Metabolic Dynamics in Escherichia coli-Based Cell-Free Systems

Optimizing Protein Production in the One-Pot PURE System: Insights into Reaction Composition and Expression Efficiency

The One-Pot PURE (Protein synthesis Using Recombinant Elements) system simplifies the preparation of traditional PURE systems by coculturing and purifying 36 essential proteins for gene expression in a single step, enhancing accessibility and affordability for widespread laboratory adoption and customization. However, replicating this protocol to match the productivity of traditional PURE systems can take considerable time and effort due to uncharacterized variability. In this work, we observed unstable PURE protein expression in the original One-Pot PURE strains, E. coli M15/pREP4 and BL21(DE3), and addressed this issue using glucose-mediated catabolite repression to minimize burdensome background expression. We also identified several limitations making the M15/pREP4 strain unsuitable for PURE protein expression, including coculture incompatibility with BL21(DE3) and uncharacterized proteolytic activity. We showed that consolidating all expression vectors into a protease-deficient BL21(DE3) strain minimized proteolysis, led to more uniform coculture cell growth at the time of induction, and improved the stoichiometry of critical translation initiation factors in the final PURE mixture for efficient cell-free protein production. In addition to optimizing the One-Pot PURE protein composition, we found that variations in commercial energy solution formulations could compensate for suboptimal PURE protein stoichiometry. Notably, altering the source of E. coli tRNAs in the energy solution alone led to significant differences in the expression capacity of cell-free reactions, highlighting the importance of tRNA codon usage in influencing protein expression yield. Taken together, this work systematically investigates the proteome and biochemical factors influencing the One-Pot PURE system productivity, offering insights to enhance its robustness and adaptability across laboratories.

Cell-Free Gene Expression: Methods and Applications

Cell-free gene expression (CFE) systems empower synthetic biologists to build biological molecules and processes outside of living intact cells. The foundational principle is that precise, complex biomolecular transformations can be conducted in purified enzyme or crude cell lysate systems. This concept circumvents mechanisms that have evolved to facilitate species survival, bypasses limitations on molecular transport across the cell wall, and provides a significant departure from traditional, cell-based processes that rely on microscopic cellular "reactors." In addition, cell-free systems are inherently distributable through freeze-drying, which allows simple distribution before rehydration at the point-of-use. Furthermore, as cell-free systems are nonliving, they provide built-in safeguards for biocontainment without the constraints attendant on genetically modified organisms. These features have led to a significant increase in the development and use of CFE systems over the past two decades. Here, we discuss recent advances in CFE systems and highlight how they are transforming efforts to build cells, control genetic networks, and manufacture biobased products.

Metabolic Perturbations to an Escherichia coli-based Cell-Free System Reveal a Trade-off between Transcription and Translation

Cell-free transcription-translation (TX-TL) systems have been used for diverse applications, but their performance and scope are limited by variability and poor predictability. To understand the drivers of this variability, we explored the effects of metabolic perturbations to anEscherichia coli (E. coli) Rosetta2 TX-TL system. We targeted three classes of molecules: energy molecules, in the form of nucleotide triphosphates (NTPs); central carbon "fuel" molecules, which regenerate NTPs; and magnesium ions (Mg2+). Using malachite green mRNA aptamer (MG aptamer) and destabilized enhanced green fluorescent protein (deGFP) as transcriptional and translational readouts, respectively, we report the presence of a trade-off between optimizing total protein yield and optimizing total mRNA yield, as measured by integrating the area under the curve for mRNA time-course dynamics. We found that a system's position along the trade-off curve is strongly determined by Mg2+ concentration, fuel type and concentration, and cell lysate preparation and that variability can be reduced by modulating these components. Our results further suggest that the trade-off arises from limitations in translation regulation and inefficient energy regeneration. This work advances our understanding of the effects of fuel and energy metabolism on TX-TL in cell-free systems and lays a foundation for improving TX-TL performance, lifetime, standardization, and prediction.

Complex Dependence of Escherichia coli-based Cell-Free Expression on Sonication Energy During Lysis

Cell lysis─by sonication or bead beating, for example─is a key step in preparing extracts for cell-free expression systems. To create high protein-production capacity extracts, standard practice is to lyse cells sufficiently to thoroughly disrupt the membrane and thus extract expression machinery but without degrading that machinery. Here, we investigate the impact of different sonication energy inputs on the protein-production capacity of Escherichia coli extracts. While the existence of operator-specific optimal sonication energy inputs is widely known, our findings show that the sonication energy input that yields maximal protein output from a given expression template may depend on plasmid concentration, transcriptional and translational features (e.g., promoter), and other expression vector components (e.g., origin of replication). These results indicate that sonication protocols cannot be standardized to a single optimum, suggest strategies for improving protein yields, and more broadly highlight the need for better metrics and protocols for characterizing cell extracts.

