Modular Transcriptional Regulation Using Switchable Transcription Terminators and Aptamers: Design and Optimization in Synthetic Biology

Abstract

Transcriptional regulation is a key step in gene expression control. While transcription factor-based regulation has been widely used and offers robust control over gene expression, it can sometimes face challenges such as achieving high specificity, rapid dynamic responses, and fine-tuned regulatory precision, which have motivated the exploration of alternative regulatory strategies. With the development of synthetic biology, novel genetic elements such as Switchable Transcription Terminators (SWT) and aptamers provide more flexible and programmable strategies for transcriptional regulation. However, the independent regulatory capabilities of these two types of elements and their combined regulatory mechanisms still require further investigation. In this study, based on an in vitro transcription system, we systematically explored the transcriptional regulation potential of SWT and aptamers. We innovatively combined these two elements to construct a modular gene expression regulation system. First, we screened and optimized a series of SWTs, obtaining high-performance SWTs with low leakage expression and high ON/OFF ratios. These were further validated for reproducibility of their regulatory performance in E. coli. Next, we constructed multi-level cascading circuits using SWTs, successfully extending the system to six levels and building four types of biological logic gates based on SWT in vitro: AND gate, NOT gate, NAND gate, and NOR gate. Furthermore, based on a previously identified thrombin aptamer capable of transcriptional regulation, we confirmed that ligand binding significantly promoted gene transcription. Finally, we integrate switchable transcription terminators (SWTs) and aptamers to create a modular, ligand-responsive system. We combined aptamers with SWTs to construct heterologous input logic gates, successfully improving the precision and dynamic range of regulation. Compared to the individual regulation of SWT and aptamer, the Aptamer-SWT synergistic regulation enhanced transcription activation by up to 3.3-fold and 7.84-fold, respectively. Additionally, we co-utilized these two genetic elements to construct heterologous input AND and OR gates in vitro. This study expands the strategies for gene expression regulation and provides new elements and theoretical support for efficient, programmable transcriptional regulation in synthetic biology. This system holds potential for biosensing, gene circuit design, and nucleic acid therapy applications.

Keywords

Synthetic biology / Transcriptional regulation / Switchable transcription terminators / Aptamers / Biological logic gates / Cascading reaction

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/550, Table S1: DNA sequences for the transcriptional regulation system ; Figure S1: Plasmid map of pSG81; Figure S2: Plasmid map of pSG22; Figure S3: Secondary structure predictions of 21 SWT variants generated by the algorithm; Figure S4: Fluorescence values of SWT under different states; Figure S5: Orthogonality prediction of candidate SWT; Figure S6: Six-layer cascade characterization; Figure S7: Characterization of the NAND gate using one-pot method; Figure S8: Candidate aptamer Mfold secondary structure prediction and AlphaFold 3 interaction structure prediction; Figure S9: Circular dichroism (CD) characterization of aptamers; Figure S10: The effect of the ligand thrombin on in vitro transcription; Figure S11: Investigation of APT-SWT heterogeneous input AND Gate construction.

Acknowledgments

We would like to thank Jongmin Kim of Pohang University of Science and Technology and Jianmin Yang, Yunquan Zheng, Feng Li, Mingmao Chen, and Li Chen of Fuzhou University for helpful discussion and suggestions.

Author Contributions

J.J. and S.G. conceived and designed the study. J.J., M.S. and S.Z. performed cloning and in vitro assay. J.J. designed multilayered circuits. J.J. wrote the original manuscript. S.G. and X.S. revised and edited the manuscript. All authors have given approval to the final version of the manuscript.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Funding

This work was supported by the Guiding Project of Fujian Provincial Department of Science and Technology (Grant No. 2023Y0006); Special project of Fujian Provincial Department of Finance (202309).

Declaration of Competing Interest

The authors declare that they have no competing interests.

References

[1] Pope SD, Medzhitov R. Emerging Principles of Gene Expression Programs and Their Regulation. Mol. Cell 2018, 71, 389-397.

