(479d) Design and Implementation of a Glucose-Responsive Genetic Switch Circuit in a Cell-Free System

Cell-free protein synthesis (CFPS) platforms have emerged as a promising technology for applications such as on-demand manufacturing of therapeutically important biologics, metabolic engineering, biosensing, prototyping genetic parts, and engineering genetic circuits. CFPS is especially promising for biosensing applications because its tolerance to cytotoxins and portability enable point-of-care sensing. Biosensor systems commonly rely on allosteric transcription factors (aTFs) to ’sense’ specific molecules (ligands); upon binding to the ligands, the aTF undergoes a conformational change that alters its DNA-binding strength and therefore the rate of a downstream reporter RNA or protein expression. The expression of the reporter protein is proportional to the amount of ligand present, enabling a direct readout of the ligand amount. These biosensor systems have recently been deployed for detecting ions, biomarkers, and antibiotics [1,2]. Having the ability to sense metabolites such as glucose, and drive downstream synthesis of proteins such as insulin or glucagon, would enable synthetic glucose switches to take on a novel therapeutic role for the management of diseases such as Type I and or Type II diabetes.

In this study, we implemented a prokaryote-derived aTF (GntR) that responds to D-gluconate, a derivative of glucose that can be rapidly synthesized from glucose in a one-step enzymatic reaction. We constructed an E. coli promoter containing the GntR operator site placed between the -35 and -10 region, and a reporter protein (Venus) placed downstream of this region. Preliminary tests of the repression efficiency were performed in CFPS by co-expressing GntR along with the Venus reporter protein in varied input concentration ratios. These sets of repression and derepression experiments were carried out in two different commercially available CFPS systems: an E. coli extract-based system (myTXTL) and a reconstituted system (PURExpress). Interestingly, derepression by D-gluconate was not observed in myTXTL. This could be attributed to the rapid consumption of D-gluconate by the metabolic enzymes present in the extract. In the reconstituted PURE system more than 6-fold decrease in reporter concentration was measured in the fully repressed case when compared to the negative control (-GntR gene) at the reaction endpoint (16 hours). Derepression by the inducer, D-gluconate, resulted in a 4-fold increase in reporter expression when compared with the fully repressed case upon inclusion of 10mM inducer at the start of the reaction. The effect of inducer concentration was also measured in PURExpress by adding varying amounts of the inducer to the reaction mixture, and reporter protein levels at the reaction endpoint in each case were used to generate a dose response curve. Taken together, by incorporating the glucose-sensitive promoter unit into genetic circuits, gene expression could be strategically altered to suit a variety of applications based upon glucose levels.

Further, to accelerate future experiment design as well as understand the interaction between different genetic parts, we also devised an effective modeling approach based on classical biophysical arguments to simulate cell free transcription (TX) and translation (TL) processes for different genetic circuits [3]. This modelling approach was used to estimate important kinetic parameters as well as perform sensitivity analyses to determine the influence of each parameter on the synthesis of mRNA and protein species for the glucose-sensor circuit presented here and two other synthetic cell-free circuits. For example, we learned kinetic parameters such as mRNA and protein half lives, promoter configuration Gibbs energies and dissociation constants using mRNA and protein measurements in conjunction with multiobjective optimization. Next we used sensitivity analyses to identify the influential parameters from over 33 parameters in the model.

Taken together, this study provides a framework for optimization and development of a variety of genetic circuits that can potentially be exploited for therapeutic and biosynthetic applications.

References:

[1] Soltani, Mehran, et al. "Reengineering cell-free protein synthesis as a biosensor: Biosensing with transcription, translation, and protein-folding." Biochemical Engineering Journal 138 (2018): 165-171.

[2] Voyvodic, Peter L., and Jerome Bonnet. "Cell-free biosensors for biomedical applications." Current Opinion in Biomedical Engineering 13 (2020): 9-15.

[3] Adhikari, Abhinav, et al. "Effective biophysical modeling of cell free transcription and translation processes." Frontiers in bioengineering and biotechnology 8 (2020).