Modular, programmable RNA sensing using ADAR editing in living cells

Daily News | Online News

Abstract

With the increasing availability of single-cell transcriptomes, RNA signatures offer a promising basis for targeting living cells. Molecular RNA sensors would enable the study of and therapeutic interventions for specific cell types/states in diverse contexts, particularly in human patients and non-model organisms. Here we describe a modular, programmable system for live RNA sensing using adenosine deaminases acting on RNA (RADAR). We validate, and then expand, our basic design, characterize its performance, and analyze its compatibility with human and mouse transcriptomes. We identify strategies to boost output levels and improve the dynamic range. Additionally, we show that RADAR enables compact AND logic. In addition to responding to transcript levels, RADAR can distinguish disease-relevant sequence alterations of transcript identities, such as point mutations and fusions. Finally, we demonstrate that RADAR is a self-contained system with the potential to function in diverse organisms.

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

Access options

Subscribe to Nature+

Get immediate online access to the entire Nature family of 50+ journals

Subscribe to Journal

Get full journal access for 1 year

$99.00

only $8.25 per issue

All prices are NET prices.

VAT will be added later in the checkout.

Tax calculation will be finalised during checkout.

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Additional access options:

• Log in
• Learn about institutional subscriptions

Data availability

Plasmids and plasmid maps have been deposited to Addgene. Next-generation sequencing data have been submitted to the Sequencing Read Archive under BioProject accession PRJNA874842. Raw flow cytometry data is available upon request from the corresponding author.

Code availability

Code for designing sensors and the bioinformatics analysis of mouse and human transcriptomes are available at https://github.com/kristjaneerik/radar-rna-sensing.

References

1. Kulkarni, A., Anderson, A. G., Merullo, D. P. & Konopka, G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58, 129–136 (2019).

   CAS 
   Article 

   Google Scholar 

2. Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R. & Benenson, Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science 333, 1307–1311 (2011).

   CAS 
   Article 

   Google Scholar 

3. Xie, Z., Liu, S. J., Bleris, L. & Benenson, Y. Logic integration of mRNA signals by an RNAi-based molecular computer. Nucleic Acids Res. 38, 2692–2701 (2010).

   CAS 
   Article 

   Google Scholar 

4. Han, S.-P. et al. Programmable siRNA pro-drugs that activate RNAi activity in response to specific cellular RNA biomarkers. Mol. Ther. Nucleic Acids 27, 797–809 (2022).

   CAS 
   Article 

   Google Scholar 

5. Ying, Z.-M., Wang, F., Chu, X., Yu, R.-Q. & Jiang, J.-H. Activatable CRISPR transcriptional circuits generate functional RNA for mRNA sensing and silencing. Angew. Chem. Int. Ed. Engl. 59, 18599–18604 (2020).

   CAS 
   Article 

   Google Scholar 

6. Lin, J., Wang, W.-J., Wang, Y., Liu, Y. & Xu, L. Building endogenous gene connections through RNA self-assembly controlled CRISPR/Cas9 sunction. J. Am. Chem. Soc. 143, 19834–19843 (2021).

   CAS 
   Article 

   Google Scholar 

7. Hochrein, L. M., Li, H. & Pierce, N. A. High-performance allosteric conditional guide RNAs for mammalian cell-selective regulation of CRISPR/Cas. ACS Synth. Biol. 10, 964–971 (2021).

   CAS 
   Article 

   Google Scholar 

8. 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).

   CAS 
   Article 

   Google Scholar 

9. Zhao, E. M. et al. RNA-responsive elements for eukaryotic translational control. Nat. Biotechnol. 40, 539–545 (2021).

   Article 

   Google Scholar 

10. Hur, S. Double-stranded RNA sensors and modulators in innate immunity. Annu. Rev. Immunol. 37, 349–375 (2019).

    CAS 
    Article 

    Google Scholar 

11. Gallo, A., Vukic, D., Michalík, D., O’Connell, M. A. & Keegan, L. P. ADAR RNA editing in human disease; more to it than meets the I. Hum. Genet. 136, 1265–1278 (2017).

    CAS 
    Article 

    Google Scholar 

12. Goodman, R. A., Macbeth, M. R. & Beal, P. A. ADAR proteins: structure and catalytic mechanism. Curr. Top. Microbiol. Immunol. 353, 1–33 (2012).

    CAS 
    PubMed 

    Google Scholar 

13. Gatsiou, A., Vlachogiannis, N., Lunella, F. F., Sachse, M. & Stellos, K. Adenosine-to-inosine RNA editing in health and disease. Antioxid. Redox Signal. 29, 846–863 (2018).

    CAS 
    Article 

    Google Scholar 

14. Katrekar, D. et al. In vivo RNA editing of point mutations via RNA-guided adenosine deaminases. Nat. Methods 16, 239–242 (2019).

    CAS 
    Article 

    Google Scholar 

15. Qu, L. et al. Programmable RNA editing by recruiting endogenous ADAR using engineered RNAs. Nat. Biotechnol. 37, 1059–1069 (2019).

    CAS 
    Article 

    Google Scholar 

16. Merkle, T. et al. Precise RNA editing by recruiting endogenous ADARs with antisense oligonucleotides. Nat. Biotechnol. 37, 133–138 (2019).

    CAS 
    Article 

    Google Scholar 

17. Reautschnig, P. et al. CLUSTER guide RNAs enable precise and efficient RNA editing with endogenous ADAR enzymes in vivo. Nat. Biotechnol. 40, 759–768 (2022).

    CAS 
    Article 

    Google Scholar 

18. Vallecillo-Viejo, I. C., Liscovitch-Brauer, N., Montiel-Gonzalez, M. F., Eisenberg, E. & Rosenthal, J. J. C. Abundant off-target edits from site-directed RNA editing can be reduced by nuclear localization of the editing enzyme. RNA Biol. 15, 104–114 (2018).

    Article 

    Google Scholar 

19. Loughran, G., Howard, M. T., Firth, A. E. & Atkins, J. F. Avoidance of reporter assay distortions from fused dual reporters. RNA 23, 1285–1289 (2017).

    Article 

    Google Scholar 

20. Vogel, P. et al. Efficient and precise editing of endogenous transcripts with SNAP-tagged ADARs. Nat. Methods 15, 535–538 (2018).

    CAS 
    Article 

    Google Scholar 

21. Luo, L., Callaway, E. M. & Svoboda, K. Genetic dissection of neural circuits. Neuron 57, 634–660 (2008).

    CAS 
    Article 

    Google Scholar 

22. Uzonyi, A. et al. Deciphering the principles of the RNA editing code via large-scale systematic probing. Mol. Cell 81, 2374–2387 (2021).

    CAS 
    Article 

    Google Scholar 

23. Biswas, J., Rahman, R., Gupta, V., Rosbash, M. & Singer, R. H. MS2-TRIBE evaluates both protein–RNA Interactions and nuclear organization of transcription by RNA editing. iScience 23, 101318 (2020).

    CAS 
    Article 

    Google Scholar 

24. Katz, N. et al. Overcoming the design, build, test bottleneck for synthesis of nonrepetitive protein-RNA cassettes. Nat. Commun. 12, 1576 (2021).

    CAS 
    Article 

    Google Scholar 

25. Rodriques, S. G. et al. RNA timestamps identify the age of single molecules in RNA sequencing. Nat. Biotechnol. 39, 320–325 (2021).

    CAS 
    Article 

    Google Scholar 

26. Yoshikawa, K. et al. Mutant p53 R248Q but not R248W enhances in vitro invasiveness of human lung cancer NCI-H1299 cells. Biomed. Res. 31, 401–411 (2010).

    CAS 
    Article 

    Google Scholar 

27. Gao, Q. et al. Driver fusions and their implications in the development and treatment of human cancers. Cell Rep. 23, 227–238.e3 (2018).

    CAS 
    Article 

    Google Scholar 

28. Gélinas, J.-F., Clerzius, G., Shaw, E. & Gatignol, A. Enhancement of replication of RNA viruses by ADAR1 via RNA editing and inhibition of RNA-activated protein kinase. J. Virol. 85, 8460–8466 (2011).

    Article 

    Google Scholar 

29. Wong, S. K., Sato, S. & Lazinski, D. W. Substrate recognition by ADAR1 and ADAR2. RNA 7, 846–858 (2001).

    CAS 
    Article 

    Google Scholar 

30. Kuttan, A. & Bass, B. L. Mechanistic insights into editing-site specificity of ADARs. Proc. Natl Acad. Sci. USA 109, E3295–E3304 (2012).

    CAS 
    Article 

    Google Scholar 

31. Kaseniit, E. RADAR RNA sensor candidates for human genes. Figshare https://doi.org/10.6084/m9.figshare.20740006 (2022).

32. Kaseniit, E. RADAR RNA sensor candidates for mouse genes. Figshare https://doi.org/10.6084/m9.figshare.20740009 (2022).

33. Dykstra, P. B., Kaplan, M. & Smolke, C. D. Engineering synthetic RNA devices for cell control. Nat. Rev. Genet. 23, 215–228 (2022).

    CAS 
    Article 

    Google Scholar 

34. Groves, B. et al. Computing in mammalian cells with nucleic acid strand exchange. Nat. Nanotechnol. 11, 287–294 (2016).

    CAS 
    Article 

    Google Scholar 

Download references

Acknowledgements

This work was funded by the National Institutes of Health (4R00EB027723-02; to X.J.G.), Seed Grant from Brain Research Foundation (to X.J.G.), NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation (to X.J.G.), Longevity Impetus Grant (to X.J.G.), Stanford Bio-X Interdisciplinary Graduate Fellowship (to K.E.K.), Fulbright Foundation (to N.K.), National Science Foundation GRFP (to N.S.K.), Stanford ChEM-H CBI training program (to N.S.K.), EDGE Doctoral Fellowship Program (to N.S.K.). N.K. is an Awardee of the Weizmann Institute of Science—Israel National Postdoctoral Award Program for Advancing Women in Science. We thank the Gao lab members for their feedback. We thank L. Luo and Y. Wu for gifts of Cre-related plasmids, J.B. Li and S. Hu for ADAR plasmids and the ADAR1 knockout cell line and advice. We thank C. Liou for technical advice on qPCR and Q. Li for technical advice on NGS.

Author information

Authors and Affiliations

1. Department of Bioengineering, Stanford University, Stanford, CA, USA

   K. Eerik Kaseniit & Natalie S. Kolber

2. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

   Noa Katz, Connor C. Call, Diego L. Wengier, Will B. Cody, Elizabeth S. Sattely & Xiaojing J. Gao

3. ChEM-H Chemistry/Biology Interface Training Program, Stanford University, Stanford, CA, USA

   Natalie S. Kolber & Xiaojing J. Gao

4. Howard Hughes Medical Institute, Stanford, CA, USA

   Elizabeth S. Sattely

Contributions

K.E.K., N.K., N.S.K., and X.J.G. designed the study. K.E.K., N.K., N.S.K., and C.C.C. performed and analyzed most of the experiments, with support from D.L.W., W.B.C., and E.S.S. for the plant experiments. K.E.K. performed bioinformatic analysis. K.E.K. and X.J.G. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to
Xiaojing J. Gao.

Ethics declarations

Competing interests

K.E.K., N.K., N.S.K, and X.J.G are co-inventors on a provisional patent filing related to RADAR sensors. All other authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Tzu-Chieh Tang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Cite this article

Kaseniit, K.E., Katz, N., Kolber, N.S. et al. Modular, programmable RNA sensing using ADAR editing in living cells.
Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01493-x

Download citation

• Received: 01 February 2022

• Accepted: 01 September 2022

• Published: 05 October 2022

• DOI: https://doi.org/10.1038/s41587-022-01493-x

Read More

Share on Google Plus