Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Area of Science:

• Synthetic Biology
• Metabolic Engineering
• Molecular Biology

Background:

• Optimizing multi-gene expression is crucial for efficient chemical production via engineered metabolic pathways.
• CRISPR-Cas transcriptional control offers promise for programming gene expression but is limited by guide RNA folding unpredictability.

Purpose of the Study:

• To correlate guide RNA folding kinetics with CRISPR activation efficacy in E. coli.
• To develop a predictive parameter for designing synthetic RNA components for CRISPR activation.
• To engineer orthogonal synthetic CRISPR activation promoters for precise multi-gene control.

Main Methods:

• Correlated modified guide RNA (scRNA) efficacy with a computational kinetic folding parameter (rS).
• Utilized the kinetic parameter for forward design of synthetic CRISPR activation promoters.
• Implemented a system of three synthetic promoters for orthogonal gene activation.
• Employed combinatorial tuning to profile a 3D design space for metabolic pathway expression.

Main Results:

• Identified a strong correlation (rS = 0.8) between scRNA folding rate and CRISPR activation efficacy.
• Successfully designed three synthetic CRISPR activation promoters with orthogonal control (>35-fold activation).
• Demonstrated variable production of pteridine and human milk oligosaccharide products through pathway optimization.

Conclusions:

• The developed RNA design approach enhances predictability and control of CRISPR activation systems.
• This method facilitates combinatorial optimization of metabolic pathways for chemical production.
• Accelerates the routine design of effective multi-gene regulation programs in bacterial hosts.