Synthetic biosensors for precise gene control and real-time monitoring of metabolites

Abstract

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Figure 1.

Induction dynamics for each of the inducible systems are reported. The relationship between fluorescent response and inducer concentration is represented as a 95% confidence band (n = 3) for both the high-copy (A) and low-copy (B) implementations of the inducible systems. The plots are log scale to capture the wide range of inducer concentrations and biosensor responses. Inducer concentrations are the same for both high and low-copy implementations. Each curve is matched to a color-coded table of inducer concentration ranges. Acrylate and anhydrotetracycline (aTC) increase in 2-fold increments while arabinose, glucarate, erythromycin and naringenin increase in 3-fold increments. The inducing chemical and biosensor name are indicated to the left and right of the table, respectively. The gray band is the fluorescent response of a control strain containing no fluorescent reporter. Fluorescence measurements are performed 15 h after addition of the inducing chemicals.

Figure 2.

Induction and growth kinetics for the high-copy glucarate (CdaR), erythromycin (MphR), acrylate (AcuR) and naringenin (TtgR) biosensors. Chemical inducers are added at time zero and fluorescence is observed for eight hours. Lower panels show the optical density of the induced cultures over time. Induction levels are indicated by shade, with darker colors indicating higher inducer concentrations. Glucarate induction levels are 40, 13, 4.4, 1.5, 0.49 mM and no inducer addition. Erythromycin induction levels are 1400, 450, 150, 51, 17 μM and no inducer addition. Acrylate induction levels are 5, 2.5, 1.3, 0.63, 0.31 mM and no inducer addition. Naringenin induction levels are 9, 3, 0.33, 0.11, 0.037 mM and no inducer addition. Fluorescence and optical density are normalized as described in the ‘Materials and Methods’ section. The standard error of the mean is represented with a 95% confidence interval (n = 3).

Figure 3.

High-copy promoter activity was fit to a model of inducible gene expression. The maximum expression velocity of each inducible promoter was determined at various levels of induction (points). The data was fit to a Hill function modified to account for basal and maximal promoter activity (green lines). The anhydrotetracycline (TetR), acrylate (AcuR) and naringenin (TtgR) biosensors all show high induction cooperativity. The arabinose (AraC), glucarate (CdaR) and erythromycin (MphR) biosensors show low or moderate levels of cooperativity. The 10 mM acrylate induction condition was omitted from the modeling data due to high toxicity (red point). Error bars reflect the 95% confidence interval for the measured expression velocity.

Figure 4.

The behavior of single cells in response to chemical induction was evaluated by flow cytometry. 100 000 cells from uninduced (gray), partially induced (green) and fully induced (blue) populations were observed for each high copy biosensor. The specific concentration of inducer is indicated in the plot. Histograms are plotted with a biexponential scale to render the wide range of biosensor activation. The absence of large, well-separated bimodal distributions indicates that bulk fluorescent measurements do indeed reflect the induction behavior of individual cells.

Figure 5.

The toxicity of each inducer chemical was evaluated over a wide range of concentrations. Growth rate was measured for each combination of chemical and concentration during the exponential phase of growth. Rates were normalized to the growth rate of cells without any added chemical and plotted as bar height. Concentration of each inducer is indicated in the table, corresponding to the bar chart by order and color. Ethanol and DMSO were included as they are the solvents for aTC and naringenin, respectively. Erythromycin was evaluated twice: with and without the erythromycin resistance gene, eryR. Inducer concentrations mirror the concentrations used in the induction experiments.

Figure 6.

The potential for the chemical inducers to activate non-target sensors was evaluated. The cross-reactivity of the new inducers, along with a selection of other commonly used inducers and inducer solvents, was evaluated against each of our six inducible systems. The growth-normalized fluorescent response of each biosensor-inducer pair is plotted as height (n = 3). No cross-reactivity was observed.

Figure 7.

Compatible CdaR-GFP, AcuR-CFP and MphR-mCherry biosensors were transformed into the same cell. The potential for these biosensors to be controlled independently was evaluated by flow cytometry. The isogenic cell population was exposed to no inducer (orange), glucarate (light blue), acrylate (dark green), erythromycin (dark blue), glucarate and acrylate (red), glucarate and erythromycin (tan), erythromycin and acrylate (pink) or glucarate, acrylate and erythromycin (light green). The eight combinations of binary induction resulted in eight distinct cell populations when characterized in the three fluorescent channels. The point clouds, each point representing 1 of 10 000 cells, are projected onto the faces of the cube in order to aid in visualization of the 3D space. All axes are log scale to capture the wide range of fluorescent responses.

Figure 8.

Activation of the CdaR biosensor is well correlated with glucarate titers. Glucarate can be produced from myo-inositol by the enzymes MIOX and Udh. MIOX orthologs were transformed into cells containing Udh and the CdaR biosensor. Fluorescence was observed 48 h after addition of myo-inositol. Glucarate titers were measured after the same period of time in identical strains without the glucarate biosensor. All coefficients of variation are less than 10% (n = 3).

Figure 9.

The TtgR biosensor was evaluated for its ability to aid in the directed evolution of enzymatic phenol production. A fluorescent response was observed in the presence of 0.1% phenol, while background levels of fluorescence were observed when the sensor was induced with the precursor molecule benzene. Catechol is a side-product of phenol production and did not activate the sensor. All experiments were carried out in triplicate.