Literature DB >> 17400499

Metabolic flux analysis in a nonstationary system: fed-batch fermentation of a high yielding strain of E. coli producing 1,3-propanediol.

Maciek R Antoniewicz1, David F Kraynie, Lisa A Laffend, Joanna González-Lergier, Joanne K Kelleher, Gregory Stephanopoulos.   

Abstract

Metabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of Escherichia coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism.

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Year:  2007        PMID: 17400499      PMCID: PMC2048574          DOI: 10.1016/j.ymben.2007.01.003

Source DB:  PubMed          Journal:  Metab Eng        ISSN: 1096-7176            Impact factor:   9.783


  20 in total

Review 1.  Flux estimation using isotopic tracers: common ground for metabolic physiology and metabolic engineering.

Authors:  J K Kelleher
Journal:  Metab Eng       Date:  2001-04       Impact factor: 9.783

Review 2.  Metabolic fluxes and metabolic engineering.

Authors:  G Stephanopoulos
Journal:  Metab Eng       Date:  1999-01       Impact factor: 9.783

3.  GC-MS analysis of amino acids rapidly provides rich information for isotopomer balancing.

Authors:  M Dauner; U Sauer
Journal:  Biotechnol Prog       Date:  2000 Jul-Aug

4.  In vivo analysis of intracellular amino acid labelings by GC/MS.

Authors:  Christoph Wittmann; Michael Hans; Elmar Heinzle
Journal:  Anal Biochem       Date:  2002-08-15       Impact factor: 3.365

Review 5.  Metabolic engineering for the microbial production of 1,3-propanediol.

Authors:  Charles E Nakamura; Gregory M Whited
Journal:  Curr Opin Biotechnol       Date:  2003-10       Impact factor: 9.740

6.  Elementary metabolite units (EMU): a novel framework for modeling isotopic distributions.

Authors:  Maciek R Antoniewicz; Joanne K Kelleher; Gregory Stephanopoulos
Journal:  Metab Eng       Date:  2006-09-17       Impact factor: 9.783

7.  Accurate assessment of amino acid mass isotopomer distributions for metabolic flux analysis.

Authors:  Maciek R Antoniewicz; Joanne K Kelleher; Gregory Stephanopoulos
Journal:  Anal Chem       Date:  2007-09-07       Impact factor: 6.986

8.  Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices.

Authors:  K Schmidt; M Carlsen; J Nielsen; J Villadsen
Journal:  Biotechnol Bioeng       Date:  1997-09-20       Impact factor: 4.530

9.  Biosynthetically directed fractional 13C-labeling of proteinogenic amino acids. An efficient analytical tool to investigate intermediary metabolism.

Authors:  T Szyperski
Journal:  Eur J Biochem       Date:  1995-09-01

10.  Production process monitoring by serial mapping of microbial carbon flux distributions using a novel Sensor Reactor approach: II--(13)C-labeling-based metabolic flux analysis and L-lysine production.

Authors:  A Drysch; M El Massaoudi; C Mack; R Takors; A A de Graaf; H Sahm
Journal:  Metab Eng       Date:  2003-04       Impact factor: 9.783

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  63 in total

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Journal:  Metab Eng       Date:  2013-09-08       Impact factor: 9.783

2.  The cellular geometry of growth drives the amino acid economy of Caenorhabditis elegans.

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3.  (13)C-based metabolic flux analysis.

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4.  Quantitation of cellular metabolic fluxes of methionine.

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Journal:  Anal Chem       Date:  2014-01-16       Impact factor: 6.986

5.  Central metabolic responses to the overproduction of fatty acids in Escherichia coli based on 13C-metabolic flux analysis.

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Journal:  Biotechnol Bioeng       Date:  2014-03       Impact factor: 4.530

6.  Co-utilization of glucose and xylose by evolved Thermus thermophilus LC113 strain elucidated by (13)C metabolic flux analysis and whole genome sequencing.

Authors:  Lauren T Cordova; Jing Lu; Robert M Cipolla; Nicholas R Sandoval; Christopher P Long; Maciek R Antoniewicz
Journal:  Metab Eng       Date:  2016-05-07       Impact factor: 9.783

7.  Fast growth phenotype of E. coli K-12 from adaptive laboratory evolution does not require intracellular flux rewiring.

Authors:  Christopher P Long; Jacqueline E Gonzalez; Adam M Feist; Bernhard O Palsson; Maciek R Antoniewicz
Journal:  Metab Eng       Date:  2017-09-23       Impact factor: 9.783

Review 8.  Methods and advances in metabolic flux analysis: a mini-review.

Authors:  Maciek R Antoniewicz
Journal:  J Ind Microbiol Biotechnol       Date:  2015-01-23       Impact factor: 3.346

9.  Allosteric inhibition of MTHFR prevents futile SAM cycling and maintains nucleotide pools in one-carbon metabolism.

Authors:  Muskan Bhatia; Jyotika Thakur; Shradha Suyal; Ruchika Oniel; Rahul Chakraborty; Shalini Pradhan; Monika Sharma; Shantanu Sengupta; Sunil Laxman; Shyam Kumar Masakapalli; Anand Kumar Bachhawat
Journal:  J Biol Chem       Date:  2020-09-15       Impact factor: 5.157

Review 10.  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

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