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Genetic circuit design automation for yeast

Nature Microbiology volume 5, pages 1349–1360 (2020)Cite this article

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Abstract

Cells can be programmed to monitor and react to their environment using genetic circuits. Design automation software maps a desired circuit function to a DNA sequence, a process that requires units of gene regulation (gates) that are simple to connect and behave predictably. This poses a challenge for eukaryotes due to their complex mechanisms of transcription and translation. To this end, we have developed gates for yeast (Saccharomyces cerevisiae) that are connected using RNA polymerase flux as the signal carrier and are insulated from each other and host regulation. They are based on minimal constitutive promoters (~120 base pairs), for which rules are developed to insert operators for DNA-binding proteins. Using this approach, we constructed nine NOT/NOR gates with nearly identical response functions and 400-fold dynamic range. In circuits, they are transcriptionally insulated from each other by placing ribozymes downstream of terminators to block nuclear export of messenger RNAs resulting from RNA polymerase readthrough. Based on these gates, Cello 2.0 was used to build circuits with up to 11 regulatory proteins. A simple dynamic model predicts the circuit response over days. Genetic circuit design automation for eukaryotes simplifies the construction of regulatory networks as part of cellular engineering projects, whether it be to stage processes during bioproduction, serve as environmental sentinels or guide living therapeutics.

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Fig. 1: S. cerevisiae circuit design using Cello 2.0.
Fig. 2: Design of minimal synthetic promoters that respond to regulators.
Fig. 3: S. cerevisiae terminator and insulators.
Fig. 4: Small molecule sensors.
Fig. 5: NOT/NOR gates.
Fig. 6: Genetic circuits.
Fig. 7: Circuit dynamics.

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Data availability

Genetic part sequences and the UCF file SC1C1G1T1.UCF are available in Supplementary Data 1. The data that support the findings of this study are available from the corresponding author upon reasonable request. Plasmids and cloning strains generated in this study will be available from Addgene (https://www.addgene.org/Christopher_Voigt/) and the corresponding author upon request. Source data are provided with this paper.

Code availability

The Cello 2.0 software and code are freely available at http://www.cellocad.org/ and https://github.com/CIDARLAB/Cello-v2.

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Acknowledgements

This work was supported by funding from the US Department of Energy (DE-SC0018368 to Y.C. and C.A.V.), NSF Synthetic Biology Engineering Research Center (SA5284-11210 to C.A.V.), NSF award (1522074 to D.D.), US Defense Advanced Research Projects Agency 1KM award (HR0011–12-C-0067 to C.A.V.) and SD2 (FA8750-17-C-0229 to S.Z. and C.A.V.), as well as a research contract from DSM (to E.M.Y.). Cello testing and feedback were provided by C. Myers and students at the University of Utah.

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Authors and Affiliations

  1. Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ye Chen, Shuyi Zhang, Eric M. Young & Christopher A. Voigt

  2. Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA

    Timothy S. Jones & Douglas Densmore

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  1. Ye Chen
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Contributions

Y.C. and C.A.V. conceived the study and designed the experiments. S.Z. performed the computational work. E.M.Y. cloned and characterized native yeast parts. T.J. and D.D. designed and developed Cello 2.0. Y.C. performed all of the other experiments and analysed the data. Y.C., E.M.Y. and C.A.V. wrote the manuscript.

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Correspondence to Christopher A. Voigt.

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Extended data

Extended Data Fig. 1 Optimization steps for building the TetR-responsive minimal promoter.

The annotated promoter sequences are provided in Supplementary Table 5, including intermediates, and all part sequences are provided in Supplementary Table 10. The promoters are evaluated through the transcriptional fusion with yfp and this cassette is carried on a plasmid (Supplementary Figure 15). The data are measured under identical experimental conditions (Methods). In the top graph, the carbon source is either 2% glucose or 2% galactose. The star indicated that there is no fluorescence detected over background. When TetRNLS is expressed from a second plasmid (Supplementary Figure 15), this is indicated by “+tetRNLS cassette”). The red arrows show the data corresponding to the promoter selected for the next round of optimization. The horizontal orange lines mark where mutations are made in order to diversify the part sequence to avoid homologous recombination in the context of a circuit. The data represent the average of three experiments performed on different days. The optimizations steps for other promoters are shown in Supplementary Figs. 2–12.

Extended Data Fig. 2 Promoter designs with varying lacOs operator spacing.

The sequences of genetic parts are provided in Supplementary Table 5. The UAS binds to Gal4 and the TSS is a 20bp sequence from ADH2 (Extended Data Figure 1). The promoters are transcriptionally fused to yfp and cloned into a plasmid backbone (Supplementary Figure 15). A second plasmid is constructed where LacI is expressed from a constitutive promoter (Supplementary Figure 15). The cytometry distributions show the fluorescence from the reporter plasmid in the absence (black) and presence (grey) of the LacI plasmid and the reported “Repression” is the ratio of the medians of these distributions. The data represent the average of three experiments performed on different days.

Extended Data Fig. 3 Upstream insulator impact on an inducible promoter.

The fluorescence values are shown when Ptet is maximally induced (100 ng/ml). Sb is a 450bp nonfunctional DNA sequence. When there is no insulator (none)-Sb, then the maximum expression is lower. The horizontal dashed line is the average of the fluorescence measurements from the strains containing an insulator. The strains are described in Supplementary Table 4 and insulator sequences are provided in Supplementary Table 5. The data represent the average of three experiments performed on different days.

Extended Data Fig. 4 Cytometry distribution for the RPU reference standard.

The cytometry distributions show the fluorescence from the strain containing the RPU standard (S. cerevisiae CY671int) (grey), S. cerevisiae BY4741 cells (white) and a reporter strain containing the strong native promoter PTDH3 (S. cerevisiae CY676int) (black). Detailed strain information is provided in Supplementary Tables 3 and 8. Three experiments were repeated on different days with similar results.

Extended Data Fig. 5 Sensor cytometry distributions and sensor dynamics.

These data correspond to Fig. 4d and Supplementary Table 1. The detailed schematic for the reporter of each sensor shown to the left and the corresponding strains are provided in Supplementary Table 4. The sequences for the genetic parts are provided in Supplementary Tables 4 and 8. The experimental conditions are as described in the Methods. Two representative cytometry distributions are shown when cells containing the sensors are grown in (0 or 100 ng/ml) aTc, (0 or 20 mM) IPTG, or (0 or 10 mM) xylose. The dynamic data was measured as described in the Methods and fit to Equations 4 and 5. The data points combine two experiments performed on different days.

Extended Data Fig. 6 Converting the x-axis of a NOT gate response function to RPU.

a, An example is shown to demonstrate how data gathered using two strains are combined to create a NOT gate response function. The PhlF gate is strain S. cerevisiae CY960-CY663int and the IPTG sensor is strain S. cerevisiae CY639int (Supplementary Table 4). Both strains are evaluated by adding different concentrations of IPTG (left to right): 0.0, 0.1, 0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 5.0, 10 and 20 mM. The growth conditions, cytometry measurements, and normalization to RPU are described in the Methods. The graph to the right is built using the RPU measurements for the input promoter (Plac) and output promoter (PphlF.1) at each inducer concentration. The data are then fit to Equation 2, the parameters for which are presented in Supplementary Table 2. b, Growth impacts are processed similarly so that the x-axis can be reported as promoter activity and not inducer concentration. As an example, the growth impact of the PhlF gate (S. cerevisiae CY1085-CY663int) is shown along with the strain used to determine the activity of the (Ptet + Plac) promoters. The strains were induced with 0, 1, 3, 5, 7.5, 10, 20 mM IPTG (0 ng/ml aTc) or 1, 2.5, 5, and 20 mM IPTG (100ng/ml aTc). The data represent three experiments performed on different days.

Extended Data Fig. 7 Detailed characterization of the NOT gates.

These data correspond to Fig. 5b and Supplementary Table 2. The detailed schematic for the gate is shown to the left and the corresponding strains are provided in Supplementary Table 4. The sequences for the genetic parts are provided in Supplementary Tables 4 and 8. The data used to fit the response functions (Equation 2) were calculated as described in Extended Data Figure 6 and the resulting parameters are provided in Supplementary Table 2. The data represent three experiments performed on different days. The experimental conditions are as described in the Methods. Two representative cytometry distributions are shown when cells containing the gates are grown in 0 or 20 mM IPTG. The dynamic data was measured as described in the Methods and fit to Equations 6, 7 and 8. The data represent two experiments performed on different days.

Extended Data Fig. 8 Detailed characterization of the NOT gates.

These data correspond to Fig. 5b and Supplementary Table 2. The detailed schematic for the gate is shown to the left and the corresponding strains are provided in Supplementary Table 4. The sequences for the genetic parts are provided in Supplementary Tables 4 and 8. The data used to fit the response functions (Equation 2) were calculated as described in Extended Data Figure 6 and the resulting parameters are provided in Supplementary Table 2. The data represent three experiments performed on different days. The experimental conditions are as described in the Methods. Two representative cytometry distributions are shown when cells containing the gates are grown in 0 or 20 mM IPTG. The dynamic data was measured as described in the Methods and fit to Equations 6, 7 and 8. The data represent two experiments performed on different days.

Extended Data Fig. 9 The failure of the 0x61 circuit.

The logic diagram is shown with gate colors corresponding to the assigned repressor. The response of the circuit is shown for different combinations of inducer: 100ng/ml aTc, 10mM xylose, and 20mM IPTG. The experimental data are shown as cytometry distributions (black) and blue/red distributions show the ON/OFF output predicted by Cello. The states behave as predicted, except for the -/+/+ state, which should be OFF but is measured as being ON. To determine where this breakage originates the output promoters of intermediate gates are fused to rfp and inserted at the HO locus. The measured responses are then compared to those predicted. The population variability in the response of the CI gate, which causes errors that propagate to the final BM3R1 gate. The DNA sequence of the circuit is provided in Supplementary Table 11, the strains are provided in Supplementary Table 4, and the reporter constructs in Supplementary Table 4. Three experiments were repeated on different days with similar results.

Extended Data Fig. 10 The impact on growth from carrying the circuits in different states.

Each point represents a different combination of inducers for a given circuit. The prediction is made using Cello and is calculated by multiplying the empirically-measured growth impact of all the gates with input promoter activities corresponding to that state [7]. Details regarding the growth assay are presented in the Methods and normalized to S. cerevisiae BY4741 in the same growth conditions. The data represent three experiments performed on different days.

Supplementary information

Supplementary Information

Supplementary note, Figs. 1–17 and Tables 1–11.

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Supplementary Data 1

UCF of yeast, specifying the strain, experimental setup, genetic location, gate library and mapping strategies.

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Source data for Fig. 2b,c.

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Source data for Fig. 3a–c.

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Source data for Fig. 4b,d,e.

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Source data for Fig. 5c,f–h.

Source Data Fig. 6

Source data for Fig. 6b.

Source Data Fig. 7

Source data for Fig. 7a,c,d.

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Chen, Y., Zhang, S., Young, E.M. et al. Genetic circuit design automation for yeast. Nat Microbiol 5, 1349–1360 (2020). https://doi.org/10.1038/s41564-020-0757-2

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