Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering
Abstract Background Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to pred...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | Microbial Cell Factories |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12934-023-02251-7 |
| _version_ | 1827602530874425344 |
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| author | Koray Malcı Rodrigo Santibáñez Nestor Jonguitud-Borrego Jorge H. Santoyo-Garcia Eduard J. Kerkhoven Leonardo Rios-Solis |
| author_facet | Koray Malcı Rodrigo Santibáñez Nestor Jonguitud-Borrego Jorge H. Santoyo-Garcia Eduard J. Kerkhoven Leonardo Rios-Solis |
| author_sort | Koray Malcı |
| collection | DOAJ |
| description | Abstract Background Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells. Results Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae. Conclusions This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®. |
| first_indexed | 2024-03-09T05:20:46Z |
| format | Article |
| id | doaj.art-fdc1b98f6ef149f0adb312ed2f365ef2 |
| institution | Directory Open Access Journal |
| issn | 1475-2859 |
| language | English |
| last_indexed | 2024-03-09T05:20:46Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbial Cell Factories |
| spelling | doaj.art-fdc1b98f6ef149f0adb312ed2f365ef22023-12-03T12:40:59ZengBMCMicrobial Cell Factories1475-28592023-11-0122112210.1186/s12934-023-02251-7Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineeringKoray Malcı0Rodrigo Santibáñez1Nestor Jonguitud-Borrego2Jorge H. Santoyo-Garcia3Eduard J. Kerkhoven4Leonardo Rios-Solis5Institute for Bioengineering, School of Engineering, University of EdinburghDepartment of Pediatrics, University of CaliforniaInstitute for Bioengineering, School of Engineering, University of EdinburghInstitute for Bioengineering, School of Engineering, University of EdinburghDepartment of Life Sciences, Chalmers University of TechnologyInstitute for Bioengineering, School of Engineering, University of EdinburghAbstract Background Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells. Results Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae. Conclusions This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.https://doi.org/10.1186/s12934-023-02251-7Computational metabolic engineeringGenome-scale modellingin silico designSynthetic biologySystems biologyMevalonate pathway |
| spellingShingle | Koray Malcı Rodrigo Santibáñez Nestor Jonguitud-Borrego Jorge H. Santoyo-Garcia Eduard J. Kerkhoven Leonardo Rios-Solis Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering Microbial Cell Factories Computational metabolic engineering Genome-scale modelling in silico design Synthetic biology Systems biology Mevalonate pathway |
| title | Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering |
| title_full | Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering |
| title_fullStr | Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering |
| title_full_unstemmed | Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering |
| title_short | Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering |
| title_sort | improved production of taxol r precursors in s cerevisiae using combinatorial in silico design and metabolic engineering |
| topic | Computational metabolic engineering Genome-scale modelling in silico design Synthetic biology Systems biology Mevalonate pathway |
| url | https://doi.org/10.1186/s12934-023-02251-7 |
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