Literature DB >> 17698812

Metabolite essentiality elucidates robustness of Escherichia coli metabolism.

Pan-Jun Kim1, Dong-Yup Lee, Tae Yong Kim, Kwang Ho Lee, Hawoong Jeong, Sang Yup Lee, Sunwon Park.   

Abstract

Complex biological systems are very robust to genetic and environmental changes at all levels of organization. Many biological functions of Escherichia coli metabolism can be sustained against single-gene or even multiple-gene mutations by using redundant or alternative pathways. Thus, only a limited number of genes have been identified to be lethal to the cell. In this regard, the reaction-centric gene deletion study has a limitation in understanding the metabolic robustness. Here, we report the use of flux-sum, which is the summation of all incoming or outgoing fluxes around a particular metabolite under pseudo-steady state conditions, as a good conserved property for elucidating such robustness of E. coli from the metabolite point of view. The functional behavior, as well as the structural and evolutionary properties of metabolites essential to the cell survival, was investigated by means of a constraints-based flux analysis under perturbed conditions. The essential metabolites are capable of maintaining a steady flux-sum even against severe perturbation by actively redistributing the relevant fluxes. Disrupting the flux-sum maintenance was found to suppress cell growth. This approach of analyzing metabolite essentiality provides insight into cellular robustness and concomitant fragility, which can be used for several applications, including the development of new drugs for treating pathogens.

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Year:  2007        PMID: 17698812      PMCID: PMC1947999          DOI: 10.1073/pnas.0703262104

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  22 in total

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Authors:  M Kanehisa; S Goto
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2.  The large-scale organization of metabolic networks.

Authors:  H Jeong; B Tombor; R Albert; Z N Oltvai; A L Barabási
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Review 3.  Metabolites: a helping hand for pathway evolution?

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Journal:  Trends Biochem Sci       Date:  2003-06       Impact factor: 13.807

4.  Lethal combinations.

Authors:  Chandra L Tucker; Stanley Fields
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5.  One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products.

Authors:  K A Datsenko; B L Wanner
Journal:  Proc Natl Acad Sci U S A       Date:  2000-06-06       Impact factor: 11.205

6.  Analysis of optimality in natural and perturbed metabolic networks.

Authors:  Daniel Segrè; Dennis Vitkup; George M Church
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-01       Impact factor: 11.205

7.  Systematic genetic analysis with ordered arrays of yeast deletion mutants.

Authors:  A H Tong; M Evangelista; A B Parsons; H Xu; G D Bader; N Pagé; M Robinson; S Raghibizadeh; C W Hogue; H Bussey; B Andrews; M Tyers; C Boone
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8.  Experimental determination and system level analysis of essential genes in Escherichia coli MG1655.

Authors:  S Y Gerdes; M D Scholle; J W Campbell; G Balázsi; E Ravasz; M D Daugherty; A L Somera; N C Kyrpides; I Anderson; M S Gelfand; A Bhattacharya; V Kapatral; M D'Souza; M V Baev; Y Grechkin; F Mseeh; M Y Fonstein; R Overbeek; A-L Barabási; Z N Oltvai; A L Osterman
Journal:  J Bacteriol       Date:  2003-10       Impact factor: 3.490

9.  Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms.

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Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

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Authors:  Jennifer L Reed; Thuy D Vo; Christophe H Schilling; Bernhard O Palsson
Journal:  Genome Biol       Date:  2003-08-28       Impact factor: 13.583

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  42 in total

Review 1.  The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli.

Authors:  Adam M Feist; Bernhard Ø Palsson
Journal:  Nat Biotechnol       Date:  2008-06       Impact factor: 54.908

Review 2.  Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

Authors:  Nathan E Lewis; Harish Nagarajan; Bernhard O Palsson
Journal:  Nat Rev Microbiol       Date:  2012-02-27       Impact factor: 60.633

Review 3.  Constraint-based models predict metabolic and associated cellular functions.

Authors:  Aarash Bordbar; Jonathan M Monk; Zachary A King; Bernhard O Palsson
Journal:  Nat Rev Genet       Date:  2014-01-16       Impact factor: 53.242

4.  Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network.

Authors:  Bevan Kai Sheng Chung; Dong-Yup Lee
Journal:  BMC Syst Biol       Date:  2009-12-19

5.  Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement.

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Journal:  Microb Cell Fact       Date:  2010-07-01       Impact factor: 5.328

6.  In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories.

Authors:  Meiyappan Lakshmanan; Kai Yu; Lokanand Koduru; Dong-Yup Lee
Journal:  J Ind Microbiol Biotechnol       Date:  2015-08-08       Impact factor: 3.346

Review 7.  Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology.

Authors:  Caroline B Milne; Pan-Jun Kim; James A Eddy; Nathan D Price
Journal:  Biotechnol J       Date:  2009-12       Impact factor: 4.677

8.  A study in molecular contingency: glutamine phosphoribosylpyrophosphate amidotransferase is a promiscuous and evolvable phosphoribosylanthranilate isomerase.

Authors:  Wayne M Patrick; Ichiro Matsumura
Journal:  J Mol Biol       Date:  2008-01-26       Impact factor: 5.469

Review 9.  Genome-scale models of bacterial metabolism: reconstruction and applications.

Authors:  Maxime Durot; Pierre-Yves Bourguignon; Vincent Schachter
Journal:  FEMS Microbiol Rev       Date:  2008-12-03       Impact factor: 16.408

10.  Genome-scale gene/reaction essentiality and synthetic lethality analysis.

Authors:  Patrick F Suthers; Alireza Zomorrodi; Costas D Maranas
Journal:  Mol Syst Biol       Date:  2009-08-18       Impact factor: 11.429

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