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Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR

Nature Biotechnology volume 41, pages 698–707 (2023)Cite this article

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Abstract

Programmable approaches to sense and respond to the presence of specific RNAs in biological systems have broad applications in research, diagnostics, and therapeutics. Here we engineer a programmable RNA-sensing technology, reprogrammable ADAR sensors (RADARS), which harnesses RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a cargo protein by the presence of endogenous RNA transcripts. Introduction of a stop codon in a guide upstream of the cargo makes translation contingent on binding of an endogenous transcript to the guide, leading to ADAR editing of the stop codon and allowing translational readthrough. Through systematic sensor engineering, we achieve 277 fold improvement in sensor activation and engineer RADARS with diverse cargo proteins, including luciferases, fluorescent proteins, recombinases, and caspases, enabling detection sensitivity on endogenous transcripts expressed at levels as low as 13 transcripts per million. We show that RADARS are functional as either expressed DNA or synthetic mRNA and with either exogenous or endogenous ADAR. We apply RADARS in multiple contexts, including tracking transcriptional states, RNA-sensing-induced cell death, cell-type identification, and control of synthetic mRNA translation.

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Fig. 1: RADARS with luciferase and fluorescence outputs can be used to sense reporter transcripts.
Fig. 2: Detection of endogenous gene expression with RADARS.
Fig. 3: RADARS do not disrupt cellular processes and has minimal off targets.
Fig. 4: Application of RADARS to multi-input sensing, inducible cell death, and targeted Cre expression.

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Data availability

All data supporting the findings of this study are available in the paper (and in its supplementary information files). Original data supporting the findings are available from the corresponding author upon reasonable request. Sequencing data will be available at Sequence Read Archive under accession PRJNA883765. Expression plasmids are available from Addgene under Uniform Biological Material Transfer Agreement. Source data are provided with this paper.

Code availability

Computational tools are available at http://www.radars.bio. Python code used for analysis can be downloaded at https://github.com/abugoot-lab/RADARS.

References

  1. Rurik, J. G. et al. CAR T cells produced in vivo to treat cardiac injury. Science 375, 91–96 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ronald, J. A., Chuang, H.-Y., Dragulescu-Andrasi, A., Hori, S. S. & Gambhir, S. S. Detecting cancers through tumor-activatable minicircles that lead to a detectable blood biomarker. Proc. Natl Acad. Sci. USA. 112, 3068–3073 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550, 451–453 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. ENCODE Project Consortium. The ENCODE (ENCyclopedia Of DNA Elements) project. Science 306, 636–640 (2004).

    Article  Google Scholar 

  5. Mich, J. K. et al. Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex. Cell Rep. 34, 108754 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Blankvoort, S., Witter, M. P., Noonan, J., Cotney, J. & Kentros, C. Marked diversity of unique cortical enhancers enables neuron-specific tools by enhancer-driven gene expression. Curr. Biol. 28, 2103–2114.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nord, A. S. & West, A. E. Neurobiological functions of transcriptional enhancers. Nat. Neurosci. 23, 5–14 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. 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 

  10. Gambill, L., Staubus, A., Ameruoso, A. & Chappell, J. A split ribozyme that links detection of a native RNA to orthogonal protein outputs. Preprint at bioRxiv https://doi.org/10.1101/2022.01.12.476080 (2022).

  11. Wang, S., Emery, N. J. & Liu, A. P. A novel synthetic toehold switch for microRNA detection in mammalian cells. ACS Synth. Biol. 8, 1079–1088 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Li, Y., Teng, X., Zhang, K., Deng, R. & Li, J. RNA strand displacement responsive CRISPR/Cas9 system for mRNA sensing. Anal. Chem. 91, 3989–3996 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. 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 

  14. 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).

    Article  CAS  PubMed  Google Scholar 

  15. Zhao, E. M. et al. RNA-responsive elements for eukaryotic translational control. Nat. Biotechnol. 40, 539–545 (2022).

