Skip to main content

Thank you for visiting 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.

hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1


The structure of messenger RNA is important for post-transcriptional regulation, mainly because it affects binding of trans-acting factors1. However, little is known about the in vivo structure of full-length mRNAs. Here we present hiCLIP, a biochemical technique for transcriptome-wide identification of RNA secondary structures interacting with RNA-binding proteins (RBPs). Using this technique to investigate RNA structures bound by Staufen 1 (STAU1) in human cells, we uncover a dominance of intra-molecular RNA duplexes, a depletion of duplexes from coding regions of highly translated mRNAs, an unexpected prevalence of long-range duplexes in 3′ untranslated regions (UTRs), and a decreased incidence of single nucleotide polymorphisms in duplex-forming regions. We also discover a duplex spanning 858 nucleotides in the 3′ UTR of the X-box binding protein 1 (XBP1) mRNA that regulates its cytoplasmic splicing and stability. Our study reveals the fundamental role of mRNA secondary structures in gene expression and introduces hiCLIP as a widely applicable method for discovering new, especially long-range, RNA duplexes.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


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

Figure 1: hiCLIP identifies RNA duplexes bound by STAU1.
Figure 2: Properties of STAU1-target RNA duplexes.
Figure 3: Insights into the secondary structures of mRNAs.
Figure 4: STAU1 regulates cytoplasmic splicing of XBP1 mRNA.

Accession codes

Primary accessions



  1. Wan, Y., Kertesz, M., Spitale, R. C., Segal, E. & Chang, H. Y. Understanding the transcriptome through RNA structure. Nature Rev. Genet. 12, 641–655 (2011)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  3. 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  CAS  ADS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  5. Li, F. et al. Global analysis of RNA secondary structure in two metazoans. Cell Rep. 1, 69–82 (2012)

    Article  CAS  Google Scholar 

  6. Goodarzi, H. et al. Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 513, 256–260 (2014)

    Article  CAS  ADS  Google Scholar 

  7. Lovci, M. T. et al. Rbfox proteins regulate alternative mRNA splicing through evolutionarily conserved RNA bridges. Nature Struct. Mol. Biol. 20, 1434–1442 (2013)

    Article  CAS  Google Scholar 

  8. 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  CAS  ADS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Heraud-Farlow, J. E. & Kiebler, M. A. The multifunctional Staufen proteins: conserved roles from neurogenesis to synaptic plasticity. Trends Neurosci. 37, 470–479 (2014)

    Article  CAS  Google Scholar 

  11. Gong, C. & Maquat, L. E. lncRNAs transactivate STAU1-mediated mRNA decay by duplexing with 3′ UTRs via Alu elements. Nature 470, 284–288 (2011)

    Article  CAS  ADS  Google Scholar 

  12. Kim, Y. K., Furic, L., Desgroseillers, L. & Maquat, L. E. Mammalian Staufen1 recruits Upf1 to specific mRNA 3′ UTRs so as to elicit mRNA decay. Cell 120, 195–208 (2005)

    Article  CAS  Google Scholar 

  13. Ricci, E. P. et al. Staufen1 senses overall transcript secondary structure to regulate translation. Nature Struct. Mol. Biol. 21, 26–35 (2014)

    Article  CAS  Google Scholar 

  14. Kim, Y. K. et al. Staufen1 regulates diverse classes of mammalian transcripts. EMBO J. 26, 2670–2681 (2007)

    Article  CAS  Google Scholar 

  15. Heraud-Farlow, J. E. et al. Staufen2 regulates neuronal target RNAs. Cell Rep. 5, 1511–1518 (2013)

    Article  CAS  Google Scholar 

  16. Laver, J. D. et al. Genome-wide analysis of Staufen-associated mRNAs identifies secondary structures that confer target specificity. Nucleic Acids Res. 41, 9438–9460 (2013)

    Article  CAS  Google Scholar 

  17. de Lucas, S., Oliveros, J. C., Chagoyen, M. & Ortin, J. Functional signature for the recognition of specific target mRNAs by human Staufen1 protein. Nucleic Acids Res. 42, 4516–4526 (2014)

    Article  CAS  Google Scholar 

  18. LeGendre, J. B. et al. RNA targets and specificity of Staufen, a double-stranded RNA-binding protein in Caenorhabditis elegans. J. Biol. Chem. 288, 2532–2545 (2013)

    Article  CAS  Google Scholar 

  19. König, J. et al. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nature Struct. Mol. Biol. 17, 909–915 (2010)

    Article  Google Scholar 

  20. Luo, M., Duchaine, T. F. & DesGroseillers, L. Molecular mapping of the determinants involved in human Staufen-ribosome association. Biochem. J. 365, 817–824 (2002)

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  22. Kretz, M. et al. Control of somatic tissue differentiation by the long non-coding RNA TINCR. Nature 493, 231–235 (2013)

    Article  CAS  ADS  Google Scholar 

  23. Elbarbary, R. A., Li, W., Tian, B. & Maquat, L. E. STAU1 binding 3′ UTR IRAlus complements nuclear retention to protect cells from PKR-mediated translational shutdown. Genes Dev. 27, 1495–1510 (2013)

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  25. Roy, B. & Jacobson, A. The intimate relationships of mRNA decay and translation. Trends Genet. 29, 691–699 (2013)

    Article  CAS  Google Scholar 

  26. Qu, X. et al. The ribosome uses two active mechanisms to unwind messenger RNA during translation. Nature 475, 118–121 (2011)

    Article  CAS  Google Scholar 

  27. Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334, 1081–1086 (2011)

    Article  CAS  ADS  Google Scholar 

  28. Marión, R. M., Fortes, P., Beloso, A., Dotti, C. & Ortin, J. A human sequence homologue of Staufen is an RNA-binding protein that is associated with polysomes and localizes to the rough endoplasmic reticulum. Mol. Cell. Biol. 19, 2212–2219 (1999)

    Article  Google Scholar 

  29. Wickham, L., Duchaine, T., Luo, M., Nabi, I. R. & DesGroseillers, L. Mammalian Staufen is a double-stranded-RNA- and tubulin-binding protein which localizes to the rough endoplasmic reticulum. Mol. Cell. Biol. 19, 2220–2230 (1999)

    Article  CAS  Google Scholar 

  30. Boulay, K. et al. Cell cycle-dependent regulation of the RNA-binding protein Staufen1. Nucleic Acids Res. 42, 7867–7883 (2014)

    Article  CAS  Google Scholar 

  31. Kiel, J. A., Emmrich, K., Meyer, H. E. & Kunau, W. H. Ubiquitination of the peroxisomal targeting signal type 1 receptor, Pex5p, suggests the presence of a quality control mechanism during peroxisomal matrix protein import. J. Biol. Chem. 280, 1921–1930 (2005)

    Article  CAS  Google Scholar 

  32. Huppertz, I. et al. iCLIP: protein–RNA interactions at nucleotide resolution. Methods 65, 274–287 (2014)

    Article  CAS  Google Scholar 

  33. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009)

    Article  CAS  ADS  Google Scholar 

  34. Guo, H., Ingolia, N. T., Weissman, J. S. & Bartel, D. P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835–840 (2010)

    Article  CAS  ADS  Google Scholar 

  35. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147, 789–802 (2011)

    Article  CAS  Google Scholar 

  36. Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nature Protocols 7, 1534–1550 (2012)

    Article  CAS  Google Scholar 

  37. Niranjanakumari, S., Lasda, E., Brazas, R. & Garcia-Blanco, M. A. Reversible cross-linking combined with immunoprecipitation to study RNA–protein interactions in vivo. Methods 26, 182–190 (2002)

    Article  CAS  Google Scholar 

  38. Li, H., Korennykh, A. V., Behrman, S. L. & Walter, P. Mammalian endoplasmic reticulum stress sensor IRE1 signals by dynamic clustering. Proc. Natl Acad. Sci. USA 107, 16113–16118 (2010)

    Article  CAS  ADS  Google Scholar 

  39. R Core Team. A language and environment for statistical computing. (2012)

  40. Wickham, H. ggplot2: Elegant Graphics For Data Analysis (Springer, 2009)

    Book  Google Scholar 

  41. Wickham, H. The Split-Apply-Combine Strategy for Data Analysis. J. Stat. Softw. 40, 1–29 (2011)

    Google Scholar 

  42. Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007)

