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Abstract
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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Affiliation(s)
- Changdai Gu
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Won Jun Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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Kim M, Park BG, Kim EJ, Kim J, Kim BG. In silico identification of metabolic engineering strategies for improved lipid production in Yarrowia lipolytica by genome-scale metabolic modeling. BIOTECHNOLOGY FOR BIOFUELS 2019; 12:187. [PMID: 31367232 PMCID: PMC6657051 DOI: 10.1186/s13068-019-1518-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 07/03/2019] [Indexed: 05/03/2023]
Abstract
BACKGROUND Yarrowia lipolytica, an oleaginous yeast, is a promising platform strain for production of biofuels and oleochemicals as it can accumulate a high level of lipids in response to nitrogen limitation. Accordingly, many metabolic engineering efforts have been made to develop engineered strains of Y. lipolytica with higher lipid yields. Genome-scale model of metabolism (GEM) is a powerful tool for identifying novel genetic designs for metabolic engineering. Several GEMs for Y. lipolytica have recently been developed; however, not many applications of the GEMs have been reported for actual metabolic engineering of Y. lipolytica. The major obstacle impeding the application of Y. lipolytica GEMs is the lack of proper methods for predicting phenotypes of the cells in the nitrogen-limited condition, or more specifically in the stationary phase of a batch culture. RESULTS In this study, we showed that environmental version of minimization of metabolic adjustment (eMOMA) can be used for predicting metabolic flux distribution of Y. lipolytica under the nitrogen-limited condition and identifying metabolic engineering strategies to improve lipid production in Y. lipolytica. Several well-characterized overexpression targets, such as diglyceride acyltransferase, acetyl-CoA carboxylase, and stearoyl-CoA desaturase, were successfully rediscovered by our eMOMA-based design method, showing the relevance of prediction results. Interestingly, the eMOMA-based design method also suggested non-intuitive knockout targets, and we experimentally validated the prediction with a mutant lacking YALI0F30745g, one of the predicted targets involved in one-carbon/methionine metabolism. The mutant accumulated 45% more lipids compared to the wild-type. CONCLUSION This study demonstrated that eMOMA is a powerful computational method for understanding and engineering the metabolism of Y. lipolytica and potentially other oleaginous microorganisms.
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Affiliation(s)
- Minsuk Kim
- Institute of Engineering Research, Seoul National University, Seoul, 08826 Republic of Korea
- Present Address: Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905 USA
| | - Beom Gi Park
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826 Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, 08826 Republic of Korea
| | - Eun-Jung Kim
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, 08826 Republic of Korea
- Bio-MAX Institute, Seoul National University, Seoul, 08826 Republic of Korea
| | - Joonwon Kim
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826 Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, 08826 Republic of Korea
| | - Byung-Gee Kim
- School of Chemical and Biological Engineering, Seoul National University, Seoul, 08826 Republic of Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, 08826 Republic of Korea
- Bio-MAX Institute, Seoul National University, Seoul, 08826 Republic of Korea
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Koduru L, Lakshmanan M, Lee DY. In silico model-guided identification of transcriptional regulator targets for efficient strain design. Microb Cell Fact 2018; 17:167. [PMID: 30359263 PMCID: PMC6201637 DOI: 10.1186/s12934-018-1015-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 10/20/2018] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Cellular metabolism is tightly regulated by hard-wired multiple layers of biological processes to achieve robust and homeostatic states given the limited resources. As a result, even the most intuitive enzyme-centric metabolic engineering endeavours through the up-/down-regulation of multiple genes in biochemical pathways often deliver insignificant improvements in the product yield. In this regard, targeted engineering of transcriptional regulators (TRs) that control several metabolic functions in modular patterns is an interesting strategy. However, only a handful of in silico model-added techniques are available for identifying the TR manipulation candidates, thus limiting its strain design application. RESULTS We developed hierarchical-Beneficial Regulatory Targeting (h-BeReTa) which employs a genome-scale metabolic model and transcriptional regulatory network (TRN) to identify the relevant TR targets suitable for strain improvement. We then applied this method to industrially relevant metabolites and cell factory hosts, Escherichia coli and Corynebacterium glutamicum. h-BeReTa suggested several promising TR targets, many of which have been validated through literature evidences. h-BeReTa considers the hierarchy of TRs in the TRN and also accounts for alternative metabolic pathways which may divert flux away from the product while identifying suitable metabolic fluxes, thereby performing superior in terms of global TR target identification. CONCLUSIONS In silico model-guided strain design framework, h-BeReTa, was presented for identifying transcriptional regulator targets. Its efficacy and applicability to microbial cell factories were successfully demonstrated via case studies involving two cell factory hosts, as such suggesting several intuitive targets for overproducing various value-added compounds.
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Affiliation(s)
- Lokanand Koduru
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117576, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore.
- School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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Genome-Scale In Silico Analysis for Enhanced Production of Succinic Acid in Zymomonas mobilis. Processes (Basel) 2018. [DOI: 10.3390/pr6040030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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Mishra P, Lee NR, Lakshmanan M, Kim M, Kim BG, Lee DY. Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica. BMC SYSTEMS BIOLOGY 2018; 12:12. [PMID: 29560822 PMCID: PMC5861505 DOI: 10.1186/s12918-018-0542-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Background Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application. Results In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica. Conclusion In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production. Electronic supplementary material The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pranjul Mishra
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore
| | - Na-Rae Lee
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore
| | - Minsuk Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Republic of Korea
| | - Byung-Gee Kim
- School of Chemical and Biological Engineering, Institute of Molecular Biology and Genetics, and Bioengineering Institute, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Republic of Korea
| | - Dong-Yup Lee
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore. .,Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore. .,School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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Chae TU, Choi SY, Kim JW, Ko YS, Lee SY. Recent advances in systems metabolic engineering tools and strategies. Curr Opin Biotechnol 2017; 47:67-82. [DOI: 10.1016/j.copbio.2017.06.007] [Citation(s) in RCA: 115] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 06/12/2017] [Indexed: 12/16/2022]
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Miklóssy I, Bodor Z, Sinkler R, Orbán KC, Lányi S, Albert B. In silico and in vivo stability analysis of a heterologous biosynthetic pathway for 1,4-butanediol production in metabolically engineered E. coli. J Biomol Struct Dyn 2016; 35:1874-1889. [PMID: 27492654 DOI: 10.1080/07391102.2016.1198721] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Recently, several approaches have been published in order to develop a functional biosynthesis route for the non-natural compound 1,4-butanediol (BDO) in E. coli using glucose as a sole carbon source or starting from xylose. Among these studies, there was reported as high as 18 g/L product concentration achieved by industrial strains, however BDO production varies greatly in case of the reviewed studies. Our motivation was to build a simple heterologous pathway for this compound in E. coli and to design an appropriate cellular chassis based on a systemic biology approach, using constraint-based flux balance analysis and bi-level optimization for gene knock-out prediction. Thus, the present study reports, at the "proof-of concept" level, our findings related to model-driven development of a metabolically engineered E. coli strain lacking key genes for ethanol, lactate and formate production (ΔpflB, ΔldhA and ΔadhE), with a three-step biosynthetic pathway. We found this strain to produce a limited quantity of 1,4-BDO (.89 mg/L BDO under microaerobic conditions and .82 mg/L under anaerobic conditions). Using glycerol as carbon source, an approach, which to our knowledge has not been tackled before, our results suggest that further metabolic optimization is needed (gene-introductions or knock-outs, promoter fine-tuning) to address the redox potential imbalance problem and to achieve development of an industrially sustainable strain. Our experimental data on culture conditions, growth dynamics and fermentation parameters can consist a base for ongoing research on gene expression profiles and genetic stability of such metabolically engineered E. coli strains.
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Affiliation(s)
- Ildikó Miklóssy
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania.,b Faculty of Applied Chemistry and Materials Science , Politehnica University of Bucharest , Bucharest , Romania
| | - Zsolt Bodor
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania
| | - Réka Sinkler
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania.,b Faculty of Applied Chemistry and Materials Science , Politehnica University of Bucharest , Bucharest , Romania
| | - Kálmán Csongor Orbán
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania
| | - Szabolcs Lányi
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania
| | - Beáta Albert
- a Department of Bioengineering , Sapientia Hungarian University of Transylvania , Libertatii Square, no. 1, 530104 Miercurea Ciuc , Romania
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Lakshmanan M, Kim TY, Chung BKS, Lee SY, Lee DY. Flux-sum analysis identifies metabolite targets for strain improvement. BMC SYSTEMS BIOLOGY 2015; 9:73. [PMID: 26510838 PMCID: PMC4625974 DOI: 10.1186/s12918-015-0198-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 08/18/2015] [Indexed: 11/21/2022]
Abstract
Background Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets. Results In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology. Conclusions The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore.
| | - Tae Yong Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. .,Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.
| | - Bevan K S Chung
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore.
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 program), Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 305-701, Republic of Korea. .,Center for Systems and Synthetic Biotechnology, Bioinformatics Research Center, Institute for the BioCentury, and Department of Bio and Brain Engineering, KAIST, Daejeon, 305-701, Republic of Korea.
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore, 138668, Singapore. .,Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117576, Singapore. .,Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore.