    Article  CAS  PubMed  Google Scholar 

  16. Ling, J. P. et al. Cell-specific regulation of gene expression using splicing-dependent frameshifting. Preprint at bioRxiv https://doi.org/10.1101/2022.03.02.481623 (2022).

  17. Stafforst, T. & Schneider, M. F. An RNA-deaminase conjugate selectively repairs point mutations. Angew. Chem. Int. Ed. 51, 11166–11169 (2012).

    Article  CAS  Google Scholar 

  18. Montiel-Gonzalez, M. F., Vallecillo-Viejo, I., Yudowski, G. A. & Rosenthal, J. J. C. Correction of mutations within the cystic fibrosis transmembrane conductance regulator by site-directed RNA editing. Proc. Natl. Acad. Sci. U. S. A. 110, 18285–18290 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cox, D. B. T. et al. RNA editing with CRISPR–Cas13. Science 358, 1019–1027 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  22. 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).

    Article  CAS  PubMed  Google Scholar 

  23. Abudayyeh, O. O. et al. A cytosine deaminase for programmable single-base RNA editing. Science 365, 382–386 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bass, B. L. & Weintraub, H. An unwinding activity that covalently modifies its double-stranded RNA substrate. Cell 55, 1089–1098 (1988).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  27. Chao, J. A., Patskovsky, Y., Almo, S. C. & Singer, R. H. Structural basis for the coevolution of a viral RNA–protein complex. Nat. Struct. Mol. Biol. 15, 103–105 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Kozak, M. Constraints on reinitiation of translation in mammals. Nucleic Acids Res. 29, 5226–5232 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ferreira, J. P., Overton, K. W. & Wang, C. L. Tuning gene expression with synthetic upstream open reading frames. Proc. Natl Acad. Sci. USA 110, 11284–11289 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dinman, J. D., Icho, T. & Wickner, R. B. A -1 ribosomal frameshift in a double-stranded RNA virus of yeast forms a gag-pol fusion protein. Proc. Natl Acad. Sci. USA 88, 174–178 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Karlsson, M. et al. A single-cell type transcriptomics map of human tissues. Sci. Adv. 7, eabh2169 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Eggington, J. M., Greene, T. & Bass, B. L. Predicting sites of ADAR editing in double-stranded RNA. Nat. Commun. 2, 319 (2011).

    Article  PubMed  Google Scholar 

  35. 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).

    Article  CAS  PubMed  Google Scholar 

  36. Zhang, J. D. et al. Detect tissue heterogeneity in gene expression data with BioQC. BMC Genomics 18, 277 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Straathof, K. C. et al. An inducible caspase 9 safety switch for T-cell therapy. Blood 105, 4247–4254 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fukuda, M. et al. Construction of a guide–RNA for site-directed RNA mutagenesis utilising intracellular A-to-I RNA editing. Sci. Rep. 7, 41478 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Montiel-González, M. F., Vallecillo-Viejo, I. C. & Rosenthal, J. J. C. An efficient system for selectively altering genetic information within mRNAs. Nucleic Acids Res. 44, e157 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. Wettengel, J., Reautschnig, P., Geisler, S., Kahle, P. J. & Stafforst, T. Harnessing human ADAR2 for RNA repair—recoding a PINK1 mutation rescues mitophagy. Nucleic Acids Res. 45, 2797–2808 (2017).

