1
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Toya Y, Shimizu H. Coupling and uncoupling growth and product formation for producing chemicals. Curr Opin Biotechnol 2024; 87:103133. [PMID: 38640846 DOI: 10.1016/j.copbio.2024.103133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 04/21/2024]
Abstract
Microbial fermentation employs two strategies: growth- and nongrowth-coupled productions. Stoichiometric metabolic models with flux balance analysis enable pathway engineering to couple target synthesis with growth, yielding numerous successful results. Growth-coupled engineering also contributes to improving bottleneck flux through subsequent adaptive evolution. However, because growth-coupled production inevitably shares resources between biomass and target syntheses, the cost-effective production of bulk chemicals mandates a nongrowth-coupled approach. In such processes, understanding how and when to transition the metabolic state from growth to production modes becomes crucial, as does maintaining cellular activity during the nongrowing state to achieve high productivity. In this paper, we review recent technologies for growth-coupled and nongrowth-coupled production, considering their advantages and disadvantages.
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Affiliation(s)
- Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
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2
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Orsi E, Schada von Borzyskowski L, Noack S, Nikel PI, Lindner SN. Automated in vivo enzyme engineering accelerates biocatalyst optimization. Nat Commun 2024; 15:3447. [PMID: 38658554 PMCID: PMC11043082 DOI: 10.1038/s41467-024-46574-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.
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Affiliation(s)
- Enrico Orsi
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | | | - Stephan Noack
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425, Jülich, Germany
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam-Golm, Germany.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität, 10117, Berlin, Germany.
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3
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Manoli MT, Gargantilla-Becerra Á, Del Cerro Sánchez C, Rivero-Buceta V, Prieto MA, Nogales J. A model-driven approach to upcycling recalcitrant feedstocks in Pseudomonas putida by decoupling PHA production from nutrient limitation. Cell Rep 2024; 43:113979. [PMID: 38517887 DOI: 10.1016/j.celrep.2024.113979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/29/2024] [Accepted: 03/06/2024] [Indexed: 03/24/2024] Open
Abstract
Bacterial polyhydroxyalkanoates (PHAs) have emerged as promising eco-friendly alternatives to petroleum-based plastics since they are synthesized from renewable resources and offer exceptional properties. However, their production is limited to the stationary growth phase under nutrient-limited conditions, requiring customized strategies and costly two-phase bioprocesses. In this study, we tackle these challenges by employing a model-driven approach to reroute carbon flux and remove regulatory constraints using synthetic biology. We construct a collection of Pseudomonas putida-overproducing strains at the expense of plastics and lignin-related compounds using growth-coupling approaches. PHA production was successfully achieved during growth phase, resulting in the production of up to 46% PHA/cell dry weight while maintaining a balanced carbon-to-nitrogen ratio. Our strains are additionally validated under an upcycling scenario using enzymatically hydrolyzed polyethylene terephthalate as a feedstock. These findings have the potential to revolutionize PHA production and address the global plastic crisis by overcoming the complexities of traditional PHA production bioprocesses.
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Affiliation(s)
- Maria-Tsampika Manoli
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Álvaro Gargantilla-Becerra
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain
| | - Carlos Del Cerro Sánchez
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - Virginia Rivero-Buceta
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain
| | - M Auxiliadora Prieto
- Polymer Biotechnology Group, Department of Microbial and Plant Biotechnology, Margarita Salas Center for Biological Research (CIB-CSIC), 28040 Madrid, Spain; Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain.
| | - Juan Nogales
- Interdisciplinary Platform for Sustainable Plastics towards a Circular Economy-Spanish National Research Council (SusPlast-CSIC), Madrid, Spain; 3Systems Biotechnology Group, Department of Systems Biology, Centro Nacional de Biotecnología, CSIC, Madrid 28049, Spain; CNB DNA Biofoundry (CNBio), CSIC, Madrid, Spain.
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4
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Lu C, Wijffels RH, Martins Dos Santos VAP, Weusthuis RA. Pseudomonas putida as a platform for medium-chain length α,ω-diol production: Opportunities and challenges. Microb Biotechnol 2024; 17:e14423. [PMID: 38528784 DOI: 10.1111/1751-7915.14423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/18/2024] [Accepted: 01/26/2024] [Indexed: 03/27/2024] Open
Abstract
Medium-chain-length α,ω-diols (mcl-diols) play an important role in polymer production, traditionally depending on energy-intensive chemical processes. Microbial cell factories offer an alternative, but conventional strains like Escherichia coli and Saccharomyces cerevisiae face challenges in mcl-diol production due to the toxicity of intermediates such as alcohols and acids. Metabolic engineering and synthetic biology enable the engineering of non-model strains for such purposes with P. putida emerging as a promising microbial platform. This study reviews the advancement in diol production using P. putida and proposes a four-module approach for the sustainable production of diols. Despite progress, challenges persist, and this study discusses current obstacles and future opportunities for leveraging P. putida as a microbial cell factory for mcl-diol production. Furthermore, this study highlights the potential of using P. putida as an efficient chassis for diol synthesis.
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Affiliation(s)
- Chunzhe Lu
- Bioprocess Engineering, Wageningen University & Research, Wageningen, The Netherlands
- Groningen Biomolecular Sciences & Biotechnology Institute, University of Groningen, Groningen, The Netherlands
| | - Rene H Wijffels
- Bioprocess Engineering, Wageningen University & Research, Wageningen, The Netherlands
- Faculty of Biosciences and Aquaculture, Nord University, Bodø, Norway
| | - Vitor A P Martins Dos Santos
- Bioprocess Engineering, Wageningen University & Research, Wageningen, The Netherlands
- Lifeglimmer GmbH, Berlin, Germany
| | - Ruud A Weusthuis
- Bioprocess Engineering, Wageningen University & Research, Wageningen, The Netherlands
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5
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Banerjee D, Yunus IS, Wang X, Kim J, Srinivasan A, Menchavez R, Chen Y, Gin JW, Petzold CJ, Martin HG, Magnuson JK, Adams PD, Simmons BA, Mukhopadhyay A, Kim J, Lee TS. Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida. Metab Eng 2024; 82:157-170. [PMID: 38369052 DOI: 10.1016/j.ymben.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/10/2024] [Accepted: 02/10/2024] [Indexed: 02/20/2024]
Abstract
Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams.
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Affiliation(s)
- Deepanwita Banerjee
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Ian S Yunus
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Xi Wang
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jinho Kim
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Aparajitha Srinivasan
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Russel Menchavez
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yan Chen
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jennifer W Gin
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Hector Garcia Martin
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jon K Magnuson
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Energy Processes & Materials Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Paul D Adams
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Blake A Simmons
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Joonhoon Kim
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Energy Processes & Materials Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
| | - Taek Soon Lee
- Joint BioEnergy Institute, 5885 Hollis St., Emeryville, CA, 94608, USA; Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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6
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization. Microb Cell Fact 2024; 23:37. [PMID: 38287320 PMCID: PMC10823710 DOI: 10.1186/s12934-023-02277-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/15/2023] [Indexed: 01/31/2024] Open
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed by making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies (with a predefined maximum number of reaction deletions) for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using various Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple- and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more beneficial and important practical solutions. The availability of all the solutions provides the opportunity to further characterize, rank and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock .
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Affiliation(s)
- Leila Hassani
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Mohammad R Moosavi
- Department of Computer Science and Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Payam Setoodeh
- Department of Chemical Engineering, School of Chemical, Petroleum and Gas Engineering, Shiraz University, Shiraz, Iran
- Booth School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada
| | - Habil Zare
- Department of Cell Systems and Anatomy, University of Texas Health Science Center, San Antonio, TX, USA.
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, USA.
