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.无细胞转录翻译（TX–TL）系统已被应用于多种研究场景，但其性能与应用范围受限于体系变异性与可预测性不足的问题。为厘清该变异性的驱动因素，我们针对大肠杆菌（E. coli）Rosetta2菌株的TX–TL系统，探究了代谢扰动带来的影响。我们靶向了三类分子：以三磷酸核苷酸（NTPs）形式存在的能量分子、可再生NTP的中心碳“燃料”分子，以及镁离子（Mg²+）。分别以孔雀绿mRNA适配体（MG适配体）与不稳定型增强绿色荧光蛋白（deGFP）作为转录与翻译层面的检测读出指标，我们发现：通过整合mRNA时间进程动力学的曲线下面积进行量化后，优化总蛋白产量与优化总mRNA产量之间存在权衡关系。研究发现，体系在该权衡曲线上的位置，主要由Mg²+浓度、燃料类型与浓度，以及细胞裂解液制备工艺所决定；且通过调控上述组分，可有效降低体系变异性。我们的结果进一步表明，该权衡关系源于翻译调控的局限性与低效的能量再生过程。本研究加深了我们对燃料与能量代谢如何影响无细胞TX–TL系统的理解，同时为提升TX–TL系统的性能、使用寿命、标准化程度与可预测性奠定了理论基础。