There has been a plethora of experimental approaches that combine traditional chemical probing techniques with next-generation sequencing. These methods have allowed the first glimpse of the 'RNA structurome'.
These studies have highlighted, on a genome-wide scale, how RNA structure controls functions and gene expression. It is now possible to identify structural motifs that affect both coding and non-coding RNA function.
Each step of protein translation is affected by RNA structure. Generally, the RNA surrounding the start and stops codons is poorly structured to allow ribosome regulation, and the coding region contains structured elements that can regulate protein expression.
Small-RNA-binding sites that regulate mRNA expression can be identified through degradome and immunoprecipitation studies to help to predict new small RNAs and their targets.
Recent in vivo studies indicate that RNA is less structured, or at least more structurally dynamic, in vivo than in vitro.
The future of this research field is likely to explore the structure of low-abundance RNAs, such as long non-coding RNAs, and tertiary RNA structure.
A comprehensive understanding of RNA structure will provide fundamental insights into the cellular function of both coding and non-coding RNAs. Although many RNA structures have been analysed by traditional biophysical and biochemical methods, the low-throughput nature of these approaches has prevented investigation of the vast majority of cellular transcripts. Triggered by advances in sequencing technology, genome-wide approaches for probing the transcriptome are beginning to reveal how RNA structure affects each step of protein expression and RNA stability. In this Review, we discuss the emerging relationships between RNA structure and the regulation of gene expression.
This is a preview of subscription content
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Sharp, P. A. The centrality of RNA. Cell 136, 577–580 (2009).
Cruz, J. A. & Westhof, E. The dynamic landscapes of RNA architecture. Cell 136, 604–609 (2009).
Warf, M. B. & Berglund, J. A. Role of RNA structure in regulating pre-mRNA splicing. Trends Biochem. Sci. 35, 169–178 (2010).
McManus, C. J. & Graveley, B. R. RNA structure and the mechanisms of alternative splicing. Curr. Opin. Genet. Dev. 21, 373–379 (2011).
Kozak, M. Regulation of translation via mRNA structure in prokaryotes and eukaryotes. Gene 361, 13–37 (2005).
Martin, K. C. & Ephrussi, A. mRNA localization: gene expression in the spatial dimension. Cell 136, 719–730 (2009).
Garneau, N. L., Wilusz, J. & Wilusz, C. J. The highways and byways of mRNA decay. Nature Rev. Mol. Cell Biol. 8, 113–126 (2007).
Mauger, D. M., Siegfried, N. A. & Weeks, K. M. The genetic code as expressed through relationships between mRNA structure and protein function. FEBS Lett. 587, 1180–1188 (2013).
Merino, E. J., Wilkinson, K. A., Coughlan, J. L. & Weeks, K. M. RNA structure analysis at single nucleotide resolution by selective 2′-hydroxyl acylation and primer extension (SHAPE). J. Am. Chem. Soc. 127, 4223–4231 (2005).
Laederach, A., Shcherbakova, I., Jonikas, M. A., Altman, R. B. & Brenowitz, M. Distinct contribution of electrostatics, initial conformational ensemble, and macromolecular stability in RNA folding. Proc. Natl Acad. Sci. 104, 7045–7050 (2007).
Adilakshmi, T., Bellur, D. L. & Woodson, S. A. Concurrent nucleation of 16S folding and induced fit in 30S ribosome assembly. Nature 455, 1268–1272 (2008).
Weeks, K. M. Advances in RNA structure analysis by chemical probing. Curr. Opin. Struct. Biol. 20, 295–304 (2010).
Hofacker, I. L. Vienna RNA secondary structure server. Nucleic Acids Res. 31, 3429–3431 (2003).
Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 31, 3406–3415 (2003).
Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M. & Turner, D. H. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. 101, 7287–7292 (2004).
Das, R. et al. Structural inference of native and partially folded RNA by high-throughput contact mapping. Proc. Natl Acad. Sci. 105, 4144–4149 (2008).
Ouyang, Z., Snyder, M. P. & Chang, H. Y. SeqFold: genome-scale reconstruction of RNA secondary structure integrating high-throughput sequencing data. Genome Res. 23, 377–387 (2013).
Smith, M. A., Gesell, T., Stadler, P. F. & Mattick, J. S. Widespread purifying selection on RNA structure in mammals. Nucleic Acids Res. 41, 8220–8236 (2013).
Lee, J. et al. RNA design rules from a massive open laboratory. Proc. Natl Acad. Sci. 111, 2122–2127 (2014).
Kertesz, M. et al. Genome-wide measurement of RNA secondary structure in yeast. Nature 467, 103–107 (2010). This paper reports the development of PARS and the secondary structure profile of budding yeast mRNA.
Zheng, Q. et al. Genome-wide double-stranded RNA sequencing reveals the functional significance of base-paired RNAs in Arabidopsis. PLoS Genet. 6, e1001141 (2010).
Underwood, J. G. et al. FragSeq: transcriptome-wide RNA structure probing using high-throughput sequencing. 7, 995–1001 (2010). This paper reports the development of FragSeq and uses it to map the structure of the mouse nuclear transcriptome.
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. 108, 11063–11068 (2011). This study presents the first multiplexed approach to structure probing using SHAPE with NGS readout.
Li, F. et al. Global analysis of RNA secondary structure in two metazoans. Cell Rep. 1, 69–82 (2012).
Li, F. et al. Regulatory impact of RNA secondary structure across the Arabidopsis transcriptome. Plant Cell 24, 4346–4359 (2012). References 24 and 25 apply ds/ssRNA-seq to the A. thaliana, D. melanogaster and C. elegans transcriptomes.
Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell 48, 169–181 (2012).
Ding, Y. et al. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature 505, 696–700 (2013).
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 (2013).
Wan, Y. et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature 505, 706–709 (2014). References 27–29 investigate the structure of RNA in vivo.
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).
Silverman, I. M., Li, F. & Gregory, B. D. Genomic era analyses of RNA secondary structure and RNA-binding proteins reveal their significance to post-transcriptional regulation in plants. Plant Science 205–206, 55–62 (2013).
Katz, L. & Burge, C. B. Widespread selection for local RNA secondary structure in coding regions of bacterial genes. Genome Res. 13, 2042–2051 (2003).
Watts, J. M. et al. Architecture and secondary structure of an entire HIV-1 RNA genome. Nature 460, 711–716 (2009). This is the first genome-wide study of RNA structure using SHAPE on ex virio HIV-1 RNA.
Mortimer, S. A. & Weeks, K. M. A fast-acting reagent for accurate analysis of RNA secondary and tertiary structure by SHAPE chemistry. J. Am. Chem. Soc. 129, 4144–4145 (2007).
Seetin, M., Kladwang, W., Bida, J. P. & Das, R. Massively parallel RNA chemical mapping with a reduced bias MAP–seq protocol. Methods Mol. Biol. 1086, 95–117 (2014).
Shabalina, S. A. A periodic pattern of mRNA secondary structure created by the genetic code. Nucleic Acids Res. 34, 2428–2437 (2006).
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).
Kudla, G., Murray, A. W., Tollervey, D. & Plotkin, J. B. Coding-sequence determinants of gene expression in Escherichia coli. Science 324, 255–258 (2009).
Scharff, L. B., Childs, L., Walther, D. & Bock, R. Local absence of secondary structure permits translation of mRNAs that lack ribosome-binding sites. PLoS Genet. 7, e1002155 (2011).
Gu, W., Zhou, T. & Wilke, C. O. A universal trend of reduced mRNA stability near the translation-initiation site in prokaryotes and eukaryotes. PLoS Comput. Biol. 6, e1000664 (2010).
Bentele, K., Saffert, P., Rauscher, R., Ignatova, Z. & Blüthgen, N. Efficient translation initiation dictates codon usage at gene start. Mol. Syst. Biol. 9, 675 (2013).
Goodman, D. B., Church, G. M. & Kosuri, S. Causes and effects of N-terminal codon bias in bacterial genes. Science 342, 475–479 (2013).
Mao, Y., Wang, W., Cheng, N., Li, Q. & Tao, S. Universally increased mRNA stability downstream of the translation initiation site in eukaryotes and prokaryotes. Gene 517, 230–235 (2013).
