Massively parallel RNA chemical mapping with a reduced bias MAP-seq protocol

The Evaluation of SHAPE-MaP RNA Structure Probing Protocols Reveals a Novel Role of Mn2+ in the Detection of 2'-OH Adducts

Chemical probing, for decades, has been one of the most popular tools for studying the secondary structure of RNA molecules. Recently, protocols for simultaneous analysis of multiple RNAs have been developed, enabling in vivo transcriptome-wide interrogation of the RNA structure dynamics. One of the most popular methods is the selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP). In this study, we describe the evaluation of this protocol by addressing the influence of the reverse transcription enzymes, buffer conditions, and chemical probes on the properties of the cDNA library and the quality of mutational profiling-derived structural signals. Our results reveal a SuperScript IV (SSIV) reverse transcriptase as a more efficient enzyme for mutational profiling of SHAPE adducts and shed new light on the role of Mn²⁺ cations in the modulation of SSIV readthrough efficiency.

RNA secondary structure packages evaluated and improved by high-throughput experiments

Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured “superfolder” mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

The authors develop an RNA sequencing-based platform, PERSIST-seq, to simultaneously delineate in-cell mRNA stability, ribosome load, and in-solution stability of a diverse mRNA library to derive design principles for improved mRNA therapeutics.

Deep learning models for predicting RNA degradation via dual crowdsourcing

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ('Stanford OpenVaccine') on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102-130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Anomalous Reverse Transcription through Chemical Modifications in Polyadenosine Stretches

Thermostable reverse transcriptases are workhorse enzymes underlying nearly all modern techniques for RNA structure mapping and for the transcriptome-wide discovery of RNA chemical modifications. Despite their wide use, these enzymes' behaviors at chemical modified nucleotides remain poorly understood. Wellington-Oguri et al. recently reported an apparent loss of chemical modification within putatively unstructured polyadenosine stretches modified by dimethyl sulfate or 2' hydroxyl acylation, as probed by reverse transcription. Here, reanalysis of these and other publicly available data, capillary electrophoresis experiments on chemically modified RNAs, and nuclear magnetic resonance spectroscopy on (A)₁₂ and variants show that this effect is unlikely to arise from an unusual structure of polyadenosine. Instead, tests of different reverse transcriptases on chemically modified RNAs and molecules synthesized with single 1-methyladenosines implicate a previously uncharacterized reverse transcriptase behavior: near-quantitative bypass through chemical modifications within polyadenosine stretches. All tested natural and engineered reverse transcriptases (MMLV; SuperScript II, III, and IV; TGIRT-III; and MarathonRT) exhibit this anomalous bypass behavior. Accurate DMS-guided structure modeling of the polyadenylated HIV-1 3' untranslated region requires taking into account this anomaly. Our results suggest that poly(rA-dT) hybrid duplexes can trigger an unexpectedly effective reverse transcriptase bypass and that chemical modifications in mRNA poly(A) tails may be generally undercounted.

Evidence of an Unusual Poly(A) RNA Signature Detected by High-Throughput Chemical Mapping

Homopolymeric adenosine RNA plays numerous roles in both cells and noncellular genetic material. We report herein an unusual poly(A) signature in chemical mapping data generated by the Eterna Massive Open Laboratory. Poly(A) sequences of length seven or more show unexpected results in the selective 2'-hydroxyl acylation read out by primer extension (SHAPE) and dimethyl sulfate (DMS) chemical probing. This unusual signature first appears in poly(A) sequences of length seven and grows to its maximum strength at length ∼10. In a long poly(A) sequence, the substitution of a single A by any other nucleotide disrupts the signature, but only for the 6 or so nucleotides on the 5' side of the substitution.

