Cell-free syhthetic biology enabled rna toehold switch system for influenza viruses

Abstract

Influenza A H1N1 continues to pose a major public health threat due to its rapid transmission and capacity to cause seasonal epidemics and pandemic outbreaks. Rapid, accurate, and cost-effective diagnostic approaches are essential for timely intervention and control of viral spread. In this study, we present a synthetic biology based diagnostic strategy that employs programmable RNA-based regulatory elements, known as toehold switches, to selectively detect Influenza A H1N1 viral RNA sequences. These switches were designed to remain translationally inactive in the absence of the viral RNA and to activate protein expression upon specific sequence recognition. The initial switch designs were computationally generated and analyzed using thermodynamic modeling tools such as NUPACK and RNAfold, allowing for assessment of structural stability and identification of high-entropy regions that could affect translation. Particular attention was given to the accessibility of the ribosome binding site and start codon regions, as local structural hindrances in these areas were found to correlate with poor performance. Based on the entropy profiles and free energy distributions, selected constructs were subjected to rational sequence redesign to enhance conformational accessibility and minimize undesired leakiness. These optimized switches were then cloned into T7 promoter-driven plasmids and tested through in vitro transcription–translation reactions. The resulting GFP-based fluorescence measurements allowed us to quantitatively compare expression levels in the presence and absence of the target RNA. Experimental data showed that optimized switch designs provided significantly higher signal-to-noise ratios, reduced background expression, and more consistent fold-change values across replicates compared to their unoptimized counterparts. Overall, this study demonstrates the potential of rationally engineered RNA-based switches as a modular, programmable, and low-cost diagnostic platform for Influenza A H1N1. Moreover, the design framework established here can be generalized to support the development of similar RNA-sensing tools for other viral pathogens.