    Article  Google Scholar 

  43. The Bioconductor Dev Team. BSgenome.Hsapiens.UCSC.hg19: Homo sapiens (human) full genome (UCSC version hg19).

  44. Aboyoun, P., Pages, H. & Lawrence, M. GenomicRanges: Representation and manipulation of genomic intervals. PloS Computational Biol. 9, e1003118

  45. Morgan, M. et al. ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 2607–2608 (2009)

    Article  CAS  Google Scholar 

  46. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

    Article  Google Scholar 

  47. Rehmsmeier, M., Steffen, P., Hochsmann, M. & Giegerich, R. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507–1517 (2004)

    Article  CAS  Google Scholar 

  48. Robinson, J. T. et al. Integrative genomics viewer. Nature Biotechnol. 29, 24–26 (2011)

    Article  CAS  Google Scholar 

  49. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013)

    Article  Google Scholar 

  50. van Helden, J. Regulatory sequence analysis tools. Nucleic Acids Res. 31, 3593–3596 (2003)

    Article  CAS  Google Scholar 

  51. Day, D. S., Luquette, L. J., Park, P. J. & Kharchenko, P. V. Estimating enrichment of repetitive elements from high-throughput sequence data. Genome Biol. 11, R69 (2010)

    Article  Google Scholar 

  52. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    Article  CAS  Google Scholar 

  53. Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009)

    Article  CAS  Google Scholar 

  54. Cho, J. et al. LIN28A is a suppressor of ER-associated translation in embryonic stem cells. Cell 151, 765–777 (2012)

    Article  CAS  Google Scholar 

  55. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  CAS  Google Scholar 

  56. Bartonicek, N. & Enright, A. J. SylArray: a web server for automated detection of miRNA effects from expression data. Bioinformatics 26, 2900–2901 (2010)

    Article  CAS  Google Scholar 

  57. Huang da. W, Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 4, 44–57 (2009)

    Article  Google Scholar 

  58. Supek, F., Bosnjak, M., Skunca, N. & Smuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011)

    Article  CAS  ADS  Google Scholar 

  59. Zarnack, K. et al. Direct competition between hnRNP C and U2AF65 protects the transcriptome from the exonization of Alu elements. Cell 152, 453–466 (2013)

    Article  CAS  Google Scholar 

Download references


We wish to thank S. Granneman and C. Sibley for discussions on the development of hiCLIP protocol; K. Zarnack, N. Haberman, C. Ravarani and B. Lang for assistance with bioinformatic analyses; D. Daujotyte and P. Lukavsky for sharing the STAU1 plasmid and helping in setting up the project; L. Maquat for sharing the ARF1 SBS plasmid; the genomic team at the Cancer Research UK Cambridge Institute for Illumina HiSeq sequencing, and M. Babu Mohan and Ule group members for support and comments on the manuscript. This work was supported by funding from Human Frontier Science Program (RGP0024/2008-C), European Research Council (206726-CLIP and 617837-Translate) and Medical Research Council (U105185858) to J.U., Cancer Research UK and UCL to N.M.L., a Wellcome Trust Joint Investigator Award to N.M.L. and J.U. (103760/Z/14/Z), the Nakajima Foundation fellowship and MRC Centenary Early Career Award to Y.S.

Author information

Authors and Affiliations



Y.S. and J.U. developed the hiCLIP protocol and designed the project; Y.S. performed the hiCLIP, mRNA-seq, ribosome profiling experiments; Y.S., A.V., E.D. and N.M.L. designed and performed bioinformatic analyses; A.D., C.M. and A.Z. performed the reporter assays. Y.S., A.D., N.M.L. and J.U. wrote the manuscript. All authors contributed to the analysis and interpretation of the data.

Corresponding authors

Correspondence to Nicholas M. Luscombe or Jernej Ule.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

The sequence data and scripts are publicly available from ArrayExpress (E-MTAB-2937, E-MTAB-2940, E-MTAB-2941) and (

Extended data figures and tables

Extended Data Figure 1 Diagrams illustrating the mapping of hybrid reads, duplex assignment and use of terms.

