Transferring a synthetic gene circuit from yeast to mammalian cells

Abstract

The emerging field of synthetic biology builds gene circuits for scientific, industrial and therapeutic needs. Adaptability of synthetic gene circuits across different organisms could enable a synthetic biology pipeline, where circuits are designed in silico, characterized in microbes and reimplemented in mammalian settings for practical usage. However, the processes affecting gene circuit adaptability have not been systematically investigated. Here we construct a mammalian version of a negative feedback-based 'linearizer' gene circuit previously developed in yeast. The first naïve mammalian prototype was non-functional, but a computational model suggested that we could recover function by improving gene expression and protein localization. After rationally developing and combining new parts as the model suggested, we regained function and could tune target gene expression in human cells linearly and precisely as in yeast. The steps we have taken should be generally relevant for transferring any gene circuit from yeast into mammalian cells.

Figure 1. Linearizer gene circuits and their performance characteristics

(a) A linearizer gene circuit consists of the TetR repressor and an arbitrary target gene, both controlled by the same promoter. In the absence of inducer (tetracycline or its derivatives), the TetR protein binds to tetO2 site(s) and physically blocks transcription from both promoters (red arrows). If inducer is added to the growth medium, it diffuses into cells and binds TetR, which dissociates from the tetO2 sites (green arrows). Protein levels start to increase (green arrows) until TetR synthesis exceeds inducer influx and TetR blocks both promoters once again. (b) Performance metrics of linearizer gene circuits based on the dose-response curve, defined as the average gene expression versus the inducer level (thick black line). The fold induction is the ratio of maximal and minimal (background) expression. The range of linearity covers the inducer concentrations where dose-response appears linear. The degree of linearity measures the straightness of dose-response between two inducer concentrations by the L1-norm (based on the shaded area). In addition, gene expression variability is measured by the coefficient of variation (CV, not shown).

Figure 2. Initial deficiency of expression and subsequent linearizer prototypes

(a) Mammalian linearizer prototype TG1, naïvely constructed based on a bifunctional repressor-reporter fusion tetR::eGFP from the original yeast circuit. (b) Prototype TG2, with an intron sequence introduced upstream of tetR::eGFP. (c) Prototype TG3, with the human codon-optimized htetR::eGFP gene (d) Prototype TG4, with a nuclear localization sequence (NLS) added between htetR and eGFP. (e) Prototype TG5, with the Woodchuck hepatitis virus post-transcriptional regulatory element (WPRE) sequence introduced into the 3′ UTR of the mRNA. (f) Prototype TG6, with the Kozak sequence (KS) introduced in the beginning of the htetR::NLS::eGFP gene. (g) Prototype TG7, using the novel pCMV-D2i promoter. (h) Gene expression distributions of MCF-7 cells stably expressing the chromosomally integrated prototype TG1 (see panel c) in 0 ng/ml doxycycline (blue) and 250 ng/ml doxycycline (red), relative to controls (black and green).

Figure 3. Computational model and simulated dose-response of the single-gene linearizer circuit

(a) Schematic representation of all chemical species, rates and reactions in the computational model. (b) Simulated mean of tetR::eGFP gene expression at different levels of inducer (I_e, doxycycline concentration, 0 to 100 ng/ml).

Figure 4. The effect of parameter changes on the dose-response of mean TetR::EGFP protein levels

Green curves represent nominal values of the parameters in the simulations. Each parameter was varied 5, 10 and 20 times up and down from the nominal value to investigate how it affects fold induction. Simulated inducer (I_e, doxycycline concentration) levels were between 0 and 1000 ng/ml. (a) Increasing the transcription rate (m) improves fold induction. (b) Increasing the translation rate (p) improves fold induction. (c) Decreasing the mRNA degradation rate (μ) improves fold induction. (d) Increasing the TetR-DNA binding rate (r) lowers minimum expression, while leaving the maximum expression unaffected, overall improving fold induction.