    CAS  PubMed  Google Scholar 

  41. Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank P. Reginato, D. Weston, and E. Boyden for MiSeq instrumentation support, N. Pirete for flow cytometry support; M. Yan for computational data analysis and Z. Tang, R. Desimone, and J. Crittenden for support and helpful discussions. We thank the members of the Chen and Abudayyeh–Gootenberg labs for helpful discussions. L.V. is supported by a Swiss National Science Foundation Postdoctoral Mobility Fellowship. J.S.G. and O.O.A. are supported by National Institutes of Health (NIH) grants 1R21-AI149694, R01-EB031957, and R56-HG011857; The McGovern Institute Neurotechnology (MINT) program; the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience; G. Harold and Leila Y. Mathers Charitable Foundation; MIT John W. Jarve (1978) Seed Fund for Science Innovation; Impetus Grants; Cystic Fibrosis Foundation Pioneer Grant; Google Ventures; FastGrants; and the McGovern Institute. F.C. acknowledges support from NIH Early Independence Award (DP5, 1DP5OD024583), the National Human Genome Research Institute (R01, R01HG010647), the Burroughs Wellcome Fund CASI award, the Searle Scholars Foundation, the Harvard Stem Cell Institute, and the Merkin Institute.

Author information

Author notes

  1. These authors contributed equally: Kaiyi Jiang, Jeremy Koob, Xi Dawn Chen, Rohan N. Krajeski, Yifan Zhang.

  2. These authors jointly supervised this work: Jonathan S. Gootenberg, Fei Chen, Omar O. Abudayyeh.

Authors and Affiliations

  1. McGovern Institute for Brain Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kaiyi Jiang, Rohan N. Krajeski, Wenyuan Zhou, Samantha R. Sgrizzi, Lukas Villiger, Jonathan S. Gootenberg & Omar O. Abudayyeh

  2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kaiyi Jiang

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jeremy Koob, Xi Dawn Chen, Yifan Zhang, Verena Volf & Fei Chen

  4. Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Jeremy Koob, Xi Dawn Chen & Fei Chen

  5. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Jeremy Koob

  6. Systems, Synthetic, and Quantitative Biology Program, Harvard Medical School, Boston, MA, USA

    Xi Dawn Chen

  7. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Verena Volf

  8. Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jonathan S. Gootenberg, Fei Chen & Omar O. Abudayyeh

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Contributions

O.O.A., J.S.G, and F.C. conceived the study and participated in the design, execution, and analysis of experiments. K.J., J.K., X.D.C., R.N.K., and Y.Z. designed and performed the experiments in this study and analyzed the data. S.R.S. helped with mRNA synthesis and next-generation sequencing. W.Z. and L.V. helped with sensor engineering strategies. V.V. and F.C. analyzed transcriptome wide sequencing results. K.J., J.K., X.D.C., R.K, Y.Z., O.O.A., J.S.G, and F.C. wrote the manuscript with help from all authors.

Corresponding authors

Correspondence to Jonathan S. Gootenberg, Fei Chen or Omar O. Abudayyeh.

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Competing interests

A patent application has been filed related to this work. J.S.G. and O.O.A. are co-founders of Sherlock Biosciences, Proof Diagnostics, Moment Biosciences, and Tome Biosciences. J.S.G. and O.O.A. were advisors for Beam Therapeutics during the course of this project. F.C. is a co-founder of Curio Biosciences. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Characterization of luciferase RADARS sensors.