Tuller, T., Waldman, Y. Y., Kupiec, M. & Ruppin, E. Translation efficiency is determined by both codon bias and folding energy. Proc. Natl Acad. Sci. 107, 3645–3650 (2010).
Komar, A. A. A pause for thought along the co-translational folding pathway. Trends Biochem. Sci. 34, 16–24 (2009).
Wolin, S. L. & Walter, P. Ribosome pausing and stacking during translation of a eukaryotic mRNA. EMBO J. 7, 3559–3569 (1988).
Shah, P., Ding, Y., Niemczyk, M., Kudla, G. & Plotkin, J. Rate-limiting steps in yeast protein translation. Cell 153, 1589–1601 (2013).
Meyer, I. M. Statistical evidence for conserved, local secondary structure in the coding regions of eukaryotic mRNAs and pre-mRNAs. Nucleic Acids Res. 33, 6338–6348 (2005).
Dana, A. & Tuller, T. Determinants of translation elongation speed and ribosomal profiling biases in mouse embryonic stem cells. PLoS Comput. Biol. 8, e1002755 (2012).
Nackley, A. G. et al. Human catechol-O-methyltransferase haplotypes modulate protein expression by altering mRNA secondary structure. Science 314, 1930–1933 (2006).
Wen, J. et al. Following translation by single ribosomes one codon at a time. Nature 452, 598–603 (2008).
Han, Y. et al. Monitoring cotranslational protein folding in mammalian cells at codon resolution. Proc. Natl Acad. Sci. 109, 12467–12472 (2012).
Zur, H. & Tuller, T. Strong association between mRNA folding strength and protein abundance in S. cerevisiae. EMBO Rep. 13, 272–277 (2012).
Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).
Newman, J. R. S. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).
Lee, M. V. et al. A dynamic model of proteome changes reveals new roles for transcript alteration in yeast. Mol. Syst. Biol. 7, 514 (2011).
Arava, Y. et al. Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. 100, 3889–3894 (2003).
Lécuyer, E. et al. Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. Cell 131, 174–187 (2007).
Holt, C. E. & Bullock, S. L. Subcellular mRNA localization in animal cells and why it matters. Science 326, 1212–1216 (2009).
Jambhekar, A. & DeRisi, J. Cis-acting determinants of asymmetric, cytoplasmic RNA transport. RNA 13, 625–642 (2007).
Chartrand, P., Meng, X. H., Huttelmaier, S., Donato, D. & Singer, R. H. Asymmetric sorting of ash1p in yeast results from inhibition of translation by localization elements in the mRNA. Mol. Cell 10, 1319–1330 (2002).
Palazzo, A. F. et al. The signal sequence coding region promotes nuclear export of mRNA. PLoS Biol. 5, e322 (2007).
Dienstbier, M., Boehl, F., Li, X. & Bullock, S. L. Egalitarian is a selective RNA-binding protein linking mRNA localization signals to the dynein motor. Genes Dev. 23, 1546–1558 (2009).
Bullock, S. L., Ringel, I., Ish-Horowicz, D. & Lukavsky, P. J. A′-form RNA helices are required for cytoplasmic mRNA transport in Drosophila. Nature Struct. Mol. Biol. 17, 703–709 (2010).
Skripkin, E. A., Adhin, M. R., de Smit, M. H. & van Duin, J. Secondary structure of the central region of bacteriophage MS2 RNA. Conservation and biological significance. J. Mol. Biol. 211, 447–463 (1990).
Kortmann, J. & Narberhaus, F. Bacterial RNA thermometers: molecular zippers and switches. Nature Rev. Microbiol. 10, 255–265 (2012).
Chowdhury, S., Maris, C., Allain, F. H. & Narberhaus, F. Molecular basis for temperature sensing by an RNA thermometer. EMBO J. 25, 2487–2497 (2006).
Meyer, M., Plass, M., Pérez-Valle, J., Eyras, E. & Vilardell, J. Deciphering 3′SS selection in the yeast genome reveals an RNA thermosensor that mediates alternative splicing. Mol. Cell 43, 1033–1039 (2011).
