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Zhou L, Wang Q, Shen J, Li Y, Zhang H, Zhang X, Yang S, Jiang Z, Wang M, Li J, Wang Y, Liu H, Zhou Z. Metabolic engineering of glycolysis in Escherichia coli for efficient production of patchoulol and τ-cadinol. BIORESOURCE TECHNOLOGY 2024; 391:130004. [PMID: 37952591 DOI: 10.1016/j.biortech.2023.130004] [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: 10/09/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/14/2023]
Abstract
Glucose metabolism suppresses the microbial synthesis of sesquiterpenes with a syndrome of "too much of a good thing can be bad". Here, patchoulol production in Escherichia coli was increased 2.02 times by engineering patchoulol synthase to obtain an initial strain. Knocking out the synthetic pathway for cyclic adenosine monophosphate relieved glucose repression and improved patchoulol titer and yield by 27.7 % and 43.1 %, respectively. A glycolysis regulation device mediated by pyruvate sensing was constructed which effectively alleviated overflow metabolism in a high-glucose environment with 10.2 % greater patchoulol titer in strain 070QA. Without fine-tuning the glucose-feeding rate, patchoulol titer further increased to 1675.1 mg/L in a 5-L bioreactor experiment, which was the highest level reported in E. coli. Using strain 070QA as a chassis, the τ-cadinol titer reached 15.2 g/L, representing the first report for microbial production of τ-cadinol. These findings will aid in the industrial production of sesquiterpene.
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Affiliation(s)
- Li Zhou
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Qin Wang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Jiawen Shen
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Yunyan Li
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Hui Zhang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Xinrui Zhang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Shiyi Yang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Ziyi Jiang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Mengxuan Wang
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Jun Li
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Yuxi Wang
- Food Micro-manufacturing Engineering and Safety Research Laboratory, Department of Food Science and Nutrition, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, People's Republic of China
| | - Haili Liu
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China
| | - Zhemin Zhou
- Key Laboratory of Industrial Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, People's Republic of China.
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Manina AS, Forlani F. Biotechnologies in Perfume Manufacturing: Metabolic Engineering of Terpenoid Biosynthesis. Int J Mol Sci 2023; 24:ijms24097874. [PMID: 37175581 PMCID: PMC10178209 DOI: 10.3390/ijms24097874] [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] [Received: 02/22/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
The fragrance industry is increasingly turning to biotechnology to produce sustainable and high-quality fragrance ingredients. Microbial-based approaches have been found to be particularly promising, as they offer a more practical, economical and sustainable alternative to plant-based biotechnological methods for producing terpene derivatives of perfumery interest. Among the evaluated works, the heterologous expression of both terpene synthase and mevalonate pathway into Escherichia coli has shown the highest yields. Biotechnology solutions have the potential to help address the growing demand for sustainable and high-quality fragrance ingredients in an economically viable and responsible manner. These approaches can help compensate for supply issues of rare or impermanent raw materials, while also meeting the increasing demand for sustainable ingredients and processes. Although scaling up biotransformation processes can present challenges, they also offer advantages in terms of safety and energy savings. Exploring microbial cell factories for the production of natural fragrance compounds is a promising solution to both supply difficulties and the demand for sustainable ingredients and processes in the fragrance industry.
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Affiliation(s)
- Alessia Shelby Manina
- Department of Food Environmental and Nutritional Science (DeFENS), University of Milan, 20133 Milan, Italy
| | - Fabio Forlani
- Department of Food Environmental and Nutritional Science (DeFENS), University of Milan, 20133 Milan, Italy
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Rinaldi MA, Ferraz CA, Scrutton NS. Alternative metabolic pathways and strategies to high-titre terpenoid production in Escherichia coli. Nat Prod Rep 2022; 39:90-118. [PMID: 34231643 PMCID: PMC8791446 DOI: 10.1039/d1np00025j] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Terpenoids are a diverse group of chemicals used in a wide range of industries. Microbial terpenoid production has the potential to displace traditional manufacturing of these compounds with renewable processes, but further titre improvements are needed to reach cost competitiveness. This review discusses strategies to increase terpenoid titres in Escherichia coli with a focus on alternative metabolic pathways. Alternative pathways can lead to improved titres by providing higher orthogonality to native metabolism that redirects carbon flux, by avoiding toxic intermediates, by bypassing highly-regulated or bottleneck steps, or by being shorter and thus more efficient and easier to manipulate. The canonical 2-C-methyl-D-erythritol 4-phosphate (MEP) and mevalonate (MVA) pathways are engineered to increase titres, sometimes using homologs from different species to address bottlenecks. Further, alternative terpenoid pathways, including additional entry points into the MEP and MVA pathways, archaeal MVA pathways, and new artificial pathways provide new tools to increase titres. Prenyl diphosphate synthases elongate terpenoid chains, and alternative homologs create orthogonal pathways and increase product diversity. Alternative sources of terpenoid synthases and modifying enzymes can also be better suited for E. coli expression. Mining the growing number of bacterial genomes for new bacterial terpenoid synthases and modifying enzymes identifies enzymes that outperform eukaryotic ones and expand microbial terpenoid production diversity. Terpenoid removal from cells is also crucial in production, and so terpenoid recovery and approaches to handle end-product toxicity increase titres. Combined, these strategies are contributing to current efforts to increase microbial terpenoid production towards commercial feasibility.
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Affiliation(s)
- Mauro A Rinaldi
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Clara A Ferraz
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
| | - Nigel S Scrutton
- Manchester Institute of Biotechnology, Department of Chemistry, School of Natural Sciences, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
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Khangwal I, Chhabra D, Shukla P. Multi-Objective Optimization Through Machine Learning Modeling for Production of Xylooligosaccharides from Alkali-Pretreated Corn-Cob Xylan Via Enzymatic Hydrolysis. Indian J Microbiol 2021; 61:458-466. [PMID: 34744201 DOI: 10.1007/s12088-021-00970-2] [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: 06/20/2021] [Accepted: 08/03/2021] [Indexed: 12/16/2022] Open
Abstract
The hemicellulose content present in corn cobs can help in producing a high amount of xylooligosaccharides (XOS) in an eco-friendly manner. In this work, the XOS was produced from alkali pre-treated corn-cobs having a true yield of 38 ± 1.4% via enzymatic hydrolysis with the help of xylanase from T. lanuginosus VAPS-24. The production process was optimized to achieve a high concentration of XOS using innovative multi-objective optimization through machine learning modeling and finding out the most suitable parameters where xylobiose production is higher than xylose. The Multi-objective connected neural networks (MOCNN) model with tangent sigmoid activation function yielded a correlation coefficient of 96.51%; there were six optimal sets where xylobiose concentration was higher than xylose. The best-optimized conditions yielded 3.03 mg/ml of xylobiose and 1.31 mg/ml of xylose. Therefore, this novel approach of machine learning can target the increasing demand for xylooligosaccharides in the growing industrial market of prebiotics.
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Affiliation(s)
- Ishu Khangwal
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana 124001 India
| | - Deepak Chhabra
- Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana 124001 India.,Present Address: School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, 221005 India
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