Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Biotechnology

Background:

• Synthetic biology offers advanced tools for optimizing complex biosynthetic pathways through modular, dynamically controlled gene expression.
• A sigma factor (σ) toolbox was previously developed for tunable, orthogonal gene expression, enabling fine-tuning of separate pathway modules without interference.
• This toolbox is applied in E. coli for the heterologous biosynthesis of naringenin, an industrially relevant plant metabolite.

Purpose of the Study:

• To implement and optimize a biosynthetic pathway for naringenin production in E. coli using a sigma factor toolbox for orthogonal gene expression.
• To develop and apply a practical workflow combining biosensor-driven screening, combinatorial engineering, and computational modeling to balance pathway steps and maximize production.
• To identify optimal pathway configurations and enzyme variants for enhanced naringenin yield.

Main Methods:

• Implementation of a sigma factor (σ) toolbox for orthogonal, tunable gene expression in E. coli.
• Development of a workflow involving biosensor-driven screening and combinatorial engineering of pathway libraries.
• Application of statistical learning techniques and computer modeling to predict optimal pathway configurations.
• Bioreactor cultivation of engineered E. coli strains for naringenin production.

Main Results:

• Achieved a 32% improvement in naringenin production titer compared to random screening.
• Generated a record 286 mg/L naringenin titer in E. coli from glycerol within 26 hours, without precursor supplementation or engineering.
• Identified key pathway configuration preferences, including specific enzyme variants and promoter strength correlations with titer, through statistical learning.

Conclusions:

• An efficient strategy utilizing orthogonal expression and a "pathway architecture designer" workflow successfully optimized flavonoid biosynthesis in E. coli to competitive levels.
• The combination of statistical learning, combinatorial optimization, and high-throughput screening enables rapid development of microbial cell factories.
• This workflow provides a versatile platform for engineering microbial production of various molecules and offers insights into pathway characteristics.