Literature DB >> 11135551

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

L M Raamsdonk1, B Teusink, D Broadhurst, N Zhang, A Hayes, M C Walsh, J A Berden, K M Brindle, D B Kell, J J Rowland, H V Westerhoff, K van Dam, S G Oliver.   

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.

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Year:  2001        PMID: 11135551     DOI: 10.1038/83496

Source DB:  PubMed          Journal:  Nat Biotechnol        ISSN: 1087-0156            Impact factor:   54.908


  214 in total

1.  Control analysis for autonomously oscillating biochemical networks.

Authors:  Karin A Reijenga; Hans V Westerhoff; Boris N Kholodenko; Jacky L Snoep
Journal:  Biophys J       Date:  2002-01       Impact factor: 4.033

Review 2.  Metabolomics--the link between genotypes and phenotypes.

Authors:  Oliver Fiehn
Journal:  Plant Mol Biol       Date:  2002-01       Impact factor: 4.076

3.  Product dependence and bifunctionality compromise the ultrasensitivity of signal transduction cascades.

Authors:  Fernando Ortega; Luis Acerenza; Hans V Westerhoff; Francesc Mas; Marta Cascante
Journal:  Proc Natl Acad Sci U S A       Date:  2002-02-05       Impact factor: 11.205

Review 4.  Thirteen years of building constraint-based in silico models of Escherichia coli.

Authors:  Jennifer L Reed; Bernhard Ø Palsson
Journal:  J Bacteriol       Date:  2003-05       Impact factor: 3.490

5.  Differential metabolic networks unravel the effects of silent plant phenotypes.

Authors:  Wolfram Weckwerth; Marcelo Ehlers Loureiro; Kathrin Wenzel; Oliver Fiehn
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-10       Impact factor: 11.205

Review 6.  6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase: head-to-head with a bifunctional enzyme that controls glycolysis.

Authors:  Mark H Rider; Luc Bertrand; Didier Vertommen; Paul A Michels; Guy G Rousseau; Louis Hue
Journal:  Biochem J       Date:  2004-08-01       Impact factor: 3.857

Review 7.  Metabolic profiles to define the genome: can we hear the phenotypes?

Authors:  Julian L Griffin
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2004-06-29       Impact factor: 6.237

Review 8.  Functional aspects of cellular microcompartmentation in the development of neurodegeneration: mutation induced aberrant protein-protein associations.

Authors:  Judit Ovádi; Ferenc Orosz; Susan Hollán
Journal:  Mol Cell Biochem       Date:  2004 Jan-Feb       Impact factor: 3.396

9.  Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses.

Authors:  Jong Myoung Park; Tae Yong Kim; Sang Yup Lee
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-02       Impact factor: 11.205

Review 10.  Integrating omics technologies to study pulmonary physiology and pathology at the systems level.

Authors:  Ravi Ramesh Pathak; Vrushank Davé
Journal:  Cell Physiol Biochem       Date:  2014-04-28
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