From zero to hero—Design-based systems metabolic engineering of Corynebacterium glutamicum for l-lysine production

Becker, Jörg; Zelder, Oskar; Hafner, S. S.; Schröder, Hartwig; Wittmann, Christoph

doi:10.1016/j.ymben.2011.01.003

Cited by 532 publications

(405 citation statements)

References 44 publications

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“…It has been reported in this organism that NADPH is generated mainly through the oxidative pentose phosphate pathway (PPP) (Marx et al, 1996). Several reports have shown that redirection of carbon from glycolysis toward the PPP leads to a significant improvement in L-lysine production (Becker et al, 2011(Becker et al, , 2007(Becker et al, , 2005; Marx et al, 2003;Ohnishi et al, 2005). Despite this positive effect, relying on the PPP is disadvantageous in terms of carbon yield, because the pathway inevitably involves the release of 1 mol of carbon dioxide accompanied by the oxidation of 1 mol of hexose (Contador et al, 2009).…”

mentioning

confidence: 99%

l -Lysine production independent of the oxidative pentose phosphate pathway by Corynebacterium glutamicum with the Streptococcus mutans gapN gene

Takeno

Hori

Ohtani

et al. 2016

Metabolic Engineering

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We have recently developed a Corynebacterium glutamicum strain that generates NADPH via the glycolytic pathway by replacing endogenous NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (GapA) with a nonphosphorylating NADP-dependent glyceraldehyde 3-phosphate dehydrogenase (GapN) from Streptococcus mutans. Strain RE2, a suppressor mutant spontaneously isolated for its improved growth on glucose from the engineered strain, was proven to be a high-potential host for L-lysine production (Takeno et al., 2010). In this study, the suppressor mutation was identified to be a point mutation in rho encoding the transcription termination factor Rho. Strain RE2 still showed retarded growth despite the mutation rho696. Our strategy for reconciling improved growth with a high level of L-lysine production was to use GapA together with GapN only in the early growth phase, and subsequently shift this combination-type glycolysis to one that depends only on GapN in the rest of the growth phase. To achieve this, we expressed gapA under the 2 myo-inositol-inducible promoter of iolT1 encoding a myo-inositol transporter in strain RE2. The resulting strain RE2A iol was engineered into an L-lysine producer by introduction of a plasmid carrying the desensitized lysC, followed by examination for culture conditions with myo-inositol supplementation. We found that as a higher concentration of myo-inositol was added to the seed culture, the following fermentation period became shorter while maintaining a high level of L-lysine production. This finally reached a fermentation period comparable to that of the control GapA strain, and yielded a 1.5-fold higher production rate compared with strain RE2. The transcript level of gapA, as well as the GapA activity, in the early growth phase increased in proportion to the myo-inositol concentration and then fell to low levels in the subsequent growth phase, indicating that improved growth was a result of increased GapA activity, especially in the early growth phase. Moreover, blockade of the pentose phosphate pathway through a defect in glucose 6-phosphate dehydrogenase did not significantly affect L-lysine production in the engineered GapN strains, while a drastic decrease in L-lysine production was observed for the control GapA strain. Determination of the intracellular NADPH/NADP + ratios revealed that the ratios in the engineered strains were significantly higher than the ratio of the control GapA strain irrespective of the pentose phosphate pathway. These results demonstrate that our strain engineering strategy allows efficient L-lysine production independent of the oxidative pentose phosphate pathway.

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mentioning

confidence: 99%

l -Lysine production independent of the oxidative pentose phosphate pathway by Corynebacterium glutamicum with the Streptococcus mutans gapN gene

Takeno

Hori

Ohtani

et al. 2016

Metabolic Engineering

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“…Beyond its contribution to obtaining better mechanistic insights into the way gene expression levels are controlled by their potential toxicity, EDGE bears considerable applicative value for biotechnologists: Genome-scale metabolic modeling has already been successfully applied to devise novel pathways for rational strain design (12,(44)(45)(46)(47)(48)(49)(50)(51), and gene overexpression has been considered in this framework as a means to produce a desired chemical (52,53). EDGE complements the existing computational methods by addressing a prime concern of metabolic engineers, who seek to foresee and mitigate the deleterious effects that often accompany the introduction of a foreign metabolic pathway into a host organism, or the overexpression of one of its native genes (17,18).…”

Section: Discussionmentioning

confidence: 99%

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Wagner¹,

Zarecki²,

Reshef

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.systems metabolic engineering | metabolic modeling | constraint-based modeling | flux balance analysis | horizontal gene transfer

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“…Using the model to predict expression differences between organisms would enable the development of host-independent SEAMAPs and the engineering of genetic systems in one organism for their eventual use in another. We selected four bacterial hosts currently used for biotechnology applications (Supplementary Table S2): E. coli BL21 for overexpression of recombinant proteins; Pseudomonas fluorescens for production of biopolymers and soil decontamination; Salmonella typhimurium LT2 for secretion of large proteins, including spider silk; and Corynebacterium glutamicum for production of enzymes and amino acids (Monti et al, 2005;Widmaier et al, 2009;Becker et al, 2011). To predict translation initiation rates, we included promoter-dependent upstream sequences in the 5 0 UTR and selected the appropriate 3 0 16S rRNA sequence for each host, 5 0 -ACCUCCUUU-3 0 for the gram-positive C. glutamicum, and 5 0 -ACCUCCUUA-3 0 for the remaining gram-negative species (Fig 2A).…”

Section: Navigation Of Expression Spaces In Diverse Bacterial Speciesmentioning

confidence: 99%

Efficient Search, Mapping, and Optimization of Multi-protein Genetic Systems in Diverse Bacteria

Farasat

Kushwaha

Collens

et al. 2013

Preprint

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Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs.

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From zero to hero—Design-based systems metabolic engineering of Corynebacterium glutamicum for l-lysine production

Cited by 532 publications

References 44 publications

l -Lysine production independent of the oxidative pentose phosphate pathway by Corynebacterium glutamicum with the Streptococcus mutans gapN gene

l -Lysine production independent of the oxidative pentose phosphate pathway by Corynebacterium glutamicum with the Streptococcus mutans gapN gene

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Efficient Search, Mapping, and Optimization of Multi-protein Genetic Systems in Diverse Bacteria

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