a, Schematic overview of the hiCLIP protocol. (1) Cells are irradiated with UV-C light. (2) After cell lysis, the unprotected sections of RNAs are digested by RNase I, and the RBP is co-immunoprecipitated with the crosslinked RNA duplex. (3) Two designated adaptors are ligated to both strands of the RNA duplex. Adaptor A (cloning adaptor) has a permanent 3′ block, while adaptor B (linker adaptor) has a removable 3′ block. (4) 3′ block of adaptor B is removed. (5) The two strands of the RNA duplex are ligated via adaptor B. (6) The RNA hybrid products are converted into a cDNA library and sequenced as in iCLIP protocol19. The resulting data comprise hybrid and non-hybrid reads. (7) Hybrid reads are selected and adaptors are trimmed to define the sequences of left (L) and right (R) arms, which are mapped independently to the transcriptome. b, c, The left arm of hybrid read locates upstream of adaptor B, and the right arm locates downstream of adaptor B. Each arm is mapped independently to transcriptome. If both arms locate into the same gene, then the duplex is considered to be formed by the same RNA. If the arms locate to different genes, then the duplex is formed by two different RNAs. d, A diagram describing how a hybrid read is used to identify an RNA duplex. e, A diagram describing how the loop (intervening sequence) is defined for each RNA duplex.

Extended Data Figure 2 Autoradiography analysis of the STAU1–RNA complex, and analysis of hybrid reads in a known STAU1 mRNA target, ARF1.

a, Autoradiograph of STAU1–RNA complex that was isolated for the hiCLIP experiment. hiCLIP experiments were performed with high and low RNase conditions, and the two controls omitted either the second intermolecular ligation or STAU1 induction. After adaptor ligation, STAU1 crosslinked RNA was radiolabelled and the complex was analysed by denaturing gel electrophoresis and membrane transfer. The size of the band is slightly higher compared to that in Fig. 1a, presumably due to the efficient adaptor ligation that adds to the size of the RNAs (the experiment shown in Fig. 1a didn’t include adaptor ligation). b, Correlation analysis of the non-hybrid read count on each RNA between the replicates of the hiCLIP experiments. c, Schematic representations of ARF1 mRNA and the known STAU1-target RNA duplex, along with the position of STAU1 hybrid reads and crosslink sites identified by non-hybrid reads. The left and right arms of hybrid reads are depicted as black boxes, and lines connect arms originating from the same hybrid read. The previously studied STAU1-target RNA duplex12,14 is indicated by green and red boxes. In addition to the known duplex, hybrid reads also identified additional duplexes in the ARF1 3′ UTR. Interestingly, two newly identified duplexes are part of overlapping secondary structures, both of which represent the minimum free energy of folding for the local sequence, as predicted by RNAfold24 (shown on the right). This suggests that some regions of the ARF1 3′ UTR may adopt alternative conformations. The overlapping region of the two structures is shaded in blue. d, The constructs of reporters (ARF1 WT and Δ) used for the validation of the STAU1 binding to the known STAU1-target RNA duplex by formaldehyde crosslinking and co-immunoprecipitation experiment are shown. The reporter has firefly luciferase (FLuc) CDS and ARF1 3′ UTR. e, The ratio of ARF1 WT and Δ in total cell lysate fraction (input) or STAU1 co-immunopreciptated fraction (Co-IPed) were analysed by RT–PCR using forward primer annealed to CDS of FLuc and reverse primer annealed to downstream of the deletion site. The ratios (log2) of two populations were compared by the two-tailed Welch’s t-test (n = 3). The corresponding Qiaxcel electropherograms are available at: (

Extended Data Figure 3 Hybrid reads identify RNA duplexes.

a, Analysis of hybrid reads in 3′ UTRs (taking into account only those where left and right arm originated from same gene) demonstrates significantly smaller minimum free energies of hybridization between the two arms of hybrid reads compared to randomly repositioned sequences as calculated by RNAhybrid47. The probability density distribution of the minimum free energies is plotted, and the distributions for hybrid reads and randomly repositioned sequences were compared using the Mann–Whitney U-test (n = 4,492 for both hybrid reads and random RNAs). b, Similar to a, but for hybrid reads in CDS (n = 958). c, Similar to a, but for intermolecular hybrid reads (that is, hybrid reads whose left arm and right arm originated from different genes; n = 257). d, Similar to a, but for hybrid reads in rRNAs (n = 3,502). e, Median normalized PARS scores were calculated around centre of all mRNA duplexes. PARS scores were obtained from Wan et al.4, and positions with 0 values were removed. PARS score represents a ratio between reads starts after cutting with dsRNase (positive) / ssRNase (negative). Assuming that the double-stranded RNase fully digests each duplex, it is expected that the positive values in PARS-seq will be highest at the last nucleotide of each duplex. This might explain why maximum PARS values occur at the positions closer to the 3′ end of duplexes. f, Metaprofiles of the distribution of STAU1 crosslink sites, identified by the start sites of non-hybrid hiCLIP reads (blue), or the randomly repositioned sites (black, mean value of 10 randomizations; grey, standard deviation of the 10 randomizations) around the positions of hiCLIP duplexes. g, Distribution of median probability of RNA bases to be single-stranded from position −50 to +100 nucleotides around the STAU1 crosslink sites.

