Tailoring microbial fitness through computational steering and CRISPRi-driven robustness regulation

Area of Science:

• Microbiology
• Synthetic Biology
• Metabolic Engineering

Background:

• Genetically modified microorganisms (GMMs) are widely used, necessitating strategies to prevent their uncontrolled release.
• Ensuring the containment of GMMs is crucial for environmental and biosafety regulations.

Purpose of the Study:

• To develop and validate a novel method for enhancing the metabolic robustness and control of GMMs.
• To engineer GMM strains with significantly reduced escape frequencies below regulatory standards.

Main Methods:

• Employed ensemble computational modeling for high-throughput in silico screening of enzymatic targets.
• Developed functional CRISPR interference (CRISPRi) systems with multiplexed gene knockdown for fitness control.
• Integrated an insulator-improved gRNA structure and an off-switch circuit with a compact Cas12m for precise GMM control.

Main Results:

• Identified key enzymatic targets for metabolic modification through computational screening.
• Successfully engineered GMM strains exhibiting escape frequencies below NIH standards.
• Demonstrated the effectiveness of the engineered control system across various experimental conditions.

Conclusions:

• The combined approach of computational modeling and CRISPRi offers a powerful strategy for secure GMM management.
• Strategically modifying microbial metabolism can halt GMM growth without significant resistance, enabling reliable control.
• This research provides a robust framework for the safe and precise application of GMMs in diverse sectors.