Strategies for Improving Small-Molecule Biosensors in Bacteria

Abstract

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Keywords: bacterial biosensor; biosensor engineering; genetic circuits; genetic engineering; protein engineering; whole-cell biosensor.

Figure 1

Functional characteristics of genetic biosensors. (A) A biosensor’s operational range describes the input range of analyte concentrations over which the sensor produces a detectable change in output. The dynamic range of a biosensor in turn refers to the range of output signal over which the biosensor produces a detectable change in response to analyte ligands. Typically, a biosensor’s dynamic range is described by its fold-induction (also known as the signal-to-noise ratio), which is calculated by dividing the biosensor’s highest measured output by its lowest measured output. (B) A biosensor’s specificity refers to the range of distinct analyte compounds to which it is capable of producing a response, with more specific biosensors responding to fewer ligands.

Figure 2

Known mechanisms of bacterial biosensors. (A) Some biosensors undergo an allosteric conformational change after binding to a ligand. The biosensor’s adoption of the ligand-bound conformation thereby results in activation of regulated genes. (B) For other biosensors, recognition of an analyte by a ligand-binding domain can cause a normally monomeric sensor to dimerize. Dimerization in turn results in activation of an effector domain, leading to activation of regulated genes. (C) A third class of bacterial biosensor relies on an activator protein that is unstable and rapidly degraded in the absence of ligand. After ligand binding, the stability of the biosensor is improved, resulting in an increased steady-state protein concentration and greater activation of regulated genes. (D) Enzymes can also be used as biosensors in bacteria. Enzymatic biosensors chemically convert their otherwise undetectable substrate into a newly detectable compound.

Figure 3

Common methods for direct engineering of biosensor genes. (A) Biosensor genes are often improved using directed evolution. In this cyclical approach, a diverse mutant library is first generated from an initial biosensor gene. A selection or screen is then used to measure the activity of variants within the library, and mutants exhibiting improved properties are then isolated. These mutants can then be used to seed subsequent rounds of evolution. (B) Prior data regarding a biosensor’s structure or activity can also be used to guide engineering efforts. This approach relies on introducing targeted mutations using an informed prediction of a protein’s properties. Computational approaches and simulations can help guide these efforts. (C) Rational design efforts can also utilize DNA sequence data to guide biosensor engineering. In this strategy, homologous or related protein variants are typically identified within genome sequence databases using bioinformatic alignments. This data is then used to guide the construction of mutant or chimeric variants of the gene of interest.

Figure 4

Genetic elements controlling bacterial gene expression. Each of the five elements shown above are commonly used to control a given gene’s steady-state concentration. Altering multiple parameters can change a gene’s expression level by several orders of magnitude.

Figure 5

Diagrams of common instrumentation used in bacterial biosensor detection. (A) Absorbance spectrophotometers are often used to detect changes of colored dyes, such as the appearance of blue color following cleavage of X-gal by the reporter lacZ. (B) Luminescence detectors are used to monitor the activity of different types of luciferase reporter genes. (C) Fluorimeters are used to monitor the presence of fluorescent proteins (such as GFP). (D) Cell growth can be assessed either by counting colonies on solid agar plates or by measuring the absorbance at 600 nm of liquid cultures.

Figure 6

Additional genetic modules for improving biosensor function. (A) Genes can be introduced to modify the concentration of ligand compounds inside cells, mediating either increased efflux or (B) increased intracellular accrual of analytes. These approaches result in changes to a biosensor’s operational range. (C) Activator genes can also be placed between the biosensor and its reporter to construct a gene cascade. The addition of an activator results in an increased biosensor signal and an extended dynamic range. (D) Leak dampener genes can also be introduced to regulate reporter genes using a type-1 coherent feed-forward loop. This strategy leads to reduced leaky reporter signal in the absence of analyte ligands, and also extends a biosensor’s dynamic range. (E) Additional genes can also be used to change the logic associated with a biosensor’s response. This approach can not only be applied to individual biosensors but can also be applied to link more than one sensor together through a multi-input logic gate.