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

Creators: Kapasiawala, Manisha¹; Murray, Richard M.¹

1. California Institute of Technology

Abstract

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.

Copyright and License

Acknowledgement

The work described here would not be possible without the initial ideas that came out of extensive discussions with William Poole. His suggestions to probe the roles of Mg²⁺ and different fuel sources helped generate new questions and analyses that ultimately led to the experiments discussed here, and we thank him for his insights. Apart from Poole, additional members of the lab─specifically Ankita Roychoudhury, David Alexander Johnson, Miryong (Miki) Yun, and Zoila Jurado─also contributed to the work shown here. Zoila was present in and contributed to many of these early discussions with William, and she also prepared the P_OR1OR2-MG aptamer-deGFP and P_T7-deGFP-MG aptamer plasmids used throughout this study. All four individuals helped prepare the cell lysate preparations used in the experiments here, and we thank them for their help. We thank Paul Freemont for his helpful comments, particularly those pertaining to cell-free metabolism. We also thank lab members John Marken, Yan Zhang, and Zoila Jurado for providing critical feedback on this manuscript. Finally, ChatGPT was used to write part of the Introduction section, which has been an interesting and helpful endeavor. Research was sponsored by the Army Research Office and was accomplished under Cooperative Agreement Number W911NF-22-2-0210. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Contributions

M.K. conceived and designed the study, performed experiments, acquired and analyzed the data, and wrote the manuscript. R.M.M. supervised the project and provided feedback on the manuscript.

Conflict of Interest

The authors declare the following competing financial interest(s): RMM has a financial stake in Tierra Biosciences, a private company that makes use of cell-free technologies such as those described in this article for protein expression and screening. The other authors have nothing to disclose.

Data Availability

The Supporting Information includes plasmid sequences for most plasmids used in this study (except for the P_T7-deGFP-MGapt plasmid), in addition to all data and code used for analysis and figure generation. The same data and code are also available on GitHub at the following link: https://github.com/mkapasiawala/txtl-tradeoff. Except for the P_T7-deGFP-MGapt plasmid, all plasmids used in this study can be obtained from Addgene using the catalog numbers #227645, #227646, #227647, and #227648.

Supplemental Material

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.4c00361.

Dependence of MG aptamer and deGFP fluorescence on pH, Mg²⁺, and 3PGA concentration; effects of Mg²⁺ concentration on the TX–TL trade-off by fuel type; effects of 3PGA concentration on the TX–TL trade-off across different lysate volume fractions; TX–TL trade-off in systems expressing MG aptamer and deGFP in different order under a P_T7 promoter; TX–TL trade-off in systems expressing MG aptamer and deGFP under different promoters; scaling of transcription and translation with increasing DNA concentration; effects of total ATP versus total GTP concentration in NTP-fueled systems; unusual and potentially desirable transcription and translation dynamics in TX–TL systems with no fuel or Mg²⁺ and with additional energy; maximum deGFP versus maximum MG aptamer slope for central carbon- and NTP-fueled systems; heatmaps of integrated MG aptamer expression and deGFP expression at different Mg²⁺ and fuel concentrations at different DNA concentrations for 3PGA, maltose, pyruvate, and succinate; tetracycline titrations; and salt calibrations for cell lysate preparations (PDF)
Analysis, additional figures, raw data, sequences, and tidy data (ZIP)

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes