Design-Build-Test-Learn guided engineering of a whole-cell pyruvate biosensor based on transcription factor

Abstract

Whole-cell biosensors are powerful tools for metabolite monitoring, yet challenges such as narrow dynamic range and high leaky expression limit their broader applications. Here, we present a systematic workflow based on two Design-Build-Test-Learn (DBTL) cycles to develop and optimize a transcription factor-based pyruvate biosensor in Escherichia coli. In the first iteration of the cycle, we constructed a biosensor that responded to intracellular pyruvate levels within 0.05-10mM range. In the second cycle, we implemented design of experiment (DoE) to systematically explore combinatorial effects of promoters and ribosome binding sites (RBSs). A first set of experiments were designed to identify factors with a significant effect on biosensor performance. The results showed RBS of report gene significantly influenced dynamic range by modulating basal and maximum expression, while RBS of transcription factor affected signal span. The Akaike Information Criterion was used to select a model incorporating two main effects and one interaction effects. The best-performing strain exhibited an 18.5-fold increase in dynamic range and a 37.2-fold reduction in leaky expression. Quantification of intracellular pyruvate confirmed an operational range of 1.23-6.81 mol/g DCW. Our work demonstrates the power of DBTL cycles with statistical modelling for biosensor engineering, enabling more precise metabolic regulation and screening applications.