Physical and Functional Analysis of Viral RNA Genomes by SHAPE

RNA viruses encode the information required to usurp cellular metabolism and gene regulation and to enable their own replication in two ways: in the linear sequence of their RNA genomes and in higher-order structures that form when the genomic RNA strand folds back on itself. Application of high-resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) structure probing to viral RNA genomes has identified numerous new regulatory elements, defined new principles by which viral RNAs interact with the cellular host and evade host immune responses, and revealed relationships between virus evolution and RNA structure. This review summarizes our current understanding of genome structure-function interrelationships for RNA viruses, as informed by SHAPE structure probing, and outlines opportunities for future studies.

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.

Guidelines for SHAPE Reagent Choice and Detection Strategy for RNA Structure Probing Studies

Chemical probing is an important tool for characterizing the complex folded structures of RNA molecules, many of which play key cellular roles. Electrophilic SHAPE reagents create adducts at the 2'-hydroxyl position on the RNA backbone of flexible ribonucleotides with relatively little dependence on nucleotide identity. Strategies for adduct detection such as mutational profiling (MaP) allow accurate, automated calculation of relative adduct frequencies for each nucleotide in a given RNA or group of RNAs. A number of alternative reagents and adduct detection strategies have been proposed, especially for use in living cells. Here we evaluate five SHAPE reagents: three previously well-validated reagents 1M7 (1-methyl-7-nitroisatoic anhydride), 1M6 (1-methyl-6-nitroisatoic anhydride), and NMIA ( N-methylisatoic anhydride), one more recently proposed NAI (2-methylnicotinic acid imidazolide), and one novel reagent 5NIA (5-nitroisatoic anhydride). We clarify the importance of carefully designed software in reading out SHAPE experiments using massively parallel sequencing approaches. We examine SHAPE modification in living cells in diverse cell lines, compare MaP and reverse transcription-truncation as SHAPE adduct detection strategies, make recommendations for SHAPE reagent choice, and outline areas for future development.

Prediction of secondary and tertiary structures of human BC200 RNA (BCYRN1) based on experimental and bioinformatic cross-validation

Based on experimental and bioinformatic approaches, we present the first empirically established complete secondary structure of human BC200 RNA. BC200 RNA is a brain-specific non-messenger RNA with a confirmed regulatory role in dendritic translation in neurons. Although the involvement of human BC200 RNA in various types of tumour and Alzheimer's disease has been repeatedly confirmed, the exact secondary structure remains not fully elucidated. To determine the secondary structure of BC200 RNA in vitro, we performed partial hydrolysis with sequence-specific nucleases and lead-induced cleavage. We also examined the availabilities of putative single-stranded regions and base-pairing interactions via specific DNAzymes and RNase H assay. To determine the complete spatial folding of BC200 RNA, we used experimental data as constraints in structure prediction programs and performed a comparison of results obtained by several algorithms using different criteria. Based on the experimental-derived secondary structure of BC200 RNA, we also predicted the tertiary structure of BC200 RNA. The presented combination of experimental and bioinformatic approaches not only enabled the determination of the most reliable secondary and tertiary structures of human BC200 RNA (largely in agreement with the previous phylogenetic model), but also verified the compatibility and potential disadvantages of utilizing in silico structure prediction programs.

nextPARS: parallel probing of RNA structures in Illumina

RNA molecules play important roles in virtually every cellular process. These functions are often mediated through the adoption of specific structures that enable RNAs to interact with other molecules. Thus, determining the secondary structures of RNAs is central to understanding their function and evolution. In recent years several sequencing-based approaches have been developed that allow probing structural features of thousands of RNA molecules present in a sample. Here, we describe nextPARS, a novel Illumina-based implementation of in vitro parallel probing of RNA structures. Our approach achieves comparable accuracy to previous implementations, while enabling higher throughput and sample multiplexing.