a) Comparison of Gluc/Cluc ratio between + target and – target conditions for IL6 #CCA8 targeting ogRNA with different length and different MS2 hairpin loops while keeping 0aa 5′ peptide length. Error bars indicate standard error of the mean. (n=3 technical replicates) b) Comparison of Gluc/Cluc ratio between + target and – target conditions for five avidity binding site (4 MS2 loops) and nine avidity binding site (8 MS2 loops) ogRNA with varying length of 5′ peptide. Error bars indicate standard error of the mean. (n=3 technical replicates) c) Comparison of Gluc/Cluc ratio between no out of frame stop codons and addition of two out of frame stop codons for a five avidity binding site ogRNA with 236 aa 5′ peptide residues. Last column represents a constitutive gluc driven under an Ef1-alpha promoter. Error bars indicate standard error of the mean. (n=3 technical replicates) d) Corresponding editing percentage of the target adenosine in the UAG stop codon for a non-targeting ogRNA and fourteen IL6-targeting ogRNAs tiling CCA sites on IL6 with exogenous ADAR1p150 supplementation using the RADARSv2 design in the presence and absence of the target IL6 transcript. Error bars indicate standard error of the mean (n=3 technical replicates). e) Correlation of IL6 RADARSv2 tiling ogRNAS luciferase fold change upon target induction and the fold change of RNA editing of the stop codon upon target induction. Pearson correlation is performed for the 14 data points and significance is determined via two tailed unpaired t-test (N.S., p>0.05). Error bars indicate standard error of the mean (n=3 technical replicates). f) Correlation of IL6 RADARSv2 tiling ogRNAS luciferase intensity (protein production) upon target induction and the RNA editing rate of the stop codon upon target induction. Pearson correlation is performed for the 14 data points and significance is determined via unpaired two tailed t-test (N.S., p>0.05). Error bars indicate standard error of the mean (n=3 technical replicates). g) Comparison of RNA editing rate of the IL6 #CCA8 ogRNA when supplemented with exogenous ADAR1p150 or MCP-ADAR2dd(E488Q, T490A). Error bars indicate standard error of the mean (n=3 technical replicates). h) Comparison of luciferase fold change between +target and –target conditions for the IL6 #CCA8 targeting ogRNA with either exogenous ADAR1p150 or MCP-ADAR2dd (E488Q, T490A).

Extended Data Fig. 2 Characterization of fluorescent RADARS and quantitation of RADARS for gene expression.

a) Gating strategy used for flow cytometry analysis of fluorescent RADARS in HEK293 cells. Gates are drawn using a control population transfected with pUC19 plasmid. b) Gates overlaid onto a population of HEK293 cells transfected with EGFP-targeting RADARS, ADARp150, and pUC19 plasmid. c) Gates overlaid onto a population of HEK293 cells transfected with EGFP-targeting RADARS, ADARp150, and EGFP-target (frame-shifted) plasmid. d) Raw Cq value of IL6 transgene normalized by subtraction to GAPDH gene Cq number and the RADARS corresponding fold activation. Error bars indicate standard error of the mean (n=3 biological replicates). e) Effect of titrating the best IL6 ogRNA (Fig. 1c) RADARS sensor amount on resulting activation and overall protein production in the plus target condition (gluc/cluc ratio). For conditions below 40 ng, the remaining plasmid amount was substituted with pUC19 plasmid. Error bars indicate standard error of the mean (n=3 technical replicates).

Extended Data Fig. 3 Validation of endogenous gene knockdown.

a) Validation of siRNA knockdown of 10 endogenous transcripts as measured by qPCR expression. Fold change is calculated by the gene expression of the target transcript in the on-target siRNA group over the non-target siRNA group. Data are mean of technical replicates (n= 3 biological replicates) ± s.e.m. b) Performance of 8 randomly selected ogRNAs targeting 10 endogenous transcripts and 8 randomly selected non-targeting ogRNAs. Fold activation of RADARS is calculated by the Gluc/Cluc ratio in the on-target siRNA group over the non-target siRNA group. The best performing targeting sensors for each gene are labeled as yellow and depicted in Fig. 2c. Horizontal line represents the mean of each group. c) Editing rate of the UAG stop codon in the best performing sensor in each gene group from b. One-tailed unpaired t-test’s carried between the ontarget siRNA group and the non-target siRNA group. (*, p<0.05. **, p<0.01. ***, p<0.001. ****, p<0.0001) Error bars indicate standard error of the mean (n=3 technical replicates). d) Editing rate of the UAG stop codon for the two ogRNA targeting HSP70 sensors in Fig. 2e. One-tailed unpaired t-test is carried out between the 37 degrees Celsius group and the heat shock (42 degrees Celsius) group (*, p<0.05. **, p<0.01). Error bars indicate standard error of the mean (n=3 technical replicates).

Extended Data Fig. 4 Characterization of RADARS-induced protein knockdown or innate immune response activation.

