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High-throughput determination of RNA structure by proximity ligation


We present an unbiased method to globally resolve RNA structures through pairwise contact measurements between interacting regions. RNA proximity ligation (RPL) uses proximity ligation of native RNA followed by deep sequencing to yield chimeric reads with ligation junctions in the vicinity of structurally proximate bases. We apply RPL in both baker's yeast (Saccharomyces cerevisiae) and human cells and generate contact probability maps for ribosomal and other abundant RNAs, including yeast snoRNAs, the RNA subunit of the signal recognition particle and the yeast U2 spliceosomal RNA homolog. RPL measurements correlate with established secondary structures for these RNA molecules, including stem-loop structures and long-range pseudoknots. We anticipate that RPL will complement the current repertoire of computational and experimental approaches in enabling the high-throughput determination of secondary and tertiary RNA structures.

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Figure 1: RNA proximity ligation identifies structurally proximate regions within the complex secondary structures of S. cerevisiae ribosomal RNAs.
Figure 2: Smoothing of ligation junction data results in ligase-dependent signal around known stem-loop formations.
Figure 3: 2D RPL contact probability maps recapitulate known and predicted nonribosomal RNA structures.
Figure 4: RPL scores demonstrate modest positive predictive value for pairs of interacting windows in RNA secondary structure.

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  1. Mortimer, S.A., Kidwell, M.A. & Doudna, J.A. Insights into RNA structure and function from genome-wide studies. Nat. Rev. Genet. 15, 469–479 (2014).

    Article  PubMed  Google Scholar 

  2. Cate, J.H. et al. Crystal structure of a group I ribozyme domain: principles of RNA packing. Science 273, 1678–1685 (1996).

    Article  PubMed  Google Scholar 

  3. Wang, Y.-H., Murphy, F.L., Cech, T.R. & Griffith, J.D. Visualization of a tertiary structural domain of the tetrahymena group I intron by electron microscopy. J. Mol. Biol. 236, 64–71 (1994).

    Article  PubMed  Google Scholar 

  4. Latham, M.P., Brown, D.J., McCallum, S.A. & Pardi, A. NMR methods for studying the structure and dynamics of RNA. ChemBioChem 6, 1492–1505 (2005).

    Article  PubMed  Google Scholar 

  5. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Reuter, J. & Mathews, D. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11, 129 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lorenz, R. et al. ViennaRNA Package 2.0. Algorithms Mol. Biol. 6, 26 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Shendure, J. & Aiden, E.L. The expanding scope of DNA sequencing. Nat. Biotechnol. 30, 1084–1094 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Rouskin, S., Zubradt, M., Washietl, S., Kellis, M. & Weissman, J.S. Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo. Nature 505, 701–705 (2014).

    Article  PubMed  Google Scholar 

  10. Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2014).

    Article  PubMed  Google Scholar 

  11. Lucks, J.B. et al. Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl. Acad. Sci. USA 108, 11063–11068 (2011).

    Article  PubMed  Google Scholar 

  12. Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010).

    Article  PubMed  Google Scholar 

  13. Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Underwood, J.G. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. Nat. Methods 7, 995–1001 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kladwang, W., VanLang, C.C., Cordero, P. & Das, R. A two-dimensional mutate-and-map strategy for non-coding RNA structure. Nat. Chem. 3, 954–962 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Siegfried, N.A., Busan, S., Rice, G.M., Nelson, J.A.E. & Weeks, K.M. RNA motif discovery by SHAPE and mutational profiling (SHAPE-MaP). Nat. Methods 9, 959–965 (2014).

    Article  Google Scholar 

  17. Fredriksson, S. et al. Protein detection using proximity-dependent DNA ligation assays. Nat. Biotechnol. 20, 473–477 (2002).

    Article  PubMed  Google Scholar 

  18. Söderberg, O. et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nat. Methods 3, 995–1000 (2006).

    Article  PubMed  Google Scholar 

  19. Dekker, J., Rippe, K., Dekker, M. & Kleckner, N. Capturing chromosome conformation. Science 295, 1306–1311 (2002).

    Article  PubMed  Google Scholar 

  20. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kudla, G., Granneman, S., Hahn, D., Beggs, J.D. & Tollervey, D. Cross-linking, ligation, and sequencing of hybrids reveals RNA–RNA interactions in yeast. Proc. Natl. Acad. Sci. USA 108, 10010–10015 (2011).

