Correlating SHAPE signatures with three-dimensional RNA structures

Consistent features observed in structural probing data of eukaryotic RNAs

Understanding RNA structure is crucial for elucidating its regulatory mechanisms. With the recent commercialization of messenger RNA vaccines, the profound impact of RNA structure on stability and translation efficiency has become increasingly evident, underscoring the importance of understanding RNA structure. Chemical probing of RNA has emerged as a powerful technique for investigating RNA structure in living cells. This approach utilizes chemical probes that selectively react with accessible regions of RNA, and by measuring reactivity, the openness and potential of RNA for protein binding or base pairing can be inferred. Extensive experimental data generated using RNA chemical probing have significantly contributed to our understanding of RNA structure in cells. However, it is crucial to acknowledge potential biases in chemical probing data to ensure an accurate interpretation. In this study, we comprehensively analyzed transcriptome-scale RNA chemical probing data in eukaryotes and report common features. Notably, in all experiments, the number of bases modified in probing was small, the bases showing the top 10% reactivity well reflected the known secondary structure, bases with high reactivity were more likely to be exposed to solvent and low reactivity did not reflect solvent exposure, which is important information for the analysis of RNA chemical probing data.

C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data

RNAs can interact with other molecules in their environment, such as ions, proteins or other RNAs, to form complexes with important biological roles. The prediction of the structure of these complexes is therefore an important issue and a difficult task. We are interested in RNA complexes composed of several (more than two) interacting RNAs. We show how available knowledge on the considered RNAs can help predict their secondary structure. We propose an interactive tool for the prediction of RNA complexes, called C-RCPRed, that considers user knowledge and probing data (which can be generated experimentally or artificially). C-RCPred is based on a multi-objective optimization algorithm. Through an extensive benchmarking procedure, which includes state-of-the-art methods, we show the efficiency of the multi-objective approach and the positive impact of considering user knowledge and probing data on the prediction results. C-RCPred is freely available as an open-source program and web server on the EvryRNA website (https://evryrna.ibisc.univ-evry.fr).

RNA:RNA interaction in ternary complexes resolved by chemical probing

RNA regulation can be performed by a second targeting RNA molecule, such as in the microRNA regulation mechanism. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) probes the structure of RNA molecules and can resolve RNA:protein interactions, but RNA:RNA interactions have not yet been addressed with this technique. Here, we apply SHAPE to investigate RNA-mediated binding processes in RNA:RNA and RNA:RNA-RBP complexes. We use RNA:RNA binding by SHAPE (RABS) to investigate microRNA-34a (miR-34a) binding its mRNA target, the silent information regulator 1 (mSIRT1), both with and without the Argonaute protein, constituting the RNA-induced silencing complex (RISC). We show that the seed of the mRNA target must be bound to the microRNA loaded into RISC to enable further binding of the compensatory region by RISC, while the naked miR-34a is able to bind the compensatory region without seed interaction. The method presented here provides complementary structural evidence for the commonly performed luciferase-assay-based evaluation of microRNA binding-site efficiency and specificity on the mRNA target site and could therefore be used in conjunction with it. The method can be applied to any nucleic acid-mediated RNA- or RBP-binding process, such as splicing, antisense RNA binding, or regulation by RISC, providing important insight into the targeted RNA structure.

Advances and opportunities in RNA structure experimental determination and computational modeling

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.

Characteristic chemical probing patterns of loop motifs improve prediction accuracy of RNA secondary structures

RNA structures play a fundamental role in nearly every aspect of cellular physiology and pathology. Gaining insights into the functions of RNA molecules requires accurate predictions of RNA secondary structures. However, the existing thermodynamic folding models remain less accurate than desired, even when chemical probing data, such as selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reactivities, are used as restraints. Unlike most SHAPE-directed algorithms that only consider SHAPE restraints for base pairing, we extract two-dimensional structural features encoded in SHAPE data and establish robust relationships between characteristic SHAPE patterns and loop motifs of various types (hairpin, internal, and bulge) and lengths (2-11 nucleotides). Such characteristic SHAPE patterns are closely related to the sugar pucker conformations of loop residues. Based on these patterns, we propose a computational method, SHAPELoop, which refines the predicted results of the existing methods, thereby further improving their prediction accuracy. In addition, SHAPELoop can provide information about local or global structural rearrangements (including pseudoknots) and help researchers to easily test their hypothesized secondary structures.

Computationally reconstructing cotranscriptional RNA folding from experimental data reveals rearrangement of non-native folding intermediates

The series of RNA folding events that occur during transcription can critically influence cellular RNA function. Here, we present reconstructing RNA dynamics from data (R2D2), a method to uncover details of cotranscriptional RNA folding. We model the folding of the Escherichia coli signal recognition particle (SRP) RNA and show that it requires specific local structural fluctuations within a key hairpin to engender efficient cotranscriptional conformational rearrangement into the functional structure. All-atom molecular dynamics simulations suggest that this rearrangement proceeds through an internal toehold-mediated strand-displacement mechanism, which can be disrupted with a point mutation that limits local structural fluctuations and rescued with compensating mutations that restore these fluctuations. Moreover, a cotranscriptional folding intermediate could be cleaved in vitro by recombinant E. coli RNase P, suggesting potential cotranscriptional processing. These results from experiment-guided multi-scale modeling demonstrate that even an RNA with a simple functional structure can undergo complex folding and processing during synthesis.

The molecular structure of long non-coding RNAs: emerging patterns and functional implications

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

Predicting RNA SHAPE scores with deep learning

Secondary structure prediction approaches rely typically on models of equilibrium free energies that are themselves based on in vitro physical chemistry. Recent transcriptome-wide experiments of in vivo RNA structure based on SHAPE-MaP experiments provide important information that may make it possible to extend current in vitro-based RNA folding models in order to improve the accuracy of computational RNA folding simulations with respect to the experimentally measured in vivo RNA secondary structure. Here we present a machine learning approach that utilizes RNA secondary structure prediction results and nucleotide sequence in order to predict in vivo SHAPE scores. We show that this approach has a higher Pearson correlation coefficient with experimental SHAPE scores than thermodynamic folding. This could be an important step towards augmenting experimental results with computational predictions and help with RNA secondary structure predictions that inherently take in-vivo folding properties into account.

Functional analysis reveals G/U pairs critical for replication and trafficking of an infectious non-coding viroid RNA

While G/U pairs are present in many RNAs, the lack of molecular studies to characterize the roles of multiple G/U pairs within a single RNA limits our understanding of their biological significance. From known RNA 3D structures, we observed that the probability a G/U will form a Watson-Crick (WC) base pair depends on sequence context. We analyzed 17 G/U pairs in the 359-nucleotide genome of Potato spindle tuber viroid (PSTVd), a circular non-coding RNA that replicates and spreads systemically in host plants. Most putative G/U base pairs were experimentally supported by selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE). Deep sequencing PSTVd genomes from plants inoculated with a cloned master sequence revealed naturally occurring variants, and showed that G/U pairs are maintained to the same extent as canonical WC base pairs. Comprehensive mutational analysis demonstrated that nearly all G/U pairs are critical for replication and/or systemic spread. Two selected G/U pairs were found to be required for PSTVd entry into, but not for exit from, the host vascular system. This study identifies critical roles for G/U pairs in the survival of an infectious RNA, and increases understanding of structure-based regulation of replication and trafficking of pathogen and cellular RNAs.

DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data

MOTIVATION

RNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution 'reactivities' for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.

RESULTS

Detection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BagheriLab/DUETT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-throughput determination of RNA structures

RNA performs and regulates a diverse range of cellular processes, with new functional roles being uncovered at a rapid pace. Interest is growing in how these functions are linked to RNA structures that form in the complex cellular environment. A growing suite of technologies that use advances in RNA structural probes, high-throughput sequencing and new computational approaches to interrogate RNA structure at unprecedented throughput are beginning to provide insights into RNA structures at new spatial, temporal and cellular scales.

The interplay between molecular flexibility and RNA chemical probing reactivities analyzed at the nucleotide level via an extensive molecular dynamics study

Determination of the tridimensional structure of ribonucleic acid molecules is fundamental for understanding their function in the cell. A common method to investigate RNA structures of large molecules is the use of chemical probes such as SHAPE (2^'-hydroxyl acylation analyzed by primer extension) reagents, DMS (dimethyl sulfate) and CMCT (1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfate), the reaction of which is dependent on the local structural properties of each nucleotide. In order to understand the interplay between local flexibility, sugar pucker, canonical pairing and chemical reactivity of the probes, we performed all-atom molecular dynamics simulations on a set of RNA molecules for which both tridimensional structure and chemical probing data are available and we analyzed the correlations between geometrical parameters and the chemical reactivity. Our study confirms that SHAPE reactivity is guided by the local flexibility of the different chemical moieties but suggests that a combination of multiple parameters is needed to better understand the implications of the reactivity at the molecular level. This is also the case for DMS and CMCT for which the reactivity appears to be more complex than commonly accepted.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

Background

Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.

Results

Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.

Conclusions

We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2645-4) contains supplementary material, which is available to authorized users.

Molecular Dynamics Simulations Reveal an Interplay between SHAPE Reagent Binding and RNA Flexibility

The function of RNA molecules usually depends on their overall fold and on the presence of specific structural motifs. Chemical probing methods are routinely used in combination with nearest-neighbor models to determine RNA secondary structure. Among the available methods, SHAPE is relevant due to its capability to probe all RNA nucleotides and the possibility to be used in vivo. However, the structural determinants for SHAPE reactivity and its mechanism of reaction are still unclear. Here molecular dynamics simulations and enhanced sampling techniques are used to predict the accessibility of nucleotide analogs and larger RNA structural motifs to SHAPE reagents. We show that local RNA reconformations are crucial in allowing reagents to reach the 2'-OH group of a particular nucleotide and that sugar pucker is a major structural factor influencing SHAPE reactivity.

Characterizing the Structure-Function Relationship of a Naturally Occurring RNA Thermometer

A large number of bacteria have been found to govern virulence and heat shock responses using temperature-sensing RNAs known as RNA thermometers. A prime example is the agsA thermometer known to regulate the production of the AgsA heat shock protein in Salmonella enterica using a "fourU" structural motif. Using the SHAPE-Seq RNA structure-probing method in vivo and in vitro, we found that the regulator functions by a subtle shift in equilibrium RNA structure populations that leads to a partial melting of the helix containing the ribosome binding site. We also demonstrate that binding of the ribosome to the agsA mRNA causes changes to the thermometer structure that appear to facilitate thermometer helix unwinding. These results demonstrate how subtle RNA structural changes can govern gene expression and illuminate the function of an important bacterial regulatory motif.

Distributed biotin-streptavidin transcription roadblocks for mapping cotranscriptional RNA folding

RNA folding during transcription directs an order of folding that can determine RNA structure and function. However, the experimental study of cotranscriptional RNA folding has been limited by the lack of easily approachable methods that can interrogate nascent RNA structure at nucleotide resolution. To address this, we previously developed cotranscriptional selective 2΄-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq) to simultaneously probe all intermediate RNA transcripts during transcription by stalling elongation complexes at catalytically dead EcoRIE111Q roadblocks. While effective, the distribution of elongation complexes using EcoRIE111Q requires laborious PCR using many different oligonucleotides for each sequence analyzed. Here, we improve the broad applicability of cotranscriptional SHAPE-Seq by developing a sequence-independent biotin-streptavidin (SAv) roadblocking strategy that simplifies the preparation of roadblocking DNA templates. We first determine the properties of biotin-SAv roadblocks. We then show that randomly distributed biotin-SAv roadblocks can be used in cotranscriptional SHAPE-Seq experiments to identify the same RNA structural transitions related to a riboswitch decision-making process that we previously identified using EcoRIE111Q. Lastly, we find that EcoRIE111Q maps nascent RNA structure to specific transcript lengths more precisely than biotin-SAv and propose guidelines to leverage the complementary strengths of each transcription roadblock in cotranscriptional SHAPE-Seq.

Cotranscriptional folding of a riboswitch at nucleotide resolution

RNAs can begin to fold immediately as they emerge from RNA polymerase. During cotranscriptional folding, interactions between nascent RNAs and ligands are able to direct the formation of alternative RNA structures, a feature exploited by noncoding RNAs called riboswitches to make gene-regulatory decisions. Despite their importance, cotranscriptional folding pathways have yet to be uncovered with sufficient resolution to reveal how cotranscriptional folding governs RNA structure and function. To access cotranscriptional folding at nucleotide resolution, we extended selective 2'-hydroxyl acylation analyzed by primer-extension sequencing (SHAPE-seq) to measure structural information of nascent RNAs during transcription. Using cotranscriptional SHAPE-seq, we determined how the cotranscriptional folding pathway of the Bacillus cereus crcB fluoride riboswitch undergoes a ligand-dependent bifurcation that delays or promotes terminator formation via a series of coordinated structural transitions. Our results directly link cotranscriptional RNA folding to a genetic decision and establish a framework for cotranscriptional analysis of RNA structure at nucleotide resolution.

Data-directed RNA secondary structure prediction using probabilistic modeling

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

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

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

For further resources related to this article, please visit the WIREs website.

Characterizing RNA structures in vitro and in vivo with selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq)

RNA molecules adopt a wide variety of structures that perform many cellular functions, including, among others, catalysis, small molecule sensing, and cellular defense. Our ability to characterize, predict, and design RNA structures are key factors for understanding and controlling the biological roles of RNAs. Fortunately, there has been rapid progress in this area, especially with respect to experimental methods that can characterize RNA structures in a high throughput fashion using chemical probing and next-generation sequencing. Here, we describe one such method, selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq), which measures nucleotide resolution flexibility information for RNAs in vitro and in vivo. We outline the process of designing and performing a SHAPE-Seq experiment and describe methods for using experimental SHAPE-Seq data to restrain computational folding algorithms to generate more accurate predictions of RNA secondary structure. We also provide a number of examples of SHAPE-Seq reactivity spectra obtained in vitro and in vivo and discuss important considerations for performing SHAPE-Seq experiments, both in terms of collecting and analyzing data. Finally, we discuss improvements and extensions of these experimental and computational techniques that promise to deepen our knowledge of RNA folding and function.

CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking

RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.

Requirement for Host RNA-Silencing Components and the Virus-Silencing Suppressor when Second-Site Mutations Compensate for Structural Defects in the 3' Untranslated Region

Turnip crinkle virus (TCV) contains a structured 3' region with hairpins and pseudoknots that form a complex network of noncanonical RNA:RNA interactions supporting higher-order structure critical for translation and replication. We investigated several second-site mutations in the p38 coat protein open reading frame (ORF) that arose in response to a mutation in the asymmetric loop of a critical 3' untranslated region (UTR) hairpin that disrupts local higher-order structure. All tested second-site mutations improved accumulation of TCV in conjunction with a partial reversion of the primary mutation (TCV-rev1) but had neutral or a negative effect on wild-type (wt) TCV or TCV with the primary mutation. SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) structure probing indicated that these second-site mutations reside in an RNA domain that includes most of p38 (domain 2), and evidence for RNA:RNA interactions between domain 2 and 3'UTR-containing domain 1 was found. However, second-site mutations were not compensatory in the absence of p38, which is also the TCV silencing suppressor, or in dcl-2/dcl4 or ago1/ago2 backgrounds. One second-site mutation reduced silencing suppressor activity of p38 by altering one of two GW motifs that are required for p38 binding to double-stranded RNAs (dsRNAs) and interaction with RNA-induced silencing complex (RISC)-associated AGO1/AGO2. Another second-site mutation substantially reduced accumulation of TCV-rev1 in the absence of p38 or DCL2/DCL4. We suggest that the second-site mutations in the p38 ORF exert positive effects through a similar downstream mechanism, either by enhancing accumulation of beneficial DCL-produced viral small RNAs that positively regulate the accumulation of TCV-rev1 or by affecting the susceptibility of TCV-rev1 to RISC loaded with viral small RNAs.

IMPORTANCE

Genomes of positive-strand RNA viruses fold into high-order RNA structures. Viruses with mutations in regions critical for translation and replication often acquire second-site mutations that exert a positive compensatory effect through reestablishment of canonical base pairing with the altered region. In this study, two distal second-site mutations that individually arose in response to a primary mutation in a critical 3' UTR hairpin in the genomic RNA of turnip crinkle virus did not directly interact with the primary mutation. Although different second-site changes had different attributes, compensation was dependent on the production of the viral p38 silencing suppressor and on the presence of silencing-required DCL and AGO proteins. Our results provide an unexpected connection between a 3' UTR primary-site mutation proposed to disrupt higher-order structure and the RNA-silencing machinery.

Simultaneous characterization of cellular RNA structure and function with in-cell SHAPE-Seq

Many non-coding RNAs form structures that interact with cellular machinery to control gene expression. A central goal of molecular and synthetic biology is to uncover design principles linking RNA structure to function to understand and engineer this relationship. Here we report a simple, high-throughput method called in-cell SHAPE-Seq that combines in-cell probing of RNA structure with a measurement of gene expression to simultaneously characterize RNA structure and function in bacterial cells. We use in-cell SHAPE-Seq to study the structure–function relationship of two RNA mechanisms that regulate translation in Escherichia coli. We find that nucleotides that participate in RNA–RNA interactions are highly accessible when their binding partner is absent and that changes in RNA structure due to RNA–RNA interactions can be quantitatively correlated to changes in gene expression. We also characterize the cellular structures of three endogenously expressed non-coding RNAs: 5S rRNA, RNase P and the btuB riboswitch. Finally, a comparison between in-cell and in vitro folded RNA structures revealed remarkable similarities for synthetic RNAs, but significant differences for RNAs that participate in complex cellular interactions. Thus, in-cell SHAPE-Seq represents an easily approachable tool for biologists and engineers to uncover relationships between sequence, structure and function of RNAs in the cell.

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.

Rational experiment design for sequencing-based RNA structure mapping

Structure mapping is a classic experimental approach for determining nucleic acid structure that has gained renewed interest in recent years following advances in chemistry, genomics, and informatics. The approach encompasses numerous techniques that use different means to introduce nucleotide-level modifications in a structure-dependent manner. Modifications are assayed via cDNA fragment analysis, using electrophoresis or next-generation sequencing (NGS). The recent advent of NGS has dramatically increased the throughput, multiplexing capacity, and scope of RNA structure mapping assays, thereby opening new possibilities for genome-scale, de novo, and in vivo studies. From an informatics standpoint, NGS is more informative than prior technologies by virtue of delivering direct molecular measurements in the form of digital sequence counts. Motivated by these new capabilities, we introduce a novel model-based in silico approach for quantitative design of large-scale multiplexed NGS structure mapping assays, which takes advantage of the direct and digital nature of NGS readouts. We use it to characterize the relationship between controllable experimental parameters and the precision of mapping measurements. Our results highlight the complexity of these dependencies and shed light on relevant tradeoffs and pitfalls, which can be difficult to discern by intuition alone. We demonstrate our approach by quantitatively assessing the robustness of SHAPE-Seq measurements, obtained by multiplexing SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) chemistry in conjunction with NGS. We then utilize it to elucidate design considerations in advanced genome-wide approaches for probing the transcriptome, which recently obtained in vivo information using dimethyl sulfate (DMS) chemistry.

Snapshots of pre-rRNA structural flexibility reveal eukaryotic 40S assembly dynamics at nucleotide resolution

Ribosome assembly in eukaryotes involves the activity of hundreds of assembly factors that direct the hierarchical assembly of ribosomal proteins and numerous ribosomal RNA folding steps. However, detailed insights into the function of assembly factors and ribosomal RNA folding events are lacking. To address this, we have developed ChemModSeq, a method that combines structure probing, high-throughput sequencing and statistical modeling, to quantitatively measure RNA structural rearrangements during the assembly of macromolecular complexes. By applying ChemModSeq to purified 40S assembly intermediates we obtained nucleotide-resolution maps of ribosomal RNA flexibility revealing structurally distinct assembly intermediates and mechanistic insights into assembly dynamics not readily observed in cryo-electron microscopy reconstructions. We show that RNA restructuring events coincide with the release of assembly factors and predict that completion of the head domain is required before the Rio1 kinase enters the assembly pathway. Collectively, our results suggest that 40S assembly factors regulate the timely incorporation of ribosomal proteins by delaying specific folding steps in the 3' major domain of the 20S pre-ribosomal RNA.

