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Improved production of Taxol(®) precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering

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 geno...

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Autores principales: Malcı, Koray, Santibáñez, Rodrigo, Jonguitud-Borrego, Nestor, Santoyo-Garcia, Jorge H., Kerkhoven, Eduard J., Rios-Solis, Leonardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687855/
https://www.ncbi.nlm.nih.gov/pubmed/38031061
http://dx.doi.org/10.1186/s12934-023-02251-7
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author Malcı, Koray
Santibáñez, Rodrigo
Jonguitud-Borrego, Nestor
Santoyo-Garcia, Jorge H.
Kerkhoven, Eduard J.
Rios-Solis, Leonardo
author_facet Malcı, Koray
Santibáñez, Rodrigo
Jonguitud-Borrego, Nestor
Santoyo-Garcia, Jorge H.
Kerkhoven, Eduard J.
Rios-Solis, Leonardo
author_sort Malcı, Koray
collection PubMed
description 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(®). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-023-02251-7.
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spelling pubmed-106878552023-11-30 Improved production of Taxol(®) precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering Malcı, Koray Santibáñez, Rodrigo Jonguitud-Borrego, Nestor Santoyo-Garcia, Jorge H. Kerkhoven, Eduard J. Rios-Solis, Leonardo Microb Cell Fact Research 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(®). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12934-023-02251-7. BioMed Central 2023-11-29 /pmc/articles/PMC10687855/ /pubmed/38031061 http://dx.doi.org/10.1186/s12934-023-02251-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Malcı, Koray
Santibáñez, Rodrigo
Jonguitud-Borrego, Nestor
Santoyo-Garcia, Jorge H.
Kerkhoven, Eduard J.
Rios-Solis, Leonardo
Improved production of Taxol(®) precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering
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(®) precursors in s. cerevisiae using combinatorial in silico design and metabolic engineering
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10687855/
https://www.ncbi.nlm.nih.gov/pubmed/38031061
http://dx.doi.org/10.1186/s12934-023-02251-7
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