Extended Data Figure 4 Analysis of duplexes identified by hiCLIP in the secondary structure of human ribosomal RNAs.

a, The position of duplexes identified by hybrid reads in the 28S rRNA secondary structure. 2,962 hybrids mapped to 28S rRNA. We first removed the duplexes with only one read, obtaining a final list of 2,816 hybrid reads uniquely mapped to 28S rRNA, and 2,020 of these reads (72%) identified duplexes that were previously determined with CryoEM structure. These duplexes are marked by blue rectangles, and the number of reads that identify each duplex is marked. 756 hybrid reads (27%) map to different double stranded regions of the 28S rRNA while the remaining 40 hybrid reads (1%) map to single strand regions of the rRNA 28S known structure. The metazoan-specific rRNA expansion segments are indicated by grey shadowing. b, Similar to a, but the position of duplexes identified by hybrid reads in the 18S rRNA secondary structure. 218 hybrid reads uniquely mapped to 18S rRNA, and 170 of these reads identified duplexes that were previously determined with CryoEM structure. Red lines mark the putative newly identified duplexes that are not part of the CryoEM structure, but are complementary and are identified by hybrid reads. The numbers next to the lines shows the number of hybrid reads that identify each of these putative duplexes, and the three newly identified duplexes (helices) that are aligned in Fig. 1e are marked as hA, hB and hC. Complementarity of the novel duplex is conserved from yeast to human as seen at: ( The rRNA secondary structure is reprinted by permission from Macmillan Publishers Ltd: Nature copyright (2013)21.

Extended Data Figure 5 Analysis of RNA types, stem lengths, and sequence motifs at STAU1-target RNA duplexes.

a, Relative proportions of RNA types that were identified by hybrid reads, where each arm maps to the same (left) or different RNAs (right). b, Distribution of duplex stem lengths for 3′ UTR duplexes. c, Sequence motifs enriched at STAU1-target 3′ UTR duplexes when compared to surrounding regions of same 3′ UTRs (controls). d, Purine content is plotted within each arm of randomly selected 2,291 3′ UTR duplexes that were detected using RNAfold24 in mRNAs that don’t contain any STAU1-target hiCLIP duplex in their 3′ UTR, and surrounding sequence up to 40 nucleotides on each side. e, Purine content is plotted within each arm of 494 CDS–CDS STAU1-target duplexes and surrounding sequence up to 40 nucleotides on each side. f, Boxplots showing the frequencies of consecutive purine tracks in the 3′ UTR STAU1-target duplexes (boxplot on the left) and in the randomly selected 3′ UTR duplexes that were detected using RNAfold in mRNAs that don’t contain any STAU1-target hiCLIP duplex in their 3′ UTR (boxplot on the right).

Extended Data Figure 6 Analysis of repeat elements and SNPs at STAU1-target RNA duplexes.