(a) Effect of sensor-target hybridization on protein production is analyzed by Western blot. PPIB protein levels are shown in response to RADARS hybridization and GAPDH is used as a normalizing protein control. b) qPCR detected gene expression fold change of four dsRNA responsive genes (IFNb, OAS1, MDA5 and RIG-1) in response to RADARS in HEK293FT cells using ACTB as the normalizing gene. Data are mean of technical replicates (n= 3) ± s.e.m. c) qPCR detected gene expression fold change of four dsRNA responsive genes (IFNb, OAS1, MDA5 and RIG-1) in response to RADARS or poly (I:C) in HepG2 cells with GAPDH as the normalizing gene. Data are mean of technical replicates (n= 3) ± s.e.m. d) 48 hours post transfection of either an ACTB targeting ogRNA RADARS or a NT ogRNA RADARS, cells are sorted for mCherry positive population and subjected to targeted next generation sequencing on the 200bp hybridization region of RADARS to the ACTB transcript. A to I (G) conversion is depicted in a heatmap. e) Quantification of RNA editing in the 300bp hybridization region of RADARS to the IL6 exogenous transcript for a IL6-targeting #CCA8 ogRNA and a NT ogRNA. A to I (G) conversion is depicted in a heatmap. f) Analysis of transcriptome-wide off target editing looking only at the repeat regions of the genome that were soft-masked out in Fig. 3. Scatter plots show the allele fraction of A->G mutations in (i) PPIB sensor and ADAR1p150 overexpression versus non-transfected HEK293T cells (n = 2.64x105 sites); (ii) same as (i) but for IL6 sensor (n = 2.35x105 sites); (iii) same as (i) but for no ADAR overexpression and a non-targeting RADAR sensor (n = 2.57 x105 sites). Sites are colored by fdr-corrected p-value (Fisher’s exact test, colorbar left). Experiments were generated from 3 independent replicates.

Source data

Extended Data Fig. 5 Whole Transcriptome off-target analysis.

a) Analysis of sequence homology between transcriptome off targets (n=23 sites) and PPIB homology region targeted. Corresponding fdr-corrected p-values for the significance of local alignment (Monte Carlo permutation test) between 200 bp surrounding each off-target site and the PPIB homology region targeted by the ogRNA (red line denotes p=0.05). b) Sequence logo analysis of the off-target edit sites from ADAR1p150 overexpression along with PPIB targeting RADARS. c) Sashimi plot of reads aligned to PPIB from polyA RNA-seq for PPIB targeting ogRNA+ADAR1p150 (red), and untransfected HEK293FT control (blue) showing no differences in exon usage/intron retention. Differential alternative splicing analysis was performed using rMATS41, yielding no significant splicing differences between samples. d) Analysis of transcriptome-wide off target editing with repeat regions soft-masked out, in HEK293FT cells transfected with MCP-ADAR2(E488Q). Scatter plots show the allele fraction of A->G mutations in MCP-ADAR2 (E488Q) overexpression versus non-transfected HEK293FT cells (n = 2.5x106 sites). Sites are colored by fdr-corrected p-value (colorbar left). e) Analysis of transcriptome-wide off target editing in the repeat regions masked out in (d), n = 1.17x105 sites, Fisher’s exact test.

Extended Data Fig. 6 Computational analysis of marker genes for cells and tissues.

a) Relative transcript abundance of 61 marker mRNAs that uniquely define a single cell type across 61 cell types. b) Summary of the number of genes needed to classify one tissue from the rest.

Extended Data Fig. 7 Characterization of mRNA RADARS and characterization of RADARS OR gate.

a) Editing rate of the UAG stop codon in the mRNA RADARS in the + target and the - target group. One-tailed unpaired t-test is carried between the two groups. (***, p<0.001.) Error bars indicate standard error of the mean (n=3 technical replicates). b) Schematic of two input OR gate logic with RADARS. c) Sensor activation of OR gate RADARS for all possible EGFP and IL6 transcript input combinations. Data are mean of technical replicates (n= 3) ± s.e.m.