[2] Mendillo ML, Santagata S, Koeva M, Bell GW, Hu R, Tamimi RM, et al. HSF1 drives a transcriptional program distinct from heat shock to support highly malignant human cancers. Cell 2012, 150, 549-562.

[3] Gao X, Fu Y, Sun S, Gu T, Li Y, Sun T, et al. Cryptococcal Hsf3 controls intramitochondrial ROS homeostasis by regulating the respiratory process. Nat. Commun. 2022, 13, 5407.

[4] Lamrabet O, Plumbridge J, Martin M, Lenski RE, Schneider D, Hindre T. Plasticity of Promoter-Core Sequences Allows Bacteria to Compensate for the Loss of a Key Global Regulatory Gene. Mol. Biol. Evol. 2019, 36, 1121-1133.

[5] Dong C, Fontana J, Patel A, Carothers JM, Zalatan JG. Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria. Nat. Commun. 2018, 9, 2489.

[6] Wang T, Simmel FC. Riboswitch-inspired toehold riboregulators for gene regulation in Escherichia coli. Nucleic Acids Res. 2022, 50, 4784-4798.

[7] Berens C, Groher F, Suess B. RNA aptamers as genetic control devices: The potential of riboswitches as synthetic elements for regulating gene expression. Biotechnol. J. 2015, 10, 246-257.

[8] Hong S, Kim J, Kim J. Multilevel Gene Regulation Using Switchable Transcription Terminator and Toehold Switch in Escherichia coli. Appl. Sci. 2021, 11, 4532.

[9] Zhao M, Kim J, Jiao J, Lim Y, Shi X, Guo S, et al. Construction of multilayered gene circuits using de-novo-designed synthetic transcriptional regulators in cell-free systems. J. Biol. Eng. 2024, 18, 64.

[10] Chappell J, Takahashi MK, Lucks JB.Creating small transcription activating RNAs. Nat. Chem. Biol. 2015, 11, 214-220.

[11] Wachsmuth M, Findeiss S, Weissheimer N, Stadler PF, Morl M. De novo design of a synthetic riboswitch that regulates transcription termination. Nucleic Acids Res. 2012, 41, 2541-2551.

[12] Brophy JA, Voigt CA. Principles of genetic circuit design. Nat. Methods 2014, 11, 508-520.

[13] Ceroni F, Boo A, Furini S, Gorochowski TE, Borkowski O, Ladak YN, et al. Burden-driven feedback control of gene expression. Nat. Methods 2018, 15, 387-393.

[14] Salvail H, Breaker RR. Riboswitches. Curr. Biol. 2023, 33, R343-R348.

[15] Wachsmuth M, Domin G, Lorenz R, Serfling R, Findeiss S, Stadler PF, et al. Design criteria for synthetic riboswitches acting on transcription. RNA Biol. 2015, 12, 221-231.

[16] Green AA, Silver PA, Collins JJ, Yin P. Toehold switches: De-novo-designed regulators of gene expression. Cell 2014, 159, 925-939.

[17] Neupert J, Karcher D, Bock R. Design of simple synthetic RNA thermometers for temperature-controlled gene expression in Escherichia coli. Nucleic Acids Res. 2008, 36, e124.

[18] Stevens JT, Carothers JM. Designing RNA-based genetic control systems for efficient production from engineered metabolic pathways. ACS Synth. Biol. 2015, 4, 107-115.

[19] Davidson EA, Ellington AD.Synthetic RNA circuits. Nat. Chem. Biol. 2007, 3, 23-28.

[20] Mutalik VK, Qi L, Guimaraes JC, Lucks JB, Arkin AP.Rationally designed families of orthogonal RNA regulators of translation. Nat. Chem. Biol. 2012, 8, 447-454.

[21] Groher F, Bofill-Bosch C, Schneider C, Braun J, Jager S, Geissler K, et al. Riboswitching with ciprofloxacin-development and characterization of a novel RNA regulator. Nucleic Acids Res. 2018, 46, 2121-2132.