a, Enrichment analysis of repeat elements. The Repeat Enrichment Estimator software51 was used to calculate the enrichment of repeat elements in the cytoplasmic STAU1 iCLIP reads (that is, hiCLIP non-hybrid reads), STAU1 iCLIP reads from the total cellular fraction compared to mRNA-Seq reads. The enrichment was calculated by comparing the proportion enrichment of repeat elements in the cytoplasmic STAU1 iCLIP (that is, hiCLIP non-hybrid reads), STAU1 iCLIP from the total cellular fraction, and hnRNP C iCLIP reads with normalized relative to mRNA-Seq reads using Repeat Enrichment Estimator software51. For comparison, we show same analysis of hnRNP C, which is known to bind Alu sequences59. The enrichment was estimated for all repeat element families (for example, AluJb, AluJo, LTR1, and LTR10A) defined by the Repeat Enrichment Estimator. The panels show the enrichment distribution of different types of repeat element in histogram (top) or box plot (bottom) format; all families of Alu repeat elements are plotted in red, and all remaining repeat elements are plotted in grey. b, Box plots showing the SNPs frequency in all the 3′ UTRs of the targets (box plot on the left) and inside the duplexes in the 3′ UTRs. A binomial test has been performed to test the statistical significance of the depletion of SNPs inside the duplexes, compared to the total 3′ UTRs (P < 10−4). c, Normalized count profile of SNPs occurrence in the long-range 3′ UTR–3′ UTR hiCLIP duplexes; those that have a loop longer than 500 nucleotides. d, Normalized count profile of SNPs occurrence in random control 3′ UTR–3′ UTR duplexes with a loop of at least 80 nucleotides that are identified by RNAfold24 but not by STAU1 hiCLIP.

Extended Data Figure 7 Analysis of mRNA abundance and translational efficiency with mRNA-seq and ribosome profiling.

a, Venn diagram shows that few mRNAs contain hiCLIP duplexes both in their 3′ UTR and CDS. b, Metagene analysis of ribosome profiling reads. The position +12 from the 5′ end of the sequence reads was used as the definition of the read positions. The number of reads mapped around start codon or stop codon is shown. The colour of dots corresponds to the positions in each codon. The sharp peak at start codon shows our definition of read position well approximated the position of ribosome A site, and trinucleotide periodicity of peak confirms that the reads captured the codon dependent positioning of ribosome. c, Translational efficiency was independent of the mRNA level (RPKN: read per kilo base per normalized library). All mRNAs that passed our filter are plotted (see Methods, for details). d, Western blotting analysis for untreated (UT), STAU1 knockdown (KD) and rescue condition (RC; knockdown with inducible expression of siRNA-resistant STAU1 in knockdown cells) for the cell sample used for ribosome profiling and mRNA-seq library generation. Duplicate experiments were performed for each condition. e, In order to examine the potential off-target effects of siRNA treatment, we analysed sequence motifs enriched in mRNAs downregulated in KD condition compared to UT using SylArray56. The plot shows the incremental hypergeometric P value of the 3 most significantly enriched 7mer motifs in the gene list sorted by the downregulation level (the leftmost gene is the most downregulated). The most significantly enriched motif corresponded to the seed sequence of siRNA, indicating that most changes in mRNA abundance between UT and KD corresponded to off-target effects. Therefore, we focused our analyses on the comparison between RC and KD, which had no significant enrichment of such motifs by the SylArray analysis. f, The cumulative fraction of mRNAs relative to their fold change of mRNA abundance or translation efficiency between STAU1 rescue (RC) and knockdown (KD) cells is plotted. The p-values were calculated by the Mann–Whitney U-test (n = 2,269, 752, and 12,122 for the RNAs containing the duplexes in their 3′ UTR or CDS or other mRNAs for the analysis of mRNA abundance and n = 1,986, 694, and 8,199 for the mRNAs containing the duplexes in their 3′ UTR or CDS or other mRNAs for the analysis of translational efficiency).

Extended Data Figure 8 STAU1 regulates the cytoplasmic splicing of XBP1 mRNA.

