Literature DB >> 16263707

A quantitative approach to catabolite repression in Escherichia coli.

Katja Bettenbrock1, Sophia Fischer, Andreas Kremling, Knut Jahreis, Thomas Sauter, Ernst-Dieter Gilles.   

Abstract

A dynamic mathematical model was developed to describe the uptake of various carbohydrates (glucose, lactose, glycerol, sucrose, and galactose) in Escherichia coli. For validation a number of isogenic strains with defined mutations were used. By considering metabolic reactions as well as signal transduction processes influencing the relevant pathways, we were able to describe quantitatively the phenomenon of catabolite repression in E. coli. We verified model predictions by measuring time courses of several extra- and intracellular components such as glycolytic intermediates, EII-ACrr phosphorylation level, both LacZ and PtsG concentrations, and total cAMP concentrations under various growth conditions. The entire data base consists of 18 experiments performed with nine different strains. The model describes the expression of 17 key enzymes, 38 enzymatic reactions, and the dynamic behavior of more than 50 metabolites. The different phenomena affecting the phosphorylation level of EIIACrr, the key regulation molecule for inducer exclusion and catabolite repression in enteric bacteria, can now be explained quantitatively.

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Year:  2005        PMID: 16263707     DOI: 10.1074/jbc.M508090200

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  37 in total

Review 1.  Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor.

Authors:  Heejoon Park; S Lee McGill; Adrienne D Arnold; Ross P Carlson
Journal:  Cell Mol Life Sci       Date:  2019-11-25       Impact factor: 9.261

2.  Correlation between growth rates, EIIACrr phosphorylation, and intracellular cyclic AMP levels in Escherichia coli K-12.

Authors:  Katja Bettenbrock; Thomas Sauter; Knut Jahreis; Andreas Kremling; Joseph W Lengeler; Ernst-Dieter Gilles
Journal:  J Bacteriol       Date:  2007-08-03       Impact factor: 3.490

3.  Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli.

Authors:  Markus W Covert; Nan Xiao; Tiffany J Chen; Jonathan R Karr
Journal:  Bioinformatics       Date:  2008-07-10       Impact factor: 6.937

4.  Diverse two-dimensional input functions control bacterial sugar genes.

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Journal:  Mol Cell       Date:  2008-03-28       Impact factor: 17.970

5.  On the identifiability of metabolic network models.

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Journal:  J Math Biol       Date:  2012-11-15       Impact factor: 2.259

6.  Functioning of a metabolic flux sensor in Escherichia coli.

Authors:  Karl Kochanowski; Benjamin Volkmer; Luca Gerosa; Bart R Haverkorn van Rijsewijk; Alexander Schmidt; Matthias Heinemann
Journal:  Proc Natl Acad Sci U S A       Date:  2012-12-31       Impact factor: 11.205

Review 7.  The bacterial phosphoenolpyruvate:carbohydrate phosphotransferase system: regulation by protein phosphorylation and phosphorylation-dependent protein-protein interactions.

Authors:  Josef Deutscher; Francine Moussan Désirée Aké; Meriem Derkaoui; Arthur Constant Zébré; Thanh Nguyen Cao; Houda Bouraoui; Takfarinas Kentache; Abdelhamid Mokhtari; Eliane Milohanic; Philippe Joyet
Journal:  Microbiol Mol Biol Rev       Date:  2014-06       Impact factor: 11.056

8.  Confirmation and elimination of xylose metabolism bottlenecks in glucose phosphoenolpyruvate-dependent phosphotransferase system-deficient Clostridium acetobutylicum for simultaneous utilization of glucose, xylose, and arabinose.

Authors:  Han Xiao; Yang Gu; Yuanyuan Ning; Yunliu Yang; Wilfrid J Mitchell; Weihong Jiang; Sheng Yang
Journal:  Appl Environ Microbiol       Date:  2011-09-16       Impact factor: 4.792

9.  The carbon assimilation network in Escherichia coli is densely connected and largely sign-determined by directions of metabolic fluxes.

Authors:  Valentina Baldazzi; Delphine Ropers; Yves Markowicz; Daniel Kahn; Johannes Geiselmann; Hidde de Jong
Journal:  PLoS Comput Biol       Date:  2010-06-10       Impact factor: 4.475

10.  Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity.

Authors:  Sarah-Maria Fendt; Joerg Martin Buescher; Florian Rudroff; Paola Picotti; Nicola Zamboni; Uwe Sauer
Journal:  Mol Syst Biol       Date:  2010-04-13       Impact factor: 11.429

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