Magnesium-dependent folding of a picornavirus IRES element modulates RNA conformation and eIF4G interaction

Internal ribosome entry site (IRES) elements are high-order RNA structures that promote internal initiation of translation to allow protein synthesis under situations that compromise the general cap-dependent translation mechanism. Picornavirus IRES elements are highly efficient elements with a modular RNA structure organization. Here we investigated the effect of Mg(2+) concentration on the local flexibility and solvent accessibility of the foot-and-mouth disease virus (FMDV) IRES element measured on the basis of selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reactivity and hydroxyl radical cleavage. We have found that Mg(2+) concentration affects the organization of discrete IRES regions, mainly the apical region of domain 3, the 10 nt loop of domain 4, and the pyrimidine tract of domain 5. In support of the effect of RNA structure on IRES activity, substitution or deletion mutants of the 10 nt loop of domain 4 impair internal initiation. In addition, divalent cations affect the binding of eIF4G, a eukaryotic initiation factor that is essential for IRES-dependent translation that interacts with domain 4. Binding of eIF4G is favored by the local RNA flexibility adopted at low Mg(2+) concentration, while eIF4B interacts with the IRES independently of the compactness of the RNA structure. Our study shows that the IRES element adopts a near-native structure in the absence of proteins, shedding light on the influence of Mg(2+) ions on the local flexibility and binding of eIF4G in a model IRES element.

In-gel probing of individual RNA conformers within a mixed population reveals a dimerization structural switch in the HIV-1 leader

Definitive secondary structural mapping of RNAs in vitro can be complicated by the presence of more than one structural conformer or multimerization of some of the molecules. Until now, probing a single structure of conformationally flexible RNA molecules has typically relied on introducing stabilizing mutations or adjusting buffer conditions or RNA concentration. Here, we present an in-gel SHAPE (selective 2'OH acylation analysed by primer extension) approach, where a mixed structural population of RNA molecules is separated by non-denaturing gel electrophoresis and the conformers are individually probed within the gel matrix. Validation of the technique using a well-characterized RNA stem-loop structure, the HIV-1 trans-activation response element, showed that authentic structure was maintained and that the method was accurate and highly reproducible. To further demonstrate the utility of in-gel SHAPE, we separated and examined monomeric and dimeric species of the HIV-1 packaging signal RNA. Extensive differences in acylation sensitivity were seen between monomer and dimer. The results support a recently proposed structural switch model of RNA genomic dimerization and packaging, and demonstrate the discriminatory power of in-gel SHAPE.

In vitro secondary structure of the genomic RNA of satellite tobacco mosaic virus

Satellite tobacco mosaic virus (STMV) is a T = 1 icosahedral virus with a single-stranded RNA genome. It is widely accepted that the RNA genome plays an important structural role during assembly of the STMV virion. While the encapsidated form of the RNA has been extensively studied, less is known about the structure of the free RNA, aside from a purported tRNA-like structure at the 3' end. Here we use selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) analysis to examine the secondary structure of in vitro transcribed STMV RNA. The predicted secondary structure is unusual in the sense that it is highly extended, which could be significant for protecting the RNA from degradation. The SHAPE data are also consistent with the previously predicted tRNA-like fold at the 3' end of the molecule, which is also known to hinder degradation. Our data are not consistent with the secondary structure proposed for the encapsidated RNA by Schroeder et al., suggesting that, if the Schroeder structure is correct, either the RNA is packaged as it emerges from the replication complex, or the RNA undergoes extensive refolding upon encapsidation. We also consider the alternative, i.e., that the structure of the encapsidated STMV RNA might be the same as the in vitro structure presented here, and we examine how this structure might be organized in the virus. This possibility is not rigorously ruled out by the available data, so it remains open to examination by experiment.

