irCLASH reveals RNA substrates recognized by human ADARs

Abstract

Adenosine deaminases acting on RNA (ADARs) convert adenosines to inosines in double-stranded RNA (dsRNA) in animals. Despite their importance, ADAR RNA substrates have not been mapped extensively in vivo. Here we develop irCLASH to map RNA substrates recognized by human ADARs and uncover features that determine their binding affinity and editing efficiency. We also observe a dominance of long-range interactions within ADAR substrates and analyze differences between ADAR1 and ADAR2 editing substrates. Moreover, we unexpectedly discovered that ADAR proteins bind dsRNA substrates tandemly in vivo, each with a 50-bp footprint. Using RNA duplexes recognized by ADARs as readout of pre-messenger RNA structures, we reveal distinct higher-order architectures between pre-messenger RNAs and mRNAs. Our transcriptome-wide atlas of ADAR substrates and the features governing RNA editing observed in our study will assist in the rational design of guide RNAs for ADAR-mediated RNA base editing.

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Fig. 1: Development and performance of irCLASH and the transcriptome-wide RNA duplexes of ADARs.
Fig. 2: The assembly and characterization of ADAR substrates.
Fig. 3: Transcriptome-wide analysis of ADAR RNA-editing substrates.
Fig. 4: Cis-regulation of RNA editing inferred from GTEx data.
Fig. 5: Insights into the RNA-RNA interactions of pre-mRNAs inferred from ADAR irCLASH data.
Fig. 6: The binding profiles and regulatory effect of ADARs on pre-mRNAs or mRNAs.
Fig. 7: Three models of ADAR-mediated binding and editing of long dsRNAs.

Data availability

The sequence data have been deposited in the NCBI GEO database under the accession code GSE136327. Source Data for Figs. 1, 2, 4 and 6 and Extended Data Figs. 1, 3, 4, 6, 9 and 10 are available with the paper online.

Code availability

All relevant codes and data processing pipelines have been deposited to GitHub (https://github.com/SYSU-zhanglab/ irCLASH).

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Acknowledgements

We thank T. Sun, B. Zarnegar, H. Chang and J. B. Li for their help on irCLASH method development, Q. Li and J. B. Li for sharing the GTEx A-to-I RNA-editing call data and critical discussion of the project. This study was supported by grants from National Key Research and Development Program of China (2018YFC1003100 to R.Z.), Guangdong Major Science and Technology Projects (2017B020226002 to R.Z.), Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06S638 to R.Z.), National Natural Science Foundation of China (31571341 and 91631108 to R.Z., 31900437 to Y.L.S.), and the Fundamental Research Funds for the Central Universities (R.Z. and Y.L.S.). The GTEx project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (commonfund.nih.gov/GTEx). Additional funds were provided by the National Cancer Institute (NCI), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), National Institute on Drug Abuse (NIDA), National Institute of Mental Health (NIMH), and National Institute of Neurological Disorders and Stroke (NINDS). Donors were enrolled at Biospecimen Source Sites funded by NCISAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170) and Roswell Park Cancer Institute (10XS171). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through a SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplement to University of Miami grant DA006227.

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Authors

Contributions

R.Z. conceived the project. W.Y., Q.F., X.Z. and Y.Z. conducted the experiments. Y.S. performed the bioinformatics analysis. R.Z., Y.S., W.Y. and L.W. wrote the manuscript.

Corresponding author

Correspondence to Rui Zhang.

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Competing interests

Sun Yat-Sen University has filed a provisional patent application on the high-affinity substrates identified, and R.Z., W.Y. and Y.S. are named as the inventors.

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Peer review information Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 The subcellular location and editing activity of the exogenously expressed ADARs.

a, IB: western blot shows the over-expressed FLAG-tagged ADARs in HEK293 cells. IP: immunoprecipitation followed by western blot confirmed the effectiveness of anti-FLAG antibody. b, Top: example of western blot of endogenously and exogenously expressed ADAR1 protein in HEK293 cells. Bottom: quantifications of western blot with odyssey infrared imaging system. Values represent mean ± s.d. (n = 3 independent experiments). c, The immunofluorescence staining shows the localization of FLAG-tagged ADARs. Scale bar, 20 µm. d, Percentages of different types of RNA variants. Only a group of RNA variants with A-to-G/T-to-C sites that comprised over 80% of all mismatches were used to compile the master list of RNA editing sites. For example, in the control sample (empty vector), only A-to-G sites of Alu and repetitive non-Alu variants were considered as genuine RNA editing sites. Replicates were merged for variant call. e, Boxplot shows the overall editing levels in ADAR overexpressed cells or control cells (Control, n = 47,527; ADAR1, n = 58,157; ADAR2, n = 91,305; ADAR3, n = 73,086). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. The significant difference between ADAR overexpressed cells and control cells was determined using two-sided Mann-Whitney U test. The sites covered by at least 30 reads were analyzed. Source data

Extended Data Fig. 2 The identification of RNA duplex from irCLASH data.

a, The computational pipeline to identify RNA duplex. b, A diagram describing how to rescue “swapped” hybrid reads. c, Correlation analysis of the non-hybrid read counts on each gene between the replicates. For each gene, the transcript with the longest length was used for analysis. In each sample, the read count of each transcript was normalized by the total mapped read count.

Extended Data Fig. 3 Characterization and verification of ADAR RNA duplexes.

a, The composition of sequence context in the arms of hybrid reads. Notably, the proportions of Alus bound by ADARs may be underestimated in our analysis, since many Alu reads were mapped to multiple positions in the transcripts and removed from the analysis. b, The proportions of Alu repeat in 5′ UTR, CDS, 3′ UTR and intron. Y-axis is defined as the relative proportion of Alu repeat in a genic region divided by the relative length of a genic region across the pre-mRNAs. c, Dot plots showing the minimum free energies of each of the hybrid reads calculated using different RNA prediction programs. Spearman’s correlation coefficient (ρ) was shown. d, Comparison of the minimum free energies of hybridization between the two arms of hybrid reads and randomly repositioned sequences using two additional RNA structure prediction programs (ADAR1, n = 5,783; ADAR2, n = 13,831; ADAR3, n = 13520). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. Data analysis was performed as described in Fig. 1f. Source data

Extended Data Fig. 4 Characterizing the features of ADAR substrates.

a, b, Metaprofiles of the base-pairing status of ADAR substrates (a) or non-Alu ADAR substrates (b). Only substrates with two arms located in the same gene were used for analysis. Position 0 means the center of substrates, and the 40 bp upstream and downstream were analyzed. Percentages of paired bases were calculated in a 5 bp sliding window. As a control, the sequence of one arm was shuffled 10 times, and each time the shuffled sequence and the sequence of the other arm were folded using RNAhybrid. c, Comparison of the percentage of substrates with editing sites in the second arm of an editing substrate. Control means the percentage of the repositioned second arms with editing sites. To include editing sites around the substrates for analysis, substrates and flanking 50 bp regions were selected. d, A species tree and the definition of editing sites in different conservation groups. N, any base; B, C or G or T. e, Comparison of the sequence conservation of substrates containing different groups of editing sites as defined in d. Two arms of a substrate were joined and the distance was calculated using MEGA7 software with Maximum Composite Likelihood model. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. f, Comparison of the secondary structure conservation of substrates containing different groups of editing sites as defined in d. Two arms of a substrate were joined and the structure conservation index was calculated using RNAz software. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. g, Distributions of interval distance of ADAR Alu substrates. Alu substrates were defined as substrates that are overlapped with Alus in the genome. h, The relationship between interval distances and arm lengths of ADAR substrates. A best-fit linear regression line was shown with Pearson correlation coefficient and p value. Source data

Extended Data Fig. 5 Verification of ADAR substrates.

a, Validation of RNA editing substrates. Substrate 1–6 and 7–16 are the substrates of ADAR1 and ADAR2, respectively. The two arms of a substrate were synthesized and subcloned into pSilencer-2.1-U6-hygro vector. Following co-transfection with a plasmid encoding either ADAR1 or ADAR2 into HEK293 cells, RNA was isolated, reverse transcribed, amplified with target-specific primers, and submitted to sanger sequencing. For each substrate, editing status of a known editing site is selected to be shown. Depending on the sequencing primer used, the edited sites were identified as A+G or T+C peaks. b, Schematics of sequence-specific RNA base editing. adRNA comprises a specificity domain (complementary to RAB7A 3′ UTR sequence with a mismatched cytidine opposite to the target adenosine) and an ADAR-recruiting domain (duplexes derived from the high-affinity substrates of ADAR1 or GluR2 transcript). dsRBD, double-stranded RNA-binding domain of ADAR1 or ADAR2; A* or I*, adenosine or inosine base at target site, respectively. c, The predicted RNA secondary structures of selected high-affinity substrates. The sequences selected as adRNA ADAR-recruiting domain (highlighted in red) and their flanking regions were shown.

