Machine-learning guided elucidation of contribution of individual steps in the mevalonate pathway and construction of a yeast platform strain for terpenoid production |
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| Institution: | 1. Department of Biological Sciences, University at Buffalo, State University of New York, Buffalo, NY, NY14260, USA;2. Department of Biostatistics, University at Buffalo, State University of New York, Buffalo, NY, NY14260, USA;1. Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China;2. Key Laboratory of Biofuels, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China;3. Shandong Energy Institute, Qingdao, 266101, China;4. Qingdao New Energy Shandong Laboratory, Qingdao, 266101, China;5. University of Chinese Academy of Sciences, Beijing, 100049, China;6. Marine Biology and Biotechnology Laboratory, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China;7. State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, 330047, China;1. Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada;2. Department of Electrical and Computer Engineering, University of Toronto, 10 King''s College Road, Ontario, Toronto, M5S 3G4, Canada;3. Centre for Environmental Biotechnology, School of Natural Sciences, Bangor University, Bangor, LL57 2UW, UK;4. Institute of Biomaterials and Biomedical Engineering, University of Toronto, University of Toronto, 200 College Street, Toronto, ON, M5S 3E5, Canada;1. Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China;2. National Technology Innovation Center of Synthetic Biology, Tianjin, 300308, China;3. Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China;4. Department of Biology and Biological Engineering, Chalmers University of Technology, SE 412 96, Gothenburg, Sweden;1. Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea;2. Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea;3. Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering, KAIST Institute for BioCentury, KAIST, Daejeon, 34141, Republic of Korea;4. BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea;1. Department of Life Sciences, Pohang University of Science and Technology, Pohang, South Korea;2. Department of Chemical Engineering, Pohang University of Science and Technology, Pohang, South Korea;3. ImmunoBiome Inc., Pohang, South Korea;4. Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, South Korea;5. Institute of Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, South Korea;6. School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, South Korea |
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| Abstract: | The production of terpenoids from engineered microbes contributes markedly to the bioeconomy by providing essential medicines, sustainable materials, and renewable fuels. The mevalonate pathway leading to the synthesis of terpenoid precursors has been extensively targeted for engineering. Nevertheless, the importance of individual pathway enzymes to the overall pathway flux and final terpenoid yield is less known, especially enzymes that are thought to be non-rate-limiting. To investigate the individual contribution of the five non-rate-limiting enzymes in the mevalonate pathway, we created a combinatorial library of 243 Saccharomyces cerevisiae strains, each having an extra copy of the mevalonate pathway integrated into the genome and expressing the non-rate-limiting enzymes from a unique combination of promoters. High-throughput screening combined with machine learning algorithms revealed that the mevalonate kinase, Erg12p, stands out as the critical enzyme that influences product titer. ERG12 is ideally expressed from a medium-strength promoter which is the ‘sweet spot’ resulting in high product yield. Additionally, a platform strain was created by targeting the mevalonate pathway to both the cytosol and peroxisomes. The dual localization synergistically increased terpenoid production and implied that some mevalonate pathway intermediates, such as mevalonate, isopentyl pyrophosphate (IPP), and dimethylallyl pyrophosphate (DMAPP), are diffusible across peroxisome membranes. The platform strain resulted in 94-fold, 60-fold, and 35-fold improved titer of monoterpene geraniol, sesquiterpene α-humulene, and triterpene squalene, respectively. The terpenoid platform strain will serve as a chassis for producing any terpenoids and terpene derivatives. |
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| Keywords: | Terpene Random forest Mevalonate kinase Metabolic engineering |
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