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Metabolome Analysis (metabolome + analysis)
Selected AbstractsMetabolome Analysis: An Introduction Wiley-Interscience Series on Mass Spectrometry.PROTEOMICS - CLINICAL APPLICATIONS, Issue 10 2007By Silas G. Villas-Bôas, Jens Nielsen, Jørn Smedsgaard, Michael A. E. Hansen, Ute Roessner No abstracts. [source] Integrated Sampling Procedure for Metabolome AnalysisBIOTECHNOLOGY PROGRESS, Issue 5 2006Jochen Schaub Metabolome analysis, the analysis of large sets of intracellular metabolites, has become an important systems analysis method in biotechnological and pharmaceutical research. In metabolic engineering, the integration of metabolome data with fluxome and proteome data into large-scale mathematical models promises to foster rational strategies for strain and cell line improvement. However, the development of reproducible sampling procedures for quantitative analysis of intracellular metabolite concentrations represents a major challenge, accomplishing (i) fast transfer of sample, (ii) efficient quenching of metabolism, (iii) quantitative metabolite extraction, and (iv) optimum sample conditioning for subsequent quantitative analysis. In addressing these requirements, we propose an integrated sampling procedure. Simultaneous quenching and quantitative extraction of intracellular metabolites were realized by short-time exposure of cells to temperatures ,95 °C, where intracellular metabolites are released quantitatively. Based on these findings, we combined principles of heat transfer with knowledge on physiology, for example, turnover rates of energy metabolites, to develop an optimized sampling procedure based on a coiled single tube heat exchanger. As a result, this sampling procedure enables reliable and reproducible measurements through (i) the integration of three unit operations into a one unit operation, (ii) the avoidance of any alteration of the sample due to chemical reagents in quenching and extraction, and (iii) automation. A sampling frequency of 5 s,1 and an overall individual sample processing time faster than 30 s allow observing responses of intracellular metabolite concentrations to extracellular stimuli on a subsecond time scale. Recovery and reliability of the unit operations were analyzed. Impact of sample conditioning on subsequent IC-MS analysis of metabolites was examined as well. The integrated sampling procedure was validated through consistent results from steady-state metabolite analysis of Escherichia coli cultivated in a chemostat at D = 0.1 h,1. [source] Metabolomics: Current technologies and future trendsPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 17 2006Katherine Hollywood Abstract The ability to sequence whole genomes has taught us that our knowledge with respect to gene function is rather limited with typically 30,40% of open reading frames having no known function. Thus, within the life sciences there is a need for determination of the biological function of these so-called orphan genes, some of which may be molecular targets for therapeutic intervention. The search for specific mRNA, proteins, or metabolites that can serve as diagnostic markers has also increased, as has the fact that these biomarkers may be useful in following and predicting disease progression or response to therapy. Functional analyses have become increasingly popular. They include investigations at the level of gene expression (transcriptomics), protein translation (proteomics) and more recently the metabolite network (metabolomics). This article provides an overview of metabolomics and discusses its complementary role with transcriptomics and proteomics, and within system biology. It highlights how metabolome analyses are conducted and how the highly complex data that are generated are analysed. Non-invasive footprinting analysis is also discussed as this has many applications to in,vitro cell systems. Finally, for studying biotic or abiotic stresses on animals, plants or microbes, we believe that metabolomics could very easily be applied to large populations, because this approach tends to be of higher throughput and generally lower cost than transcriptomics and proteomics, whilst also providing indications of which area of metabolism may be affected by external perturbation. [source] Gas chromatographic,mass spectrometric urinary metabolome analysis to study mutations of inborn errors of metabolismMASS SPECTROMETRY REVIEWS, Issue 6 2005Tomiko Kuhara 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. © 2004 Wiley Periodicals, Inc., Mass Spec Rev 24:814,827, 2005 [source] Biosynthesis of cellulose-enriched tension wood in Populus: global analysis of transcripts and metabolites identifies biochemical and developmental regulators in secondary wall biosynthesisTHE PLANT JOURNAL, Issue 2 2006Sara Andersson-Gunnerås Summary Stems and branches of angiosperm trees form tension wood (TW) when exposed to a gravitational stimulus. One of the main characteristics of TW, which distinguishes it from normal wood, is the formation of fibers with a thick inner gelatinous cell wall layer mainly composed of crystalline cellulose. Hence TW is enriched in cellulose, and deficient in lignin and hemicelluloses. An expressed sequence tag library made from TW-forming tissues in Populus tremula (L.) × tremuloides (Michx.) and data from transcript profiling using microarray and metabolite analysis were obtained during TW formation in Populus tremula (L.) in two growing seasons. The data were examined with the aim of identifying the genes responsible for the change in carbon (C) flow into various cell wall components, and the mechanisms important for the formation of the gelatinous cell wall layer (G-layer). A specific effort was made to identify carbohydrate-active enzymes with a putative function in cell wall biosynthesis. An increased C flux to cellulose was suggested by a higher abundance of sucrose synthase transcripts. However, genes related to the cellulose biosynthetic machinery were not generally affected, although the expression of secondary wall-specific CesA genes was modified in both directions. Other pathways for which the data suggested increased activity included lipid and glucosamine biosynthesis and the pectin degradation machinery. In addition, transcripts encoding fasciclin-like arabinogalactan proteins were particularly increased and found to lack true Arabidopsis orthologs. Major pathways for which the transcriptome and metabolome analysis suggested decreased activity were the pathway for C flux through guanosine 5,-diphosphate (GDP) sugars to mannans, the pentose phosphate pathway, lignin biosynthesis, and biosynthesis of cell wall matrix carbohydrates. Several differentially expressed auxin- and ethylene-related genes and transcription factors were also identified. [source] | |||||||||||||