Skip to main navigation Skip to search Skip to main content

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

  • Koray Malcı*
  • , Rodrigo Santibáñez
  • , Nestor Jonguitud-Borrego
  • , Jorge H. Santoyo-Garcia
  • , Eduard J. Kerkhoven
  • , Leonardo Rios-Solis*
  • *Corresponding author for this work
    • University of Edinburgh
    • University of California at San Diego

    Research output: Contribution to journalJournal articleResearchpeer-review

    63 Downloads (Orbit)

    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®.

    Original languageEnglish
    Article number243
    JournalMicrobial Cell Factories
    Volume22
    Number of pages22
    ISSN1475-2859
    DOIs
    Publication statusPublished - 2023

    Keywords

    • Computational metabolic engineering
    • Genome-scale modelling
    • In silico design
    • Synthetic biology
    • Systems biology
    • Mevalonate pathway
    • Saccharomyces cerevisiae
    • Taxadiene
    • Taxol

    Fingerprint

    Dive into the research topics of 'Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering'. Together they form a unique fingerprint.

    Cite this