computer-automated peak identification, mathematical compensation for chromatographic drift, etc.) and biological variability (individual variability, cohort-cohort variability, outliers). Attention to these issues suggests ∼250 compounds in serum are sufficiently reliable, both analytically and biologically, for potential use in building mathematical models of serotype.

Capillary electrophoretic techniques toward the metabolome analysis

Abstract: Metabolome analysis is a systematic chemical analysis of metabolites, which may be used to investigate the metabolic activity in the cell. Capillary electrophoresis (CE) is one of the most promising techniques for the metabolome analysis, because it gives high-resolu- tion separations in a reasonable time and requires a minimum amount of samples. General characteristics of CE are discussed from the viewpoint of metabolome analysis. Micellar electrokinetic chromatography (MEKC), a separation mode of CE, enables the separation of neutral analytes by using micelles as pseudostationary phases. MEKC is also powerful for the separation of ionic analytes to improve selectivity. To solve relatively poor concentration sensitivity with UV absorbance detection, on-line sample preconcentration techniques were developed resulting in up to few thousand-fold increases in sensitivity. Laser-induced fluo- rescence detection is another solution to increase concentration sensitivity, but most analytes are not natively fluorescent. Therefore, several derivatization reactions were performed to selectively detect a class of analytes with high sensitivity. Some preliminary results are shown with formic acid extracts of Bacillus subtilis.