Ensemble analysis of primary microRNA structure reveals an extensive capacity to deform near the Drosha cleavage site

Most noncoding RNAs function properly only when folded into complex three-dimensional (3D) structures, but the experimental determination of these structures remains challenging. Understanding of primary microRNA (miRNA) maturation is currently limited by a lack of determined structures for nonprocessed forms of the RNA. SHAPE chemistry efficiently determines RNA secondary structural information with single-nucleotide resolution, providing constraints suitable for input into MC-Pipeline for refinement of 3D structure models. Here we combine these approaches to analyze three structurally diverse primary microRNAs, revealing deviations from canonical double-stranded RNA structure in the stem adjacent to the Drosha cut site for all three. The necessity of these deformable sites for efficient processing is demonstrated through Drosha processing assays. The structure models generated herein support the hypothesis that deformable sequences spaced roughly once per turn of A-form helix, created by noncanonical structure elements, combine with the necessary single-stranded RNA-double-stranded RNA junction to define the correct Drosha cleavage site.

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence.

Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity

Messenger RNA is a key component of an intricate regulatory network of its own. It accommodates numerous nucleotide signals that overlap protein coding sequences and are responsible for multiple levels of regulation and generation of biological complexity. A wealth of structural and regulatory information, which mRNA carries in addition to the encoded amino acid sequence, raises the question of how these signals and overlapping codes are delineated along non-synonymous and synonymous positions in protein coding regions, especially in eukaryotes. Silent or synonymous codon positions, which do not determine amino acid sequences of the encoded proteins, define mRNA secondary structure and stability and affect the rate of translation, folding and post-translational modifications of nascent polypeptides. The RNA level selection is acting on synonymous sites in both prokaryotes and eukaryotes and is more common than previously thought. Selection pressure on the coding gene regions follows three-nucleotide periodic pattern of nucleotide base-pairing in mRNA, which is imposed by the genetic code. Synonymous positions of the coding regions have a higher level of hybridization potential relative to non-synonymous positions, and are multifunctional in their regulatory and structural roles. Recent experimental evidence and analysis of mRNA structure and interspecies conservation suggest that there is an evolutionary tradeoff between selective pressure acting at the RNA and protein levels. Here we provide a comprehensive overview of the studies that define the role of silent positions in regulating RNA structure and processing that exert downstream effects on proteins and their functions.

Squaring theory with practice in RNA design

Ribonucleic acid (RNA) design offers unique opportunities for engineering genetic networks and nanostructures that self-assemble within living cells. Recent years have seen the creation of increasingly complex RNA devices, including proof-of-concept applications for in vivo three-dimensional scaffolding, imaging, computing, and control of biological behaviors. Expert intuition and simple design rules--the stability of double helices, the modularity of noncanonical RNA motifs, and geometric closure--have enabled these successful applications. Going beyond heuristics, emerging algorithms may enable automated design of RNAs with nucleotide-level accuracy but, as illustrated on a recent RNA square design, are not yet fully predictive. Looking ahead, technological advances in RNA synthesis and interrogation are poised to radically accelerate the discovery and stringent testing of design methods.

The mechanisms of RNA SHAPE chemistry

The biological functions of RNA are ultimately governed by the local environment at each nucleotide. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemistry is a powerful approach for measuring nucleotide structure and dynamics in diverse biological environments. SHAPE reagents acylate the 2'-hydroxyl group at flexible nucleotides because unconstrained nucleotides preferentially sample rare conformations that enhance the nucleophilicity of the 2'-hydroxyl. The critical corollary is that some constrained nucleotides must be poised for efficient reaction at the 2'-hydroxyl group. To identify such nucleotides, we performed SHAPE on intact crystals of the Escherichia coli ribosome, monitored the reactivity of 1490 nucleotides in 16S rRNA, and examined those nucleotides that were hyper-reactive toward SHAPE and had well-defined crystallographic conformations. Analysis of these conformations revealed that 2'-hydroxyl reactivity is broadly facilitated by general base catalysis involving multiple RNA functional groups and by two specific orientations of the bridging 3'-phosphate group. Nucleotide analog studies confirmed the contributions of these mechanisms to SHAPE reactivity. These results provide a strong mechanistic explanation for the relationship between SHAPE reactivity and local RNA dynamics and will facilitate interpretation of SHAPE information in the many technologies that make use of this chemistry.