Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Predictable control of RNA lifetime using engineered degradation-tuning RNAs

Nature Chemical Biology volume 17, pages 828–836 (2021)Cite this article

Subjects

Abstract

The ability to tune RNA and gene expression dynamics is greatly needed for biotechnological applications. Native RNA stabilizers or engineered 5′ stability hairpins have been used to regulate transcript half-life to control recombinant protein expression. However, these methods have been mostly ad hoc and hence lack predictability and modularity. Here, we report a library of RNA modules called degradation-tuning RNAs (dtRNAs) that can increase or decrease transcript stability in vivo and in vitro. dtRNAs enable modulation of transcript stability over a 40-fold dynamic range in Escherichia coli with minimal influence on translation initiation. We harness dtRNAs in messenger RNAs and noncoding RNAs to tune gene circuit dynamics and enhance CRISPR interference in vivo. Use of stabilizing dtRNAs in cell-free transcription-translation reactions also tunes gene and RNA aptamer production. Finally, we combine dtRNAs with toehold switch sensors to enhance the performance of paper-based norovirus diagnostics, illustrating the potential of dtRNAs for biotechnological applications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Modulation of RNA stability by native ompA stabilizer variants.
Fig. 2: Identifying functional structural features of synthetic dtRNAs.
Fig. 3: Using dtRNAs to modulate gene circuit dynamics and noncoding RNA levels.
Fig. 4: In vitro regulation of gene expression and RNA aptamer production via synthetic dtRNAs.
Fig. 5: Redesigned dtRNA/toehold switch sensors improve the performance of paper-based viral diagnostics.

Similar content being viewed by others

Data availability

The data generated and/or analyzed during the current study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

The codes that support this study are available from the corresponding authors upon reasonable request.

References

  1. Cambray, G., Guimaraes, J. C. & Arkin, A. P. Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat. Biotechnol. 36, 1005–1015 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Delivering the promise of RNA therapeutics. Nat. Med. 25, 1321 (2019).

  3. Kole, R., Krainer, A. R. & Altman, S. RNA therapeutics: beyond RNA interference and antisense oligonucleotides. Nat. Rev. Drug Disco. 11, 125–140 (2012).

    Article  CAS  Google Scholar 

  4. Pardee, K. et al. Rapid, low-cost detection of zika virus using programmable biomolecular components. Cell 165, 1255–1266 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Slomovic, S., Pardee, K. & Collins, J. J. Synthetic biology devices for in vitro and in vivo diagnostics. Proc. Natl Acad. Sci. USA 112, 14429–14435 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chen, Y. Y., Galloway, K. E. & Smolke, C. D. Synthetic biology: advancing biological frontiers by building synthetic systems. Genome Biol. 13, 240 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Khalil, A. S. & Collins, J. J. Synthetic biology: applications come of age. Nat. Rev. Genet. 11, 367–379 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen, Y.-J. et al. Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat. Methods 10, 659–664 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Gardner, P. P., Barquist, L., Bateman, A., Nawrocki, E. P. & Weinberg, Z. RNIE: genome-wide prediction of bacterial intrinsic terminators. Nucleic Acids Res. 39, 5845–5852 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Green, A. A., Silver, P. A., Collins, J. J. & Yin, P. Toehold switches: de-novo-designed regulators of gene expression. Cell 159, 925–939 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chappell, J., Takahashi, M. K. & Lucks, J. B. Creating small transcription activating RNAs. Nat. Chem. Biol. 11, 214–220 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Green, A. A. et al. Complex cellular logic computation using ribocomputing devices. Nature 548, 117–121 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kim, J. et al. De novo-designed translation-repressing riboregulators for multi-input cellular logic. Nat. Chem. Biol. 15, 1173–1182 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hoynes-O’Connor, A., Hinman, K., Kirchner, L. & Moon, T. S. De novo design of heat-repressible RNA thermosensors in E. coli. Nucleic Acids Res. 43, 6166–6179 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lou, C., Stanton, B., Chen, Y.-J., Munsky, B. & Voigt, C. A. Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol. 30, 1137–1142 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Matharu, N. et al. CRISPR-mediated activation of a promoter or enhancer rescues obesity caused by haploinsufficiency. Science 363, eaau0629 (2019).

