Transcription factor-based biosensors in biotechnology: current state and future prospects

Abstract

Living organisms have evolved a plethora of sensing systems for the intra- and extracellular detection of small molecules, ions or physical parameters. Several recent studies have demonstrated that these principles can be exploited to devise synthetic regulatory circuits for metabolic engineering strategies. In this context, transcription factors (TFs) controlling microbial physiology at the level of transcription play a major role in biosensor design, since they can be implemented in synthetic circuits controlling gene expression in dependency of, for example, small molecule production. Here, we review recent progress on the utilization of TF-based biosensors in microbial biotechnology highlighting different areas of application. Recent advances in metabolic engineering reveal TF-based sensors to be versatile tools for strain and enzyme development using high-throughput (HT) screening strategies and adaptive laboratory evolution, the optimization of heterologous pathways via the implementation of dynamic control circuits and for the monitoring of single-cell productivity in live cell imaging studies. These examples underline the immense potential of TF-based biosensor circuits but also identify limitations and room for further optimization.

Keywords: Biosensor; Evolution; Metabolic engineering; Screening; Single-cell analysis; Transcriptional regulator.

Fig. 1

Principles for the architecture of transcription factor-based biosensors. a A transcriptional activator may be used to activate expression of an actuator gene (circuit) in response to effector molecules. In contrast, repressors block the expression of actuators. By setting the expression of a second repressor under the control of the TF-biosensor repressor, the signalling can be inverted, resulting in a positive output of the actuator module. b Depending on the final function, different actuators are available as biosensor readout. The expression of e.g. autofluorescent proteins (AFP) results in an optical output, while the insertion of the biosensor into regulatory circuits can trigger and dynamically control biosynthetic pathways. Sensors can further be used to generate an artificial selection scheme by the choice of a suitable actuator (e.g. antibiotics, toxins or auxotrophy) controlling the survival of strains with desired traits

Fig. 2

Versatile applications of TF-based biosensors. Biosensors with an optical readout, e.g. production of an autofluorescent protein (AFP), are efficient tools for the high-throughput (HT) screening of large mutant libraries using fluorescence-activated cell sorting (FACS). Biosensor-driven evolution has proven a convenient strategy to increase production by iteratively imposing an artificial selective pressure on the fluorescent output of a biosensor using FACS or selection schemes. Integrated into synthetic regulatory circuits, biosensors can be used for the dynamic control of biosynthetic pathways in order to avoid, for example, the accumulation of toxic intermediates. Finally, biosensors are convenient tools for non-invasive online monitoring of production processes and for analysis at single-cell resolution using FACS and live cell imaging in microfluidic chip devices

Fig. 3

Examples of biosensor engineering for altered performance characteristics or orthogonal applications. a The dynamic range, describing the maximum fold change of a reporter output to a given input signal (Mustafi et al. 2015), was increased by introducing two FadR binding sites from the fadAB promoter into the strong lambda phage promoter P_L (Zhang et al. 2012). b To increase the sensitivity as rate of increase in reporter output (depicted by the slope of the transfer curve) to 3-methylbenzoate (3MBz), the truncated operator site Omp-d upstream of the operator site Omp-p in the P_b promoter was completed enabling the binding of two benzoate-binding transcription factors (TF) (Silva-Rocha and de Lorenzo 2012). c Furthermore, screening of an AraC mutein library for effectors of interest resulted in the identification of transcription factors with altered specificities (Tang and Cirino ; Tang et al. 2013). d The orthogonal transfer of biosensors to host organisms is challenging. Umeyama and co-workers equipped the S-adenosylmethionine (SAM)-responsive transcription factor MetJ of E. coli with the transcriptional activator domain B42 resulting in SAM detection in S. cerevisiae (Umeyama et al. 2013)