Self-controlled in silico gene knockdown strategies to enhance the sustainable production of heterologous terpenoid by Saccharomyces cerevisiae.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • Additional Information
    • Source:
      Publisher: Academic Press Country of Publication: Belgium NLM ID: 9815657 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-7184 (Electronic) Linking ISSN: 10967176 NLM ISO Abbreviation: Metab Eng Subsets: MEDLINE
    • Publication Information:
      Original Publication: Brugge, Belgium ; Orlando, FL : Academic Press, c1999-
    • Subject Terms:
    • Abstract:
      Microbial bioengineering is a growing field for producing plant natural products (PNPs) in recent decades, using heterologous metabolic pathways in host cells. Once heterologous metabolic pathways have been introduced into host cells, traditional metabolic engineering techniques are employed to enhance the productivity and yield of PNP biosynthetic routes, as well as to manage competing pathways. The advent of computational biology has marked the beginning of a novel epoch in strain design through in silico methods. These methods utilize genome-scale metabolic models (GEMs) and flux optimization algorithms to facilitate rational design across the entire cellular metabolic network. However, the implementation of in silico strategies can often result in an uneven distribution of metabolic fluxes due to the rigid knocking out of endogenous genes, which can impede cell growth and ultimately impact the accumulation of target products. In this study, we creatively utilized synthetic biology to refine in silico strain design for efficient PNPs production. OptKnock simulation was performed on the GEM of Saccharomyces cerevisiae OA07, an engineered strain for oleanolic acid (OA) bioproduction that has been reported previously. The simulation predicted that the single deletion of fol1, fol2, fol3, abz1, and abz2, or a combined knockout of hfd1, ald2 and ald3 could improve its OA production. Consequently, strains EK1∼EK7 were constructed and cultivated. EK3 (OA07△fol3), EK5 (OA07△abz1), and EK6 (OA07△abz2) had significantly higher OA titers in a batch cultivation compared to the original strain OA07. However, these increases were less pronounced in the fed-batch mode, indicating that gene deletion did not support sustainable OA production. To address this, we designed a negative feedback circuit regulated by malonyl-CoA, a growth-associated intermediate whose synthesis served as a bypass to OA synthesis, at fol3, abz1, abz2, and at acetyl-CoA carboxylase-encoding gene acc1, to dynamically and autonomously regulate the expression of these genes in OA07. The constructed strains R_3A, R_5A and R_6A had significantly higher OA titers than the initial strain and the responding gene-knockout mutants in either batch or fed-batch culture modes. Among them, strain R_3A stand out with the highest OA titer reported to date. Its OA titer doubled that of the initial strain in the flask-level fed-batch cultivation, and achieved at 1.23 ± 0.04 g L -1 in 96 h in the fermenter-level fed-batch mode. This indicated that the integration of optimization algorithm and synthetic biology approaches was efficiently rational for PNP-producing strain design.
      Competing Interests: Declaration of competing interest The authors declare that they have no competing interests that could have appeared to influence the work reported in this paper.
      (Copyright © 2024 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)
    • Comments:
      Erratum in: Metab Eng. 2024 May 10;:. (PMID: 38734582)
    • Contributed Indexing:
      Keywords: Dynamic regulation; Genome-scale metabolic network model; Oleanolic acid; OptKnock; Saccharomyces cerevisiae
    • Publication Date:
      Date Created: 20240422 Date Completed: 20240518 Latest Revision: 20240518
    • Publication Date:
      20240519
    • Accession Number:
      10.1016/j.ymben.2024.04.005
    • Accession Number:
      38648878