Structure-seq2: sensitive and accurate genome-wide profiling of RNA structure in vivo

RNA serves many functions in biology such as splicing, temperature sensing, and innate immunity. These functions are often determined by the structure of RNA. There is thus a pressing need to understand RNA structure and how it changes during diverse biological processes both in vivo and genome-wide. Here, we present Structure-seq2, which provides nucleotide-resolution RNA structural information in vivo and genome-wide. This optimized version of our original Structure-seq method increases sensitivity by at least 4-fold and improves data quality by minimizing formation of a deleterious by-product, reducing ligation bias, and improving read coverage. We also present a variation of Structure-seq2 in which a biotinylated nucleotide is incorporated during reverse transcription, which greatly facilitates the protocol by eliminating two PAGE purification steps. We benchmark Structure-seq2 on both mRNA and rRNA structure in rice (Oryza sativa). We demonstrate that Structure-seq2 can lead to new biological insights. Our Structure-seq2 datasets uncover hidden breaks in chloroplast rRNA and identify a previously unreported N¹-methyladenosine (m¹A) in a nuclear-encoded Oryza sativa rRNA. Overall, Structure-seq2 is a rapid, sensitive, and unbiased method to probe RNA in vivo and genome-wide that facilitates new insights into RNA biology.

RNA structure inference through chemical mapping after accidental or intentional mutations

Despite the critical roles RNA structures play in regulating gene expression, sequencing-based methods for experimentally determining RNA base pairs have remained inaccurate. Here, we describe a multidimensional chemical-mapping method called "mutate-and-map read out through next-generation sequencing" (M2-seq) that takes advantage of sparsely mutated nucleotides to induce structural perturbations at partner nucleotides and then detects these events through dimethyl sulfate (DMS) probing and mutational profiling. In special cases, fortuitous errors introduced during DNA template preparation and RNA transcription are sufficient to give M2-seq helix signatures; these signals were previously overlooked or mistaken for correlated double-DMS events. When mutations are enhanced through error-prone PCR, in vitro M2-seq experimentally resolves 33 of 68 helices in diverse structured RNAs including ribozyme domains, riboswitch aptamers, and viral RNA domains with a single false positive. These inferences do not require energy minimization algorithms and can be made by either direct visual inspection or by a neural-network-inspired algorithm called M2-net. Measurements on the P4-P6 domain of the Tetrahymena group I ribozyme embedded in Xenopus egg extract demonstrate the ability of M2-seq to detect RNA helices in a complex biological environment.

Dawn of the in vivo RNA structurome and interactome

RNA is one of the most fascinating biomolecules in living systems given its structural versatility to fold into elaborate architectures for important biological functions such as gene regulation, catalysis, and information storage. Knowledge of RNA structures and interactions can provide deep insights into their functional roles in vivo For decades, RNA structural studies have been conducted on a transcript-by-transcript basis. The advent of next-generation sequencing (NGS) has enabled the development of transcriptome-wide structural probing methods to profile the global landscape of RNA structures and interactions, also known as the RNA structurome and interactome, which transformed our understanding of the RNA structure-function relationship on a transcriptomic scale. In this review, molecular tools and NGS methods used for RNA structure probing are presented, novel insights uncovered by RNA structurome and interactome studies are highlighted, and perspectives on current challenges and potential future directions are discussed. A more complete understanding of the RNA structures and interactions in vivo will help illuminate the novel roles of RNA in gene regulation, development, and diseases.

RNA Structure: Advances and Assessment of 3D Structure Prediction

Biological functions of RNA molecules are dependent upon sustained specific three-dimensional (3D) structures of RNA, with or without the help of proteins. Understanding of RNA structure is frequently based on 2D structures, which describe only the Watson-Crick (WC) base pairs. Here, we hierarchically review the structural elements of RNA and how they contribute to RNA 3D structure. We focus our analysis on the non-WC base pairs and on RNA modules. Several computer programs have now been designed to predict RNA modules. We describe the RNA-Puzzles initiative, which is a community-wide, blind assessment of RNA 3D structure prediction programs to determine the capabilities and bottlenecks of current predictions. The assessment metrics used in RNA-Puzzles are briefly described. The detection of RNA 3D modules from sequence data and their automatic implementation belong to the current challenges in RNA 3D structure prediction.