  18. Siu, K.-H. & Chen, W. Riboregulated toehold-gated gRNA for programmable CRISPR–Cas9 function. Nat. Chem. Biol. 15, 217–220 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Hanewich-Hollatz, M. H., Chen, Z., Hochrein, L. M., Huang, J. & Pierce, N. A. Conditional guide RNAs: programmable conditional regulation of CRISPR/Cas function in bacterial and mammalian cells via dynamic RNA nanotechnology. ACS Cent. Sci. 5, 1241–1249 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wu, F., Zhang, Q. & Wang, X. Design of adjacent transcriptional regions to tune gene expression and facilitate circuit construction. Cell Syst. 6, 206–215.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pfleger, B. F., Pitera, D. J., Smolke, C. D. & Keasling, J. D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol. 24, 1027–1032 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Win, M. N. & Smolke, C. D. Higher-order cellular information processing with synthetic RNA devices. Science 322, 456–460 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Yang, T. T., Cheng, L. & Kain, S. R. Optimized codon usage and chromophore mutations provide enhanced sensitivity with the green fluorescent protein. Nucleic Acids Res. 24, 4592–4593 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hui, M. P., Foley, P. L. & Belasco, J. G. Messenger RNA degradation in bacterial cells. Annu. Rev. Genet. 48, 537–559 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mohanty, B. K. & Kushner, S. R. Enzymes involved in posttranscriptional RNA metabolism in gram-negative bacteria. Microbiol. Spectr. 6, https://doi.org/10.1128/microbiolspec.RWR-0011-2017 (2018).

  26. Mohanty, B. K. & Kushner, S. R. Regulation of mRNA decay in bacteria. Annu. Rev. Microbiol. 70, 25–44 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Case, C. C., Simons, E. L. & Simons, R. W. The IS10 transposase mRNA is destabilized during antisense RNA control. EMBO J. 9, 1259–1266 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Arraiano, C. M. et al. The critical role of RNA processing and degradation in the control of gene expression. FEMS Microbiol. Rev. 34, 883–923 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. Arraiano, C. M., Mauxion, F., Viegas, S. C., Matos, R. G. & Séraphin, B. Intracellular ribonucleases involved in transcript processing and decay: Precision tools for RNA. Biochim. Biophys. Acta - Gene Reg. Mech. 1829, 491–513 (2013).

    Article  CAS  Google Scholar 

  30. Bouvet, P. & Belasco, J. G. Control of RNase E-mediated RNA degradation by 5′-terminal base pairing in E. coli. Nature 360, 488–491 (1992).

    Article  CAS  PubMed  Google Scholar 

  31. Carrier, T. A. & Keasling, J. D. Library of synthetic 5′ secondary structures to manipulate mRNA stability in Escherichia coli. Biotechnol. Prog. 15, 58–64 (1999).

    Article  CAS  PubMed  Google Scholar 

  32. Cetnar, D. P. & Salis, H. M. Systematic quantification of sequence and structural determinants controlling mRNA stability in bacterial operons. ACS Synth. Biol. 10, 318–332 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Viegas, S. C., Apura, P., Martínez-García, E., de Lorenzo, V. & Arraiano, C. M. Modulating heterologous gene expression with portable mRNA-stabilizing 5′-UTR sequences. ACS Synth. Biol. 7, 2177–2188 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Arnold, T. E., Yu, J. & Belasco, J. G. mRNA stabilization by the ompA 5′ untranslated region: two protective elements hinder distinct pathways for mRNA degradation. RNA 4, 319–330 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Emory, S. A., Bouvet, P. & Belasco, J. G. A 5′-terminal stem-loop structure can stabilize mRNA in Escherichia coli. Genes Dev. 6, 135–148 (1992).

    Article  CAS  PubMed  Google Scholar 

  36. Salis, H. M., Mirsky, E. A. & Voigt, C. A. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946–950 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Gusarov, I. & Nudler, E. The mechanism of intrinsic transcription termination. Mol. Cell 3, 495–504 (1999).

    Article  CAS  PubMed  Google Scholar 

  38. d’Aubenton Carafa, Y., Brody, E. & Thermes, C. Prediction of rho-independent Escherichia coli transcription terminators. A statistical analysis of their RNA stem-loop structures. J. Mol. Biol. 216, 835–858 (1990).

