Dynamic flux responses in riboflavin overproducing <i>Bacillus subtilis</i> to increasing glucose limitation in fed‐batch culture

Rühl, Martin; Zamboni, Nicola; Sauer, Uwe

doi:10.1002/bit.22591

Cited by 31 publications

(17 citation statements)

References 47 publications

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“…Cell metabolisms are highly dependent on environmental conditions, so the metabolic state often shifts during the cultivation period [1], [2], [3]. Characterizing the transience of metabolic fluxes is important for understanding how cells responded to environmental changes.…”

Section: Introductionmentioning

confidence: 99%

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Feng

Chen

et al. 2012

PLoS Comput Biol

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Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of “maximizing growth rate” and “minimizing overall flux”) to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.

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Section: Introductionmentioning

confidence: 99%

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Feng

Chen

et al. 2012

PLoS Comput Biol

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“…This result confirms that the FAAs-based method can reduce labeling time for MFA. Although the faster turnover rate for FAAs is preferable for an MFA study of batch and fed-batch culture [12,13,14,15], it remains unclear whether a reliable result can be produced by FAAs-based MFA. It is not also obvious which FAAs are reproducibly observed from E. coli cells in various culture conditions, and whether a precise metabolic flux can be estimated by using the observed amino acids.…”

Section: Resultsmentioning

confidence: 99%

“…For the MFA study of batch and fed-batch culture, intracellular free amino acids (FAAs) with faster turnover rates are more promising targets for a 13 C enrichment measurement [12,13,14,15,16]. The time course analysis of 13 C enrichment of FAAs demonstrated that the experimental time required to reach an isotopic steady state of the FAAs is 2 to 10 times faster than that for PAAs [17].…”

Section: Introductionmentioning

confidence: 99%

Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids

et al. 2014

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13C metabolic flux analysis (MFA) is a tool of metabolic engineering for investigation of in vivo flux distribution. A direct 13C enrichment analysis of intracellular free amino acids (FAAs) is expected to reduce time for labeling experiments of the MFA. Measurable FAAs should, however, vary among the MFA experiments since the pool sizes of intracellular free metabolites depend on cellular metabolic conditions. In this study, minimal 13C enrichment data of FAAs was investigated to perform the FAAs-based MFA. An examination of a continuous culture of Escherichia coli using 13C-labeled glucose showed that the time required to reach an isotopically steady state for FAAs is rather faster than that for conventional method using proteinogenic amino acids (PAAs). Considering 95% confidence intervals, it was found that the metabolic flux distribution estimated using FAAs has a similar reliability to that of the PAAs-based method. The comparative analysis identified glutamate, aspartate, alanine and phenylalanine as the common amino acids observed in E. coli under different culture conditions. The results of MFA also demonstrated that the 13C enrichment data of the four amino acids is required for a reliable analysis of the flux distribution.

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“…In the biotechnology industry, non-stationary 13 C-metabolic flux ratio analyses via isotopic tracing have obtained the snapshots of the metabolic fluxes through several key nodes in B. subtilis [108]. Time-dependent fluxomics has also been proposed to investigate B. subtilis fermentation processes [110]. …”

Section: Application Of Isotope-assisted Metabolomicsmentioning

confidence: 99%

Application of Stable Isotope-Assisted Metabolomics for Cell Metabolism Studies

2014

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The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other “omics” analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research.

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Dynamic flux responses in riboflavin overproducing Bacillus subtilis to increasing glucose limitation in fed‐batch culture

Cited by 31 publications

References 47 publications

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Integrating Flux Balance Analysis into Kinetic Models to Decipher the Dynamic Metabolism of Shewanella oneidensis MR-1

Reliable Metabolic Flux Estimation in Escherichia coli Central Carbon Metabolism Using Intracellular Free Amino Acids

Application of Stable Isotope-Assisted Metabolomics for Cell Metabolism Studies

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