Tuning the performance of a TphR-based terephthalate biosensor with a design of experiments approach

Abstract

Transcription factor-based biosensors are genetic tools that aim to predictability link the presence of a specific input stimuli to a tailored gene expression output. The performance characteristics of a biosensor fundamentally determines its potential applications. However, current methods to engineer and optimise tailored biosensor responses are highly nonintuitive, and struggle to investigate multidimensional sequence/design space efficiently. In this study we employ a design of experiments (DoE) approach to build a framework for efficiently engineering activator-based biosensors with tailored performances, and we apply the framework for the development of biosensors for the polyethylene terephthalate (PET) plastic degradation monomer terephthalate (TPA). We simultaneously engineer the core promoter and operator regions of the responsive promoter, and by employing a dual refactoring approach, we were able to explore an enhanced biosensor design space and assign their causative performance effects. The approach employed here serves as a foundational framework for engineering transcriptional biosensors and enabled development of tailored biosensors with enhanced dynamic range and diverse signal output, sensitivity, and steepness. We further demonstrate its applicability on the development of tailored biosensors for primary screening of PET hydrolases and enzyme condition screening, demonstrating the potential of statistical modelling in optimizing biosensors for tailored industrial and environmental applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/600737v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@2db5d6org.highwire.dtl.DTLVardef@fd0935org.highwire.dtl.DTLVardef@67a01dorg.highwire.dtl.DTLVardef@140da62_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. Employment of a DoE framework for fine-tuning biosensor performance. C_FIG HighlightsO_LIBioinformatic mining of allosteric transcription factors to produce TPA biosensors C_LIO_LIEfficient sampling of complex sequence-function relationships of genetic circuits C_LIO_LIModelling to learn and optimise biosensor genetic circuits C_LIO_LIApplication of biosensors for primary and secondary enzyme screening applications C_LI