13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol

Yao, Ruilian; Li, Jiawei; Feng, Lei; Zhang, Xuehong; Hu, Hongbo

doi:10.1186/s13068-019-1372-4

Cited by 21 publications

(15 citation statements)

References 57 publications

(71 reference statements)

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“…NADPH also provides the main anabolic reducing power for biomass growth, lipid formation, and also for natural product biosynthesis [ 12 ]. Indeed, cofactor engineering has been reported to improve productivities in the bacterial cell factories Escherichia coli [ 13 , 14 ], and Corynebacterium glutamicum , as well as in the yeast cell factory Yarrowia lipolytica [ 15 ]. Two common strategies have mainly been employed to optimize the availability of NADPH.…”

Section: Introductionmentioning

confidence: 99%

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

et al. 2020

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Background Nicotinamide adenine dinucleotide phosphate (NADPH) is an important cofactor ensuring intracellular redox balance, anabolism and cell growth in all living systems. Our recent multi-omics analyses of glucoamylase (GlaA) biosynthesis in the filamentous fungal cell factory Aspergillus niger indicated that low availability of NADPH might be a limiting factor for GlaA overproduction. Results We thus employed the Design-Build-Test-Learn cycle for metabolic engineering to identify and prioritize effective cofactor engineering strategies for GlaA overproduction. Based on available metabolomics and 13C metabolic flux analysis data, we individually overexpressed seven predicted genes encoding NADPH generation enzymes under the control of the Tet-on gene switch in two A. niger recipient strains, one carrying a single and one carrying seven glaA gene copies, respectively, to test their individual effects on GlaA and total protein overproduction. Both strains were selected to understand if a strong pull towards glaA biosynthesis (seven gene copies) mandates a higher NADPH supply compared to the native condition (one gene copy). Detailed analysis of all 14 strains cultivated in shake flask cultures uncovered that overexpression of the gsdA gene (glucose 6-phosphate dehydrogenase), gndA gene (6-phosphogluconate dehydrogenase) and maeA gene (NADP-dependent malic enzyme) supported GlaA production on a subtle (10%) but significant level in the background strain carrying seven glaA gene copies. We thus performed maltose-limited chemostat cultures combining metabolome analysis for these three isolates to characterize metabolic-level fluctuations caused by cofactor engineering. In these cultures, overexpression of either the gndA or maeA gene increased the intracellular NADPH pool by 45% and 66%, and the yield of GlaA by 65% and 30%, respectively. In contrast, overexpression of the gsdA gene had a negative effect on both total protein and glucoamylase production. Conclusions This data suggests for the first time that increased NADPH availability can indeed underpin protein and especially GlaA production in strains where a strong pull towards GlaA biosynthesis exists. This data also indicates that the highest impact on GlaA production can be engineered on a genetic level by increasing the flux through the pentose phosphate pathway (gndA gene) followed by engineering the flux through the reverse TCA cycle (maeA gene). We thus propose that NADPH cofactor engineering is indeed a valid strategy for metabolic engineering of A. niger to improve GlaA production, a strategy which is certainly also applicable to the rational design of other microbial cell factories.

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Section: Introductionmentioning

confidence: 99%

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

et al. 2020

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“…Although the original model was developed and validated for growth on glucose, the steady-state flux distribution (when using glycerol as carbon source) was compared with values determined experimentally [ 12 , 13 ]. This assessment unveiled a significantly different flux distribution between the dynamic model and experimental data ( S1 Appendix , section 1. and Figs 1 and S1), which was considered when analysing the results under glycerol consumption.…”

Section: Resultsmentioning

confidence: 99%

“… (A) Steady-State flux distribution from glycerol obtained from the extended kinetic model of E.coli ’s CCM; (B) Flux distribution from glycerol obtained experimentaly by Toya et al (2018) [ 12 ]; (C) Flux distribution from glycerol obtained experimentaly by Yao et al (2019) [ 13 ]. …”

Section: Supporting Informationmentioning

confidence: 99%

A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coli

et al. 2021

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Acrylic acid is a value-added chemical used in industry to produce diapers, coatings, paints, and adhesives, among many others. Due to its economic importance, there is currently a need for new and sustainable ways to synthesise it. Recently, the focus has been laid in the use of Escherichia coli to express the full bio-based pathway using 3-hydroxypropionate as an intermediary through three distinct pathways (glycerol, malonyl-CoA, and β-alanine). Hence, the goals of this work were to use COPASI software to assess which of the three pathways has a higher potential for industrial-scale production, from either glucose or glycerol, and identify potential targets to improve the biosynthetic pathways yields. When compared to the available literature, the models developed during this work successfully predict the production of 3-hydroxypropionate, using glycerol as carbon source in the glycerol pathway, and using glucose as a carbon source in the malonyl-CoA and β-alanine pathways. Finally, this work allowed to identify four potential over-expression targets (glycerol-3-phosphate dehydrogenase (G3pD), acetyl-CoA carboxylase (AccC), aspartate aminotransferase (AspAT), and aspartate carboxylase (AspC)) that should, theoretically, result in higher AA yields.

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“…Metabolic flux analysis with 13 C-labeled glycerol has been reported in previous studies (Alagesan et al, 2018;Maruyama et al, 2018;Tomàs-Gamisans et al, 2019;Toya et al, 2018;Yao et al, 2019), but, until now, has not been reported for S. cerevisiae. In the present study, to understand intracellular metabolism of S. cerevisiae obtained by ALE and the RIM15 disruptant grown on glycerol as a main carbon source, we performed metabolic flux analysis of evolved S. cerevisiae grown on a mixture of naturally-labeled and 13 C-labeled glycerol.…”

Section: Introductionmentioning

confidence: 93%

<sup>13</sup>C-metabolic flux analysis in glycerol-assimilating strains of <i>Saccharomyces cerevisiae</i>

Yuzawa

Shirai

Orishimo

et al. 2021

J. Gen. Appl. Microbiol.

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Glycerol is an attractive raw material for the production of useful chemicals using microbial cells. We previously identified metabolic engineering targets for the improvement of glycerol assimilation ability in Saccharomyces cerevisiae based on adaptive laboratory evolution (ALE) and transcriptome analysis of the evolved cells. We also successfully improved glycerol assimilation ability by the disruption of the RIM15 gene encoding a Greatwall protein kinase together with overexpression of the STL1 gene encoding the glycerol/H + symporter. To understand glycerol assimilation metabolism in the evolved glycerol-assimilating strains and STL1overexpressing RIM15 disruptant, we performed metabolic flux analysis using 13 C-labeled glycerol. Significant differences in metabolic flux distributions between the strains obtained from the culture after 35 and 85 generations in ALE were not found, indicating that metabolic flux changes might occur in the early phase of ALE (i.e., before 35 generations at least). Similarly, metabolic flux distribution was not significantly changed by RIM15 gene disruption. However, fluxes for the lower part of glycolysis and the TCA cycle were larger and, as a result, flux for the pentose phosphate pathway was smaller in the STL1-overexpressing RIM15 disruptant than in the strain obtained from the culture after 85 generations in ALE. It could be effective to increase flux for the pentose phosphate pathway to improve the glycerol assimilation ability in S. cerevisiae.

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13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol

Cited by 21 publications

References 57 publications

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coli

<sup>13</sup>C-metabolic flux analysis in glycerol-assimilating strains of <i>Saccharomyces cerevisiae</i>

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