Vandivier, L. et al. Arabidopsis mRNA secondary structure correlates with protein function and domains. Plant Signal. Behav. 8, e24301 (2013).
Gasch, A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000).
Bonneau, F. et al. The yeast exosome functions as a macromolecular cage to channel RNA substrates for degradation. Cell 139, 547–559 (2009).
Panniers, R. Translational control during heat shock. Cell 76, 737–747 (1994).
Wilson, R. C. & Doudna, J. A. Molecular mechanisms of RNA interference. 42, 217–239 (2013).
Gantier, M. P. & Williams, B. R. G. The response of mammalian cells to double-stranded RNA. Cytokine Growth Factor Rev. 18, 363–371 (2007).
Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U. & Segal, E. The role of site accessibility in microRNA target recognition. Nature Genet. 39, 1278–1284 (2007).
Low, J. T. et al. SHAPE-directed discovery of potent shRNA inhibitors of HIV-1. Mol. Ther. 20, 820–828 (2012).
Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).
Ruby, J. G. et al. Evolution, biogenesis, expression, and target predictions of a substantially expanded set of Drosophila microRNAs. Genome Res. 17, 1850–1864 (2007).
Dai, X., Zhuang, Z. & Zhao, P. X. Computational analysis of miRNA targets in plants: current status and challenges. Brief. Bioinform. 12, 115–121 (2011).
Addo-Quaye, C., Eshoo, T. W., Bartel, D. P. & Axtell, M. J. Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr. Biol. 18, 758–762 (2008).
German, M. A. et al. Global identification of microRNA–target RNA pairs by parallel analysis of RNA ends. Nature Biotech. 26, 941–946 (2008).
Gregory, B. D. et al. A link between RNA metabolism and silencing affecting Arabidopsis development. Dev. Cell 14, 854–866 (2008).
Willmann, M. R., Berkowitz, N. D. & Gregory, B. D. Improved genome-wide mapping of uncapped and cleaved transcripts in eukaryotes—GMUCT 2.0. Methods http://dx.doi.org/10.1016/j.ymeth.2013.07.003 (2013).
Chi, S. W., Zang, J. B., Mele, A. & Darnell, R. B. Argonaute HITS–CLIP decodes microRNA–mRNA interaction maps. Nature 479, 479–486 (2009).
Zisoulis, D. G. et al. Comprehensive discovery of endogenous Argonaute binding sites in Caenorhabditis elegans. Nature Struct. Mol. Biol. 17, 173–179 (2010).
Yang, J. et al. starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP–seq and degradome–seq data. Nucleic Acids Res. 39, D202–209 (2011).
Li, J., Liu, S., Zhou, H., Qu, L. & Yang, J. starBase v2.0: decoding miRNA–ceRNA, miRNA–ncRNA and protein–RNA interaction networks from large-scale CLIP–seq data. Nucleic Acids Res. 42, D92–D97 (2014).
Wells, S. E., Hughes, J. M., Igel, A. H. & Ares, M. Jr. Use of dimethyl sulfate to probe RNA structure in vivo. Methods Enzymol. 318, 479–493 (2000).
Ziehler, W. A. & Engelke, D. R. in Current Protocols in Nucleic Acid Chemistry Unit 6.1 (Wiley, 2001).
Spitale, R. C. et al. RNA SHAPE analysis in living cells. Nature Chem. Biol. 9, 18–20 (2012).
Tyrrell, J., McGinnis, J. L., Weeks, K. M. & Pielak, G. J. The cellular environment stabilizes adenine riboswitch RNA structure. Biochemistry 52, 8777–8785 (2013).
Kwok, C. K., Ding, Y., Tang, Y., Assmann, S. M. & Bevilacqua, P. C. Determination of in vivo RNA structure in low-abundance transcripts. Nature Commun. 4, 2971 (2013).
Tijerina, P., Mohr, S. & Russell, R. DMS footprinting of structured RNAs and RNA–protein complexes. Nature Protoc. 2, 2608–2623 (2007).
Halvorsen, M., Martin, J. S., Broadaway, S. & Laederach, A. Disease-associated mutations that alter the RNA structural ensemble. PLoS Genet. 6, e1001074 (2010).