    Article  PubMed  Google Scholar 

  22. Helwak, A., Kudla, G., Dudnakova, T. & Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153, 654–665 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  PubMed  Google Scholar 

  24. Rao, S.S.P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Ben-Shem, A. et al. The structure of the eukaryotic ribosome at 3.0 Å resolution. Science 334, 1524–1529 (2011).

    Article  PubMed  Google Scholar 

  26. Nawrocki, E.P. & Eddy, S.R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Burge, S.W. et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. D226–D232 (2013).

  28. Engreitz, J.M. et al. RNA-RNA interactions enable specific targeting of noncoding RNAs to nascent pre-mRNAs and chromatin sites. Cell 159, 188–199 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Grosswendt, S. et al. Unambiguous identification of miRNA:target site interactions by different types of ligation reactions. Mol. Cell 54, 1042–1054 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cordero, P., Lucks, J.B. & Das, R. An RNA Mapping DataBase for curating RNA structure mapping experiments. Bioinformatics 28, 3006–3008 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Cameron, V. & Uhlenbeck, O.C. 3′-Phosphatase activity in T4 polynucleotide kinase. Biochemistry 16, 5120–5126 (1977).

    Article  PubMed  Google Scholar 

  32. Cannone, J. et al. The Comparative RNA Web (CRW) Site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinformatics 3, 2 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Anger, A.M. et al. Structures of the human and Drosophila 80S ribosome. Nature 497, 80–85 (2013).

    Article  PubMed  Google Scholar 

  34. Lu, X. & Olson, W.K. 3DNA: a software package for the analysis, rebuilding and visualization of three-dimensional nucleic acid structures. Nucleic Acids Res. 31, 5108–5121 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

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We thank members of the Shendure laboratory (particularly D. Cusanovich, M. Kircher, A. McKenna and M. Snyder), D. Fowler, C. Trapnell and J. Underwood for helpful discussions and comments on the manuscript. We thank G. Kudla, A. Helwak and D. Tollervey for answering questions pertaining to the CLASH protocol. We would also like to acknowledge A. Dobin for making auxiliary scripts for processing STAR alignments publicly available. This work was funded by National Institutes of Health (NIH) Director's Pioneer Award (1DP1HG007811 to J.S.) and an NIH National Human Genome Research Institute (NHGRI) Genome Training Grant (5T32HG000035 to V.R.).

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Authors and Affiliations



V.R. and J.S. conceived of the project and devised experiments. V.R. and R.Q. carried out the experiments. V.R. performed computational analyses. V.R. and J.S. wrote the manuscript.

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Correspondence to Jay Shendure.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Samples treated with exogenous ligase are enriched for “gapped,” or intramolecular chimeric, reads.

We observe enrichment for gapped, or intramolecular chimeric reads, over most gap sizes in our RPL sample compared to a control sample in which no T4 RNA Ligase was added. For gap sizes > 495 bases, we observed 627 reads per million analyzed in the (+) ligase sample, versus 25 reads per million analyzed in the (−) ligase sample. This ligase-dependent enrichment for long gap sizes suggests that the long-distance ligation products generated RPL are neither a result of gross mapping artifacts, nor the result of biological artifacts such as RT template switching.

Supplementary Figure 2 Mixing of RNA from two species during an RPL experiment to quantify the extent of non-specific product generation during the RPL protocol.

We carried out, in duplicate and with matched (−) ligase controls, the RPL protocols for yeast and human cells separately (1 colony picked for yeast; 5E5 GM12878 cells used for human) and then mixed the two slurries together prior to overnight proximity ligation by T4 RNA Ligase I. Comparing the (+) ligase and (−) ligase samples, we observe the strongest enrichments for intraspecies, intramolecular ligations.

Supplementary Figure 3 RPL signal recapitulates known long-range base-pairing interactions, and is dependent on exogenous ligase.

a.-b) The distribution of ligation junctions shows enrichment centered at known secondary structure base-pairs even when only considering long-range ligation junctions. Shown here are distributions for the 5.8S/25S rRNA, (a) excluding all ligation events ≤ 50 bp or (b) excluding all ligation events ≤ 100 bp. Results from a matched (−) ligase control are shown in blue.