Extended Data Fig. 8 Cell Type specific protein expression.

a) Left: Schematics of dual loxP EGFP Cre reporter and IL6 RADARS-CRE. Right: HEK293FT fluorescence at 48 hours post-transfection of dual loxP EGFP reporter, ADAR1p150, and IL6-targeting RADARS with Cre cargo with or without IL6 target. Images are shown for the – target and + target conditions. White scale bar denotes 100 microns. Imaging experiments are repeated 3 times with independent biological replicates and a representative image is shown. b) Cells from part (a) are harvested for flow analysis of EGFP expression. c) EGFP expression quantified by flow cytometry at 48 hours post-transfection of CRE reporter, ADAR1p150, and IL6-targeting RADARS with Cre cargo with or without IL6 target. Distribution of EGFP signals is analyzed by flow cytometry for Hela, A549 and HepG2 cells. For all three cell types, GFP positive cells are defined as a population of cells with FITC channel EGFP intensity above 107. d) Cell viability of HepG2, Hela and A549 cells are determined 48 hours after transfection of a SERPINA1 or non-targeting RADARS expressing iCaspase9 construct and ADAR1p150 using MTS assay and normalized to a control group transfected with only a GFP expressing plasmid. Data are mean of technical replicates (n= 3) ± s.e.m.

Extended Data Fig. 9 Characterization of RADARSv2 with endogenous ADAR.

a) Comparison of fold activation of #CCA8 IL6 ogRNA with 171 nt guide and 4 MS2 loops (Fig. 1c) when used in conjunction with exogenous supplemented ADAR1p150 or with endogenous ADAR. Data are mean of technical replicates (n= 3) ± s.e.m. b) Editing rate of the UAG stop codon of the #CCA8 IL6 ogRNA when supplemented with ADAR1p150 or with endogenous ADAR in the presence and absence of exogenous IL6 target. Data are mean of technical replicates (n= 3) ± s.e.m. c) qPCR and RADARSv2 detected HSP70 expression differences between the 37 degrees Celsius (control) and 42 degrees Celsius (heat shock) groups. Top HSP70 targeting RADARS construct on the HSP70 transcript and a scrambled non-targeting (NT) RADARS construct, without exogenous ADAR were transfected into HeLa cells followed by 24 hours at 42 °C or 37 °C. Sensor activation is calculated between the 42 °C and 37 °C groups and is normalized to the NT condition, which is a sensor with a scrambled non-targeting ogRNA (NT) to control for changes in protein production. Data are mean of technical replicates (n= 3) ± s.e.m. d) Editing rate of the UAG stop codon for HSP70 #CCA42 in the ambient group (37 degrees Celsius) and the heat shock group (42 degrees Celsius) when transfected without exogenous ADAR1p150 supplementation. Data are mean of technical replicates (n= 3) ± s.e.m. e) RADARSv2 with ogRNAs targeting the highest TPM gene (RPS5), the lowest TPM gene (KRAS), or as a non-targeting scrambled sequence used either with exogenous ADAR1p150 supplementation or with endogenous ADAR to sense the downregulation of their corresponding gene via gene-specific siRNA. Fold activation is calculated as the activation of cargo in the on-target siRNA group over non-target siRNA group. Error bars indicate standard error of the mean (n=3 technical replicates). f) Editing rate of the UAG stop codon of the RPS5 sensor and KRAS sensor in the on-target siRNA and non-target siRNA groups. Error bars indicate standard error of the mean (n=3 technical replicates).

Extended Data Fig. 10 Schematic illustrating design of RADARSv2 sensors.

The workflow for engineering an ogRNA targeting a new site, involving target site selection, generation of center homology, addition of MS2 loops, assembly of multiple binding sites, and mutation of in-frame stop codons.

Supplementary information

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Source Data Extended Data Fig. 4

Unprocessed western blots.

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Jiang, K., Koob, J., Chen, X.D. et al. Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR. Nat Biotechnol 41, 698–707 (2023). https://doi.org/10.1038/s41587-022-01534-5

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