Figure 5. Improving fold induction in prototypes TG2 to TG6

(a) Gene expression distributions of MCF-7 cells transiently nucleofected with plasmids harboring prototypes TG1, TG2, and TG3 in saturating concentration of inducer (1000 ng/ml anhydrotetracycline). Median fluorescence was calculated for the cells carrying plasmid DNA. For control cells lacking plasmid DNA the same statistics was calculated based on the entire population. (b) Fluorescent images of MCF-7 cells harboring genome-integrated prototypes TG3 (no NLS, panels on the left) and TG4 (with NLS, panels on the right). EGFP fluorescence overlaid with DAPI nuclear staining shows preferentially nuclear localization of TetR::NLS::eGFP in prototype TG4 compared to prototype TG3. The scale bar represents 50 μm. (c) Gene expression distributions of MCF-7 cells stably expressing genome-integrated prototypes TG3 and TG4 in 0 ng/ml doxycycline (blue and red) and in 250 ng/ml doxycycline (cyan and magenta), indicating the corresponding fold induction. (d) Gene expression distributions of MCF-7 cells transiently nucleofected with plasmids harboring prototypes TG4, TG5, and TG6 in saturating inducer concentration (250 ng/ml doxycycline). Median statistics was calculated as in (a).

Figure 6. A library of novel TetR-repressible promoters

(a) A schematic representation of wild-type pCMV, pCMV-2xtetO (Invitrogen) and two sets of novel TetR-repressible promoters with different numbers and positions of tetO2 sites and with (pCMV-D2i, pCMV-D2t, pCMV-D3, pCMV-D4, and pCMV-D5) or without (pCMV-C3 and pCMV-C4) wild-type distance between the TATA box and the Inr motif. (b) Fold induction (mean ± SD, n=5) in MCF-7 cells bulk-transfected and stably expressing genome-integrated prototypes with the newly engineered promoters from (a). (c) Maximum expression (mean ± SD, n=5, a.u., at 250 ng/ml doxycycline) in the same MCF-7 cells as in (b). (d) Difference in fold induction between sets of clonal MCF-7 cell lines stably expressing TG prototypes based on the pCMV-2xtetO (mean ± SD, n = 7), pCMV-D2t (mean ± SD, n = 12), and pCMV-D2i (mean ± SD, n = 12) promoters (ANOVA, overall p = 0.003, followed by a Tukey HSD test: pCMV-D2i vs. pCMV-2xtetO, p = 0.042; pCMV-D2i vs. pCMV-D2t, p = 0.004).

Figure 7. Selection and assessment of prototype TG7

(a) Dose-response curve averaged for three independent assessments of MCF-7 cells stably expressing the genome-integrated prototype TG7 at increasing concentrations of doxycycline inducer (mean ± SD). (b) Representative gene expression distributions of MCF-7 cells stably expressing the genome-integrated prototype TG7 at different levels of induction. (c) Variability of gene expression (CV) averaged for three independent assessments of MCF-7 cells stably expressing genome-integrated prototype TG7 at increasing concentrations of doxycycline inducer (mean ± SD).

Figure 8. Two-gene mammalian linearizer system

(a) Two-gene mammalian linearizer based on the TG7 prototype driving the expression of the fluorescent reporter gene mCherry. (b) Dose-response curves of htetR::NLS::eGFP and mCherry expression averaged for three independent measurements of MCF-7 cells stably expressing genome-integrated two-gene linearizer system at increasing concentrations of doxycycline inducer (mean ± SD). (c) Variability (CV) of gene expression for htetR::NLS::eGFP and mCherry genes measured as described for panel (b) (mean ± SD). (d) Representative distributions of hTetR::NLS::EGFP measured by flow cytometry for the same cells as in panels (b) and (c). (e) Representative distributions of mCherry measured by flow cytometry for the same cells as in panels (b) and (c)