RNA structure through multidimensional chemical mapping

The discoveries of myriad non-coding RNA molecules, each transiting through multiple flexible states in cells or virions, present major challenges for structure determination. Advances in high-throughput chemical mapping give new routes for characterizing entire transcriptomes in vivo, but the resulting one-dimensional data generally remain too information-poor to allow accurate de novo structure determination. Multidimensional chemical mapping (MCM) methods seek to address this challenge. Mutate-and-map (M2), RNA interaction groups by mutational profiling (RING-MaP and MaP-2D analysis) and multiplexed •OH cleavage analysis (MOHCA) measure how the chemical reactivities of every nucleotide in an RNA molecule change in response to modifications at every other nucleotide. A growing body of in vitro blind tests and compensatory mutation/rescue experiments indicate that MCM methods give consistently accurate secondary structures and global tertiary structures for ribozymes, ribosomal domains and ligand-bound riboswitch aptamers up to 200 nucleotides in length. Importantly, MCM analyses provide detailed information on structurally heterogeneous RNA states, such as ligand-free riboswitches that are functionally important but difficult to resolve with other approaches. The sequencing requirements of currently available MCM protocols scale at least quadratically with RNA length, precluding general application to transcriptomes or viral genomes at present. We propose a modify-cross-link-map (MXM) expansion to overcome this and other current limitations to resolving the in vivo 'RNA structurome'.

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

BACKGROUND

Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data.

RESULTS

We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy.

CONCLUSIONS

To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

The RNA Epistructurome: Uncovering RNA Function by Studying Structure and Post-Transcriptional Modifications

A large fraction of higher metazoan genomes transcribe RNA molecules whose functions extend far beyond carrying instructions for protein synthesis. Although RNA is apparently a simple molecule, the ways in which it performs many of its functions have remained highly elusive for decades. As learned from studying ribosomal and transfer RNAs, two of the key features influencing the function of RNA are its structure and post-transcriptional modifications. A deep understanding of RNA function therefore requires rapid and straightforward approaches to study the complex and intricate landscape of RNA structures and modifications. In this review we summarize and discuss the most recent methods and findings in the field of RNA biology, with an eye toward new frontiers and open questions.

Metrics for rapid quality control in RNA structure probing experiments

MOTIVATION

The diverse functionalities of RNA can be attributed to its capacity to form complex and varied structures. The recent proliferation of new structure probing techniques coupled with high-throughput sequencing has helped RNA studies expand in both scope and depth. Despite differences in techniques, most experiments face similar challenges in reproducibility due to the stochastic nature of chemical probing and sequencing. As these protocols expand to transcriptome-wide studies, quality control becomes a more daunting task. General and efficient methodologies are needed to quantify variability and quality in the wide range of current and emerging structure probing experiments.

RESULTS

We develop metrics to rapidly and quantitatively evaluate data quality from structure probing experiments, demonstrating their efficacy on both small synthetic libraries and transcriptome-wide datasets. We use a signal-to-noise ratio concept to evaluate replicate agreement, which has the capacity to identify high-quality data. We also consider and compare two methods to assess variability inherent in probing experiments, which we then utilize to evaluate the coverage adjustments needed to meet desired quality. The developed metrics and tools will be useful in summarizing large-scale datasets and will help standardize quality control in the field.

AVAILABILITY AND IMPLEMENTATION

The data and methods used in this article are freely available at: http://bme.ucdavis.edu/aviranlab/SPEQC_software CONTACT: saviran@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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.