    Article  PubMed  Google Scholar 

  39. Varani, G. Exceptionally stable nucleic acid hairpins. Annu. Rev. Biophys. Biomol. Struct. 24, 379–404 (1995).

    Article  CAS  PubMed  Google Scholar 

  40. Caron, M.-P. et al. Dual-acting riboswitch control of translation initiation and mRNA decay. Proc. Natl Acad. Sci. USA 109, E3444–E3453 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wu, F., Menn, D. J. & Wang, X. Quorum-sensing crosstalk-driven synthetic circuits: from unimodality to trimodality. Chem. Biol. 21, 1629–1638 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wu, M. et al. Engineering of regulated stochastic cell fate determination. Proc. Natl Acad. Sci. USA 110, 10610–10615 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Pardee, K. et al. Paper-based synthetic gene networks. Cell 159, 940–954 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Dudley, Q. M., Karim, A. S. & Jewett, M. C. Cell-free metabolic engineering: biomanufacturing beyond the cell. Biotechnol. J. 10, 69–82 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Ma, D., Shen, L., Wu, K., Diehnelt, C. W. & Green, A. A. Low-cost detection of norovirus using paper-based cell-free systems and synbody-based viral enrichment. Synth. Biol. 3, ysy018 (2018).

    Article  CAS  Google Scholar 

  46. Huang, A. et al. BioBitsTM Explorer: a modular synthetic biology education kit. Sci. Adv. 4, eaat5105 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ceroni, F. et al. Burden-driven feedback control of gene expression. Nat. Methods 15, 387–393 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Kwon, Y.-C. & Jewett, M. C. High-throughput preparation methods of crude extract for robust cell-free protein synthesis. Sci. Rep. 5, 8663 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lavickova, B. & Maerkl, S. J. A simple, robust, and low-cost method to produce the pure cell-free system. ACS Synth. Biol. 8, 455–462 (2019).

    Article  CAS  PubMed  Google Scholar 

  50. Silverman, A. D., Kelley-Loughnane, N., Lucks, J. B. & Jewett, M. C. Deconstructing cell-free extract preparation for in vitro activation of transcriptional genetic circuitry. ACS Synth. Biol. 8, 403–414 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wu, F., Su, R.-Q., Lai, Y.-C. & Wang, X. Engineering of a synthetic quadrastable gene network to approach Waddington landscape and cell fate determination. eLife 6, e23702 (2017).

  52. Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Standage-Beier, K., Zhang, Q. & Wang, X. Targeted large-scale deletion of bacterial genomes using CRISPR-nickases. ACS Synth. Biol. 4, 1217–1225 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Ermentrout, B. in Computational Systems Neurobiology (ed. Le Novère, N.) 519–531 (Springer, 2012).

  57. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Article  CAS  PubMed  Google Scholar 

  58. Zhang, R. et al. Topology-dependent interference of synthetic gene circuit function by growth feedback. Nat. Chem. Biol. 16, 695–701 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Shin, J. & Noireaux, V. Study of messenger RNA inactivation and protein degradation in an Escherichia coli cell-free expression system. J. Biol. Eng. 4, 9 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Z. Yan for assistance with RNA preparation and in vitro gene expression experiments, and J. Cutts and N. Brookhouser for technical guidance for RT–qPCR experiments and data analysis. We also thank X. Tian, R. Zhang and Z. Mi for MATLAB assistance and useful discussions. This work was financially supported by a National Science Foundation grant (no. DMS-1100309) and National Institutes of Health (NIH) grant (nos. GM106081, GM131405) to X.W.; an NIH Director’s New Innovator Award (no. 1DP2GM126892), the Gates Foundation (grant no. OPP1160667), an Arizona Biomedical Research Commission New Investigator Award (no. ADHS16-162400), an Alfred P. Sloan Fellowship (no. FG-2017-9108), Gordon and Betty Moore Foundation funds (no. 6984), NIH funds (no. 1R21AI136571) and Arizona State University funds to A.A.G. The views, opinions and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the NIH.

