Improving the design of an oxidative stress sensing biosensor in yeast

Abstract

Transcription factor (TF)-based biosensors have proven useful for increasing biomanufacturing yields, large-scale functional screening, and in environmental monitoring. Most yeast TF-based biosensors are built from natural promoters, resulting in large DNA parts retaining considerable homology to the host genome, which can complicate biological engineering efforts. There is a need to explore smaller, synthetic biosensors to expand the options for regulating gene expression in yeast. Here, we present a systematic approach to improving the design of an existing oxidative stress sensing biosensor in Saccharomyces cerevisiae based on the Yap1 transcription factor. Starting from a synthetic core promoter, we optimized the activity of a Yap1-dependent promoter through rational modification of a minimalist Yap1 upstream activating sequence. Our novel promoter achieves dynamic ranges of activation surpassing those of the previously engineered Yap1-dependent promoter, while reducing it to only 171 base pairs. We demonstrate that coupling the promoter to a positive-feedback-regulated TF further improves the biosensor by increasing its dynamic range of activation and reducing its limit of detection. We have illustrated the robustness and transferability of the biosensor by reproducing its activity in an unconventional probiotic yeast strain, Saccharomyces boulardii. Our findings can provide guidance in the general process of TF-based biosensor design.

Keywords: Saccharomyces boulardii; cell-based biosensor; oxidative stress; promoter engineering; reactive oxygen species; synthetic biology.

Figure 1.

Optimizing the synthetic oxidative stress responsive promoter through promoter modifications. A) Schematic of the synthetic oxidative stress biosensor. Various copies of the Yap1 binding site were added in the UAS region of a synthetic core promoter separated by a spacer element. A 30 bp 5’UTR was added after the core promoter.Upon increasing levels of reactive oxygen species, Yap1 will be recruited to the promoter and increase expression of the fluorescent reporter (mCherry). B) The effect of different spacer region length on promoter activity. Synthetic promoters were designed with either 0, 9, 19, or 29 nucleotides spacer length between 4 Yap1 binding sites. Spacer sequences were derived from the TRX2 promoter in yeast directly upstream of the first Yap1 binding site. C) The effect of increasing Yap1 binding units on promoter activity. 0 to 8 copies of a minimal Yap1 binding element were added upstream of the synthetic core promoter. D) The effect of different 5’UTR length on promoter activity. Synthetic promoters were created with 5’UTRs of either 0, 10, 20, or 30 nucleotides in length. Sequences of the 5’UTR were derived from the TRX2 promoter in yeast directly upstream of the start codon. E) The effect of alternative Yap1 binding sites on promoter activity. Synthetic promoters were created using two alternative Yap1 binding sites (pSynOS_4(alt1): TTAGTAA and pSynOS_4(alt2): TTAGTCA). Bar graph illustrates the mean OD-normalized mCherry fluorescence plus standard deviation of three biological replicates under indicated concentration of the hydrogen peroxide. Fold fluorescent change of biosensor compared to basal, non-stressed conditions (0 µM) is reported over each bar.

Figure 2.

Implementing a positive feedback loop to improve the fold activation of the oxidative stress biosensor. A) Schematic of the positive feedback loop for controlling the expression of Yap1 with two different oxidative stress responsive promoters: pTRX2 and pSynOS_4. We tested the activity of the oxidative stress biosensor pSynOS_4-mCherry with the positive feedback loop design using either B) plasmids or C) genomically integrated Yap1 reporters under two different oxidative stress responsive promoters. D) Testing the effect of integrated copy numbers of the positive feedback loop on the activity of the oxidative stress biosensor. Bar graph illustrates the mean OD-normalized mCherry fluorescence, with error bars indicating one standard deviation over three biological replicates. Fold fluorescent change of biosensor output compared to basal, non-stressed conditions (0 µM) is reported over each bar.

Figure 3.

Comparing the dose response of the fully optimized oxidative stress sensing biosensor with previous biosensors in Saccharomyces cerevisiae. Comparison of the dose response curves of the fully optimized biosensor (pSynOS_4(alt1)+) with the synthetic promoter (pSynOS_4(alt1)), the natural yeast promoter (pTRX2), and the previously published Yap1-dependent promoter (5XUAS-pTRX2) against the ROSs A) hydrogen peroxide, B) diethyl maleate, C) tert-butyl hydroperoxide, and D) diamide. 16 to 18 different concentrations of each ROS were used ranging from 10^1 to 10^4 µM. Mean fluorescence levels of the output (mCherry) from three biological replicates is normalized to cell concentration (OD-normalized) and relative to basal levels of expression at 0 µM (±S.D.). Nonlinear regression models (lines) were fitted to the datasets up to an appropriately chosen concentration. If nonlinear regression could not be appropriately fitted, certain variables (Hill coefficient, top value) were fixed ( = ) to allow an approximation of the other values. Table beneath the graph summarizes the calculated maximal dynamic range of activation, limit of detection (L.O.D.), and Hill coefficient from the fitted curves.

Figure 4.

Translating the oxidative stress biosensor into Saccharomyces boulardii. A) Pictures of plated S. boulardii strains to illustrate how we created S. boulardii strains containing one to three Yap1 cassettes at different integration sites. Three separate GFP reporters were integrated at three different sites (see Methods) then used as visual markers for successful integration of the Yap1 cassettes using a CRISPR-Cas9 system with a single gRNA designed to efficiently and specifically cut within the GFP gene. B) Testing oxidative stress responsive promoters in S. boulardii. The three different oxidative stress sensing promoters with variant Yap1 binding sites (pSynOS_4,pSynOS_4(alt1), pSynOS_4(alt2)) were transformed in S. boulardii and tested for their response to two concentrations of hydrogen peroxide, as described in the Methods section. Bar graph illustrates the mean OD-normalized mCherry fluorescence plus standard deviation of three biological replicates. Fold fluorescent change of biosensor compared to basal, non-stressed conditions (0 μM) is reported over each bar. C) Testing the effect of the positive feedback loop to control Yap1 expression on the activity of the oxidative stress biosensor in S. boulardii. Three strains were generated from S. boulardii with one to three Yap1 copies under the control of the pTRX2 promoter. Oxidative stress biosensor pSynOS_4(alt1) was transformed in each strains and tested for its response to two concentrations of hydrogen peroxide, as described above.

Figure 5.

Evaluating the dose response of the fully optimized oxidative stress sensing biosensor in Saccharomyces boulardii. Dose response curves of the fully optimized biosensor (pSynOS_4(alt1)+) integrated into S. boulardii against the ROSs A) hydrogen peroxide, B) diethyl maleate, C) tert-butyl hydroperoxide, and D) diamide. 16 different concentrations of each ROSs were used ranging from 10^1 to 10^4 µM. Mean fluorescence levels of the output (mCherry) from three biological replicates is normalized to cell concentration (OD-normalized) and relative to basal levels of expression at 0 µM (±S.D.). Nonlinear regression models (lines) were fitted to each dataset up to an appropriately chosen concentration. Calculated dynamic range of activation, limit of detection (L.O.D.), and Hill coefficient from the fitted curves are presented next to each graph.