Genetic programs constructed from layered logic gates in single cells

Abstract

Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits, and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits. Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator-chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells.

Figure 1. Mining circuits from genomic islands

a, The truth table for an AND gate. b, The architecture of an AND gate. The protein–protein and protein–DNA interactions that can lead to crosstalk between gates are shown as red rectangles. c, The gene cluster from SPI-1 and the needle structure^,. The transcription factor InvF is shown in red, the chaperone SicA in blue, and the SipC effector in green.

Figure 2. Part engineering to improve dynamic range and orthogonality

a, Sequences for the InvF activator (top), the pipaH promoter variant (middle) and the SicA mutant (bottom). b, A comparison in the induction of the psicA promoter by the short (annotated) and long (correct) InvF sequences. c, A comparison of the wild-type and mutant (pipaH*) promoters. d, A comparison of the wild-type and mutant (SicA*) chaperones. Activation of either the pipaH* promoter by MxiE (left) or the psicA promoter by InvF (right) is shown. In b–d the chaperone (SicA and IpgC) and activator (InvF and MxiE) are expressed from the pBAD and pTet promoters, respectively: −, no inducer; +, 5 mM Ara and 50 ng ml⁻¹ aTc. e, The orthogonality of protein–protein interactions. This figure shows the fold change, calculated by dividing the fluorescence values (with both inputs on) by the minimum fluorescence from each promoter (with both inputs off). The inducers were 5 mM Ara for sicA* and ipgC, 1 µM 3OC6 for exsC, and 50 ng ml⁻¹ aTc for invF, mxiE and exsDA. f, The orthogonality of protein–DNA interactions. All of the error bars in these figures were calculated as the s.d. of three replicates performed on different days. The error bars for e and f are shown in Supplementary Fig. 4.

Figure 3. Three 2-input AND gates constructed using Salmonella (left), Shigella (middle) and Pseudomonas (right) parts

a, The architecture of three AND gates. b, The transfer functions obtained by measuring fluorescence. The inducers used were Ara (0, 0.0016, 0.008, 0.04, 0.2, 1, 5 and 25 mM) and 3OC6 (0, 0.32, 1.6, 8, 40, 200, 1,000 and 5,000 nM) from bottom to top; and aTc (0, 0.0032, 0.016, 0.08, 0.4, 2, 10 and 50 ng ml⁻¹) from left to right. Data are averages of three replicates performed on different days. c, The transfer functions as fitted to mathematical models. The white boxes show the experiment ranges obtained by the inducible promoters. Note that b and c cannot be visually compared to determine the goodness of the fit because the axes are rescaled. Supplementary Fig. 10 shows a quantitative comparison, which yields an R² of 0.9.

Figure 4. Genetic programs formed by layering AND gates

a, 3-input AND gate. This system consists of three sensors, an integrated circuit and a reporter gene. b, The fluorescence measured from cells containing the 3-input AND gate. The three inducers used for the on (1) input are Ara (5 mM), IPTG (0.1 mM) and aTc (10 ng ml⁻¹). Data are means and s.d. for three replicates performed on different days. c, Raw cytometry data for all sets of input states. The thick line is for the [111] set of inducers. d, 4-input AND gate. e, The output fluorescence for different combinations of inputs. The four inducers used for the on input were Ara (5 mM), IPTG (0.1 mM), 3OC6 (5 µM) and aTc (10 ng ml⁻¹). f, Raw cytometry data for all input states. The thick line is for [1111].

Figure 5. Performance of genetic programs

a, The dynamic behaviour for the induction and relaxation of the 4-input program. The input states were switched either [0000] to [1111] (at D₆₀₀ = 0.25; black diamonds) or [1111] to [0000] (at D₆₀₀ = 0.05; blue diamonds). The off and on states are shown as a reference (red dashed lines, values from Fig. 4). b, A comparison for the output ‘expected’ from combining the independently measured transfer functions of each gate with that ‘measured’ in the final context. Each data point is from a different combination of inducers for the 3-input (blue diamonds) and 4-input (black squares) programs. The line shown is y = x. c, The dynamic behaviour for the 3-input gate switching from [110] to [011] (at D₆₀₀ = 0.25). The input states are listed as [Ara IPTG aTc]. d, The dynamic behaviour for the 4-input gate switching from [1110] to [0111]. The input states are listed as [Ara IPTG 3OC6 aTc]. The four inducer concentrations used for the on input for the 3- and 4-input AND gate were Ara (5 mM), IPTG (0.1 mM), 3OC6 (5 µM) and aTc (10 ng ml⁻¹). The on state is shown as a reference (orange dashed lines). The red dashed lines indicate the steady-state outputs for the corresponding inputs (Fig. 4). The error bars show s.d. calculated on the basis of three replicates performed on different days.