Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Abstract

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Keywords: biosensor applications; biosensor tuning; dynamic regulation; high-throughput screening; metabolic heterogeneity; transcription factor; transcriptional control; translational control.

Figure 1

Biosensor performance criteria. (a) Specificity of a biosensor for the target metabolite compared to alternative metabolites; (b) sensitivity of the biosensor as represented by the slope of the sensor’s response curve; (c) detection range reflects the limits of the metabolite concentration detected by a sensor; (d) dynamic range reflects the ratio of maximal output over minimal output levels; (e) response time quantifies the time it takes for a sensor to reach half of its steady-state signal; (f) mechanisms affecting biosensor cooperativity.

Figure 2

Tuning strategies for biosensors. (a) Promoter engineering through changing the number and location of operator sites, mutating the −35 or −10 region of the promoter, or mutating the transcription factor (TF)-binding operator sites (the black bar represents the DNA sequence; the blue segment represents the RNA polymerase binding site; the orange segment represents the TF-binding site; additional icons are defined in the Figure); (b) TF engineering via mutations to the ligand-binding pocket or the DNA-binding domain; (c) ribosome-binding site (RBS) engineering (the green segment represents the RBS sequence; the purple segment represents the gene of interest; additional icons are defined in the Figure).

Figure 3

Applications of TF-based metabolite biosensors. (a) High-throughput screening of genetic libraries to select active enzymes or high-producing strains; (b) metabolite sensor-enabled dynamic regulation of metabolic pathways (example of a negative feedback loop to control metabolite production); (c) studying metabolite heterogeneity within an isogenic cell population using metabolite biosensors.

Figure 4

Roadmap of notable work related to the development and application of TF-based metabolite biosensors [77,86,94,95,96,97].