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7
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Huang J, Liu J, Dong H, Shi J, You X, Zhang Y. Engineering of a Substrate Affinity Reduced S-Adenosyl-methionine Synthetase as a Novel Biosensor for Growth-Coupling Selection of L-Methionine Overproducers. Appl Biochem Biotechnol 2023:10.1007/s12010-023-04807-0. [PMID: 38150159 DOI: 10.1007/s12010-023-04807-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2023] [Indexed: 12/28/2023]
Abstract
Biosensors are powerful tools for monitoring specific metabolites or controlling metabolic flux towards the products in a single cell, which play important roles in microbial cell factory construction. Despite their potential role in metabolic flux monitoring, the development of biosensors for small molecules is still limited. Reported biosensors often exhibit bottlenecks of poor specificity and a narrow dynamic range. Moreover, fine-tuning the substrate binding affinity of a crucial enzyme can decrease its catalytic activity, which ultimately results in the repression of the corresponding essential metabolite biosynthesis and impairs cell growth. However, increasing intracellular substrate concentration can elevate the availability of the essential metabolite and may lead to restore cellular growth. Herein, a new strategy was proposed for constructing whole-cell biosensors based on enzyme encoded by essential gene that offer inherent specificity and universality. Specifically, S-adenosyl-methionine synthetase (MetK) in E. coli was chosen as the crucial enzyme, and a series of MetK variants were identified that were sensitive to L-methionine concentration. This occurrence enabled the engineered cell to sense L-methionine and exhibit L-methionine dose-dependent cell growth. To improve the biosensor's dynamic range, an S-adenosyl-methionine catabolic enzyme was overexpressed to reduce the intracellular availability of S-adenosyl-methionine. The resulting whole-cell biosensor effectively coupled the intracellular concentration of L-methionine with growth and was successfully applied to select strains with enhanced L-methionine biosynthesis from random mutagenesis libraries. Overall, our study presents a universal strategy for designing and constructing growth-coupled biosensors based on crucial enzyme, which can be applied to select strains overproducing high value-added metabolites in cellular metabolism.
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Affiliation(s)
- Jianfeng Huang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, People's Republic of China
| | - Jinhui Liu
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China
- Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, People's Republic of China
| | - Huaming Dong
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China
- School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan, 430205, People's Republic of China
| | - Jingjing Shi
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, People's Republic of China
| | - Xiaoyan You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China.
- Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023, People's Republic of China.
| | - Yanfei Zhang
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, People's Republic of China.
- National Center of Technology Innovation for Synthetic Biology, Tianjin, 300308, People's Republic of China.
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8
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Kulakowski S, Banerjee D, Scown CD, Mukhopadhyay A. Improving microbial bioproduction under low-oxygen conditions. Curr Opin Biotechnol 2023; 84:103016. [PMID: 37924688 DOI: 10.1016/j.copbio.2023.103016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/17/2023] [Accepted: 10/07/2023] [Indexed: 11/06/2023]
Abstract
Microbial bioconversion provides access to a wide range of sustainably produced chemicals and commodities. However, industrial-scale bioproduction process operations are preferred to be anaerobic due to the cost associated with oxygen transfer. Anaerobic bioconversion generally offers limited substrate utilization profiles, lower product yields, and reduced final product diversity compared with aerobic processes. Bioproduction under conditions of reduced oxygen can overcome the limitations of fully aerobic and anaerobic bioprocesses, but many microbial hosts are not developed for low-oxygen bioproduction. Here, we describe advances in microbial strain engineering involving the use of redox cofactor engineering, genome-scale metabolic modeling, and functional genomics to enable improved bioproduction processes under low oxygen and provide a viable path for scaling these bioproduction systems to industrial scales.
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Affiliation(s)
- Shawn Kulakowski
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Corinne D Scown
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Energy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics & Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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9
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Motamedian E, Berzins K, Muiznieks R, Stalidzans E. OptEnvelope: A target point guided method for growth-coupled production using knockouts. PLoS One 2023; 18:e0294313. [PMID: 37972019 PMCID: PMC10653430 DOI: 10.1371/journal.pone.0294313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.
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Affiliation(s)
- Ehsan Motamedian
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Kristaps Berzins
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Reinis Muiznieks
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, Computational Systems Biology Group, University of Latvia, Riga, Latvia
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10
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Eng T, Banerjee D, Menasalvas J, Chen Y, Gin J, Choudhary H, Baidoo E, Chen JH, Ekman A, Kakumanu R, Diercks YL, Codik A, Larabell C, Gladden J, Simmons BA, Keasling JD, Petzold CJ, Mukhopadhyay A. Maximizing microbial bioproduction from sustainable carbon sources using iterative systems engineering. Cell Rep 2023; 42:113087. [PMID: 37665664 DOI: 10.1016/j.celrep.2023.113087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/10/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
Maximizing the production of heterologous biomolecules is a complex problem that can be addressed with a systems-level understanding of cellular metabolism and regulation. Specifically, growth-coupling approaches can increase product titers and yields and also enhance production rates. However, implementing these methods for non-canonical carbon streams is challenging due to gaps in metabolic models. Over four design-build-test-learn cycles, we rewire Pseudomonas putida KT2440 for growth-coupled production of indigoidine from para-coumarate. We explore 4,114 potential growth-coupling solutions and refine one design through laboratory evolution and ensemble data-driven methods. The final growth-coupled strain produces 7.3 g/L indigoidine at 77% maximum theoretical yield in para-coumarate minimal medium. The iterative use of growth-coupling designs and functional genomics with experimental validation was highly effective and agnostic to specific hosts, carbon streams, and final products and thus generalizable across many systems.
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Affiliation(s)
- Thomas Eng
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Deepanwita Banerjee
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Javier Menasalvas
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yan Chen
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jennifer Gin
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Hemant Choudhary
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biomanufacturing and Biomaterials Department, Sandia National Laboratories, Livermore, CA, USA
| | - Edward Baidoo
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jian Hua Chen
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Axel Ekman
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ramu Kakumanu
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yuzhong Liu Diercks
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Alex Codik
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Carolyn Larabell
- Department of Anatomy, University of California, San Francisco, San Francisco, CA, USA; National Center for X-ray Tomography, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - John Gladden
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biomanufacturing and Biomaterials Department, Sandia National Laboratories, Livermore, CA, USA
| | - Blake A Simmons
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Jay D Keasling
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; QB3 Institute, University of California, Berkeley, 5885 Hollis Street, 4th Floor, Emeryville, CA 94608, USA; Department of Chemical & Biomolecular Engineering, University of California, Berkeley, Berkeley, CA 94720, USA; Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, 2970 Horsholm, Denmark; Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Christopher J Petzold
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Aindrila Mukhopadhyay
- The Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
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11
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Pentjuss A, Bolmanis E, Suleiko A, Didrihsone E, Suleiko A, Dubencovs K, Liepins J, Kazaks A, Vanags J. Pichia pastoris growth-coupled heme biosynthesis analysis using metabolic modelling. Sci Rep 2023; 13:15816. [PMID: 37739976 PMCID: PMC10516909 DOI: 10.1038/s41598-023-42865-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/15/2023] [Indexed: 09/24/2023] Open
Abstract
Soy leghemoglobin is one of the most important and key ingredients in plant-based meat substitutes that can imitate the colour and flavour of the meat. To improve the high-yield production of leghemoglobin protein and its main component-heme in the yeast Pichia pastoris, glycerol and methanol cultivation conditions were studied. Additionally, in-silico metabolic modelling analysis of growth-coupled enzyme quantity, suggests metabolic gene up/down-regulation strategies for heme production. First, cultivations and metabolic modelling analysis of P. pastoris were performed on glycerol and methanol in different growth media. Glycerol cultivation uptake and production rates can be increased by 50% according to metabolic modelling results, but methanol cultivation-is near the theoretical maximum. Growth-coupled metabolic optimisation results revealed the best feasible upregulation (33 reactions) (1.47% of total reactions) and 66 downregulation/deletion (2.98% of total) reaction suggestions. Finally, we describe reaction regulation suggestions with the highest potential to increase heme production yields.
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Affiliation(s)
- Agris Pentjuss
- Microbiology and Biotechnology Institute, University of Latvia, Jelgavas Street 1, Riga, 1004, Latvia.
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia.
| | - Emils Bolmanis
- Latvian Biomedical Research and Study Centre, Ratsupites Street 1 K-1, Riga, 1067, Latvia
| | - Anastasija Suleiko
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia
- Bioreactors.Net AS, Dzerbenes Street 27, Riga, 1006, Latvia
| | - Elina Didrihsone
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia
| | - Arturs Suleiko
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia
- Bioreactors.Net AS, Dzerbenes Street 27, Riga, 1006, Latvia
| | - Konstantins Dubencovs
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia
- Bioreactors.Net AS, Dzerbenes Street 27, Riga, 1006, Latvia
| | - Janis Liepins
- Microbiology and Biotechnology Institute, University of Latvia, Jelgavas Street 1, Riga, 1004, Latvia
| | - Andris Kazaks
- Latvian Biomedical Research and Study Centre, Ratsupites Street 1 K-1, Riga, 1067, Latvia
| | - Juris Vanags
- Latvian State Institute of Wood Chemistry, Dzerbenes Street 27, Riga, 1006, Latvia
- Bioreactors.Net AS, Dzerbenes Street 27, Riga, 1006, Latvia
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12
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Bezold F, Scheffer J, Wendering P, Razaghi-Moghadam Z, Trauth J, Pook B, Nußhär H, Hasenjäger S, Nikoloski Z, Essen LO, Taxis C. Optogenetic control of Cdc48 for dynamic metabolic engineering in yeast. Metab Eng 2023; 79:97-107. [PMID: 37422133 DOI: 10.1016/j.ymben.2023.06.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/10/2023]
Abstract
Dynamic metabolic engineering is a strategy to switch key metabolic pathways in microbial cell factories from biomass generation to accumulation of target products. Here, we demonstrate that optogenetic intervention in the cell cycle of budding yeast can be used to increase production of valuable chemicals, such as the terpenoid β-carotene or the nucleoside analog cordycepin. We achieved optogenetic cell-cycle arrest in the G2/M phase by controlling activity of the ubiquitin-proteasome system hub Cdc48. To analyze the metabolic capacities in the cell cycle arrested yeast strain, we studied their proteomes by timsTOF mass spectrometry. This revealed widespread, but highly distinct abundance changes of metabolic key enzymes. Integration of the proteomics data in protein-constrained metabolic models demonstrated modulation of fluxes directly associated with terpenoid production as well as metabolic subsystems involved in protein biosynthesis, cell wall synthesis, and cofactor biosynthesis. These results demonstrate that optogenetically triggered cell cycle intervention is an option to increase the yields of compounds synthesized in a cellular factory by reallocation of metabolic resources.
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Affiliation(s)
- Filipp Bezold
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Johannes Scheffer
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Philipp Wendering
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Jonathan Trauth
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Bastian Pook
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Hagen Nußhär
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Sophia Hasenjäger
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Lars-Oliver Essen
- Unit for Structural Biochemistry, Department of Chemistry, Philipps-University Marburg, 35032, Marburg, Germany.
| | - Christof Taxis
- Department of Biology/Genetics, Philipps-University Marburg, 35032, Marburg, Germany; School of Science and Technology, University Siegen, 57076, Siegen, Germany.
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13
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Cai J, Liao X, Mao Y, Wang R, Li H, Ma H. Designing gene manipulation schedules for high throughput parallel construction of objective strains. Biotechnol J 2023; 18:e2200578. [PMID: 37300341 DOI: 10.1002/biot.202200578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 05/24/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Recent advances in biofoundries have enabled the construction of a large quantity of strains in parallel, accelerating the design-build-test-learn (DBTL) cycles for strain development. However, the construction of a large number of strains by iterative gene manipulation is still time-consuming and costly, posing a challenge for the development of commercial strains. Common gene manipulations among different objective strains open up the possibility of reducing cost and time for strain construction in biofoundries by optimizing genetic manipulation schedules. A method is introduced consisting of two complementary algorithms for designing optimal parent-children manipulation schedules for strain construction: greedy search of common ancestor strains (GSCAS) and minimizing total manipulations (MTM). By reusing common ancestor strains, the number of strains to be constructed can be effectively reduced, resulting in a tree-like structure of descendants instead of linear lineages for each strain. The GSCAS algorithm can quickly find common ancestor strains and clusters them together based on their genetic makeup, and the MTM algorithm subsequently minimize the genetic manipulations required, resulting in a further reduction in the total number of genetic manipulations. The effectiveness of our method is demonstrated through a case study of 94 target strains, where GSCAS reduces an average of 36% of the total gene manipulations, and MTM reduces an additional 10%. The performance of both algorithms is robust among case studies with different average occurrences of gene manipulations across objective strains. Our method potentially improves cost efficiency and accelerate the development of commercial strains significantly. The implementation of the methods can be freely accessed via https://gscas-mtm.biodesign.ac.cn/.
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Affiliation(s)
- Jingyi Cai
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Xiaoping Liao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
- Haihe Laboratory of Synthetic Biology, Tianjin, China
| | - Yufeng Mao
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Ruoyu Wang
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Haoran Li
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
| | - Hongwu Ma
- Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin, China
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14
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Hassani L, Moosavi MR, Setoodeh P, Zare H. FastKnock: An efficient next-generation approach to identify all knockout strategies for strain optimization. RESEARCH SQUARE 2023:rs.3.rs-3126389. [PMID: 37503204 PMCID: PMC10371132 DOI: 10.21203/rs.3.rs-3126389/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Overproduction of desired native or nonnative biochemical(s) in (micro)organisms can be achieved through metabolic engineering. Appropriate rewiring of cell metabolism is performed making rational changes such as insertion, up-/down-regulation and knockout of genes and consequently metabolic reactions. Finding appropriate targets (including proper sets of reactions to be knocked out) for metabolic engineering to design optimal production strains has been the goal of a number of computational algorithms. We developed FastKnock, an efficient next-generation algorithm for identifying all possible knockout strategies for the growth-coupled overproduction of biochemical(s) of interest. We achieve this by developing a special depth-first traversal algorithm that allows us to prune the search space significantly. This leads to a drastic reduction in execution time. We evaluate the performance of the FastKnock algorithm using three Escherichia coli genome-scale metabolic models in different conditions (minimal and rich mediums) for the overproduction of a number of desired metabolites. FastKnock efficiently prunes the search space to less than 0.2% for quadruple and 0.02% for quintuple-reaction knockouts. Compared to the classic approaches such as OptKnock and the state-of-the-art techniques such as MCSEnumerator methods, FastKnock found many more useful and important practical solutions. The availability of all the solutions provides the opportunity to further characterize and select the most appropriate intervention strategy based on any desired evaluation index. Our implementation of the FastKnock method in Python is publicly available at https://github.com/leilahsn/FastKnock.
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Affiliation(s)
| | | | | | - Habil Zare
- University of Texas Health Science Center
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15
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Chen JP, Gong JS, Su C, Li H, Xu ZH, Shi JS. Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies. Metab Eng 2023; 78:99-114. [PMID: 37244368 DOI: 10.1016/j.ymben.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023]
Abstract
Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.
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Affiliation(s)
- Jin-Ping Chen
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Gong
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China.
| | - Chang Su
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Heng Li
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China
| | - Zheng-Hong Xu
- National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, School of Biotechnology, Jiangnan University, Wuxi, 214122, PR China; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
| | - Jin-Song Shi
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Life Sciences and Health Engineering, Jiangnan University, Wuxi, 214122, PR China; Yixing Institute of Food and Biotechnology Co., Ltd, Yixing, 214200, PR China
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16
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Nielsen JR, Weusthuis RA, Huang WE. Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration. Biotechnol Adv 2023; 63:108102. [PMID: 36681133 DOI: 10.1016/j.biotechadv.2023.108102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 01/11/2023] [Accepted: 01/15/2023] [Indexed: 01/20/2023]
Abstract
Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD+/NADH and NADP+/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD+/NADH and NADP+/NADPH pairs, and of non-canonical redox cofactor pairs NMN+/NMNH and NCD+/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.
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Affiliation(s)
- Jochem R Nielsen
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
| | - Ruud A Weusthuis
- Department of Bioprocess Engineering, Wageningen University & Research, Wageningen 6700AA, the Netherlands.
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
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17
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Hu M, Dinh HV, Shen Y, Suthers PF, Foster CJ, Call CM, Ye X, Pratas J, Fatma Z, Zhao H, Rabinowitz JD, Maranas CD. Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale. Metab Eng 2023; 76:1-17. [PMID: 36603705 DOI: 10.1016/j.ymben.2023.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/22/2022] [Accepted: 01/01/2023] [Indexed: 01/04/2023]
Abstract
The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.
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Affiliation(s)
- Mengqi Hu
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Xuanjia Ye
- Department of Molecular Biology, Princeton University, Princeton, NJ, 08544, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, 08544, USA; Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, 08544, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, USA.
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18
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Tamura T, Muto-Fujita A, Tohsato Y, Kosaka T. Gene Deletion Algorithms for Minimum Reaction Network Design by Mixed-Integer Linear Programming for Metabolite Production in Constraint-Based Models: gDel_minRN. J Comput Biol 2023; 30:553-568. [PMID: 36809057 DOI: 10.1089/cmb.2022.0352] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Genome-scale constraint-based metabolic networks play an important role in the simulation of growth-coupled production, which means that cell growth and target metabolite production are simultaneously achieved. For growth-coupled production, a minimal reaction-network-based design is known to be effective. However, the obtained reaction networks often fail to be realized by gene deletions due to conflicts with gene-protein-reaction (GPR) relations. Here, we developed gDel_minRN that determines gene deletion strategies using mixed-integer linear programming to achieve growth-coupled production by repressing the maximum number of reactions via GPR relations. The results of computational experiments showed that gDel_minRN could determine the core parts, which include only 30% to 55% of whole genes, for stoichiometrically feasible growth-coupled production for many target metabolites, which include useful vitamins such as biotin (vitamin B7), riboflavin (vitamin B2), and pantothenate (vitamin B5). Since gDel_minRN calculates a constraint-based model of the minimum number of gene-associated reactions without conflict with GPR relations, it helps biological analysis of the core parts essential for growth-coupled production for each target metabolite. The source codes, implemented in MATLAB using CPLEX and COBRA Toolbox, are available on https://github.com/MetNetComp/gDel-minRN.
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Affiliation(s)
- Takeyuki Tamura
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
| | - Ai Muto-Fujita
- RIKEN Center for Biosystems Dynamics Research, Suita, Osaka, Japan
| | - Yukako Tohsato
- Faculty of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
| | - Tomoyuki Kosaka
- Research Center for Thermotolerant Microbial Resources (RCTMR), Yamaguchi University, Yoshida, Yamaguchi, Japan.,Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yoshida, Yamaguchi, Japan
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19
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Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications. Comput Struct Biotechnol J 2022; 21:563-573. [PMID: 36659921 PMCID: PMC9816911 DOI: 10.1016/j.csbj.2022.12.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/23/2022] [Accepted: 12/23/2022] [Indexed: 12/25/2022] Open
Abstract
Adaptive laboratory evolution (ALE) has long been used as the tool of choice for microbial engineering applications, ranging from the production of commodity chemicals to the innovation of complex phenotypes. With the advent of systems and synthetic biology, the ALE experimental design has become increasingly sophisticated. For instance, implementation of in silico metabolic model reconstruction and advanced synthetic biology tools have facilitated the effective coupling of desired traits to adaptive phenotypes. Furthermore, various multi-omic tools now enable in-depth analysis of cellular states, providing a comprehensive understanding of the biology of even the most genomically perturbed systems. Emerging machine learning approaches would assist in streamlining the interpretation of massive and multiplexed datasets and promoting our understanding of complexity in biology. This review covers some of the representative case studies among the 700 independent ALE studies reported to date, outlining key ideas, principles, and important mechanisms underlying ALE designs in bioproduction and synthetic cell engineering, with evidence from literatures to aid comprehension.
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Yilmaz S, Nyerges A, van der Oost J, Church GM, Claassens NJ. Towards next-generation cell factories by rational genome-scale engineering. Nat Catal 2022. [DOI: 10.1038/s41929-022-00836-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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21
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Legon L, Corre C, Bates DG, Mannan AA. gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs. Bioinformatics 2022; 38:3657-3659. [PMID: 35642935 PMCID: PMC9272801 DOI: 10.1093/bioinformatics/btac376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/09/2022] [Accepted: 05/30/2022] [Indexed: 11/14/2022] Open
Abstract
Motivation A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow. Results To address this multi-objective optimization problem, we developed a software tool named gcFront—using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run—granting users flexibility in selecting designs to take to the lab. Availability and implementation gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Laurence Legon
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry CV4 7AL, UK
- Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Christophe Corre
- Warwick Integrative Synthetic Biology Centre, School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
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22
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Rang J, Cao L, Shuai L, Liu Y, Zhu Z, Xia Z, Jin D, Sun Y, Yu Z, Hu S, Xie Q, Xia L. Promoting Butenyl-spinosyn Production Based on Omics Research and Metabolic Network Construction in Saccharopolyspora pogona. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:3557-3567. [PMID: 35245059 DOI: 10.1021/acs.jafc.2c00285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Understanding the metabolism of Saccharopolyspora pogona on a global scale is essential for manipulating its metabolic capabilities to improve butenyl-spinosyn biosynthesis. Here, we combined multiomics analysis to parse S. pogona genomic information, construct a metabolic network, and mine important functional genes that affect the butenyl-spinosyn biosynthesis. This research not only elucidated the relationship between butenyl-spinosyn biosynthesis and the primary metabolic pathway but also showed that the low expression level and continuous downregulation of the bus cluster and the competitive utilization of acetyl-CoA were the main reasons for reduced butenyl-spinosyn production. Our framework identified 148 genes related to butenyl-spinosyn biosynthesis that were significantly differentially expressed, confirming that butenyl-spinosyn polyketide synthase (PKS) and succinic semialdehyde dehydrogenase (GabD) play an important role in regulating butenyl-spinosyn biosynthesis. Combined modification of these genes increased overall butenyl-spinosyn production by 6.38-fold to 154.1 ± 10.98 mg/L. Our results provide an important strategy for further promoting the butenyl-spinosyn titer.
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Affiliation(s)
- Jie Rang
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (MOE of China), National & Local Joint Engineering Laboratory for New Petro-chemical Materials and Fine Utilization of Resources, College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Li Cao
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Ling Shuai
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Yang Liu
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Zirong Zhu
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Ziyuan Xia
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Duo Jin
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Yunjun Sun
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Ziquan Yu
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Shengbiao Hu
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Qingji Xie
- Key Laboratory of Chemical Biology and Traditional Chinese Medicine Research (MOE of China), National & Local Joint Engineering Laboratory for New Petro-chemical Materials and Fine Utilization of Resources, College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
| | - Liqiu Xia
- Hunan Provincial Key Laboratory for Microbial Molecular Biology, State Key Laboratory of Development Biology of Freshwater Fish, College of Life Science, Hunan Normal University, Changsha, Hunan 410081, People's Republic of China
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23
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Yu J, Moon SK, Kim YH, Min J. Isoprene production by Rhodobacter sphaeroides and its antimicrobial activity. Res Microbiol 2022; 173:103938. [DOI: 10.1016/j.resmic.2022.103938] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/02/2022] [Accepted: 03/09/2022] [Indexed: 11/25/2022]
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24
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Testa RL, Delpino C, Estrada V, Diaz MS. Development of in silico strategies to photoautotrophically produce poly-β-hydroxybutyrate (PHB) by cyanobacteria. ALGAL RES 2022. [DOI: 10.1016/j.algal.2021.102621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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25
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Keasling J, Garcia Martin H, Lee TS, Mukhopadhyay A, Singer SW, Sundstrom E. Microbial production of advanced biofuels. Nat Rev Microbiol 2021; 19:701-715. [PMID: 34172951 DOI: 10.1038/s41579-021-00577-w] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/13/2021] [Indexed: 02/06/2023]
Abstract
Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.
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Affiliation(s)
- Jay Keasling
- Joint BioEnergy Institute, Emeryville, CA, USA. .,Department of Chemical & Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA. .,Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA. .,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. .,Center for Biosustainability, Danish Technical University, Lyngby, Denmark. .,Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, China.
| | - Hector Garcia Martin
- Joint BioEnergy Institute, Emeryville, CA, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,DOE Agile BioFoundry, Emeryville, CA, USA.,BCAM,Basque Center for Applied Mathematics, Bilbao, Spain.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Taek Soon Lee
- Joint BioEnergy Institute, Emeryville, CA, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Emeryville, CA, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Steven W Singer
- Joint BioEnergy Institute, Emeryville, CA, USA.,Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Eric Sundstrom
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.,Advanced Biofuels and Bioproducts Process Development Unit, Emeryville, CA, USA
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26
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Zhang G, Chen Y, Li Q, Zhou J, Li J, Du G. Growth-coupled evolution and high-throughput screening assisted rapid enhancement for amylase-producing Bacillus licheniformis. BIORESOURCE TECHNOLOGY 2021; 337:125467. [PMID: 34320747 DOI: 10.1016/j.biortech.2021.125467] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/22/2021] [Accepted: 06/24/2021] [Indexed: 06/13/2023]
Abstract
Bacillus licheniformis α-amylase is a thermostable enzyme used in industrial starch hydrolysis. However, difficulties in the genetic manipulation of B. licheniformis hamper further enhancement of α-amylase production. In this regard, adaptive evolution is a useful strategy for promoting the productivity of microbial hosts, although the success of this approach requires the application of suitable evolutionary stress. In this study, we designed a growth-coupled adaptive evolution model to enrich B. licheniformis strains with enhanced amylase productivity and utilization capacity of starch substrates. Single cells of high α-amylase-producing B. licheniformis were isolated using a droplet-based microfluidic platform. Clones with 67% higher α-amylase yield were obtained and analyzed by genome resequencing. Our findings confirmed that growth-coupled evolution combined with high-throughput screening is an efficient strategy for enhanced α-amylase production. In addition, we identified several potential target genes to guide further modification of the B. licheniformis host for efficient protein expression.
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Affiliation(s)
- Guoqiang Zhang
- National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
| | - Yukun Chen
- National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Qinghua Li
- National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- National Engineering Laboratory for Cereal Fermentation Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jianghua Li
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Guocheng Du
- School of Biotechnology and Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
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27
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Shimizu H, Toya Y. Recent advances in metabolic engineering-integration of in silico design and experimental analysis of metabolic pathways. J Biosci Bioeng 2021; 132:429-436. [PMID: 34509367 DOI: 10.1016/j.jbiosc.2021.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/07/2021] [Indexed: 11/29/2022]
Abstract
Microorganisms are widely used to produce valuable compounds. Because thousands of metabolic reactions occur simultaneously and many metabolic reactions are related to target production and cell growth, the development of a rational design method for metabolic pathway modification to optimize target production is needed. In this paper, recent advances in metabolic engineering are reviewed, specifically considering computational pathway modification design and experimental evaluation of metabolic fluxes by 13C-metabolic flux analysis. Computational tools for seeking effective gene deletion targets and recruiting heterologous genes are described in flux balance analysis approaches. A kinetic model and adaptive laboratory evolution were applied to identify and eliminate the rate-limiting step in metabolic pathways. Data science-based approaches for process monitoring and control are described to maximize the performance of engineered cells in bioreactors.
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Affiliation(s)
- Hiroshi Shimizu
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
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28
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Orsi E, Claassens NJ, Nikel PI, Lindner SN. Growth-coupled selection of synthetic modules to accelerate cell factory development. Nat Commun 2021; 12:5295. [PMID: 34489458 PMCID: PMC8421431 DOI: 10.1038/s41467-021-25665-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/23/2021] [Indexed: 11/18/2022] Open
Abstract
Synthetic biology has brought about a conceptual shift in our ability to redesign microbial metabolic networks. Combining metabolic pathway-modularization with growth-coupled selection schemes is a powerful tool that enables deep rewiring of the cell factories’ biochemistry for rational bioproduction.
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Affiliation(s)
- Enrico Orsi
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Nico J Claassens
- Laboratory of Microbiology, Wageningen University, Wageningen, The Netherlands
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Steffen N Lindner
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany.
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29
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Schneider P, Mahadevan R, Klamt S. Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs. Biotechnol J 2021; 16:e2100236. [PMID: 34432943 DOI: 10.1002/biot.202100236] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/08/2022]
Abstract
A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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30
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Ma Y, Tamura T. Dynamic Solution Space Division-Based Methods for Calculating Reaction Deletion Strategies for Constraint-Based Metabolic Networks for Substance Production: DynCubeProd. FRONTIERS IN BIOINFORMATICS 2021; 1:716112. [DOI: 10.3389/fbinf.2021.716112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/13/2021] [Indexed: 11/13/2022] Open
Abstract
Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of Escherichia coli. Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.
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31
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Razaghi-Moghadam Z, Nikoloski Z. GeneReg: a constraint-based approach for design of feasible metabolic engineering strategies at the gene level. Bioinformatics 2021; 37:1717-1723. [PMID: 33245091 PMCID: PMC8289378 DOI: 10.1093/bioinformatics/btaa996] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/24/2020] [Accepted: 11/17/2020] [Indexed: 11/18/2022] Open
Abstract
Motivation Large-scale metabolic models are widely used to design metabolic engineering strategies for diverse biotechnological applications. However, the existing computational approaches focus on alteration of reaction fluxes and often neglect the manipulations of gene expression to implement these strategies. Results Here, we find that the association of genes with multiple reactions leads to infeasibility of engineering strategies at the flux level, since they require contradicting manipulations of gene expression. Moreover, we identify that all of the existing approaches to design gene knockout strategies do not ensure that the resulting design may also require other gene alterations, such as up- or downregulations, to match the desired flux distribution. To address these issues, we propose a constraint-based approach, termed GeneReg, that facilitates the design of feasible metabolic engineering strategies at the gene level and that is readily applicable to large-scale metabolic networks. We show that GeneReg can identify feasible strategies to overproduce ethanol in Escherichia coli and lactate in Saccharomyces cerevisiae, but overproduction of the TCA cycle intermediates is not feasible in five organisms used as cell factories under default growth conditions. Therefore, GeneReg points at the need to couple gene regulation and metabolism to design rational metabolic engineering strategies. Availability and implementation https://github.com/MonaRazaghi/GeneReg Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zahra Razaghi-Moghadam
- Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Department of Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Department of Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany.,Department of Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
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32
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Garcia S, Trinh CT. Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules. Metab Eng 2021; 67:453-463. [PMID: 34339856 DOI: 10.1016/j.ymben.2021.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 07/15/2021] [Accepted: 07/21/2021] [Indexed: 11/19/2022]
Abstract
Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable production modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all of 161 production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell to be re-purposed towards production of new molecules. Overall, ModCell-HPC is a useful tool for understanding modularity of biological systems and guiding more efficient and generalizable design of modular cells that help reduce research and development cost in biocatalysis.
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Affiliation(s)
- Sergio Garcia
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, United States; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Cong T Trinh
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, United States; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
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33
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Vaud S, Pearcy N, Hanževački M, Van Hagen AMW, Abdelrazig S, Safo L, Ehsaan M, Jonczyk M, Millat T, Craig S, Spence E, Fothergill J, Bommareddy RR, Colin PY, Twycross J, Dalby PA, Minton NP, Jäger CM, Kim DH, Yu J, Maness PC, Lynch S, Eckert CA, Conradie A, Bryan SJ. Engineering improved ethylene production: Leveraging systems biology and adaptive laboratory evolution. Metab Eng 2021; 67:308-320. [PMID: 34245888 DOI: 10.1016/j.ymben.2021.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 05/26/2021] [Accepted: 07/02/2021] [Indexed: 10/20/2022]
Abstract
Ethylene is a small hydrocarbon gas widely used in the chemical industry. Annual worldwide production currently exceeds 150 million tons, producing considerable amounts of CO2 contributing to climate change. The need for a sustainable alternative is therefore imperative. Ethylene is natively produced by several different microorganisms, including Pseudomonas syringae pv. phaseolicola via a process catalyzed by the ethylene-forming enzyme (EFE), subsequent heterologous expression of EFE has led to ethylene production in non-native bacterial hosts including Escherichia coli and cyanobacteria. However, solubility of EFE and substrate availability remain rate-limiting steps in biological ethylene production. We employed a combination of genome-scale metabolic modelling, continuous fermentation, and protein evolution to enable the accelerated development of a high efficiency ethylene producing E. coli strain, yielding a 49-fold increase in production, the most significant improvement reported to date. Furthermore, we have clearly demonstrated that this increased yield resulted from metabolic adaptations that were uniquely linked to EFE (wild type versus mutant). Our findings provide a novel solution to deregulate metabolic bottlenecks in key pathways, which can be readily applied to address other engineering challenges.
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Affiliation(s)
- Sophie Vaud
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Nicole Pearcy
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Marko Hanževački
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Alexander M W Van Hagen
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Salah Abdelrazig
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, NG7 2RD, UK
| | - Laudina Safo
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, NG7 2RD, UK
| | - Muhammad Ehsaan
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Magdalene Jonczyk
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Thomas Millat
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Sean Craig
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Edward Spence
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - James Fothergill
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Rajesh Reddy Bommareddy
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Pierre-Yves Colin
- Department of Biochemical Engineering, Bernard Katz Building, University College London, WC1E 6BT, UK
| | - Jamie Twycross
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK; School of Computer Science, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Paul A Dalby
- Department of Biochemical Engineering, Bernard Katz Building, University College London, WC1E 6BT, UK
| | - Nigel P Minton
- BBSRC/EPSRC Synthetic Biology Research Centre, The Biodiscovery Institute, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Christof M Jäger
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Dong-Hyun Kim
- Centre for Analytical Bioscience, Advanced Materials and Healthcare Technologies Division, School of Pharmacy, University of Nottingham, NG7 2RD, UK
| | - Jianping Yu
- Biosciences Center, National Renewable Energy Laboratory (NREL), Golden, CO, USA
| | - Pin-Ching Maness
- Biosciences Center, National Renewable Energy Laboratory (NREL), Golden, CO, USA
| | - Sean Lynch
- Biosciences Center, National Renewable Energy Laboratory (NREL), Golden, CO, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, USA; Somalogic, Inc., Boulder, CO, USA
| | - Carrie A Eckert
- Biosciences Center, National Renewable Energy Laboratory (NREL), Golden, CO, USA; Renewable and Sustainable Energy Institute (RASEI), University of Colorado Boulder, USA
| | - Alex Conradie
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Samantha J Bryan
- Department of Chemical and Environmental Engineering, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, UK.
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34
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Guo L, Lu J, Gao C, Zhang L, Liu L, Chen X. Dynamic control of the distribution of carbon flux between cell growth and butyrate biosynthesis in Escherichia coli. Appl Microbiol Biotechnol 2021; 105:5173-5187. [PMID: 34115183 DOI: 10.1007/s00253-021-11385-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 05/05/2021] [Accepted: 06/02/2021] [Indexed: 12/14/2022]
Abstract
Microbial cell factories offer an economic and environmentally friendly method for the biosynthesis of acetyl-CoA-derived chemicals. However, the static control of carbon flux can cause direct and indirect competition for acetyl-CoA between cell growth and chemical biosynthesis, limiting the efficiency of microbial cell factories. Herein, recombinase-based genetic circuits were developed to achieve the optimal distribution of acetyl-CoA between cell growth and butyrate biosynthesis. First, three dynamic devices-a turn-on switch, a turn-off switch, and a recombinase-based inverter (RBI)-were constructed based on Bxb1 recombinase. Then, the turn-on switch was used to dynamically control the butyrate biosynthetic pathway, which directly improved the consumption of acetyl-CoA. Next, the turn-off switch was applied to dynamically control cell growth, which indirectly enhanced the supply of acetyl-CoA. Finally, an RBI was adopted for the dynamic dual control of the distribution of acetyl-CoA between cell growth and butyrate biosynthesis. The final butyrate production rate was increased to 34 g/L, with a productivity of 0.405 g/L/h. The strategy described herein will pave the way for the development of high-performance microbial cell factories for the production of other desirable chemicals. KEY POINTS: • Competition for acetyl-CoA between cell growth and synthesis limits productivity. • Recombinase-based genetic circuits were developed to dynamic control of acetyl-CoA. • Optimal distribution of acetyl-CoA between cell growth and synthesis was achieved.
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Affiliation(s)
- Liang Guo
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, China
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, 214122, China
| | - Jiaxin Lu
- School of Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Cong Gao
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, China
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, 214122, China
| | - Linpei Zhang
- School of Biotechnology, Jiangnan University, Wuxi, 214122, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, China
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, 214122, China
| | - Xiulai Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, 214122, China.
- International Joint Laboratory on Food Safety, Jiangnan University, Wuxi, 214122, China.
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35
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Mannan AA, Bates DG. Designing an irreversible metabolic switch for scalable induction of microbial chemical production. Nat Commun 2021; 12:3419. [PMID: 34103495 PMCID: PMC8187666 DOI: 10.1038/s41467-021-23606-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/07/2021] [Indexed: 01/05/2023] Open
Abstract
Bacteria can be harnessed to synthesise high-value chemicals. A promising strategy for increasing productivity uses inducible control systems to switch metabolism from growth to chemical synthesis once a large population of cell factories are generated. However, use of expensive chemical inducers limits scalability of this approach for biotechnological applications. Switching using cheap nutrients is an appealing alternative, but their tightly regulated uptake and consumption again limits scalability. Here, using mathematical models of fatty acid uptake in E. coli as an exemplary case study, we unravel how the cell's native regulation and program of induction can be engineered to minimise inducer usage. We show that integrating positive feedback loops into the circuitry creates an irreversible metabolic switch, which, requiring only temporary induction, drastically reduces inducer usage. Our proposed switch should be widely applicable, irrespective of the product of interest, and brings closer the realization of scalable and sustainable microbial chemical production.
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Affiliation(s)
- Ahmad A Mannan
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Declan G Bates
- Warwick Integrative Synthetic Biology Centre, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom.
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36
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Escherichia coli as a platform microbial host for systems metabolic engineering. Essays Biochem 2021; 65:225-246. [PMID: 33956149 DOI: 10.1042/ebc20200172] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/12/2021] [Accepted: 04/14/2021] [Indexed: 12/19/2022]
Abstract
Bio-based production of industrially important chemicals and materials from non-edible and renewable biomass has become increasingly important to resolve the urgent worldwide issues including climate change. Also, bio-based production, instead of chemical synthesis, of food ingredients and natural products has gained ever increasing interest for health benefits. Systems metabolic engineering allows more efficient development of microbial cell factories capable of sustainable, green, and human-friendly production of diverse chemicals and materials. Escherichia coli is unarguably the most widely employed host strain for the bio-based production of chemicals and materials. In the present paper, we review the tools and strategies employed for systems metabolic engineering of E. coli. Next, representative examples and strategies for the production of chemicals including biofuels, bulk and specialty chemicals, and natural products are discussed, followed by discussion on materials including polyhydroxyalkanoates (PHAs), proteins, and nanomaterials. Lastly, future perspectives and challenges remaining for systems metabolic engineering of E. coli are discussed.
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37
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Wu Y, Jameel A, Xing XH, Zhang C. Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution. Trends Biotechnol 2021; 40:38-59. [PMID: 33958227 DOI: 10.1016/j.tibtech.2021.04.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/17/2021] [Accepted: 04/01/2021] [Indexed: 12/18/2022]
Abstract
Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.
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Affiliation(s)
- Yinan Wu
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Aysha Jameel
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Xin-Hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
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38
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Martin-Pascual M, Batianis C, Bruinsma L, Asin-Garcia E, Garcia-Morales L, Weusthuis RA, van Kranenburg R, Martins Dos Santos VAP. A navigation guide of synthetic biology tools for Pseudomonas putida. Biotechnol Adv 2021; 49:107732. [PMID: 33785373 DOI: 10.1016/j.biotechadv.2021.107732] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 03/12/2021] [Accepted: 03/18/2021] [Indexed: 12/12/2022]
Abstract
Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.
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Affiliation(s)
- Maria Martin-Pascual
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Christos Batianis
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Lyon Bruinsma
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Enrique Asin-Garcia
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Luis Garcia-Morales
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands
| | - Ruud A Weusthuis
- Bioprocess Engineering, Wageningen University and Research, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
| | - Richard van Kranenburg
- Corbion, Gorinchem 4206 AC, The Netherlands; Laboratory of Microbiology, Wageningen University & Research, Wageningen 6708 WE, the Netherlands
| | - Vitor A P Martins Dos Santos
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen 6708 WE, The Netherlands; LifeGlimmer GmbH, Berlin 12163, Germany.
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39
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Santala S, Santala V, Liu N, Stephanopoulos G. Partitioning metabolism between growth and product synthesis for coordinated production of wax esters in Acinetobacter baylyi ADP1. Biotechnol Bioeng 2021; 118:2283-2292. [PMID: 33666232 DOI: 10.1002/bit.27740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/17/2021] [Accepted: 02/27/2021] [Indexed: 11/11/2022]
Abstract
Microbial storage compounds, such as wax esters (WE), are potential high-value lipids for the production of specialty chemicals and medicines. Their synthesis, however, is strictly regulated and competes with cell growth, which leads to trade-offs between biomass and product formation. Here, we use metabolic engineering and synergistic substrate cofeeding to partition the metabolism of Acinetobacter baylyi ADP1 into two distinct modules, each dedicated to cell growth and WE biosynthesis, respectively. We first blocked the glyoxylate shunt and upregulated the WE synthesis pathway to direct the acetate substrate exclusively for WE synthesis, then we controlled the supply of gluconate so it could be used exclusively for cell growth and maintenance. We show that the two modules are functionally independent from each other, allowing efficient lipid accumulation while maintaining active cell growth. Our strategy resulted in 7.2- and 4.2-fold improvements in WE content and productivity, respectively, and the product titer was enhanced by 8.3-fold over the wild type strain. Notably, during a 24-h cultivation, a yield of 18% C-WE/C-total-substrates was achieved, being the highest reported for WE biosynthesis. This study provides a simple, yet powerful, means of controlling cellular operations and overcoming some of the fundamental challenges in microbial storage lipid production.
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Affiliation(s)
- Suvi Santala
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
| | - Ville Santala
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland
| | - Nian Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Gregory Stephanopoulos
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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40
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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41
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Klamt S, Mahadevan R, von Kamp A. Speeding up the core algorithm for the dual calculation of minimal cut sets in large metabolic networks. BMC Bioinformatics 2020; 21:510. [PMID: 33167871 PMCID: PMC7654042 DOI: 10.1186/s12859-020-03837-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 10/23/2020] [Indexed: 12/16/2022] Open
Abstract
Background The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS2 approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS2 approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations. Results In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS2 approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger number of (more explicit) constraints and involved variables. Conclusions Our results further enhance the algorithmic toolbox for MCS calculations and are of general importance for theoretical developments as well as for practical applications of the MCS framework.
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Affiliation(s)
- Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106, Magdeburg, Germany.
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106, Magdeburg, Germany
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42
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Rugbjerg P, Olsson L. The future of self-selecting and stable fermentations. J Ind Microbiol Biotechnol 2020; 47:993-1004. [PMID: 33136197 PMCID: PMC7695646 DOI: 10.1007/s10295-020-02325-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/17/2020] [Indexed: 02/08/2023]
Abstract
Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.
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Affiliation(s)
- Peter Rugbjerg
- Enduro Genetics ApS, Copenhagen, Denmark. .,Department of Biology and Biological Engineering, Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden.
| | - Lisbeth Olsson
- Department of Biology and Biological Engineering, Industrial Biotechnology, Chalmers University of Technology, Gothenburg, Sweden
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43
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Banerjee D, Eng T, Lau AK, Sasaki Y, Wang B, Chen Y, Prahl JP, Singan VR, Herbert RA, Liu Y, Tanjore D, Petzold CJ, Keasling JD, Mukhopadhyay A. Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale. Nat Commun 2020; 11:5385. [PMID: 33097726 PMCID: PMC7584609 DOI: 10.1038/s41467-020-19171-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/30/2020] [Indexed: 01/06/2023] Open
Abstract
High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).
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Affiliation(s)
- Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Thomas Eng
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Andrew K Lau
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yusuke Sasaki
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Brenda Wang
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jan-Philip Prahl
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Advanced Biofuel and Bioproduct Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Vasanth R Singan
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Robin A Herbert
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Yuzhong Liu
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Deepti Tanjore
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Advanced Biofuel and Bioproduct Process Development Unit, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jay D Keasling
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- QB3 Institute, University of California-Berkeley, 5885 Hollis Street, 4th Floor, Emeryville, CA, 94608, USA
- Department of Chemical & Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA
- Department of Bioengineering, University of California, Berkeley, CA, 94720, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University Denmark, 2970, Horsholm, Denmark
- Synthetic Biochemistry Center, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, 94608, USA.
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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44
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Godara A, Kao KC. Adaptive laboratory evolution for growth coupled microbial production. World J Microbiol Biotechnol 2020; 36:175. [DOI: 10.1007/s11274-020-02946-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 10/08/2020] [Indexed: 12/18/2022]
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45
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Han Y, Zhang F. Control strategies to manage trade-offs during microbial production. Curr Opin Biotechnol 2020; 66:158-164. [PMID: 32810759 PMCID: PMC8021483 DOI: 10.1016/j.copbio.2020.07.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/04/2020] [Accepted: 07/05/2020] [Indexed: 12/31/2022]
Abstract
When engineering microbes to overproduce a target molecule, engineers face multiple layers of trade-offs to allocate limited cellular resources between the target pathway and native cellular systems. These trade-offs arise from limited free ribosomes during translation, competition for metabolic precursors, as well as the negative relationship between production and growth rate. To achieve high production performance, microbes need to spontaneously make decisions in the dynamic and heterogeneous fermentation environment. In this review, we discuss recent advances in microbial control strategies that are used to manage these trade-offs and to improve microbial production. This review focuses on design principles and compares different implementations, with the hope to provide guidelines to future microbial engineering.
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Affiliation(s)
- Yichao Han
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Fuzhong Zhang
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Division of Biological & Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Institute of Materials Science & Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
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46
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Woodley JM. Towards the sustainable production of bulk-chemicals using biotechnology. N Biotechnol 2020; 59:59-64. [PMID: 32693028 DOI: 10.1016/j.nbt.2020.07.002] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/06/2020] [Accepted: 07/07/2020] [Indexed: 01/06/2023]
Abstract
The design and development of new routes for the production of sustainable bulk-chemicals requires focus on feedstock, conversion technology and downstream product recovery. This brief article discusses some of the constraints with using fermentation and suggests the removal of some constraints by using microbial biocatalysis or enzyme biocatalysis, which give a number of benefits in the context of the requirements for bulk-chemical production. Some potential process concepts are described, for products in the suitable low-price range. These examples (biodiesel, furfurals and amines) are used to illustrate the power of biocatalysis. Suggestions for future research efforts beyond molecular biology, involving process-based concepts, are also discussed.
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Affiliation(s)
- John M Woodley
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, DK-2800, Lyngby, Denmark.
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47
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Garcia S, Trinh CT. Harnessing Natural Modularity of Metabolism with Goal Attainment Optimization to Design a Modular Chassis Cell for Production of Diverse Chemicals. ACS Synth Biol 2020; 9:1665-1681. [PMID: 32470305 DOI: 10.1021/acssynbio.9b00518] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modular design is key to achieve efficient and robust systems across engineering disciplines. Modular design potentially offers advantages to engineer microbial systems for biocatalysis, bioremediation, and biosensing, overcoming the slow and costly design-build-test-learn cycles in the conventional cell engineering approach. These systems consist of a modular (chassis) cell compatible with exchangeable modules that enable programmed functions such as overproduction of a desirable chemical. We previously proposed a multiobjective optimization framework coupled with metabolic flux models to design modular cells and solved it using multiobjective evolutionary algorithms. However, such approach might not achieve solution optimality and hence limits design options for experimental implementation. In this study, we developed the goal attainment formulation compatible with optimization algorithms that guarantee solution optimality. We applied goal attainment to design an Escherichia coli modular cell capable of synthesizing all molecules in a biochemically diverse library at high yields and rates with only a few genetic manipulations. To elucidate modular organization of the designed cells, we developed a flux variance clustering (FVC) method by identifying reactions with high flux variance and clustering them to identify metabolic modules. Using FVC, we identified reaction usage patterns for different modules in the modular cell, revealing that its broad pathway compatibility is enabled by the natural modularity and flexible flux capacity of endogenous core metabolism. Overall, this study not only sheds light on modularity in metabolic networks from their topology and metabolic functions but also presents a useful synthetic biology toolbox to design modular cells with broad applications.
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Affiliation(s)
- Sergio Garcia
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory Oak Ridge, Tennessee 37830, United States
| | - Cong T. Trinh
- Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory Oak Ridge, Tennessee 37830, United States
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Schneider P, von Kamp A, Klamt S. An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets. PLoS Comput Biol 2020; 16:e1008110. [PMID: 32716928 PMCID: PMC7410339 DOI: 10.1371/journal.pcbi.1008110] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 08/06/2020] [Accepted: 06/30/2020] [Indexed: 02/07/2023] Open
Abstract
The concept of minimal cut sets (MCS) provides a flexible framework for analyzing properties of metabolic networks and for computing metabolic intervention strategies. In particular, it has been used to support the targeted design of microbial strains for bio-based production processes. Herein we present a number of major extensions that generalize the existing MCS approach and broaden its scope for applications in metabolic engineering. We first introduce a modified approach to integrate gene-protein-reaction associations (GPR) in the metabolic network structure for the computation of gene-based intervention strategies. In particular, we present a set of novel compression rules for GPR associations, which effectively speedup the computation of gene-based MCS by a factor of up to one order of magnitude. These rules are not specific for MCS and as well applicable to other computational strain design methods. Second, we enhance the MCS framework by allowing the definition of multiple target (undesired) and multiple protected (desired) regions. This enables precise tailoring of the metabolic solution space of the designed strain with unlimited flexibility. Together with further generalizations such as individual cost factors for each intervention, direct combinations of reaction/gene deletions and additions as well as the possibility to search for substrate co-feeding strategies, the scope of the MCS framework could be broadly extended. We demonstrate the applicability and performance benefits of the described developments by computing (gene-based) Escherichia coli strain designs for the bio-based production of 2,3-butanediol, a chemical, that has recently received much attention in the field of metabolic engineering. With our extended framework, we could identify promising strain designs that were formerly unpredictable, including those based on substrate co-feeding.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Axel von Kamp
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany
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Schneider P, Klamt S. Characterizing and ranking computed metabolic engineering strategies. Bioinformatics 2020; 35:3063-3072. [PMID: 30649194 PMCID: PMC6735923 DOI: 10.1093/bioinformatics/bty1065] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 11/28/2018] [Accepted: 01/07/2019] [Indexed: 01/06/2023] Open
Abstract
MOTIVATION The computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected. RESULTS This work focuses on how to evaluate and rank, in a meaningful way, a given pool of computed metabolic engineering strategies for growth-coupled product synthesis. Apart from straightforward criteria, such as a preferably small number of necessary interventions, a reasonable growth rate and a high product yield, we present several new criteria useful to pick the most suitable intervention strategy. Among others, we investigate the robustness of the intervention strategies by searching for metabolites that may disrupt growth coupling when accumulated or secreted and by checking whether the interventions interrupt pathways at their origin (preferable) or at downstream steps. We also assess thermodynamic properties of the pathway(s) favored by the intervention strategy. Furthermore, strategies that have a significant overlap with alternative solutions are ranked higher because they provide flexibility in implementation. We also introduce the notion of equivalence classes for grouping intervention strategies with identical solution spaces. Our ranking procedure involves in total ten criteria and we demonstrate its applicability by assessing knockout-based intervention strategies computed in a genome-scale model of E.coli for the growth-coupled synthesis of l-methionine and of the heterologous product 1,4-butanediol. AVAILABILITY AND IMPLEMENTATION The MATLAB scripts that were used to characterize and rank the example intervention strategies are available at http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Philipp Schneider
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
| | - Steffen Klamt
- Max Planck Institute for Dynamics of Complex Technical Systems, Analysis and Redesign of Biological Networks, Magdeburg, Germany
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Röhl A, Riou T, Bockmayr A. Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection. Bioinformatics 2020; 35:2618-2625. [PMID: 30590390 DOI: 10.1093/bioinformatics/bty1027] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 12/06/2018] [Accepted: 12/14/2018] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks. RESULTS Here we introduce irreversible minimal cut sets (iMCSs). These are MCSs that consist of irreversible reactions only. The advantage of iMCSs is that they can be computed by projecting the flux cone of the metabolic network on the set of irreversible reactions, which usually leads to a smaller cone. Using oriented matroid theory, we show how the projected cone can be computed efficiently and how this can be applied to find iMCSs even in large genome-scale networks. AVAILABILITY AND IMPLEMENTATION Software is freely available at https://sourceforge.net/projects/irreversibleminimalcutsets/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Annika Röhl
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
| | - Tanguy Riou
- Department FRANCE, Ecole Centrale de Nantes, Nantes, France
| | - Alexander Bockmayr
- Department of Mathematics and Computer Science, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany
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