scholarly journals A Lysate Proteome Engineering Strategy for Enhancing Cell-Free Metabolite Production

2020 ◽  
Author(s):  
David C. Garcia ◽  
Jaime Lorenzo N. Dinglasan ◽  
Him Shrestha ◽  
Paul E. Abraham ◽  
Robert L. Hettich ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Cell Growth ◽  
Metabolic Flux ◽  
Selective Removal ◽  
List Type ◽  
Metabolic Potential ◽  
Metabolite Production ◽  
Genomic Engineering ◽  
Crude Extracts ◽  
Engineering Strategy

AbstractCell-free systems present a significant opportunity to harness the metabolic potential of diverse organisms. Removing the cellular context provides the ability to produce biological products without the need to maintain cell viability and enables metabolic engineers to explore novel chemical transformation systems. Crude extracts maintain much of a cell’s capabilities. However, only limited tools are available for engineering the contents of the extracts used for cell-free systems. Thus, our ability to take full advantage of the potential of crude extracts for cell-free metabolic engineering is limited. Here, we employ Multiplex Automated Genomic Engineering (MAGE) to tag proteins for selective removal from crude extracts so as to specifically direct chemical production. Specific edits to central metabolism are possible without significantly impacting cell growth. Selective removal of pyruvate degrading enzymes are demonstrated that result in engineered crude lysates that are capable of 10 to 20-fold increases of pyruvate production when compared to the non-engineered extract. The described approach melds the tools of systems and synthetic biology to develop cell-free metabolic engineering into a practical platform for both bioprototyping and bioproduction.HighlightsA novel method of engineering crude cell lysates for enhancing specific metabolic processes is described.Multiplex Automated Genomic Engineering (MAGE) can be used to engineer donor strains for improving cell-free metabolite production with minimal impact on cell-growth.The described lysate engineering strategy can specifically direct metabolic flux and create metabolic states not possible in living cells.Pooling of the central precursor pyruvate was significantly improved through use of this lysate proteome engineering strategy.

scholarly journals Combinatorial Metabolic Engineering in Saccharomyces cerevisiae for the Enhanced Production of the FPP-Derived Sesquiterpene Germacrene

2020 ◽  
Vol 7 (4) ◽  
pp. 135
Author(s):  
Jan Niklas Bröker ◽  
Boje Müller ◽  
Dirk Prüfer ◽  
Christian Schulze Gronover
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Metabolic Engineering ◽  
Metabolic Flux ◽  
Mevalonate Pathway ◽  
Plant Secondary Metabolites ◽  
Product Formation ◽  
Culture Conditions ◽  
Efficient Production ◽  
Enhanced Production ◽  
Engineering Strategy

Farnesyl diphosphate (FPP)-derived isoprenoids represent a diverse group of plant secondary metabolites with great economic potential. To enable their efficient production in the heterologous host Saccharomyces cerevisiae, we refined a metabolic engineering strategy using the CRISPR/Cas9 system with the aim of increasing the availability of FPP for downstream reactions. The strategy included the overexpression of mevalonate pathway (MVA) genes, the redirection of metabolic flux towards desired product formation and the knockout of genes responsible for competitive reactions. Following the optimisation of culture conditions, the availability of the improved FPP biosynthesis for downstream reactions was demonstrated by the expression of a germacrene synthase from dandelion. Subsequently, biosynthesis of significant amounts of germacrene-A was observed in the most productive strain compared to the wild type. Thus, the presented strategy is an excellent tool to increase FPP-derived isoprenoid biosynthesis in yeast.

scholarly journals Elucidating the potential of crude cell extracts for producing pyruvate from glucose

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
David C Garcia ◽  
Benjamin P Mohr ◽  
Jakob T Dovgan ◽  
Gregory B Hurst ◽  
Robert F Standaert ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Cell Growth ◽  
Enzymatic Activity ◽  
Crude Extract ◽  
Growth Conditions ◽  
Living Systems ◽  
Fine Chemicals ◽  
Pathway Optimization ◽  
Cell Extracts ◽  
Crude Extracts

Abstract Living systems possess a rich biochemistry that can be harnessed through metabolic engineering to produce valuable therapeutics, fuels and fine chemicals. In spite of the tools created for this purpose, many organisms tend to be recalcitrant to modification or difficult to optimize. Crude cellular extracts, made by lysis of cells, possess much of the same biochemical capability, but in an easier to manipulate context. Metabolic engineering in crude extracts, or cell-free metabolic engineering, can harness these capabilities to feed heterologous pathways for metabolite production and serve as a platform for pathway optimization. However, the inherent biochemical potential of a crude extract remains ill-defined, and consequently, the use of such extracts can result in inefficient processes and unintended side products. Herein, we show that changes in cell growth conditions lead to changes in the enzymatic activity of crude cell extracts and result in different abilities to produce the central biochemical precursor pyruvate when fed glucose. Proteomic analyses coupled with metabolite measurements uncover the diverse biochemical capabilities of these different crude extract preparations and provide a framework for how analytical measurements can be used to inform and improve crude extract performance. Such informed developments can allow enrichment of crude extracts with pathways that promote or deplete particular metabolic processes and aid in the metabolic engineering of defined products.

