Design, Optimization and Application of Small Molecule Biosensor in Metabolic Engineering

Abstract

The development of synthetic biology and metabolic engineering has painted a great future for the bio-based economy, including fuels, chemicals, and drugs produced from renewable feedstocks. With the rapid advance of genome-scale modeling, pathway assembling and genome engineering/editing, our ability to design and generate microbial cell factories with various phenotype becomes almost limitless. However, our lack of ability to measure and exert precise control over metabolite concentration related phenotypes becomes a bottleneck in metabolic engineering. Genetically encoded small molecule biosensors, which provide the means to couple metabolite concentration to measurable or actionable outputs, are highly promising solutions to the bottleneck. Here we review recent advances in the design, optimization and application of small molecule biosensor in metabolic engineering, with particular focus on optimization strategies for transcription factor (TF) based biosensors.

Keywords: industrial application; metabolic engineering; optimization strategy; small molecule biosensor; synthetic biology; transcription factor.

FIGURE 1

Three major categories of genetically encoded small molecule biosensors surrounding the central dogma. (A) Transcription factor (TF) based biosensor: the transcriptional inhibitory factor that can interact with its operator in the absence of its ligand (effector molecule) and dissociate from DNA when the ligand is abundant. TF activates expression of a reporter protein (e.g., GFP) in response to a target metabolite (‘X’); (B) Riboswitches: The RNA folds in local regions of complementarity, presumably, while transcription is proceeding. Cellular concentration of metabolites (‘Y’) under threshold concentration, transcription and intramolecular RNA folding are continue. Cellular concentration of metabolite (Y) above threshold concentration will be specifically sensed by sensor domains of riboswitches, and RNA folding can lead to an alternate conformation. (C) FÖrster resonance energy transfer (FRET) based biosensor: the binding of a metabolite (‘Z’) induces a FRET signal change.

FIGURE 2

Applications of small molecule biosensors in metabolic engineering. (A) High throughput screening: the concentration of target metabolite is converted to fluorescence output at a single cell level, through fluorescence activated cell sorting (FACS) high-yield strains are enriched. (B) Growth selection: biosensors can be used to control the survival of strains with the desired traits by selecting suitable actuators (e.g., antibiotics, auxotrophs or toxins), which can directly enrich and select high-yield strains. (C) Dynamic pathway control: biosensors can further be used to build regulatory circuits to dynamically control and optimize the metabolic biosynthetic pathway.