Literature DB >> 12568740

Metabolic flux analysis of Escherichia coli K12 grown on 13C-labeled acetate and glucose using GC-MS and powerful flux calculation method.

Jiao Zhao1, Kazuyuki Shimizu.   

Abstract

A new algorithm was developed for the estimation of the metabolic flux distribution based on GC-MS data of proteinogenic amino acids. By using a sensitive GC-MS protocol as well as by combining the global search algorithm such as the genetic algorithm with the local search algorithm such as the Levenberg-Marquardt algorithm, not only the distribution of the net fluxes in the entire network, but also certain exchange fluxes which contribute significantly to the isotopomer distribution could be quantified. This mass isotopomer analysis could identify the biochemical changes involved in the regulation where acetate or glucose was used as a main carbon source. The metabolic flux analysis clearly revealed that when the specific growth rate increased, only a slight change in flux distribution was observed for acetate metabolism, indicating that subtle regulation mechanism exists in certain key junctions of this network system. Different from acetate metabolism, when glucose was used as a carbon source, as the growth rate increased, a significant increase in relative pentose phosphate pathway (PPP) flux was observed for Escherichia coli K12 at the expense of the citric acid cycle, suggesting that when growing on glucose, the flux catalyzed by isocitrate dehydrogenase could not fully fulfill the NADPH demand for cell growth, causing the oxidative PPP to be utilized to a larger extent so as to complement the NADPH demand. The GC-MS protocol as well as the new algorithm demonstrated here proved to be a powerful tool for characterizing metabolic regulation and can be utilized for strain improvement and bioprocess optimization. Copyright 2002 Elsevier Science B.V.

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Year:  2003        PMID: 12568740     DOI: 10.1016/s0168-1656(02)00316-4

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  47 in total

1.  CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics.

Authors:  Zhengdong Zhang; Tie Shen; Bin Rui; Wenwei Zhou; Xiangfei Zhou; Chuanyu Shang; Chenwei Xin; Xiaoguang Liu; Gang Li; Jiansi Jiang; Chao Li; Ruiyuan Li; Mengshu Han; Shanping You; Guojun Yu; Yin Yi; Han Wen; Zhijie Liu; Xiaoyao Xie
Journal:  Nucleic Acids Res       Date:  2014-11-11       Impact factor: 16.971

2.  Pathway confirmation and flux analysis of central metabolic pathways in Desulfovibrio vulgaris hildenborough using gas chromatography-mass spectrometry and Fourier transform-ion cyclotron resonance mass spectrometry.

Authors:  Yinjie Tang; Francesco Pingitore; Aindrila Mukhopadhyay; Richard Phan; Terry C Hazen; Jay D Keasling
Journal:  J Bacteriol       Date:  2006-11-17       Impact factor: 3.490

3.  Decoding how a soil bacterium extracts building blocks and metabolic energy from ligninolysis provides road map for lignin valorization.

Authors:  Arul M Varman; Lian He; Rhiannon Follenfant; Weihua Wu; Sarah Wemmer; Steven A Wrobel; Yinjie J Tang; Seema Singh
Journal:  Proc Natl Acad Sci U S A       Date:  2016-09-15       Impact factor: 11.205

4.  Metabolic flux analysis of Escherichia coli creB and arcA mutants reveals shared control of carbon catabolism under microaerobic growth conditions.

Authors:  Pablo I Nikel; Jiangfeng Zhu; Ka-Yiu San; Beatriz S Méndez; George N Bennett
Journal:  J Bacteriol       Date:  2009-06-26       Impact factor: 3.490

5.  2H/1H variation in microbial lipids is controlled by NADPH metabolism.

Authors:  Reto S Wijker; Alex L Sessions; Tobias Fuhrer; Michelle Phan
Journal:  Proc Natl Acad Sci U S A       Date:  2019-05-31       Impact factor: 11.205

6.  Role of phosphoenolpyruvate in the NADP-isocitrate dehydrogenase and isocitrate lyase reaction in Escherichia coli.

Authors:  Tadashi Ogawa; Keiko Murakami; Hirotada Mori; Nobuyoshi Ishii; Masaru Tomita; Masataka Yoshin
Journal:  J Bacteriol       Date:  2006-12-01       Impact factor: 3.490

7.  Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling.

Authors:  Srinath Garg; Laurence Yang; Radhakrishnan Mahadevan
Journal:  BMC Res Notes       Date:  2010-05-05

8.  A systematic investigation of Escherichia coli central carbon metabolism in response to superoxide stress.

Authors:  Bin Rui; Tie Shen; Hong Zhou; Jianping Liu; Jiusheng Chen; Xiaosong Pan; Haiyan Liu; Jihui Wu; Haoran Zheng; Yunyu Shi
Journal:  BMC Syst Biol       Date:  2010-09-01

9.  Modeling and simulation of the main metabolism in Escherichia coli and its several single-gene knockout mutants with experimental verification.

Authors:  Tuty Asmawaty Abdul Kadir; Ahmad A Mannan; Andrzej M Kierzek; Johnjoe McFadden; Kazuyuki Shimizu
Journal:  Microb Cell Fact       Date:  2010-11-19       Impact factor: 5.328

10.  Large D/H variations in bacterial lipids reflect central metabolic pathways.

Authors:  Xinning Zhang; Aimee L Gillespie; Alex L Sessions
Journal:  Proc Natl Acad Sci U S A       Date:  2009-07-17       Impact factor: 11.205

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