Extended Data Fig. 6 Editing-independent ADAR substrates differ from editing-dependent substrates.

a, The cumulative distributions of the distances between the substrates and the nearest editing sites. Editing-dependent (ED) substrates were defined as the ones containing editing sites. Editing-independent (EI) substrates were defined as the ones without editing sites within 1 kb of both arms. b, Comparison of the repetitive sequence features between ED and EI substrates. c, Comparison of the minimum free energies of hybridization between the two arms of hybrid reads and randomly repositioned sequences. ED and EI substrates were analyzed, respectively. Reads from mRNAs and long non-coding RNAs were used. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. P-values were calculated using two-sided Mann-Whitney U test. d, Comparison of the arm lengths between ED and EI substrates. The significant difference between different groups of substrates was determined using two-sided Mann-Whitney U test. e, Comparison of minimum free energies between ED and EI substrates. To control the effect of arm length, 15 bp upstream and downstream sequences of the center of each arm were used in this analysis. P-values were calculated using the Mann-Whitney U test. f, Comparison of ADAR binding affinities between ED and EI substrates. The significant difference between different groups of substrates was determined using two-sided Mann-Whitney U test. Source data

Extended Data Fig. 7 Metagene analysis of the crosslinking site, sequence context and editing site.

a, The number of crosslinking sites across ADAR substrates. b, The GC content across ADAR substrates. Only the positions with > 100 (ADAR1) or > 200 (ADAR2 and ADAR3) substrates were plotted. c, Sequence context around the peaks. 10 bp upstream and downstream of the peak summit were plotted with WebLogo v.3.5. d, The distribution of editing sites across RNA editing substrates. Position 0 is the center of the arm.

Extended Data Fig. 8 edQTL analysis using GTEx data.

a, The distributions of editing levels in GTEx samples. Editing levels for the editing sites that are located within the ADAR substrates and flanking 100 bp region were plotted in each tissue. Editing sites were ranked by their mean editing levels in a tissue. The number of editing sites in each tissue is indicated. b, The number of edQTL associations shared by tissues. c, Boxplot showing the relationship between an edQTL SNP in the RPL37A gene and the associated editing site in the non-brain tissues. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. d, The number of associated editing pairs shared by tissues.

Extended Data Fig. 9 Comparison of RNA substrates bound by endogenously and exogenously expressed ADAR1.

a, Comparison of sequence context in the arms of hybrid reads bound by endogenously expressed ADAR1 (EnEA) and exogenously expressed ADAR1 (ExEA). b, The proportion of the genic locations of the uniquely annotated arms of EnEA and ExEA hybrid reads. c, Comparison of the minimum free energies of hybridization between the two arms of EnEA hybrid reads and randomly repositioned sequences (n = 25,537). Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ±1.5× IQR. Data analysis was performed as described in Fig. 1f. d, The cumulative distributions of the distance between EnEA hybrid arms and the nearest editing site or between repositioned arms and the nearest editing site. P-values were calculated using the Mann-Whitney U test. e, Median normalized PARS scores in EnEA duplexes and flanking regions. Data analysis was performed as described in Fig. 1h. f, Metaprofiles of the base-pairing status of EnEA and ExEA substrates. Data analysis was performed as described in Extended Data Fig. 4a. g, Distributions of arm lengths of EnEA and ExEA substrates. h, Distributions of interval distances of EnEA and ExEA substrates. i, The mean number of crosslinking events along EnEA substrates. Only editing-dependent substrates were plotted. Editing-independent substrates were not shown because of the limited positions that pass the coverage filter. Position 0 is the 5’start of the arm. A 5 nt sliding window was used and only positions with ≥ 10 crosslinking sites were plotted. j, The mean editing level and number of crosslinking events across RNA editing substrates. Position 0 is the center of the arm. A 5 nt sliding window was used. Editing levels in HEK293 cells were used for analysis. Positions with < 60 editing sites were not shown. k, The percentage of editing sites in different structural features of EnEA substrates. l, The proportions of different bulge types in EnEA substrates. m, The percentage and mean representative editing level of editing sites in internal loops of EnEA substrates. Loop types with length < 3 nt and with sites ≥ 5 were shown. The mean representative editing levels for each type were shown as diamond symbols. The edited “A” was highlighted in red. The control percentages were obtained as in k. Source data

Extended Data Fig. 10 The effect of flanking dsRNA region on RNA editing.

a, Two editing substrates were selected and either the upstream or downstream region of the edited sites was deleted, separately. Following co-transfection with a plasmid encoding ADAR1 into HEK293 cells, RNA was isolated, reverse transcribed, amplified with target-specific primers, and submitted to sanger sequencing. b, Editing level measurement derived from sanger sequencing. Allelic ratio was measured using QSVanalyser. Values represent mean ± s.e.m. (n = 3 independent experiments). Source data

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Table 1

Comparison of CLASH, hiCLIP and irCLASH methods.

Supplementary Table 2

Library list and the mapping information.

Supplementary Table 3

Annotation of hybrid reads.

Supplementary Table 4

Annotation of ADAR substrates.

Supplementary Table 5

Sanger sequencing verification of base editing.

Supplementary Table 6

edQTL SNP information.

Supplementary Table 7

Oligo information.

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Song, Y., Yang, W., Fu, Q. et al. irCLASH reveals RNA substrates recognized by human ADARs. Nat Struct Mol Biol 27, 351–362 (2020). https://doi.org/10.1038/s41594-020-0398-4

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