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Optimizing multicopy chromosomal integration for stable high-performing strains.

Fei Du1, Zijia Li1, Xin Li1

  • 1School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, China.

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|June 10, 2024
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Summary
This summary is machine-generated.

This study introduces a novel method using nonrepetitive DNA sequences and machine learning to optimize gene copy numbers for microbial cell factories. Engineered strains show enhanced production and genetic stability, overcoming previous limitations.

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Area of Science:

  • Synthetic Biology
  • Metabolic Engineering
  • Biotechnology

Background:

  • Chromosomal integration for microbial cell factories can lead to genetic instability due to repetitive DNA sequences and homologous recombination.
  • Optimizing gene copy numbers for metabolic pathways is a complex, time-consuming process.

Purpose of the Study:

  • To develop a method to overcome genetic instability and optimize gene copy numbers in engineered microbial strains.
  • To improve the efficiency of microbial cell factories for the biosynthesis of valuable compounds.

Main Methods:

  • Application of a calculator for generating multiple nonrepetitive coding DNA sequence (CDS) variants.
  • Development of a machine learning model to determine optimal gene copy number combinations.
  • Engineering of Yarrowia lipolytica for eicosapentaenoic acid (EPA) and Escherichia coli for lycopene production.

Main Results:

  • An engineered Yarrowia lipolytica strain achieved a record titer of 27.5 g/L EPA in a 50-liter bioreactor.
  • Significant improvements in lycopene production were observed in engineered Escherichia coli.
  • All engineered strains (Y. lipolytica, E. coli, Saccharomyces cerevisiae) using nonrepetitive CDSs demonstrated high genetic stability.

Conclusions:

  • The developed method effectively enhances the production of target compounds in microbial cell factories.
  • Utilizing nonrepetitive CDSs and machine learning ensures genetic stability in engineered microorganisms.
  • This approach offers a robust strategy for constructing efficient and stable microbial cell factories.