Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs RNA
Impact Factor: 4.928

Transcending the prediction paradigm: novel applications of SHAPE to RNA function and evolution

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Selective 2′‐hydroxyl acylation analyzed by primer extension (SHAPE) provides information on RNA structure at single‐nucleotide resolution. It is most often used in conjunction with RNA secondary structure prediction algorithms as a probabilistic or thermodynamic restraint. With the recent advent of ultra‐high‐throughput approaches for collecting SHAPE data, the applications of this technology are extending beyond structure prediction. In this review, we discuss recent applications of SHAPE data in the transcriptomic context and how this new experimental paradigm is changing our understanding of these experiments and RNA folding in general. SHAPE experiments probe both the secondary and tertiary structure of an RNA, suggesting that model‐free approaches for within and comparative RNA structure analysis can provide significant structural insight without the need for a full structural model. New methods incorporating SHAPE at different nucleotide resolutions are required to parse these transcriptomic data sets to transcend secondary structure modeling with global structural metrics. These ‘multiscale’ approaches provide deeper insights into RNA global structure, evolution, and function in the cell. WIREs RNA 2017, 8:e1374. doi: 10.1002/wrna.1374 This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA
SHAPE data for related RNAs follow similar but not identical patterns. (a) Examples of SHAPE data from high‐SHAPE regions (left) and low‐SHAPE regions (right) from the SIVmac239 SHAPE data from Pollom et al. High SHAPE nucleotides are indicated in red, medium in orange, and low in black. Although both profiles have low‐ and high‐SHAPE nucleotides, the frequencies of each are distinct between the two regions. (b) SHAPE data for SIVmac239 (top) and HIV‐1 (bottom) aligned genomes. SHAPE data are from Pollom et al., annotations are from Pollom et al. and the Los Alamos HIV database (http://www.hiv.lanl.gov/), and the sequences were aligned using MAFFT. Regional SHAPE represents the windowed median SHAPE over a 75‐nt window, with respect to the global median SHAPE value for each transcript. Values above the line are regionally unstructured, and below the line are regionally structured. Alignment regions where both viruses are regionally structured are annotated in gray. These regions correspond to known structural elements of the SIVmac virus (above, red). (c) SHAPE reactivities aligned by sequence for the RB1 5′ UTR in human (blue), cow (brown), and manatee (gray) from Kutchko et al. The SHAPE profiles for each species are similar across most of the UTR. (Reprinted with permission from Ref . Copyright 2015 Kutchko et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society).
[ Normal View | Magnified View ]
SHAPE reactivities distinguish between paired and unpaired nucleotides. (a) Distributions of SHAPE reactivities for paired and unpaired nucleotides recreated from Cordero et al. (left) and Sükösd et al. (right; data from Deigan et al.). SHAPE reactivities for paired nucleotides follow a generalized extreme value distribution in both data sets. SHAPE reactivities for unpaired nucleotides follow a generalized extreme value distribution in Cordero et al. and an exponential distribution in Sükösd et al. In both data sets, the distributions for paired and unpaired nucleotides differ, signifying that SHAPE reactivities are drawn from multiple probability distributions. (b) Example of RNA structure (the RB1 5′ UTR from Kutchko et al.) overlaid with SHAPE data. Red: high SHAPE; orange: medium SHAPE; black: low SHAPE; gray: no data. Most paired positions have low SHAPE and many unpaired positions have high SHAPE, but SHAPE does not completely distinguish between paired and unpaired nucleotides, in part because this RNA forms multiple conformations.
[ Normal View | Magnified View ]
SHAPE reactivities can help identify conservation of multiple RNA structures. (a) Figure from Ritz et al. Multiple conformations of an RNA are evolutionarily conserved. Top: Purine riboswitch consensus structure with the anti‐terminator pairs in red lines. Both the P1/terminator conformation and the anti‐terminator conformation must be conserved. Bottom: Evolutionary analysis of bases involved in the P1, terminator, and anti‐terminator stems. Blue indicates conservation of each base. Mutual information shows that the pairs involved in the anti‐terminator stem preserve their ability to base pair, but also to pair with their partners in the P1 and terminator stems. (Reprinted with permission from Ref . Copyright 2013 Ritz et al.; PLOS Computational Biology) (b) The 5′ UTR of RB1 forms multiple distinct structures in humans (blue), cow (brown), and manatee (gray), with SHAPE‐directed Boltzmann sampled structures indicated by blue dots. Structures and arc diagrams on the side show representative structures from each conformation. Green and orange stems are conserved across all three organisms. Thus, SHAPE‐directed structure prediction allows us to confidently identify the conservation of multiple RNA structures. (Reprinted with permission from Ref . Copyright 2015 Kutchko et al.; Cold Spring Harbor Laboratory Press).
[ Normal View | Magnified View ]
SHAPE data of homologous RNAs can facilitate sequence alignments. (a) Schematic of SHAPE‐directed sequence alignment used in Lavender et al. (1) SHAPE reactivities, and optionally nucleotide identity, can be used as parameters in the Gotoh alignment algorithm for pairwise alignment. Pairwise alignments can then be combined to create a SHAPE‐informed multiple sequence alignment. (b) Scoring function from Lavender et al. (1) used to align SHAPE reactivities for two sequences. More similar SHAPE reactivities have a higher score for alignment. If SHAPE reactivities differ by more than 1, the scoring function treats them as unrelated. (c) SHAPE‐only alignment of a section from three 16S rRNA sequences. Data from Lavender et al. (1) The aligned SHAPE reactivities are very similar, reflecting structural homology. (d) SHAPE‐directed multiple sequence alignment of three lentivirus sequences. The aligned SHAPE profiles of each virus have similar patterns. (e) Significance of the correlation of SHAPE between the three viruses, determined by linear regression. Lower p‐values indicate more similarity in SHAPE profiles. The SHAPE profiles have regions of significant correlation across the alignment, particularly in regions known to have functional RNA structures. (Reprinted with permission from Ref Copyright 2015 Lavender et al.; PLOS Computational Biology and Ref Copyright 2015 Lavender et al.; PLoS Computational Biology).
[ Normal View | Magnified View ]

Related Articles

The potential of the riboSNitch in personalized medicine
RNA structural analysis by evolving SHAPE chemistry

Browse by Topic

RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution
RNA Evolution and Genomics > Computational Analyses of RNA
RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts