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Published on 01 February 2022

A synthetic biology study of bicistronic designs supporting gene expression in vivo & in vitro environments

bicistronic designs group img scaled
bicistronic designs group img scaled

This short review article is based on the synthetic biology research paper titled “Synthetic Biology Bicistronic Designs support gene expression equally well in vitro and in vivo”, authored by Owen Koucky, Jacob Wagner, Sofia Aguilera, Benjamin Bashaw, Queena Chen, Anthony Eckdahl, Elise Edman, Paul Gomez, Nick Hanlan, Nick Kempf, Devin Mattoon, Sam McKlin, Christopher Mazariegos, Alex Morehead, Shi Qing Ong, Andy Peterson, Maria Rojas, Kyla Roland, Kaitlyn Schildknecht, Haley Seligmann, Kaden Slater, Ali Tauchen, Raechel Tittor, Tatianna Travieso, Dannie Urban, Caroline Willis, John Zhou, Nicole L. Snyder, Laurie J. Heyer, Jeffrey L. Poet, Todd T. Eckdahl, & A. Malcolm Campbell

The research paper was published in The American Journal of Undergraduate Research in June, 2020. The study explores if BCDs (Bicistronic designs) would be able to operate as effectively in vitro as they do in vivo to produce a predictable level of protein. Pressure-driven flow controlled microfluidics enabled the researchers to effectively perform this study and test their hypothesis.

Abstract

Synthetic biology is responsible for utilizing molecular biology tools with the help of engineering vision to solve problems in medicine, agriculture, bioremediation, and biomanufacturing. A problem that has always remained in synthetic biology has been the ability to design genetic circuits that produce expected levels of protein. Mutalik et al, (2013) built bicistronic designs (BCDs) that promote the predictability of protein production in bacterial cells (in vivo). The generation of proteins outside a cell have seen a growth in attention recently. Due to this growing interest, the researchers wanted to investigate if BCD’s would operate as effectively in cell-free protein synthesis (CFPS) as they do in E. coli cells. 20 BCD’s were checked in CFPS. Very similar behaviour was observed both in vivo & in vitro. As a step towards developing a protein fabrication method in artificial cells, 3 BCDs were tested inside nano-liter-scaled microfluidic droplets.The BCDs worked well in the microfluidic droplets, but their relative protein production levels were not as expected. The key findings from this study, that have been explained in detail later in the article. They suggest that conditions under which gene expressions happen in droplets result in a different relationship between genetic control elements such as BCDs and protein production than what exists in batch CFPS or in cells.

Introduction

Synthetic biology is a novel field of research that combines molecular biology, engineering, chemistry, mathematics and computer science. [1] 

Scientists involved in synthetic biology research invented numerous useful devices that produce drugs [2,3,4], improve agriculture [5], achieve bioremediation [6] and make biofuels or chemical compounds with the help of biomanufacturing [7]. Even though synthetic biology had valuable contributions in various fields of research, one of the main challenges for synthetic biology remained to produce reliable levels of proteins. 

To solve this challenge, bicistronic designs were invented by a group of synthetic biology researchers (Mutalik et al, 2013). These BCDs produced predictable levels of protein in E. coli. [8] 

BCDs (Bicistronic designs) are encoded in the DNA, but function as part of mRNA. A BCD consists of two segments of DNA, called cistrons that encode polypeptides. The first cistron begins with a ribosomal binding site (RBS) followed by a start codon that marks the beginning of a coding segment of a leader polypeptide of 16 amino acids.

Although cells are very good at producing proteins, they often cannot produce considerable amounts of orthogonal proteins and sometimes even fail to produce these proteins at all. [9] For example, E. coli does not make antimicrobial proteins that can kill bacteria. Due to this, synthetic biology scientists often switch from in vivo production of proteins, and prefer in vitro production.

Aim & objectives

The primary objectives from this study can be explained in 4 sections : 

  • To evaluate and compare BCDs performances in vivo vs in-vitro
  • To develop a cell-free protein synthesis procedure (CFPS)
  • To test 20 of the BCDs made by Mutalik et al during CFPS
  • To compare protein production directed by 3 BCDs in CFPS droplets to batch CFPS reactions with the same 3 BCDs.

Materials & methods

The OB1 MK3 Microfluidic Flow control system, from Elveflow was used to generate droplets for CFPS (Cell-free protein synthesis). CFPS reactions for use in microfluidic droplets were arranged for in vitro batch reactions. 

For a detailed understanding of the protocols for CFPS & microfluidics, please refer to the appendix in the original research paper. All methods followed for this study have been compiled there, and have been adapted from multiple existing protocols. [14,15,16,17] 

[droplet_pack_rebound]

Key findings

Producing the E. coli lysate went smoothly but optimizing CFPS (Cell-free Protein Synthesis) was a much more complicated task (Figure 1). The primary roadblock for this study was preparing the amino acid cocktail without precipitating any of the components. The various factors involved in this stage of the study are explained in the graphs of Fig 1(A,B,C,D).

bicistronic designs fig 1
bicistronic designs fig 1

After optimizations of the CFPS, 20 BCDs were tested. The results are shown in Figure 2. The GFP output was ranked from highest (BCD19) to lowest (BCD22). Finally, when the GFP fluorescence levels from Fig 2 were compared to those from the Mutalik paper with Spearman’s Rank Order Correlation, a high degree of correlation (r=0.88) was observed. It can thus be concluded that the BCDs developed for in vivo environment, perform equally well in vitro.