Principles for Predicting RNA Secondary Structure Design Difficulty

Designing RNAs that form specific secondary structures is enabling better understanding and control of living systems through RNA-guided silencing, genome editing and protein organization. Little is known, however, about which RNA secondary structures might be tractable for downstream sequence design, increasing the time and expense of design efforts due to inefficient secondary structure choices. Here, we present insights into specific structural features that increase the difficulty of finding sequences that fold into a target RNA secondary structure, summarizing the design efforts of tens of thousands of human participants and three automated algorithms (RNAInverse, INFO-RNA and RNA-SSD) in the Eterna massive open laboratory. Subsequent tests through three independent RNA design algorithms (NUPACK, DSS-Opt and MODENA) confirmed the hypothesized importance of several features in determining design difficulty, including sequence length, mean stem length, symmetry and specific difficult-to-design motifs such as zigzags. Based on these results, we have compiled an Eterna100 benchmark of 100 secondary structure design challenges that span a large range in design difficulty to help test future efforts. Our in silico results suggest new routes for improving computational RNA design methods and for extending these insights to assess "designability" of single RNA structures, as well as of switches for in vitro and in vivo applications.

Genome-Wide Approaches for RNA Structure Probing

RNA molecules of all types fold into complex secondary and tertiary structures that are important for their function and regulation. Structural and catalytic RNAs such as ribosomal RNA (rRNA) and transfer RNA (tRNA) are central players in protein synthesis, and only function through their proper folding into intricate three-dimensional structures. Studies of messenger RNA (mRNA) regulation have also revealed that structural elements embedded within these RNA species are important for the proper regulation of their total level in the transcriptome. More recently, the discovery of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) has shed light on the importance of RNA structure to genome, transcriptome, and proteome regulation. Due to the relatively small number, high conservation, and importance of structural and catalytic RNAs to all life, much early work in RNA structure analysis mapped out a detailed view of these molecules. Computational and physical methods were used in concert with enzymatic and chemical structure probing to create high-resolution models of these fundamental biological molecules. However, the recent expansion in our knowledge of the importance of RNA structure to coding and regulatory RNAs has left the field in need of faster and scalable methods for high-throughput structural analysis. To address this, nuclease and chemical RNA structure probing methodologies have been adapted for genome-wide analysis. These methods have been deployed to globally characterize thousands of RNA structures in a single experiment. Here, we review these experimental methodologies for high-throughput RNA structure determination and discuss the insights gained from each approach.

Using Molecular Replacement Phasing to Study the Structure and Function of RNA

In recent years a wide variety of RNA molecules regulating fundamental cellular processes has been discovered. Therefore, RNA structure determination is experiencing a boost and many more RNA structures are likely to be determined in the years to come. The broader availability of experimentally determined RNA structures implies that molecular replacement (MR) will be used more and more frequently as a method for phasing future crystallographic structures. In this report we describe various aspects relative to RNA structure determination by MR. First, we describe how to select and create MR search models for nucleic acids. Second, we describe how to perform MR searches on RNA using available crystallographic software. Finally, we describe how to refine and interpret the successful MR solutions. These protocols are applicable to determine novel RNA structures as well as to establish structural-functional relationships on existing RNA structures.

The interplay of long non-coding RNAs and MYC in cancer

Long non-coding RNAs (lncRNAs) are a class of RNA molecules that are changing how researchers view eukaryotic gene regulation. Once considered to be non-functional products of low-level aberrant transcription from non-coding regions of the genome, lncRNAs are now viewed as important epigenetic regulators and several lncRNAs have now been demonstrated to be critical players in the development and/or maintenance of cancer. Similarly, the emerging variety of interactions between lncRNAs and MYC, a well-known oncogenic transcription factor linked to most types of cancer, have caught the attention of many biomedical researchers. Investigations exploring the dynamic interactions between lncRNAs and MYC, referred to as the lncRNA-MYC network, have proven to be especially complex. Genome-wide studies have shown that MYC transcriptionally regulates many lncRNA genes. Conversely, recent reports identified lncRNAs that regulate MYC expression both at the transcriptional and post-transcriptional levels. These findings are of particular interest because they suggest roles of lncRNAs as regulators of MYC oncogenic functions and the possibility that targeting lncRNAs could represent a novel avenue to cancer treatment. Here, we briefly review the current understanding of how lncRNAs regulate chromatin structure and gene transcription, and then focus on the new developments in the emerging field exploring the lncRNA-MYC network in cancer.

Rich RNA Structure Landscapes Revealed by Mutate-and-Map Analysis

Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral infection [corrected]. We report herein that high-throughput chemical experiments can isolate an RNA's multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M2) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states' structures and populations. In an in silico benchmark on non-coding RNAs with complex landscapes, M2-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M2-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M2-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.

Consistent global structures of complex RNA states through multidimensional chemical mapping

Accelerating discoveries of non-coding RNA (ncRNA) in myriad biological processes pose major challenges to structural and functional analysis. Despite progress in secondary structure modeling, high-throughput methods have generally failed to determine ncRNA tertiary structures, even at the 1-nm resolution that enables visualization of how helices and functional motifs are positioned in three dimensions. We report that integrating a new method called MOHCA-seq (Multiplexed •OHCleavage Analysis with paired-end sequencing) with mutate-and-map secondary structure inference guides Rosetta 3D modeling to consistent 1-nm accuracy for intricately folded ncRNAs with lengths up to 188 nucleotides, including a blind RNA-puzzle challenge, the lariat-capping ribozyme. This multidimensional chemical mapping (MCM) pipeline resolves unexpected tertiary proximities for cyclic-di-GMP, glycine, and adenosylcobalamin riboswitch aptamers without their ligands and a loose structure for the recently discovered human HoxA9D internal ribosome entry site regulon. MCM offers a sequencing-based route to uncovering ncRNA 3D structure, applicable to functionally important but potentially heterogeneous states.

DOI:http://dx.doi.org/10.7554/eLife.07600.001

Our genetic material, in the form of molecules of DNA, provides instructions for many different processes in our cells. To issue these instructions, particular sections of DNA are copied to make a type of molecule called ribonucleic acid (RNA). Some of these RNA molecules contain instructions to make proteins, but others—known as non-coding RNAs—regulate the activity of genes in cells.

The genetic information within RNA is encoded by the sequence of four different chemical parts called ‘nucleotides’. RNA can exist as a single strand of nucleotides, but the nucleotides can also pair up in specific combinations to form sections of double-stranded RNA. Therefore, a single strand of non-coding RNA can fold into a complex three-dimensional shape that contains loops, twists, and bulges.

The three-dimensional structures of non-coding RNAs are crucial for their roles in cells, but the variety and complexity of shapes that they can form makes it technically difficult to study them. In 2008, researchers developed a new method called MOHCA that can map the positions of nucleotides that are close together in the three-dimensional structure. Highly reactive chemicals are attached to the nucleotides and these can react with, and damage, other nearby nucleotides. By detecting which nucleotides have been damaged, it is possible to map the positions of these nucleotides and decipher the structure of the RNA molecule using computer algorithms.

MOHCA is a promising approach, but the initial methods to find the damaged nucleotides were tedious and required specialized equipment. Now, Cheng, Das et al.—including some of the researchers involved in the 2008 work—have developed an improved version of MOHCA that uses readily available RNA sequencing techniques to find the damaged nucleotides. The RNA sequencing data are then analyzed by a new algorithm in the Rosetta computer modeling software.

Cheng, Das et al. used this newly developed ‘MOHCA-seq’ and Rosetta to reveal the structures of a human non-coding RNA and several other non-coding RNA molecules to a much higher level of detail than before. Together, MOHCA-seq and Rosetta provide a rapid method for researchers to decipher the three-dimensional structure of non-coding RNAs. This method is likely to speed up the analysis of the complex structures of non-coding RNAs. It will be useful in future efforts to work out what roles these RNAs play in cells, including their activity in cancer, neurodegeneration, and other diseases.