Supplementary Figure 4 The raw ligation count data is noisy.

a.-c) We randomly sampled 10 (a), 25 (b), and 50 (c) paired bases from the 5.8S/25S rRNA and plotted the distribution of ligation junctions as a function distance to pairing partner. d-f.) Same as above, but for the 18S rRNA. We find that the enrichment evident when averaging over all base-pairs in the molecule (Figure 1b,c) is apparent but much nosier.

Supplementary Figure 5 RPL ligation junctions demonstrate a slight sequence composition bias.

Shown here is a sequence-logo representation for the five bases upstream and downstream of observed ligation products. We observe a slight enrichment for A/T (~1.2 fold with respect to background frequency) immediately proximal to the ligation junction in our sequencing products, consistent with kinase and/or T4 RNA Ligase I bias during proximity ligation. We compute the background distribution by first calculating the individual nucleotide frequencies of all transcripts with at least one alignment, then calculating a weighted background frequency from these based on the number of reads aligning to each transcript.

Supplementary Figure 6 RPL contact probability maps broadly recapitulate the proximity implied by base-pairing relationships in structurally complex yeast ribosomal RNAs.

a.) RPL contact probability map for the 5.8S/25S rRNAs mirrored against all interacting 21 nt windows that contain paired bases in the known structures. b.) Same as above, but for the 18S rRNA. In both cases, high RPL scores broadly agree with the interacting windows in the known RNA secondary structures.

Supplementary Figure 7 2D RPL contact probability map for the S. cerevisiae U2 spliceosomal RNA homolog LSR1.

Anti-diagonal RPL scores imply the formation of a long stem in this molecule. This analysis was carried out using 21 nt window-based RPL scores.

Supplementary Figure 8 RPL signal is predominantly intramolecular.

We tallied ligation counts among and between the six species analyzed in this study and normalized them on a species-by-species basis using the coverage normalization procedure used for intramolecular contact maps (Methods). We find that RPL signal lies predominantly along the diagonal (i.e. intramolecular ligation events), although there is modest signal for intermolecular events between the 5.8S/25S rRNAs (LSU) and 18S rRNA (SSU), which interact as the ribosome. Inset: Contact probability map showing RPL scores computed for all interacting 21 nt windows between and within the 5.8S/25S rRNAs and 18S rRNA (outlined in black).

Supplementary Figure 9 Extension of RPL to RNA secondary structures in mammalian cell culture.

a.) Contact probability map for the LSU 28S rRNA mirrored against interacting windows containing paired bases. b.) Contact probability map of the SSU 18S rRNA mirrored against interacting windows containing paired bases. Secondary structures for these molecules were derived from a cryo-EM structure of the human ribosome. All RPL scores shown here were calculated using 21 nt windows.

Supplementary Figure 10 Mammalian RPL (−) RNase, (−) Ligase control demonstrates weak signal for structure-related ligation junctions.

a.) A distribution of ligation junctions centered at known base-pairing partners for the mammalian LSU rRNA displays a weak enrichment in signal at known pairing partners, in a (−) ligase sample also untreated by RNases. This suggests that endogenous ligases/RNases may be active to a small degree in lymphoblastoid cell lines. However, the signal is much stronger in the (+) RNase, (+) ligase sample. b.) A contact probability map illustrates the extent of noise in these potentially endogenous ligations, though certain highly scoring regions do appear consistent with known structures within the molecule (shown mirrored). RPL scores were calculated using 21 nt windows.

Supplementary Figure 11 The Yeast RPL protocol demonstrates limited degradation of RNA products following PNK treatment.

Bioanalyzer gel representation of purified RNA at three conditions: 1.) Following PNK treatment; 2.) Following a negative control incubation at 16°C overnight in the absence of ligase; 3.) Following incubation at 16°C overnight in the presence of T4 RNA Ligase I. RNA Integrity Numbers (RIN) for the three samples were 7.0, 7.2, and 7.0, respectively.

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Ramani, V., Qiu, R. & Shendure, J. High-throughput determination of RNA structure by proximity ligation. Nat Biotechnol 33, 980–984 (2015).

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