Stoddard, C. D. et al. Free state conformational sampling of the SAM-I riboswitch aptamer domain. Structure 18, 787–797 (2010).
Fang, X. et al. An unusual topological structure of the HIV-1 rev response element. Cell 155, 594–605 (2013).
Kladwang, W. & Van Lang, C. C., Cordero, P. & Das, R. A two-dimensional mutate-and-map strategy for non-coding RNA structure. Nature Chem. 3, 954–962 (2011).
Kielpinski, L. J. & Vinther, J. Massive parallel-sequencing-based hydroxyl radical probing of RNA accessibility. Nucleic Acids Res. 42, e70 (2014).
Mercer, T. R. & Mattick, J. S. Structure and function of long noncoding RNAs in epigenetic regulation. Nature Struct. Mol. Biol. 20, 300–307 (2013).
Novikova, I. V., Hennelly, S. P., Tung, C. & Sanbonmatsu, K. Y. Rise of the RNA machines: exploring the structure of long non-coding RNAs. J. Mol. Biol. 425, 3731–3746 (2013).
Clote, P. Structural RNA has lower folding energy than random RNA of the same dinucleotide frequency. RNA 11, 578–591 (2005).
Aviran, S. et al. Modeling and automation of sequencing-based characterization of RNA structure. Proc. Natl Acad. Sci. 108, 11069–11074 (2011).
Kelly, B. N. et al. Implications for viral capsid assembly from crystal structures of HIV-1 Gag1–278 and CAN133–278 . Biochemistry 45, 11257–11266 (2006).
Worthylake, D. K., Wang, H., Yoo, S., Sundquist, W. I. & Hill, C. P. Structures of the HIV-1 capsid protein dimerization domain at 2.6 Å resolution. Acta Crystallogr. D Biol. Crystallogr. 55, 85–92 (1999).
The authors apologize to colleagues whose work was not cited owing to space limitation. They thank Y. Bai, R. Wilson, S. Floor, M. Hammond and members of J.A.D.'s laboratory for discussions; K. Weeks for sharing HIV-1 SHAPE data; and J. Ji for reading the manuscript. This work was supported in part by a grant from the US National Institutes of Health (to J.A.D.). J.A.D. is a Howard Hughes Medical Institute Investigator.
The authors declare no competing financial interests.
- Secondary structures
Two-dimensional non-covalent interactions within an RNA molecule that consist of contiguous base pairs and loops. RNA molecules contain both canonical Watson–Crick base pairs and many non-canonical base pairs.
- Tertiary structures
Non-covalent interactions of separate secondary structures within an RNA molecule. These interactions occur between the bases and 2′OH ribose to create precise three- dimensional structures.
- Next-generation sequencing
(NGS). A high-throughput technology that simultaneously sequences millions of different DNA molecules by non-Sanger sequencing methods (most commonly sequencing by synthesis).
- RNA structurome
The collective group of secondary and tertiary structures formed within the transcriptome of a given organism.
- Shine–Dalgarno sequences
Ribosome-binding sites in bacterial mRNA. They are generally located eight bases upstream of the AUG start codon and contain the six-base consensus sequence AGGAGG.
- Folding energies
The Gibbs free energies associated with particular RNA folds or structures.
- Ribosome profiling
Qualitative and quantitative sequencing of the RNAs attached to ribosomes as a signature of RNAs that are being translated.
- Signal peptide
A short peptide of 5–30 amino acids at the amino terminus of newly synthesized proteins in the secretory pathway.
Single-nucleotide variants that alter RNA structure and that potentially affect gene regulation.
About this article
Cite this article
Mortimer, S., Kidwell, M. & Doudna, J. Insights into RNA structure and function from genome-wide studies. Nat Rev Genet 15, 469–479 (2014). https://doi.org/10.1038/nrg3681
BMC Bioinformatics (2022)
Nature Methods (2022)
Structural landscape of the complete genomes of dengue virus serotypes and other viral hemorrhagic fevers
BMC Genomics (2021)
A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network
BMC Bioinformatics (2021)
Structural-profiling of low molecular weight RNAs by nanopore trapping/translocation using Mycobacterium smegmatis porin A
Nature Communications (2021)