Exploring the Nonconserved Sequence Space of Synthetic Expression Modules in Bacillus subtilis

Area of Science:

• Biotechnology
• Molecular Biology
• Synthetic Biology

Background:

• Optimizing protein expression is crucial for industrial enzyme production.
• Predicting promoter activity and translation efficiency from DNA sequences remains challenging.

Purpose of the Study:

• To develop a high-throughput method for enhancing protein expression in Bacillus subtilis.
• To identify key sequence elements that regulate gene expression levels.

Main Methods:

• Designed and synthesized a library of approximately 12,000 unique synthetic expression modules (SEMs).
• Utilized Green Fluorescent Protein (GFP) as a reporter to screen SEMs for expression levels.
• Analyzed sequence features of high-performing SEMs to understand their impact on translation initiation.

Main Results:

• Achieved a 5-order of magnitude dynamic range in gene expression.
• Identified SEMs that increased expression up to 13-fold compared to a strong native promoter.
• Found that 5'-end mRNA sequences significantly influenced expression levels, likely by affecting secondary structure and translation initiation.
• Observed a weaker correlation between GFP and xylanase expression, indicating protein-specific responses.

Conclusions:

• Large libraries of SEMs combined with high-throughput screening are effective for protein production optimization.
• The impact of SEMs on expression is protein-dependent and cannot be universally extrapolated.
• 5'-end mRNA sequence optimization is critical for enhancing translation initiation and overall protein yield.