A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis

Liu, Yanfeng; Link, Hannes; Liu, Long; Du, Guocheng; Chen, Jian; Sauer, Uwe

doi:10.1038/ncomms11933

Cited by 45 publications

(16 citation statements)

References 33 publications

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“…A parsimonious explanation is that metabolic bottlenecks somewhere along these unique entry routes render cells unable to increase glycolysis to match their energy demands. While pinpointing metabolic bottlenecks remains challenging even in microbial systems 43 , we reasoned that intracellular metabolite data may enable us to distinguish between two potential bottlenecks for each sugar: the internal conversion step (which often manifests as an accumulation of the immediate substrate) or the sugar transport (where no such accumulation is observed). Our metabolomics data cannot distinguish the hexose-phosphate species unique to each sugar utilization pathway (Figure 6A).…”

Section: Resultsmentioning

confidence: 99%

Dissecting the impact of metabolic environment on three common cancer cell phenotypes

Kochanowski

Sander

Link

et al. 2020

Preprint

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8The impact of different metabolic environments on cancer cell behavior is poorly understood. Here, we 9 systematically altered nutrient composition of cell culture media and examined the impact on three 10 phenotypes-drug-treatment survival, cell migration, and lactate overflow-that are frequently studied 11 in cancer cells. These perturbations across diverse metabolic environments revealed simple relationships 12 between cell growth rate and drug-treatment survival or migration. In contrast, lactate overflow was 13 highly sensitive to changes in sugar availability but largely insensitive to changes in amino acid availability, 14regardless of the growth rate. Further investigation suggested that the degree of lactate overflow across 15 metabolic environments is largely determined by the cells' ability to maintain high rates of sugar uptake. 16This study enabled us to elucidate quantitative relationships between metabolic environment and cancer 17 cell phenotypes, which echo empirical growth laws discovered to govern analogous phenotypes in 18 microbes. 19

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Section: Resultsmentioning

confidence: 99%

Dissecting the impact of metabolic environment on three common cancer cell phenotypes

Kochanowski

Sander

Link

et al. 2020

Preprint

Self Cite

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“…Studying metabolite dynamics provides a way to assess the in vivo kinetic parameters and to assemble more accurate models for metabolic engineering . Furthermore, metabolite dynamics can be used to identify unknown network interactions including those that cause pathway bottlenecks . Different bottlenecks produce different metabolite dynamics which allows rapid bottleneck identification by comparing metabolite time course data to a kinetic model.…”

Section: Metabolite Dynamicsmentioning

confidence: 99%

Engineering Microbial Metabolite Dynamics and Heterogeneity

Schmitz

Hartline

Zhang

2017

Biotechnology Journal

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As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.

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“…Both challenges may benefit from the development of systems biology and synthetic biology. To identify and remove potential bottlenecks, several strategies have been developed, such as dynamic pathway analysis [ 9 ], X-omic technology [ 10 ], reverse metabolic engineering [ 11 ], in vitro metabolic engineering [ 12 ], and CRISPRi system [ 7 ]. To engineer and improve its transmission efficiency, many strategies have shown great potential, such as periplasmic engineering [ 13 ], mitochondrial engineering [ 14 ], DNA scaffold [ 15 ], protein scaffold [ 16 ], enzyme engineering [ 17 ], modular pathway engineering [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Engineering the transmission efficiency of the noncyclic glyoxylate pathway for fumarate production in Escherichia coli

Chen

Liu

et al. 2020

Biotechnol Biofuels

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Background Fumarate is a multifunctional dicarboxylic acid in the tricarboxylic acid cycle, but microbial engineering for fumarate production is limited by the transmission efficiency of its biosynthetic pathway. Results Here, pathway engineering was used to construct the noncyclic glyoxylate pathway for fumarate production. To improve the transmission efficiency of intermediate metabolites, pathway optimization was conducted by fluctuating gene expression levels to identify potential bottlenecks and then remove them, resulting in a large increase in fumarate production from 8.7 to 16.2 g/L. To further enhance its transmission efficiency of targeted metabolites, transporter engineering was used by screening the C 4 -dicarboxylate transporters and then strengthening the capacity of fumarate export, leading to fumarate production up to 18.9 g/L. Finally, the engineered strain E. coli W3110△4-P (H) CAI (H) SC produced 22.4 g/L fumarate in a 5-L fed-batch bioreactor. Conclusions In this study, we offered rational metabolic engineering and flux optimization strategies for efficient production of fumarate. These strategies have great potential in developing efficient microbial cell factories for production of high-value added chemicals.

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A dynamic pathway analysis approach reveals a limiting futile cycle in N-acetylglucosamine overproducing Bacillus subtilis

Cited by 45 publications

References 33 publications

Dissecting the impact of metabolic environment on three common cancer cell phenotypes

Dissecting the impact of metabolic environment on three common cancer cell phenotypes

Engineering Microbial Metabolite Dynamics and Heterogeneity

Engineering the transmission efficiency of the noncyclic glyoxylate pathway for fumarate production in Escherichia coli

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