Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset

Abstract

Engineering allosteric transcriptional repressors containing an environmental sensing module (ESM) and a DNA recognition module (DRM) has the potential to unlock a combinatorial set of rationally designed biological responses. We demonstrated that constructing hybrid repressors by fusing distinct ESMs and DRMs provides a means to flexibly rewire genetic networks for complex signal processing. We have used coevolutionary traits among LacI homologs to develop a model for predicting compatibility between ESMs and DRMs. Our predictions accurately agree with the performance of 40 engineered repressors. We have harnessed this framework to develop a system of multiple toggle switches with a master OFF signal that produces a unique behavior: each engineered biological activity is switched to a stable ON state by different chemicals and returned to OFF in response to a common signal. One promising application of this design is to develop living diagnostics for monitoring multiple parameters in complex physiological environments and it represents one of many circuit topologies that can be explored with modular repressors designed with coevolutionary information.

Figure 1.

Development of a computational model to predict compatibility among DRMs and ESMs from LacI homologs. (A) Module swapping strategy. By combining DRMs and ESMs from different LacI family members, we can mix and match DNA recognition and allosteric response properties originated from different repressors (20). In this study, we used a computational strategy for the prediction of compatibility between DRMs and ESMs. (B–G). Computational strategy for the prediction of hybrid repressor performance. Direct Coupling Analysis was used to infer the parameters of the global joint probability distribution estimated for the LacI homologs. First, these parameters, e_ij and h_i, were used to compute direct information values for residue pairs to discern the most important residue-residue pairs between the ESM and the DRM domains. Then, a compatibility score C(S) of a specific repressor sequence was calculated using e_ij, only for those residue position pairs with the highest DI values. C(S) provides a metric of functionality of the designed hybrid that is validated experimentally.

Figure 2.

Characterization of LacI family hybrid repressors via experimental design and coevolutionary models. (A) Experimental characterization of hybrid repressors. We assessed the ability of hybrid repressors in gene expression regulation and allosteric response, by using a transcriptional reporter assay in a strain of E. coli with GFP as a reporter of transcription activities. A number of 40 repressors (including 35 hybrids) were characterized with this assay. Results shown in each column and each row are from repressors with the same DRM and ESM, respectively. Repressors with the same ESM were exposed to the same signaling molecule as indicated in the bracket on the left of each row. Repressors with the same DRM were used to control GFP expression driven by the same promoter (shown for each column). Data points in bar graphs represent GFP fluorescence in cells that were uninduced (grey) and induced (red). The blue number above each plot represents the corresponding fold-change of GFP induction. All data points represent mean ± S.D. of three biological replicates. (B) Heatmap of compatibility scores, C(S), predicted from coevolutionary information for 40 hybrid repressors. The hybrid repressors with the same ESM are presented in rows, while the repressors with the same DRM are shown in columns. The more negative score is shown in darker color, indicating more favorable compatibility. (C) Agreement between experimental observations and coevolutionary compatibility predictions. Orange and red cells indicate successful prediction of negative or positive for hybrid functionality (induction threshold at 20), respectively. White cells indicate disagreement between computational predictions and experimental results.

Figure 3.

Global inference performance metrics of the hybrid compatibility score C(S) on predicting functional hybrid repressors. (A) Collection of hybrids with their corresponding experimental fold change measurements (x-axis) and C(S) scores (y-axis). The dashed line depicts the threshold for the experimental fold change, classifying the hybrids into two classes: inducible hybrids with fold increase ≥20 and non-inducible hybrids with fold change <20. The dash-dotted line denotes the threshold for compatibility score, classifying the hybrids into two classes: predicted to be compatible for hybrids with score ≤–69 and non-compatible hybrids with C(S) > –69. The shaded areas delimited by C(S) and fold response thresholds are regions where the model was able to correctly classify the response of the hybrid. The repressors used for building double toggle system described are indicated with arrows. (B) The ROC curve of the compatibility score model with fold change threshold at 20. The area under the curve is 0.88. The optimal operating point is highlighted by a dot in the curve. The black line indicates the performance of a random predictor. (C) Positive prediction rate of a C(S)-ranked set of hybrid repressors sharing a common DRM. There are 8 sets that contain five hybrids with different ESMs for each set. The positive prediction rate (y-axis) was calculated as the proportion, out of these eight repressors, with fold change ≥20 for each compatibility score rank category (x-axis). The best-ranked hybrid has a high probability to be functional. (D) Positive prediction rate of a C(S)-ranked set of hybrid repressors sharing a common ESM. In this case, there are five groups of hybrids with the same ESM and for each set there are eight different DRMs. The positive prediction rate is computed as in (C) for these hybrids. The best ranked scores indicate with high probability that the hybrid will be functional.

Figure 4.

Construction and characterization of a MuTMOS system with ribose as the master OFF signal. (A) A schematic of the MutMOS circuit that uses MalR-RbsR and native RbsR for detecting ribose. Engineered cells exposed to IPTG and ATc are switched to mCherry-ON state and GFP-ON state, respectively. An exposure to ribose turns off the expression of both mCherry and GFP. (B) Stochastic simulation of concentration changes of 4 repressors after induction (ON) by IPTG plus ATc (in shade). Ribose induces a subsequent OFF signal (in shade). A.U. stands for arbitrary units. The simulation was conducted by using the Gillespie algorithm. The levels of RbsR and MalR-RbsR increase while the levels of TetR and LacI decrease with the induction of IPTG plus ATc for the first 100,000 steps, and then maintained the equilibrium state even after the removal of ligands (upper row). The addition of ribose after 500,000 steps switched the toggle switches back to the state with high levels of LacI and TetR (lower row). (C) Experimental characterization of the MuTMOS system with ribose as the OFF signal. As shown on the first row, cells at the OFF state were exposed to IPTG only to switch to a stable mCherry-ON only state (left column); exposing cells only at the mCherry-ON state to ribose switched them back to a stable OFF state (right column). Similarly, results shown on the second row illustrates that ATc switched cells to a stable GFP-ON state (left), and ribose switched cells back to the OFF state (right). When cells were exposed to both ATc and IPTG (third row), cells became both mCherry-ON and GFP-ON (left), whereas ribose deactivated the expression of both mCherry and GFP to return cells back to a stable OFF state (right). Representative flow cytometry data are illustrated on the fourth row for the switching process between the state with both mCherry-ON and GFP-ON and the OFF state. All data points represent the mean ± S.D. of three biological replicates.