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Lakshmanan M, Yu K, Koduru L, Lee DY. In silico model-driven cofactor engineering strategies for improving the overall NADP(H) turnover in microbial cell factories. J Ind Microbiol Biotechnol 2015; 42:1401-14. [PMID: 26254041 DOI: 10.1007/s10295-015-1663-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 07/23/2015] [Indexed: 02/03/2023]
Abstract
Optimizing the overall NADPH turnover is one of the key challenges in various value-added biochemical syntheses. In this work, we first analyzed the NADPH regeneration potentials of common cell factories, including Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and Pichia pastoris across multiple environmental conditions and determined E. coli and glycerol as the best microbial chassis and most suitable carbon source, respectively. In addition, we identified optimal cofactor specificity engineering (CSE) enzyme targets, whose cofactors when switched from NAD(H) to NADP(H) improve the overall NADP(H) turnover. Among several enzyme targets, glyceraldehyde-3-phosphate dehydrogenase was recognized as a global candidate since its CSE improved the NADP(H) regeneration under most of the conditions examined. Finally, by analyzing the protein structures of all CSE enzyme targets via homology modeling, we established that the replacement of conserved glutamate or aspartate with serine in the loop region could change the cofactor dependence from NAD(H) to NADP(H).
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Affiliation(s)
- Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore
| | - Kai Yu
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore
| | - Lokanand Koduru
- Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, Centros, Singapore, 138668, Singapore. .,Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore. .,NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), Life Sciences Institute, National University of Singapore, 28 Medical Drive, Singapore, 117456, Singapore.
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Kim B, Kim WJ, Kim DI, Lee SY. Applications of genome-scale metabolic network model in metabolic engineering. ACTA ACUST UNITED AC 2015; 42:339-48. [DOI: 10.1007/s10295-014-1554-9] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Accepted: 11/19/2014] [Indexed: 12/11/2022]
Abstract
Abstract
Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.
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Affiliation(s)
- Byoungjin Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Won Jun Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Dong In Kim
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
| | - Sang Yup Lee
- grid.37172.30 0000000122920500 Department of Chemical and Biomolecular Engineering (BK21 Plus Program), BioProcess Engineering Research Center, Bioinformatics Research Center, Center for Systems and Synthetic Biotechnology Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak-ro, Yuseong-gu 305-701 Daejeon Republic of Korea
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Chou HH, Marx CJ, Sauer U. Transhydrogenase promotes the robustness and evolvability of E. coli deficient in NADPH production. PLoS Genet 2015; 11:e1005007. [PMID: 25715029 PMCID: PMC4340650 DOI: 10.1371/journal.pgen.1005007] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 01/14/2015] [Indexed: 11/18/2022] Open
Abstract
Metabolic networks revolve around few metabolites recognized by diverse enzymes and involved in myriad reactions. Though hub metabolites are considered as stepping stones to facilitate the evolutionary expansion of biochemical pathways, changes in their production or consumption often impair cellular physiology through their system-wide connections. How does metabolism endure perturbations brought immediately by pathway modification and restore hub homeostasis in the long run? To address this question we studied laboratory evolution of pathway-engineered Escherichia coli that underproduces the redox cofactor NADPH on glucose. Literature suggests multiple possibilities to restore NADPH homeostasis. Surprisingly, genetic dissection of isolates from our twelve evolved populations revealed merely two solutions: (1) modulating the expression of membrane-bound transhydrogenase (mTH) in every population; (2) simultaneously consuming glucose with acetate, an unfavored byproduct normally excreted during glucose catabolism, in two subpopulations. Notably, mTH displays broad phylogenetic distribution and has also played a predominant role in laboratory evolution of Methylobacterium extorquens deficient in NADPH production. Convergent evolution of two phylogenetically and metabolically distinct species suggests mTH as a conserved buffering mechanism that promotes the robustness and evolvability of metabolism. Moreover, adaptive diversification via evolving dual substrate consumption highlights the flexibility of physiological systems to exploit ecological opportunities.
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Affiliation(s)
- Hsin-Hung Chou
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| | - Christopher J. Marx
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Uwe Sauer
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
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Mahalik S, Sharma AK, Mukherjee KJ. Genome engineering for improved recombinant protein expression in Escherichia coli. Microb Cell Fact 2014; 13:177. [PMID: 25523647 PMCID: PMC4300154 DOI: 10.1186/s12934-014-0177-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Accepted: 12/05/2014] [Indexed: 01/09/2023] Open
Abstract
A metabolic engineering perspective which views recombinant protein
expression as a multistep pathway allows us to move beyond vector design and
identify the downstream rate limiting steps in expression. In E.coli these are typically at the translational level
and the supply of precursors in the form of energy, amino acids and nucleotides.
Further recombinant protein production triggers a global cellular stress response
which feedback inhibits both growth and product formation. Countering this requires
a system level analysis followed by a rational host cell engineering to sustain
expression for longer time periods. Another strategy to increase protein yields
could be to divert the metabolic flux away from biomass formation and towards
recombinant protein production. This would require a growth stoppage mechanism which
does not affect the metabolic activity of the cell or the transcriptional or
translational efficiencies. Finally cells have to be designed for efficient export
to prevent buildup of proteins inside the cytoplasm and also simplify downstream
processing. The rational and the high throughput strategies that can be used for the
construction of such improved host cell platforms for recombinant protein expression
is the focus of this review.
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Affiliation(s)
- Shubhashree Mahalik
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
| | - Ashish K Sharma
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
| | - Krishna J Mukherjee
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
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