a, Schematic represents the unspliced (XBP1(u)) and spliced (XBP1(s)) mRNAs, together with hybrid reads that identified a long-range STAU1-target RNA structure. The constructs of reporters (XBP1 wt and Δ) used for the validation by formaldehyde crosslinking, co-immunoprecipitation or reporter assay experiment are shown at the bottom. b, STAU1 interacts with XBP1 mRNA via the long-range RNA structure. The ratio of XBP1 wt and Δ in total cell lysate fraction (Input) or STAU1 co-immunoprecipitated fraction (Co-IPed) were analysed by RT–PCR. The ratios (log2) of two populations were compared by the two-tailed Welch’s t-test (n = 3). The corresponding Qiaxcel electropherograms are available at: ( c, Overview of XBP1(u) and XBP1(s) mRNAs. The position of cytoplasmic splicing site is indicated by an arrow. The longer RNA duplex overlaps with the region translated in XBP1(s). d, Thapsigargin induces endoplasmic reticulum stress and cytoplasmic splicing of XBP1 mRNA. After the induction of endoplasmic reticulum stress by thapsigargin, cells were lysed, and the alternative region of XBP1(s) and XBP1(u) mRNAs was amplified by PCR. The PCR products of XBP1(s) and XBP1(u) mRNAs were examined by the electropherogram (the expected sizes of PCR products from XBP1(s) and XBP1(u) mRNAs are indicated as s and u respectively, while a hybrid PCR product is indicated by 38. We confirmed that after 30 min of the endoplasmic reticulum stress induction, XBP1 mRNA was actively spliced. e, Real-time PCR analysis of reporter mRNA levels containing wt, mut or com XBP1 3′ UTR (as marked in a). Analysis is done in untreated cells (UT), in cells treated with siRNA against STAU1 (KD), or in cells where siRNA-resistant STAU1 is induced with doxycycline to rescue expression of STAU1 in spite of knockdown (RC). Differences in expression were compared by the two-tailed Student’s t-test. The two independent experiments are marked by black and blue colour, and each dot represents replicates performed on separate wells of cells as part of the same experiment. Disruption of duplex destabilizes the mRNA, and compensatory mutation restores the stability slightly above the wt level. This may be because the duplex in the ‘com’ reporter is longer by 2 nucleotides compared to ‘wt’. The mechanism whereby the long-range duplex impacts mRNA stability remains to be determined.

Extended Data Figure 9 Gene Ontology analysis and schematic of STAU1 function in 293 cells.

Gene ontology analysis of the genes bound by STAU1 in the 3′ UTR (red) and in the CDS (purple), using the DAVID Gene Ontology Tool57 and visualized using ReviGO58. Node colour indicates the P value (threshold: P value <10−6, FDR <0.01), and node size indicates the frequency of the GO term in the GOA database. Each gene is mapped only to the most specific terms that are applicable to it (in each ontology). Highly similar GO terms are linked by edges in the graph, with the edge width depicting the degree of similarity. a, Diagram summarizing the enriched Biological Processes GO terms. 3′ UTR-bound mRNAs tend to encode proteins that function in intracellular transport (in red), whereas CDS-bound mRNAs tend to encode proteins that function in the cell cycle M phase (in blue). b, Diagram summarizing the enriched Cellular Components GO terms in the context of their location in the cell. 3′ UTR-bound mRNAs tend to encode membrane proteins that are translated at the endoplasmic reticulum (in red), whereas CDS-bound mRNAs tend to encode nuclear proteins (in blue). c, Schematic diagram of the functional analyses of CDS and 3′ UTR-bound mRNAs. 3′ UTR-bound mRNAs tend to be highly translated and encode membrane proteins that are translated at the endoplasmic reticulum (in grey). CDS-bound mRNAs tend to be lowly translated and encode nuclear proteins that function in the cell cycle M phase. Loops formed by RNA duplexes in the 3′ UTR tend to be longer than in the CDS, and 3′ UTRs have higher density of bound duplexes.

Extended Data Figure 10 Analysis of confident duplexes that were identified by >1 hybrid read.

a, Sequence motifs enriched at confident STAU1-target RNA duplexes (those identified by >1 unique hybrid read). b, Visualization of purine content at confident duplexes (as in Fig. 2a). c, Normalized count profile of SNPs occurrence in confident 3′ UTR–3′ UTR duplexes with a loop of at least 80 nucleotides. d, Comparison of range (the length of loop plus duplex) distribution among all duplexes and confident duplexes located in 3′ UTRs. e, Gene Ontology analysis of mRNAs containing confident STAU1 duplexes in 3′UTRs (as in Extended Data Fig. 9).

Supplementary information

Supplementary Information

This file contains a discussion of challenges of the experimental methods available to study RNA structures in vivo. (PDF 146 kb)

Supplementary Table 1

This table contains mapping statistics of high-throughput sequencing data. (XLSX 36 kb)

Supplementary Table 2

This table contains genomic positions and sequences of STAU1 target mRNA duplexes that are located in CDS or 3'UTR. (XLSX 767 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sugimoto, Y., Vigilante, A., Darbo, E. et al. hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1. Nature 519, 491–494 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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