scholarly journals Metabolic Engineering of Enterobacter aerogenes for Improved 2,3-Butanediol Production by Manipulating NADH Levels and Overexpressing the Small RNA RyhB

2021 ◽  
Vol 12 ◽  
Author(s):  
Yan Wu ◽  
Wanying Chu ◽  
Jiayao Yang ◽  
Yudong Xu ◽  
Qi Shen ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Parent Strain ◽  
Nadh Dehydrogenase ◽  
Metabolic Flux ◽  
Enterobacter Aerogenes ◽  
Flux Analysis ◽  
Wild Type ◽  
Biotechnological Production ◽  
Engineering Strategy ◽  
Competing Pathways

Biotechnological production of 2,3-butanediol (2,3-BD), a versatile platform bio-chemical and a potential biofuel, is limited due to by-product toxicity. In this study, we aimed to redirect the metabolic flux toward 2,3-BD in Enterobacter aerogenes (E. aerogenes) by increasing the intracellular NADH pool. Increasing the NADH/NAD+ ratio by knocking out the NADH dehydrogenase genes (nuoC/nuoD) enhanced 2,3-BD production by up to 67% compared with wild-type E. aerogenes. When lactate dehydrogenase (ldh) was knocked out, the yield of 2,3-BD was increased by 71.2% compared to the wild type. Metabolic flux analysis revealed that upregulated expression of the sRNA RyhB led to a noteworthy shift in metabolism. The 2,3-BD titer of the best mutant Ea-2 was almost seven times higher than that of the parent strain in a 5-L fermenter. In this study, an effective metabolic engineering strategy for improved 2,3-BD production was implemented by increasing the NADH/NAD+ ratio and blocking competing pathways.

scholarly journals Metabolic engineering strategy for synthetizing trans-4-hydroxy-l-proline in microorganisms

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenyu Zhang ◽  
Pengfu Liu ◽  
Weike Su ◽  
Huawei Zhang ◽  
Wenqian Xu ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Biosynthetic Pathway ◽  
Industrial Applications ◽  
Microbial Cell Factories ◽  
Important Amino Acid ◽  
Cell Factories ◽  
Complex Process ◽  
Engineering Strategy ◽  
Hydrolysis Of ◽  
New Strategies

AbstractTrans-4-hydroxy-l-proline is an important amino acid that is widely used in medicinal and industrial applications, particularly as a valuable chiral building block for the organic synthesis of pharmaceuticals. Traditionally, trans-4-hydroxy-l-proline is produced by the acidic hydrolysis of collagen, but this process has serious drawbacks, such as low productivity, a complex process and heavy environmental pollution. Presently, trans-4-hydroxy-l-proline is mainly produced via fermentative production by microorganisms. Some recently published advances in metabolic engineering have been used to effectively construct microbial cell factories that have improved the trans-4-hydroxy-l-proline biosynthetic pathway. To probe the potential of microorganisms for trans-4-hydroxy-l-proline production, new strategies and tools must be proposed. In this review, we provide a comprehensive understanding of trans-4-hydroxy-l-proline, including its biosynthetic pathway, proline hydroxylases and production by metabolic engineering, with a focus on improving its production.

scholarly journals Constraint-based metabolic control analysis for rational strain engineering

2020 ◽  
Author(s):  
Sophia Tsouka ◽  
Meric Ataman ◽  
Tuure Hameri ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis
Keyword(s):  
Metabolic Engineering ◽  
Genome Editing ◽  
Metabolic Control ◽  
Metabolic Flux ◽  
Strain Engineering ◽  
Metabolic Control Analysis ◽  
Physiological Data ◽  
Response Analysis ◽  
Control Analysis ◽  
Wide Range

AbstractThe advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.

scholarly journals A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

2020 ◽  
Vol 117 (31) ◽  
pp. 18869-18879 ◽  
Author(s):  
Christopher Culley ◽  
Supreeta Vijayakumar ◽  
Guido Zampieri ◽  
Claudio Angione
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Cell Growth ◽  
Yeast Cell ◽  
Metabolic Flux ◽  
Data Augmentation ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Metabolic Reconstruction

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

scholarly journals Metabolic Flux and the Regulation of Mammalian Cell Growth

2011 ◽  
Vol 14 (4) ◽  
pp. 443-451 ◽  
Author(s):  
Jason W. Locasale ◽  
Lewis C. Cantley
Keyword(s):  
Cell Growth ◽  
Mammalian Cell ◽  
Metabolic Flux ◽  
Mammalian Cell Growth

Construction of yeast producing patchoulol by global metabolic engineering strategy

2020 ◽  
Vol 117 (5) ◽  
pp. 1348-1356 ◽  
Author(s):  
Ryosuke Mitsui ◽  
Riru Nishikawa ◽  
Ryosuke Yamada ◽  
Takuya Matsumoto ◽  
Hiroyasu Ogino
Keyword(s):  
Metabolic Engineering ◽  
Metabolic Engineering Strategy ◽  
Engineering Strategy
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