Author information

Authors and Affiliations

  1. School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA

    Qi Zhang, Fuqing Wu, Kylie Standage-Beier, Xingwen Chen & Xiao Wang

  2. Biodesign Center for Molecular Design and Biomimetics, The Biodesign Institute and School of Molecular Sciences, Arizona State University, Tempe, AZ, USA

    Duo Ma

  3. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Kaiyue Wu & Alexander A. Green

  4. Molecular Biology, Cell Biology & Biochemistry Program, Graduate School of Arts and Sciences, Boston University, Boston, MA, USA

    Kaiyue Wu & Alexander A. Green

Authors
  1. Qi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Duo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fuqing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kylie Standage-Beier
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingwen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaiyue Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexander A. Green
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Q.Z. and X.W. conceived the research. Q.Z. and F.W. conducted in vivo gene expression regulation analysis by naturally occurring stabilizer variants and engineered the dtRNA library. X.C. and X.W. conducted mathematical modeling and data fitting analysis. Q.Z. performed dtRNA-regulated dynamic analysis. Q.Z. and K.S.-B. designed and tested the CRISPR interference system regulated by dtRNAs. Q.Z., D.M. and K.W. designed and performed the in vitro aptamer and GFP regulation assay. Q.Z., D.M. and K.W. redesigned the toehold sensors and performed in vitro norovirus diagnostics. Q.Z., D.M., K.S.-B., X.C., X.W. and A.A.G. analyzed the data. A.A.G and X.W. supervised the work. Q.Z., A.A.G. and X.W. wrote the paper.

Corresponding authors

Correspondence to Alexander A. Green or Xiao Wang.

Ethics declarations

Competing interests

A patent application has been filed relating to this work.

Additional information

Peer review information Nature Chemical Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Structure of ompA stabilizer and GFP expression measurement driven by a strong promoter.

a, Schematic showing the structure of naturally occurring ompA stabilizer. (b-c) GFP fluorescence measurement results for circuits driven a strong promoter. b, Design WT, Hp1 and Hp2 exhibits comparable GFP fluorescence. c, Each design with small structure formation nearby RBS region shows low GFP fluorescence levels. Data represent the mean ± s.d. of four biological replicates. P (WT_I) = 0.0083, P (Hp1_I) = 0.0084, and P (Hp2_I) = 0.0497. n.s. (not significant) P > 0.05, * P < 0.05, ** P < 0.01, P value is measured by two-tailed student’s t-test.

Source data

Extended Data Fig. 2 Fluorescence measurements on synthetic dtRNAs with inserted RNase E cleavage sites.

a, Fifteen synthetic dtRNAs are designed without (-) or with single/multiple RNase E cleavage sites (UCUUCC) engineered into different structural regions of the stable dtRNA. The inserted regions are marked yellow (right). Fluorescence measurement result shows that insertion of cleavage sites have minor effects on RNA stability. b, Fluorescence measurement for dtRNAs with multiple RNase E cleavage sites inserted into 18-nt loop region. c, Characterize the effect of dtRNA 5’ spacing length on GFP expression. Seven dtRNAs with 5’ spacing lengths from 1-nt to 18-nt are designed to measurement their effect on GFP expression. d, Fluorescence measurement of dtRNAs with RNase E cleavage sites engineered into 12-nt 5’ spacing region. All data represent the mean ± s.d. of six biological replicates.

Source data

Extended Data Fig. 3 Factors have minor effects on dtRNA function.

a, Relative GFP expression of circuits regulated by dtRNAs with or without the three-nucleotide bulge introduced in stem region. b, Fluorescence measurement result for designs with the same stem feature but varying loop GC content. c, Relative mRFP fluorescence regulated by selected dtRNAs with varying stabilizing abilities. Colors of the bar represent the fold enhancement of each dtRNA on GFP reporter. d, Comparison between relative mRFP fluorescence and relative GFP fluorescence regulated by selected dtRNAs. The result exhibits high correlation (R2 = 0.8681) between the report gene expression suggesting dtRNA performance is transferable to the other genes with different sequence composition. e, Commonality test for circuits with different promoters. Two promoters are selected (Biobrick number: J23105 and J23109, Supplementary Table 1) and engineered into the circuit with identical constructions. f, Commonality test for circuits with different RBSs. Two RBSs (Biobrick number: B0031 and B0032) are engineered into the circuit with identical constructions (Supplementary Table 1). All data represent the mean ± s.d. of six biological replicates.

