Systematic optimization of TF-based carboxylic acid biosensors in cell-free system

Rapid and precise detection of small-molecule metabolites is crucial for optimizing the bioproduction processes. Cell-free systems (CFSs) offer an ideal platform for developing such biosensors due to their speed and suitability for automation. However, transcription factor (TF)-based biosensors, which are key elements for metabolite sensing, suffer from a severe bottleneck in in vitro environments. Their performance is often compromised due to the absence of nucleoid-associated proteins and different DNA topology compared to in vivo conditions. Here, we present a systematic framework for rationally engineering high-performance TF biosensors optimized for CFSs through integrated control of TF availability and promoter sequence. Using an itaconate (ITA)-responsive biosensor regulated by the LysR-type transcriptional regulator ItcR as a model system, we demonstrate that modulating TF supply and redesigning promoter elements substantially enhance sensitivity and dynamic range. Promoter dissection revealed that upstream sequences that function normally in vivo interfered with regulated transcription in CFSs, and that truncation to remove this region restored inducible behavior. Subsequent fine-tuning of the -35 and -10 motifs enhanced RNA polymerase recruitment and regulator interaction, resulting in 19-fold higher maximum signal, a 3.3-fold lower detection limit (0.003 g/L ITA), and a steeper dose-response curve (Hill slope increase from 2.7 to 34.7). The same promoter engineering strategy also improved a 3-hydroxypropionate-responsive biosensor, demonstrating its generality across distinct TF-promoter systems. Collectively, this framework establishes a rational, modular approach for constructing high-performance, topology-aware biosensors in CFSs, directly enabling high-throughput screening and automated biofoundry integration for synthetic biology and metabolic engineering applications.