(D-132) Bioinformatics-guided design of transcriptional regulators for implementing decision-making behaviors in cellular systems

Introduction:: Transcriptional regulators are used robustly to control gene expression in response to specific signaling molecules. However, native regulators can only connect a signaling molecule to a specific promoter for transcription control; difficulty in rewiring connections between signals and genetic elements obstructs the development of genetic systems to create complex cellular response behaviors. To address this problem, we aim to develop a series of novel transcriptional regulators that facilitate the use of a single signal to control multiple promoters and also, the use of multiple signals to control the same promoter.

Materials and Methods:: Our approach is to use a module swapping strategy, in which DNA-binding modules (DBMs) and ligand-binding modules (LBMs) from different regulators are fused to generate hybrid regulators, which possess a new combination of DNA recognition and signal detection properties. We also used a bioinformatics approach to identify co-evolutionary traits within a regulator family, which reveal important interactions between DBMs and LBMs. Based on these DBM-LBM interactions, we evaluate the compatibility between different DBMs and LBMs. Furthermore, we design mutants of hybrid regulators to restore important DBM-LBM interactions, improving their regulator activities.

Results, Conclusions, and Discussions:: With our module swapping approach, we generated a wide range of hybrid regulators. Approximately half of these hybrids are efficient regulators. With the computational model based on coevolutionary traits, we accurately predicted the performance of these hybrid regulators. The use of this predictive model led us to design mutants to improve the performance of hybrid regulators. Overall, these results demonstrate that our multidisciplinary approach is an efficient way to study and design genetic tools to serve as biosensors.

Acknowledgements (Optional): : This work was supported by funding from the National Institutes of Health/National Institute of General Medical Sciences grants R15GM135813, R35GM142421, and the National Science Foundation grant MCB-1914538.

References (Optional): : 1. Jiang, X., Dimas, R.P., Chan, C.T.Y. , and Morcos, F. (2021). Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions. Nature Communications. 3:937.
2. Dimas, R.P., Jordoan, B.R., Jiang, X., Martini, C., Glavy, J.S., Patterson, D.P., Morcos, F., and Chan, C.T.Y. (2019). Engineering DNA recognition and allosteric response properties of TetR family proteins by using a module-swapping strategy. Nucleic Acids Research. 47(16):8913-8925.
Clement T. Y. Chan | Curriculum Vitae, page 3
3. Dimas, R.P., Jiang, X., Alberto de la Paz, J., Morcos, F., and Chan, C.T.Y. (2019). Engineering repressors with coevolutionary cues facilitates toggle switches with a master reset. Nucleic Acids Research. 47(10):5449-5463.
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