The 3 boxed BCDs from Fig 2, are used to conduct CFPS in Figure 3.

After analyzing the functionality of BCDs in CFPS batch reactions, the potential for its use in nanoliter-scale microfluidic droplets (Fig 3A) is explored.

bicistronic designs fig 2
bicistronic designs fig 2
bicistronic designs fig 3A
bicistronic designs fig 3A

During the study, it was observed that normal CFPS reaction mixture was too viscous to produce reliable droplets, thus the droplet reaction mixture was diluted with water upto 75% of the CFPS batch reaction concentration (Fig 3B). The results from this section of the study suggests that the reaction conditions under which gene expression happens in droplets result in a different relationship between BCDs & protein production compared to what exists in batch CFPS or in cells.

bicistronic designs fig 3B
bicistronic designs fig 3B

Conclusion

This groundbreaking study showcases the power of bicistronic designs in synthetic biology, demonstrating their consistent performance across both in vitro (CFPS) and in vivo systems. Through an innovative, cost-effective approach using locally produced cell lysate (Figure 1), the research team validated that bicistronic constructs maintain functional integrity in:

  • Pooled CFPS reactions
  • Droplet microfluidic environments

Key findings from Figures 2 & 3 reveal:
✓ Identical performance metrics (Spearman’s Rank Order Correlation)
✓ Enhanced GFP detection in droplet formats
✓ Reproducible expression patterns matching in vivo results

These precision results were enabled by Elveflow’s OB1 microfluidic system, providing:
✔ Ultra-accurate pressure control for droplet generation
✔ Bubble-free operation critical for bicistronic design analysis
✔ Seamless integration with synthetic biology workflows

For researchers working with bicistronic designs, this study:

  1. Validates their reliability across platforms
  2. Demonstrates cost-effective implementation strategies
  3. Highlights microfluidics as an essential validation tool

Elevate your bicistronic design research with Elveflow’s cutting-edge microfluidic solutions – delivering the precision and reproducibility required for tomorrow’s synthetic biology breakthroughs.

Access the full study for detailed methodology. 

  1. ampbell, A. M. (2005) Meeting Report: Synthetic Biology Jamboree for Undergraduates, Cell Biology Education 4(1), 19–23. doi:10.1187/cbe.04-11-0047
  2. Denby, C. M., Li, R. A., Vu, V. T., Costello, A., Lin, W., Chan, L. J. G., Williams, J., Donaldson, B., Bamforth, C. W., Petzold, C. J., Scheller, H. V., Martin, H. G., Keasling, J. D. (2018) Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer, Nat Commun 9, 965. https://www.nature.com/articles/s41467-018-03293-x
  3. Ro, D.-K., Paradise, E. M., Ouellet, M., Fisher, K. J., Newman, K. L., Ndungu, J. M., Ho, K. A., Eachus, R. A., Ham, T. S., Kirby, J., Chang, M. C. Y., Withers, S. T., Shiba, Y., Sarpong, R., and Keasling, J. D. (2006) Production of the antimalarial drug precursor artemisinic acid in engineered yeast, Nature 440(7086), 940–943. https://doi:10.1038/nature04640
  4. Bourdeau, R. W., Lee-Gosselin, A., Lakshmanan, A., Farhadi, A., Kumar, S. R., Nety, S. P., and Shapiro, M. G. (2018) Acoustic reporter genes for noninvasive imaging of microorganisms in mammalian hosts, Nature 553(7686), 86–90. https://doi:10.1038/nature25021
  5. Liu, W., and Stewart, C. N. (2015) Plant synthetic biology, Trends in Plant Science 20(5), 309–317. https://doi:10.1016/j.tplants.2015.02.004
  6. Karig, D. K. (2017) Cell-free synthetic biology for environmental sensing and remediation, Current Opinion in Biotechnology 45, 69–75. https://doi:10.1016/j.copbio.2017.01.010
  7. Clomburg, J. M., and Gonzalez, R. (2010) Biofuel production in Escherichia coli: the role of metabolic engineering and synthetic biology, Applied Microbiology and Biotechnology 86(2), 419–434. https://doi:10.1007/s00253-010-2446-1
  8. Mutalik, V. K., Guimaraes, J. C., Cambray, G., Lam, C., Christoffersen, M. J., Mai, Q.-A., Tran, A. B., Paull, M., Keasling, J. D., Arkin, A. P., and Endy, D. (2013) Precise and reliable gene expression via standard transcription and translation initiation elements, Nature Methods 10(4), 354–360. https://doi:10.1038/nmeth.2404
  9. Ruiz, R. C. H., Kiatwuthinon, P., Kahn, J. S., Roh, Y. H., and Luo, D. (2012) Cell-Free Protein Expression from DNA-Based Hydrogel (P-Gel) Droplets for Scale-Up Production, Industrial Biotechnology 8(6), 372–377. https://doi:10.1089/ind.2012.0024
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