Modular and signal-responsive transcriptional regulation using CRISPRi-aided genetic switches in Escherichia coli

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BACKGROUND: Precise and dynamic transcriptional regulation is a cornerstone of synthetic biology, enabling the construction of robust genetic circuits and programmable cellular systems. However, existing regulatory tools are often limited by issues such as leaky transcription and insufficient tunability, particularly in high-expression or complex genetic contexts. This study aimed to develop a CRISPRi-aided genetic switch platform that overcomes these limitations and expands the functionality of transcriptional regulation tools in synthetic biology. RESULTS: We established a versatile CRISPRi-aided genetic switch platform by integrating transcription factor-based biosensors with the Type V-A FnCas12a CRISPR system. Exploiting the RNase activity of FndCas12a, this system processes CRISPR RNAs (crRNAs) directly from biosensor-responsive mRNA transcripts, enabling precise, signal-dependent transcriptional regulation. To mitigate basal transcription and enhance regulatory precision, transcriptional terminator filters were incorporated, reducing leaky expression and increasing the dynamic range of target gene regulation. The platform demonstrated exceptional adaptability across diverse applications, including ligand-inducible genetic switches for transcriptional control, signal amplification circuits for enhanced output, and metabolic genetic switches for pathway reprogramming. Notably, the metabolic genetic switch dynamically repressed the endogenous gapA gene while compensating with orthologous gapC expression, effectively redirecting metabolic flux to balance cell growth. CONCLUSIONS: The CRISPRi-aided genetic switch provides a powerful and flexible toolkit for synthetic biology, addressing the limitations of existing systems. By enabling precise and tunable transcriptional regulation, it offers robust solutions for a wide array of biotechnological applications, including pathway engineering and synthetic gene networks.

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