(160aq) Modeling and Analysis of Energy Generation in Commercial E. coli Cell Free Extracts

We previously used sequence specific flux balance analysis (ssFBA) to evaluate the performance of cell free protein production of Chloramphenicol acetyltransferase (CAT) in a PANOx-SP based E. coli system [4][5]. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Metabolic fluxes were constrained by experimental time series measurements for 37 metabolites including glucose, nucleotides, amino acids and organic acids for the first hour of the reaction. The difference in flux distribution between the experimentally and theoretically constrained solution indicated the need for experimentally derived constraints for more accurate predictions. The experimentally constrained model accurately simulated CAT production for one hour of the CFPS reaction. Analysis of energy efficiency in CFPS revealed that the theoretical energy efficiency of cell free protein synthesis is much higher than that observed experimentally. This result highlighted opportunities for optimizing cell free systems for more efficient energy production and improved protein yields. Towards this opportunity, we developed a more comprehensive model for understanding performance limitations and resource allocation in CFPS that integrated additional types of experimental data with the flux prediction.

In this study, we expanded the earlier model by integrating kinetic turnover rates, enzyme levels, kinetic descriptions of transcription and translation processes, enzyme activity assays and absolute metabolite levels to describe CFPS metabolism. Additionally, we applied the ssFBA approach to the commercially available E. Coli based CFPS system, myTXTL (Arbor Biosciences). In particular, myTXTL was used for green fluorescent protein (GFP) production, and simulations were performed using the new model. Experimental data along with time course flux estimations reveal that oxidative phosphorylation is active in myTXTL and is coupled with central carbon metabolism to power transcription and translation for the production of GFP for 16 hours. Metabolic constraints and enzyme activity assays for glutamate dehydrogenase reveal glutamate powers the tricarboxylic acid (TCA) cycle along with succinate dehydrogenase to provide energy support for oxidative phosphorylation. The myTXTL system is also reported to rely on maltodextrin and 3-phosphoglycerate (3PG) to power transcription and translation anaerobically. Thus, CFPS of GFP relies on a mixture of aerobic and anaerobic processes to provide the necessary energy requirements for transcription and translation.

To study activation of oxidative phosphorylation in the myTXTL system, inhibitors were used to block oxidative phosphorylation reactions and analyse the downstream effect on protein production. The myTXTL cell-free reaction was incubated with two different inhibitors: thenoyltrifluoroacetone (TTA), an electron transport inhibitor in Complex II, and 2-4-dinitrophenol (DNP), a membrane gradient uncoupler. In both cases, the observed protein accumulation was significantly less compared to the case where inhibitors were not used. To simulate protein production, the mathematical model was constrained by absolute experimental measurements for 63 metabolites and gene abundance data from literature for 104 reactions [6]. Time-resolved FBA simulations accurately predicted the effect of DNP and TTA on CFPS metabolism, as well as mRNA and protein production. The experimentally-validated model was used to understand the role of oxidative phosphorylation in CFPS, which estimated a reduction of 94% and 51% in oxidative phosphorylation activity for the DNP and TTA cases respectively. Thus, the mathematical model governed by metabolic and kinetic constraints described metabolic perturbations when biochemical inhibitors were introduced into the reaction.

We will further explore the response of myTXTL to inhibitors using Minimization of Metabolic Adjustment (MOMA) [7]. In particular, we will use MOMA to predict GFP production in response to the inhibitors and compare the predicted flux distribution with FBA results. We anticipate identifying pathways that re-route flux in response to inhibition of the oxidative phosphorylation pathway. MOMA is designed as a quadratic programming problem which assumes that the metabolic flux distribution immediately following a perturbation will resemble the wild type flux distribution. Thus, this approach takes into account the time required for regulatory controls to bring the system to a new steady state. MOMA has been shown to be useful for predicting flux distributions in perturbed systems well in agreement with experimental measurements [8]. This work will enable better understanding of metabolic processes used for powering transcription and translation in cell free systems. Furthermore, future extensions of this model will be useful for redirecting energy resources for improving the performance of cell free protein synthesis. Taken together, we provide a modeling framework that describes and predicts CFPS metabolism with reasonable accuracy that can be potentially used to identify strategies for cell-free metabolic engineering applications.

References

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