[22] Felletti M, Stifel J, Wurmthaler LA, Geiger S, Hartig JS. Twister ribozymes as highly versatile expression platforms for artificial riboswitches. Nat. Commun. 2016, 7, 12834.

[23] Wurmthaler LA, Sack M, Gense K, Hartig JS, Gamerdinger M. A tetracycline-dependent ribozyme switch allows conditional induction of gene expression in Caenorhabditis elegans. Nat. Commun. 2019, 10, 491.

[24] Larson MH, Greenleaf WJ, Landick R, Block SM. Applied Force Reveals Mechanistic and Energetic Details of Transcription Termination. Cell 2008, 132, 971-982.

[25] Shin J, Noireaux V. Efficient cell-free expression with the endogenous E. coli RNA polymerase and sigma factor 70. J. Biol. Eng. 2010, 4, 8.

[26] Alam KK, Tawiah KD, Lichte MF, Porciani D, Burke DH. A Fluorescent Split Aptamer for Visualizing RNA-RNA Assembly In Vivo. ACS Synth. Biol. 2017, 6, 1710-1721.

[27] Reif R, Haque F, Guo P. Fluorogenic RNA nanoparticles for monitoring RNA folding and degradation in real time in living cells. Nucleic Acid. Ther. 2012, 22, 428-437.

[28] Haque F, Shu D, Shu Y, Shlyakhtenko LS, Rychahou PG, Evers BM, et al. Ultrastable synergistic tetravalent RNA nanoparticles for targeting to cancers. Nano Today 2012, 7, 245-257.

[29] Shu D, Khisamutdinov EF, Zhang L, Guo P. Programmable folding of fusion RNA in vivo and in vitro driven by pRNA 3WJ motif of phi 29 DNA packaging motor. Nucleic Acids Res. 2014, 42, e10.

[30] Filonov GS, Moon JD, Svensen N, Jaffrey SR. Broccoli: Rapid Selection of an RNA Mimic of Green Fluorescent Protein by Fluorescence-Based Selection and Directed Evolution. J. Am. Chem. Soc. 2014, 136, 16299-16308.

[31] Bhadra S, Ellington AD. Design and application of cotranscriptional non-enzymatic RNA circuits and signal trans ducers. Nucleic Acids Res. 2014, 42, e58.

[32] Akter F, Yokobayashi Y. RNA signal amplifier circuit with integrated fluorescence output. ACS Synth. Biol. 2015, 4, 655-658.

[33] Hofer K, Langejurgen LV, Jaschke A. Universal aptamer-based real-time monitoring of enzymatic RNA synthesis. J. Am. Chem. Soc. 2013, 135, 13692-13694.

[34] Pothoulakis G, Ceroni F, Reeve B, Ellis T. The spinach RNA aptamer as a characterization tool for synthetic biology. ACS Synth. Biol. 2014, 3, 182-187.

[35] Kocalar S, Miller BM, Huang A, Gleason E, Martin K, Foley K, et al. Validation of Cell-Free Protein Synthesis Aboard the International Space Station. ACS Synth. Biol. 2024, 13, 942-950.

[36] Huizenga DE, Szostak JW. A DNA aptamer that binds adenosine and ATP. Biochemistry 1995, 34, 656-665.

[37] Hermann T, Patel DJ. Adaptive recognition by nucleic acid aptamers. Science 2000, 287, 820-825.

[38] Daniels DA, Chen H, Hicke BJ, Swiderek KM, Gold L. A tenascin-C aptamer identified by tumor cell SELEX: Systematic evolution of ligands by exponential enrichment. Proc. Natl. Acad. Sci. USA 2003, 100, 15416-15421.

[39] Parekh P, Tang Z, Turner PC, Moyer RW, Tan W. Aptamers recognizing glycosylated hemagglutinin expressed on the surface of vaccinia virus-infected cells. Anal. Chem. 2010, 82, 8642-8649.

[40] Keefe AD, Pai S, Ellington A.Aptamers as therapeutics. Nat. Rev. Drug Discov. 2010, 9, 537-550.

[41] Guo KT, Schafer R, Paul A, Gerber A, Ziemer G, Wendel HP. A new technique for the isolation and surface immobilization of mesenchymal stem cells from whole bone marrow using high-specific DNA aptamers. Stem Cells 2006, 24, 2220-2231.

[42] Shangguan D, Li Y, Tang Z, Cao ZC, Chen HW, Mallikaratchy P, et al. Aptamers evolved from live cells as effective molecular probes for cancer study. Proc. Natl. Acad. Sci. USA 2006, 103, 11838-11843.

[43] Ellington AD, Szostak JW. In vitro selection of RNA molecules that bind specific ligands. Nature 1990, 346, 818-822.

[44] Tuerk C, Gold L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T 4 DNA polymerase. Science 1990, 249, 505-510.

[45] Guo S, Xu Z, Lin L, Guo Y, Li J, Lu C, et al. Using CIVT-SELEX to Select Aptamers as Genetic Parts to Regulate Gene Circuits in a Cell-Free System. Int. J. Mol. Sci. 2023, 24, 2833.

[46] Lee H, Xie T, Kang B, Yu X, Schaffter SW, Schulman R. Plug-and-play protein biosensors using aptamer-regulated in vitro transcription. Nat. Commun. 2024, 15, 7973.

[47] Fornace ME, Huang J, Newman CT, Porubsky NJ, Pierce MB, Pierce NA. NUPACK: Analysis and Design of Nucleic Acid Structures, Devices, and Systems. 2022. accessed on 1 September 2023).

[48] Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold3. Nature 2024, 630, 493-500.

[49] Yeung E, Dy AJ, Martin KB, Ng AH, Del Vecchio D, Beck JL, et al. Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks. Cell Syst. 2017, 5, 11-24 e12.

[50] Avino A, Fabrega C, Tintore M, Eritja R. Thrombin binding aptamer, more than a simple aptamer: chemically modified derivatives and biomedical applications. Curr. Pharm. Des. 2012, 18, 2036-2047.

[51] Iyer S, Doktycz MJ. Thrombin-Mediated Transcriptional Regulation Using DNA Aptamers in DNA-Based Cell-Free Protein Synthesis. ACS Synth. Biol. 2013, 3, 340-346.

[52] Wang J, Yang L, Cui X, Zhang Z, Dong L, Guan N. A DNA Bubble-Mediated Gene Regulation System Based on Thrombin-Bound DNA Aptamers. ACS Synth. Biol. 2017, 6, 758-765.

[53] Li Z, Zhang Y, Peng B, Qin S, Zhang Q, Chen Y, et al. A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity. Nucleic Acids Res. 2024, 52, 13447-13468.

[54] Van Der Linden AJ, Pieters PA, Bartelds MW, Nathalia BL, Yin P, Huck WTS, et al. DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron. ACS Synth. Biol. 2022, 11, 1510-1520.

[55] Hong F, Ma D, Wu K, Mina LA, Luiten RC, Liu Y, et al. Precise and Programmable Detection of Mutations Using Ultraspecific Riboregulators. Cell 2020, 180, 1018-1032 e16.

[56] Canoura J, Liu Y, Perry J, Willis C, Xiao Y. Suite of Aptamer-Based Sensors for the Detection of Fentanyl and Its Analogues. ACS Sens. 2023, 8, 1901-1911.

[57] Xie Y, She JP, Zheng JX, Salminen K, Sun JJ. Rapid nanomolar detection of Delta (9)-tetrahydrocannabinol in biofluids via electrochemical aptamer-based biosensor. Anal. Chim. Acta 2024, 1295, 342304.

[58] Angenent-Mari NM, Garruss AS, Soenksen LR, Church G, Collins JJ. A deep learning approach to programmable RNA switches. Nat. Commun. 2020, 11, 5057.