Characterizing the Black Box of Eukaryotic Cell Free Systems Using Metabolomics

CFE systems have several benefits over using whole cells, such as being easier to scale up, having longer shelf life, and being more portable. So far, cell -free systems of less complex organisms such as Escherichia coli are being used for several applications such as protein production, biosensors, and biomanufacturing [1]. However, these systems cannot produce the complex proteins needed for many therapeutics due to their lack of the required evolved cellular machinery. Yeast systems have the potential to produce more complicated proteins, but they face challenges, such as lower protein yields and shorter reaction times due to energy loss and toxic buildup [2].

The crude cellular extract is a crucial part of a functional cell free reaction. Its development through lysing whole cells to release their internal machinery, is often the most intensive step due to considerable variation in protocol details from growth medium to lysis methods. Several known and unknown variables, including the presence of endogenous nucleases and the inherent metabolism of the lysate, can affect the performance of the lysate [3]. Thus, this step is often considered a ‘black box’, due to relatively little knowledge on the processes occurring within the lysate.

Systems biology (proteomics, metabolomics, etc.) is a useful tool often utilized to characterize such an unknown system. It is a ‘top-down’, systems-scale approach that measures the biological system as a whole. Metabolomics, the analysis of the small molecules present in a system, can be used to identify possible inherent bottlenecks within the yeast cell free system that are restricting protein production [1]. This approach has proven successful on E. coli systems, where the adjustment of specific metabolite concentrations has been shown to increase protein yield. These studies have also made it possible to determine the effects of specific steps within the CFE protocol on the final target protein production [4]. By determining possible metabolomic bottlenecks, the system can be fine-tuned to account for these previously unknown metabolic changes leading to an increased protein yield.

We have produced a viable crude cell extract in Saccharomyces cerevisiae, or brewer’s yeast, and performed cell free reactions to verify a baseline productivity of the system. S. cerevisiae was chosen for this experimentation as it is a model organism due to its fast growth, annotated genome, and similarity to more complex mutli-cellular eukaryotic organisms. Originally a luciferase reporter system was chosen, enabling luminescence correlating to protein yield to be read by a microplate reader. Luciferase was chosen as an initial reporter due to its sensitivity towards low outputs, which were expected from an untested system. However, luciferase requires addition of a luciferin substrate to report luminescence, which prohibits the ability to perform a continuous time course reaction. Thus, we switched to a GFP reporter for continuous monitoring, and fluorescence was read by a microplate reader and correlated to protein yield going forward [2].

We have worked to optimize the system to maximize output by varying sonication energy input during lysis and various reactant concentrations including T7 RNA Polymerase, glycerol, and lysate percentage. The effects of different variables affecting the final protein production such as the physiological state of the yeast cells, inherent metabolism of the lysate, and different lysis methods such as sonication and homogenization will be studied. To enable comparison and analysis of the effects of different variables, samples lysed with different methods will be collected at varying stages of growth to determine the effect of the cell’s physiological state on the performance of the CFE system. The metabolomics workflow, consisting of various steps such as quenching and extraction, prepares the sample for GC-MS testing [5]. The resultant dataset will be analyzed using various statistical tools such as multivariate analysis using PCA and univariate analysis using ANOVA. As was observed with E. coli CFE systems, the resulting data can shed light onto the black box of processes within the lysate and CFE systems. Once one or more of these bottlenecks are determined, it will be possible to further fine-tune the system by harnessing the tools of synthetic biology resulting in a substantially more efficient yeast cell free system.

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