DOI:http://dx.doi.org/10.7554/eLife.07600.002

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

SHAPE Selection (SHAPES) enrich for RNA structure signal in SHAPE sequencing-based probing data

Selective 2' Hydroxyl Acylation analyzed by Primer Extension (SHAPE) is an accurate method for probing of RNA secondary structure. In existing SHAPE methods, the SHAPE probing signal is normalized to a no-reagent control to correct for the background caused by premature termination of the reverse transcriptase. Here, we introduce a SHAPE Selection (SHAPES) reagent, N-propanone isatoic anhydride (NPIA), which retains the ability of SHAPE reagents to accurately probe RNA structure, but also allows covalent coupling between the SHAPES reagent and a biotin molecule. We demonstrate that SHAPES-based selection of cDNA-RNA hybrids on streptavidin beads effectively removes the large majority of background signal present in SHAPE probing data and that sequencing-based SHAPES data contain the same amount of RNA structure data as regular sequencing-based SHAPE data obtained through normalization to a no-reagent control. Moreover, the selection efficiently enriches for probed RNAs, suggesting that the SHAPES strategy will be useful for applications with high-background and low-probing signal such as in vivo RNA structure probing.

Reproducible Analysis of Sequencing-Based RNA Structure Probing Data with User-Friendly Tools

RNA structure-probing data can improve the prediction of RNA secondary and tertiary structure and allow structural changes to be identified and investigated. In recent years, massive parallel sequencing has dramatically improved the throughput of RNA structure probing experiments, but at the same time also made analysis of the data challenging for scientists without formal training in computational biology. Here, we discuss different strategies for data analysis of massive parallel sequencing-based structure-probing data. To facilitate reproducible and standardized analysis of this type of data, we have made a collection of tools, which allow raw sequencing reads to be converted to normalized probing values using different published strategies. In addition, we also provide tools for visualization of the probing data in the UCSC Genome Browser and for converting RNA coordinates to genomic coordinates and vice versa. The collection is implemented as functions in the R statistical environment and as tools in the Galaxy platform, making them easily accessible for the scientific community. We demonstrate the usefulness of the collection by applying it to the analysis of sequencing-based hydroxyl radical probing data and comparing different normalization strategies.

Computational analysis of RNA structures with chemical probing data

RNAs play various roles, not only as the genetic codes to synthesize proteins, but also as the direct participants of biological functions determined by their underlying high-order structures. Although many computational methods have been proposed for analyzing RNA structures, their accuracy and efficiency are limited, especially when applied to the large RNAs and the genome-wide data sets. Recently, advances in parallel sequencing and high-throughput chemical probing technologies have prompted the development of numerous new algorithms, which can incorporate the auxiliary structural information obtained from those experiments. Their potential has been revealed by the secondary structure prediction of ribosomal RNAs and the genome-wide ncRNA function annotation. In this review, the existing probing-directed computational methods for RNA secondary and tertiary structure analysis are discussed.

Modeling complex RNA tertiary folds with Rosetta

Reliable modeling of RNA tertiary structures is key to both understanding these structures' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1 nm) resolution. Our laboratory's current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

Improving RNA secondary structure prediction with structure mapping data

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide.

RNA regulons in Hox 5' UTRs confer ribosome specificity to gene regulation

Emerging evidence suggests that the ribosome has a regulatory function in directing how the genome is translated in time and space. However, how this regulation is encoded in the messenger RNA sequence remains largely unknown. Here we uncover unique RNA regulons embedded in homeobox (Hox) 5' untranslated regions (UTRs) that confer ribosome-mediated control of gene expression. These structured RNA elements, resembling viral internal ribosome entry sites (IRESs), are found in subsets of Hox mRNAs. They facilitate ribosome recruitment and require the ribosomal protein RPL38 for their activity. Despite numerous layers of Hox gene regulation, these IRES elements are essential for converting Hox transcripts into proteins to pattern the mammalian body plan. This specialized mode of IRES-dependent translation is enabled by an additional regulatory element that we term the translation inhibitory element (TIE), which blocks cap-dependent translation of transcripts. Together, these data uncover a new paradigm for ribosome-mediated control of gene expression and organismal development.

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.

High-throughput mutate-map-rescue evaluates SHAPE-directed RNA structure and uncovers excited states

The three-dimensional conformations of noncoding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a 16S rRNA domain for which SHAPE (selective 2'-hydroxyl acylation with primer extension) and limited mutational analysis suggested a conformational change between apo- and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M(2)) experiments highlight issues of prior methodology and instead give a near-crystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M(2)-based secondary structure. The data further uncover the functional conformation as an excited state (20 ± 10% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change, expose critical limitations of conventional structure mapping methods, and illustrate practical steps for more incisively dissecting RNA dynamic structure landscapes.

SHAPE-Seq 2.0: systematic optimization and extension of high-throughput chemical probing of RNA secondary structure with next generation sequencing

RNA structure is a primary determinant of its function, and methods that merge chemical probing with next generation sequencing have created breakthroughs in the throughput and scale of RNA structure characterization. However, little work has been done to examine the effects of library preparation and sequencing on the measured chemical probe reactivities that encode RNA structural information. Here, we present the first analysis and optimization of these effects for selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). We first optimize SHAPE-Seq, and show that it provides highly reproducible reactivity data over a wide range of RNA structural contexts with no apparent biases. As part of this optimization, we present SHAPE-Seq v2.0, a ‘universal’ method that can obtain reactivity information for every nucleotide of an RNA without having to use or introduce a specific reverse transcriptase priming site within the RNA. We show that SHAPE-Seq v2.0 is highly reproducible, with reactivity data that can be used as constraints in RNA folding algorithms to predict structures on par with those generated using data from other SHAPE methods. We anticipate SHAPE-Seq v2.0 to be broadly applicable to understanding the RNA sequence–structure relationship at the heart of some of life's most fundamental processes.

Scientific rigor through videogames

Hypothesis-driven experimentation - the scientific method - can be subverted by fraud, irreproducibility, and lack of rigorous predictive tests. A robust solution to these problems may be the 'massive open laboratory' model, recently embodied in the internet-scale videogame EteRNA. Deploying similar platforms throughout biology could enforce the scientific method more broadly.

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.

Insights into RNA structure and function from genome-wide studies

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.

Standardization of RNA chemical mapping experiments

Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated in vivo) illustrates the utility and necessity of standardization for quantitative RNA mapping.

Massive parallel-sequencing-based hydroxyl radical probing of RNA accessibility

Hydroxyl Radical Footprinting (HRF) is a tried-and-tested method for analysis of the tertiary structure of RNA and for identification of protein footprints on RNA. The hydroxyl radical reaction breaks accessible parts of the RNA backbone, thereby allowing ribose accessibility to be determined by detection of reverse transcriptase termination sites. Current methods for HRF rely on reverse transcription of a single primer and detection by fluorescent fragments by capillary electrophoresis. Here, we describe an accurate and efficient massive parallel-sequencing-based method for probing RNA accessibility with hydroxyl radicals, called HRF-Seq. Using random priming and a novel barcoding scheme, we show that HRF-Seq dramatically increases the throughput of HRF experiments and facilitates the parallel analysis of multiple RNAs or experimental conditions. Moreover, we demonstrate that HRF-Seq data for the Escherichia coli 16S rRNA correlates well with the ribose accessible surface area as determined by X-ray crystallography and have a resolution that readily allows the difference in accessibility caused by exposure of one side of RNA helices to be observed.

RNA design rules from a massive open laboratory

Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models--even at the secondary structure level--hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies--including several previously unrecognized negative design rules--were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.