Plasmid Crosstalk in Cell-Free Expression Systems

Although cell-free protein expression has been widely used for the synthesis of single proteins, cell-free synthetic biology has rapidly expanded to new, more complex applications. One such application is the prototyping or implementation of complex genetic networks involving the expression of multiple proteins at precise ratios, often from different plasmids. However, expression of multiple proteins from multiple plasmids may inadvertently result in unexpected, off-target changes to the levels of the proteins being expressed, a phenomenon termed plasmid crosstalk. Here, we show that the effects of plasmid crosstalk─even at the qualitative level of increases vs decreases in protein expression─depend on the concentration of plasmids in the reaction and the type of transcriptional machinery involved in the expression. This crosstalk can have a significant impact on genetic circuitry function and even interpretation of simple experimental results and thus should be taken into consideration during the development of cell-free applications.

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

Mechanistic Insights into Cell-Free Gene Expression through an Integrated -Omics Analysis of Extract Processing Methods

Cell-free systems derived from crude cell extracts have developed into tools for gene expression, with applications in prototyping, biosensing, and protein production. Key to the development of these systems is optimization of cell extract preparation methods. However, the applied nature of these optimizations often limits investigation into the complex nature of the extracts themselves, which contain thousands of proteins and reaction networks with hundreds of metabolites. Here, we sought to uncover the black box of proteins and metabolites in Escherichia coli cell-free reactions based on different extract preparation methods. We assess changes in transcription and translation activity from σ⁷⁰ promoters in extracts prepared with acetate or glutamate buffer and the common post-lysis processing steps of a runoff incubation and dialysis. We then utilize proteomic and metabolomic analyses to uncover potential mechanisms behind these changes in gene expression, highlighting the impact of cold shock-like proteins and the role of buffer composition.

Rapid and Finely-Tuned Expression for Deployable Sensing Applications

Organisms from across the tree of life have evolved highly efficient mechanisms for sensing molecules of interest using biomolecular machinery that can in turn be quite valuable for the development of biosensors. However, purification of such machinery for use in in vitro biosensors is costly, while the use of whole cells as in vivo biosensors often leads to long sensor response times and unacceptable sensitivity to the chemical makeup of the sample. Cell-free expression systems overcome these weaknesses by removing the requirements associated with maintaining living sensor cells, allowing for increased function in toxic environments and rapid sensor readout at a production cost that is often more reasonable than purification. Here, we focus on the challenge of implementing cell-free protein expression systems that meet the stringent criteria required for them to serve as the basis for field-deployable biosensors. Fine-tuning expression to meet these requirements can be achieved through careful selection of the sensing and output elements, as well as through optimization of reaction conditions via tuning of DNA/RNA concentrations, lysate preparation methods, and buffer conditions. Through careful sensor engineering, cell-free systems can continue to be successfully used for the production of tightly regulated, rapidly expressing genetic circuits for biosensors.

Systems biology-based analysis of cell-free systems

Cell-free expression systems are becoming increasingly widely used due to their diverse applications in biotechnology. Despite this rapid expansion in adoption, many aspects of cell-free systems remain surprisingly poorly understood. Systems biology approaches make it possible to characterize cell-free systems deeply and broadly to better understand their underlying complexity. Here, we review recent systems biology studies that have provided insight into cell-free systems. We focus on characterization of the cell-free proteome, including its dependence on preparation protocol and host strain, as well as the cell-free metabolome and the relationship of endogenous metabolism to system performance. We conclude by highlighting promising future research directions.

In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology

Living systems are studied using three complementary approaches: living cells, cell-free systems and computer-mediated modelling. Progresses in understanding, allowing researchers to create novel chassis and industrial processes rest on a cycle that combines in vivo, in vitro and in silico studies. This design-build-test-learn iteration loop cycle between experiments and analyses combines together physiology, genetics, biochemistry and bioinformatics in a way that keeps going forward. Because computer-aided approaches are not directly constrained by the material nature of the entities of interest, we illustrate here how this virtuous cycle allows researchers to explore chemistry which is foreign to that present in extant life, from whole chassis to novel metabolic cycles. Particular emphasis is placed on the importance of evolution.

Point-of-Care Analyte Quantification and Digital Readout via Lysate-Based Cell-Free Biosensors Interfaced with Personal Glucose Monitors

Field-deployable diagnostics based on cell-free systems have advanced greatly, but on-site quantification of target analytes remains a challenge. Here we demonstrate that Escherichia coli lysate-based cell-free biosensors coupled to a personal glucose monitor (PGM) can enable on-site analyte quantification, with the potential for straightforward reconfigurability to diverse types of analytes. We show that analyte-responsive regulators of transcription and translation can modulate the production of the reporter enzyme β-galactosidase, which in turn converts lactose into glucose for PGM quantification. Because glycolysis is active in the lysate and would readily deplete converted glucose, we decoupled enzyme production and glucose conversion to increase the end point signal output. However, this lysate metabolism did allow for one-pot removal of glucose present in complex samples (like human serum) without confounding target quantification. Taken together, our results show that integrating lysate-based cell-free biosensors with PGMs enables accessible target detection and quantification at the point of need.