Source data

Extended Data Fig. 4 qPCR measurement and dtRNAs function prediction.

a, RT–qPCR measurement of relative RNA levels for dtRNAs with diverse stabilizing efficiency. The result displays a strong correlation between relative RNA levels and relative GFP fluorescence (R2 = 0.9406). Data represent the mean ± s.d. of at least three biological replicates. b, Relative fluorescence comparison between predicted relative GFP and observed relative GFP of circuits constructed followed by combined design rules (Supplementary Table 3). N is the total number for 54 single measurement regulated by additional designed dtRNAs (R2 = 0.5005). c, Fluorescence measurement of dtRNA design f (Supplementary Table 3) without (left) or with (right) 18 nt 5’ spacing. Data represent the mean ± s.d. of six biological replicates. d, Scatter plot reveals that structure MFE is not significantly correlated with GFP fluorescence enhancement regulated by synthetic dtRNA library (R2 = 0.000068). Data represent the mean ± s.d. of six biological replicates.

Source data

Extended Data Fig. 5 Hysteresis measurement for dtRNA-regulated positive feedback loop.

a, Schematic showing the construction of positive feedback loop, dtRNA is only inserted at 5’ upstream of the LuxR gene. All genetic components are sharing the same colors as showed in Fig. 3a. b, The hysteresis result of Fig. 3c regulated by dR1 and dR82 induced by 0 to 2 nM 3OC6HSL concentration. This figure serves to zoom in on lower induction doses shown in Fig. 3c to better visualize low dosage dynamics. c, Hysteresis results for synthetic positive feedback circuit regulated by dR6 and dR81. Various concentrations of 3OC6HSL are applied to induce the circuit. The top panel is the enlarged result induced by 0 to 2 nM 3OC6HSL concentration. All data in b-c represent the mean ± s.d. of three biological replicates.

Source data

Extended Data Fig. 6 In vitro regulation of gene expression via synthetic dtRNAs.

a, GFP fluorescence measurement results of designs without RNase inhibitor treatment. b, GFP fluorescence measurement results of designs with RNase inhibitor treatment. All data represent the mean ± s.d. of three biological replicates. GFP fluorescence is measured every 50 seconds.

Extended Data Fig. 7 Relative GFP fluorescence comparison and in vitro dtRNA-regulated aptamer assay.

a, Relative GFP fluorescence comparison among circuits regulated by the same dtRNAs in vitro and in vivo. Data represent the mean ± s.d. of at least three biological replicates. b, Aptamer fluorescence measurement assay. c, Comparison between in vivo relative GFP fluorescence and relative aptamer fluorescence in cell-free expression system. The result shows little correlation between relative GFP and aptamer fluorescence. Interestingly, dtRNAs with short stem-loop hairpins tend to exert stronger positive effect on aptamer fluorescence (green dots). d, Aptamer fluorescence measurements with varying 5’ single-stranded length.

Source data

Extended Data Fig. 8 Two-hour in vitro norovirus diagnostics and the toehold sensor expression leakage.

a, Leaky expression of sensors Ori, dR19_1 dR19_4 and dR19_5 without RNase inhibitor treatment. Leaky expression indicates the false positive result that reporter expresses even without viral input. b, Plate reader measurement shows two-hour viral diagnostic result without RNase inhibitor treatment. ‘+’ represents groups induced by synthetic norovirus RNA and ‘–’ represents the negative control; The dash line indicates the detection threshold (ΔOD575 = 0.4). Data represent the mean ± s.d. of five biological replicates. c, Plate reader measurement shows device dR19_2 and dR19_3 exhibit high expression leakage. Data represents the mean ± s.d. of five biological replicates. d, Expression leakage of sensors Ori, dR19_1 dR19_4 and dR19_5 with RNase inhibitor treatment.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–3, Tables 1–5 and Note.

Reporting Summary

Supplementary Data 1

Sequence information.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Ma, D., Wu, F. et al. Predictable control of RNA lifetime using engineered degradation-tuning RNAs. Nat Chem Biol 17, 828–836 (2021). https://doi.org/10.1038/s41589-021-00816-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-021-00816-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing