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Jiang S, Wu H, Yao Z, Li R, Ma Q, Xie X. Phenotype-genotype mapping reveals the betaine-triggered L-arginine overproduction mechanism in Escherichia coli. BIORESOURCE TECHNOLOGY 2023; 386:129540. [PMID: 37488018 DOI: 10.1016/j.biortech.2023.129540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
The production phenotype improvement of industrial microbes is extremely needed and challenging. Environmental factors optimization provides insightful ideas to trigger the superior production phenotype by activating potential genetic determiners. Here, phenotype-genotype mapping was used to dissect the betaine-triggered L-arginine overproduction mechanism and mine beneficial genes for further improving production phenotype. The comparative transcriptomic analysis revealed a novel role for betaine in modulating global gene transcription. Guided by this finding, 4 novel genes (cynX, cynT, pyrB, and rhaB) for L-arginine biosynthesis were identified via reverse engineering. Moreover, the rhaB deletion was demonstrated as a common metabolic engineering strategy to improve ATP pool in E. coli. By combinatorial genes manipulation, the L-arginine titer and yield increased by 17.9% and 28.9% in a 5-L bioreactor without betaine addition. This study revealed the molecular mechanism of gene transcription regulation by betaine and developed a superior L-arginine overproducer that does not require betaine.
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Affiliation(s)
- Shuai Jiang
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China
| | - Heyun Wu
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China
| | - Zhuoyue Yao
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China
| | - Ran Li
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China
| | - Qian Ma
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China
| | - Xixian Xie
- Key Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin University of Science & Technology, Tianjin 300457, PR China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, PR China.
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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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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Khaleghi MK, Savizi ISP, Lewis NE, Shojaosadati SA. Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters. Biotechnol J 2021; 16:e2100212. [PMID: 34390201 DOI: 10.1002/biot.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/10/2021] [Accepted: 08/11/2021] [Indexed: 11/06/2022]
Abstract
Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Mohammad Karim Khaleghi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Iman Shahidi Pour Savizi
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Nathan E Lewis
- Department of Bioengineering, University of California, San Diego, USA.,Department of Pediatrics, University of California, San Diego, USA
| | - Seyed Abbas Shojaosadati
- Biotechnology Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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Tokuyama K, Shimodaira Y, Kodama Y, Matsui R, Kusunose Y, Fukushima S, Nakai H, Tsuji Y, Toya Y, Matsuda F, Shimizu H. Soft-sensor development for monitoring the lysine fermentation process. J Biosci Bioeng 2021; 132:183-189. [PMID: 33958301 DOI: 10.1016/j.jbiosc.2021.04.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/06/2021] [Accepted: 04/11/2021] [Indexed: 10/21/2022]
Abstract
Monitoring cell growth and target production in working fermentors is important for stabilizing high level production. In this study, we developed a novel soft sensor for estimating the concentration of a target product (lysine), substrate (sucrose), and bacterial cell in commercially working fermentors using machine learning combined with available on-line process data. The lysine concentration was accurately estimated in both linear and nonlinear models; however, the nonlinear models were also suitable for estimating the concentrations of sucrose and bacterial cells. Data enhancement by time interpolation improved the model prediction accuracy and eliminated unnecessary fluctuations. Furthermore, the soft sensor developed based on the dataset of the same process parameters in multiple fermentor tanks successfully estimated the fermentation behavior of each tank. Machine learning-based soft sensors may represent a novel monitoring system for digital transformation in the field of biotechnological processes.
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Affiliation(s)
- Kento Tokuyama
- DX Promotion Department, Ajinomoto Co., Inc., 1-15-1 Kyobashi, Chuo-ku, Tokyo 104-8315, Japan
| | - Yoshiki Shimodaira
- DX Promotion Department, Ajinomoto Co., Inc., 1-15-1 Kyobashi, Chuo-ku, Tokyo 104-8315, Japan
| | - Yohei Kodama
- Institute for Innovation, Ajinomoto Co., Inc., 1-1 Suzuki-cho, Kawasaki-ku, Kawasaki, Kanagawa 210-8681, Japan
| | - Ryuzo Matsui
- Institute for Innovation, Ajinomoto Co., Inc., 1-1 Suzuki-cho, Kawasaki-ku, Kawasaki, Kanagawa 210-8681, Japan
| | - Yasuhiro Kusunose
- Institute for Innovation, Ajinomoto Co., Inc., 1-1 Suzuki-cho, Kawasaki-ku, Kawasaki, Kanagawa 210-8681, Japan
| | - Shunsuke Fukushima
- Ajinomoto Animal Nutrition Europe S.A.S., 60, rue de Vaux, CS18018, 80084 Amiens Cedex 2, France
| | - Hiroaki Nakai
- Ajinomoto Animal Nutrition Europe S.A.S., 60, rue de Vaux, CS18018, 80084 Amiens Cedex 2, France
| | - Yuichiro Tsuji
- Ajinomoto Animal Nutrition Europe S.A.S., 60, rue de Vaux, CS18018, 80084 Amiens Cedex 2, France
| | - Yoshihiro Toya
- Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Fumio Matsuda
- 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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