Model-Based Design of Synthetic Antisense RNA for Predictable Gene Repression

Area of Science:

• Molecular Biology
• Synthetic Biology
• Genetics

Background:

• Small RNA regulators, including synthetic antisense RNA (asRNA), are increasingly used due to advances in understanding RNA folding and function.
• Synthetic asRNAs function by binding to complementary target messenger RNAs (mRNAs), leading to gene repression through mRNA degradation or blocked ribosome access.
• Predictable and tunable gene repression using synthetic asRNAs has been a significant challenge despite their simple complementarity mechanism.

Purpose of the Study:

• To describe a model-based protocol for designing synthetic antisense RNAs (asRNAs).
• To enable predictable and tunable gene repression through a novel asRNA design strategy.
• To validate the model's predictive capability across different bacterial species and genetic contexts.

Main Methods:

• Development of a predictive model for asRNA-mediated gene repression efficiency.
• Utilizing two key parameters: change in free energy of complex formation (ΔG_CF) and percent mismatch in the target binding region (TBR).
• Experimental validation of the model in both Gram-positive and Gram-negative bacteria, targeting both plasmid and chromosomal genes.

Main Results:

• The model accurately predicts synthetic asRNA repression efficiency based on ΔG_CF and TBR.
• Experimental validation confirmed the model's efficacy in diverse bacterial settings.
• The protocol allows for straightforward asRNA creation by modifying the TBR sequence.

Conclusions:

• A model-based approach enables predictable and tunable gene repression using synthetic antisense RNAs (asRNAs).
• The developed protocol is broadly applicable across various bacterial organisms and genetic elements (plasmids, chromosomes).
• This advancement facilitates the design of asRNAs as versatile regulators for diverse synthetic biology applications.