Structure Probing Experiments Research Articles

The RNA secondary structure of androgen receptor-FL and V7 transcripts reveals novel regulatory regions.

The androgen receptor (AR) is a ligand-dependent nuclear transcription factor belonging to the steroid hormone nuclear receptor family. Due to its roles in regulating cell proliferation and differentiation, AR is tightly regulated to maintain proper levels of itself and the many genes it controls. AR dysregulation is a driver of many human diseases including prostate cancer. Though this dysregulation often occurs at the RNA level, there are many unknowns surrounding post-transcriptional regulation of AR mRNA, particularly the role that RNA secondary structure plays. Thus, a comprehensive analysis of AR transcript secondary structure is needed. We address this through the computational and experimental analyses of two key isoforms, full length (AR-FL) and truncated (AR-V7). Here, a combination of in-cell RNA secondary structure probing experiments (targeted DMS-MaPseq) and computational predictions were used to characterize the static structural landscape and conformational dynamics of both isoforms. Additionally, in-cell assays were used to identify functionally relevant structures in the 5' and 3' UTRs of AR-FL. A notable example is a conserved stem loop structure in the 5'UTR of AR-FL that can bind to Poly(RC) Binding Protein 2 (PCBP2). Taken together, our results reveal novel features that regulate AR expression.

Hierarchical Hydration Dynamics of RNA with Nano-Water-Pool at Its Core.

Many functional RNAs fold into a compact, roughly globular shape by minimizing the electrostatic repulsion between their negatively charged phosphodiester backbone. The fold of such close, compact RNA architecture is often so designed that its outer surface and complex core both are predominately populated by phosphate groups loosely sequestering bases in the intermediate layers. A number of helical junctions maintain the RNA core and its nano-water-pool. While the folding of RNA is manifested by its counterion environment composed of mixed mono- and divalent salts, the concerted role of ion and water in maintaining an RNA fold is yet to be explored. In this work, detailed atomistic simulations of SAM-I and Add Adenine riboswitch aptamers, and subgenomic flavivirus RNA (sfRNA) have been performed in a physiological mixed mono- and divalent salt environment. All three RNA systems have compact folds with a core diameter of range 1-1.7 nm. The spatiotemporal heterogeneity of RNA hydration was probed in a layer-wise manner by distinguishing the core, the intermediate, and the outer layers. The layer-wise decomposition of hydrogen bonds and collective single-particle reorientational dynamics reveal a nonmonotonic relaxation pattern with the slowest relaxation observed at the intermediate layers that involves functionally important tertiary motifs. The slowness of this intermediate layer is attributed to two types of long-resident water molecules: (i) water from ion-hydration layers and (ii) structurally trapped water (distant from ions). The relaxation kinetics of the core and the surface water essentially exposed to the phosphate groups show well-separated time scales from the intermediate layers. In the slow intermediate layers, site-specific ions and water control the functional dynamics of important RNA motifs like kink-turn, observed in different structure-probing experiments. Most interestingly, we find that as the size of the RNA core increases (SAM1 core < sfRNAcore < Add adenine core), its hydration tends to show faster relaxation. The hierarchical hydration and the layer-wise base-phosphate composition uniquely portray the globular RNA to act like a soft vesicle with a quasi-dynamic nano-water-pool at its core.

Understanding cooperativity effects in RNA structure probing experiments.

Small molecule chemical probes, which form covalent bonds with atoms of flexible nucleotides, are commonly used to gain insight into the structure adopted by an RNA. In this work, we use selective 2’ hydroxyl acylation analyzed by primer extension (SHAPE) and nuclear magnetic resonance (NMR) spectroscopy experiments to explore whether the structure of an RNA is modulated by chemical probe binding events. We find that as the concentration of a chemical probe increases, modified nucleotides locally modulate the RNA structure, resulting in the increase or decrease of SHAPE reactivity in surrounding nucleotides. This cooperative effect is dependent on both chemical probe concentration and size. Critically, this effect impacts the accuracy of predicted RNA secondary structures when guided by chemical probing experiments. Altogether, our work highlights the importance of optimizing the conditions of chemical probing experiments to obtain secondary structural information about an RNA.

A Nucleic Acid Sequence That is Catalytically Active in Both RNA and TNA Backbones.

Threose nucleic acid (TNA) is considered a potential RNA progenitor due to its chemical simplicity, base pairing property, and capability of folding into a functional tertiary structure. However, it is unknown whether the functional property can be maintained during transition from TNA to RNA. Here, we use a toggle in vitro selection to identify nucleic acid catalyst sequences that are active in both TNA and RNA backbones. One such nucleic acid enzyme with exchangeable backbone (CAMELEON) catalyzes an RNA cleavage reaction when prepared as TNA (T) and RNA (R). Further biochemical characterization reveals that CAMELEON R and T exhibit different catalytic behaviors such as rate enhancement and magnesium dependence. Structural probing and mutagenesis experiments suggest that they likely fold into distinct tertiary structures. This work demonstrates that the catalytic activity can be preserved during backbone transition from TNA to RNA and provides further experimental support for TNA as an RNA precursor in evolution.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

Exploring the Druggability of Conserved RNA Regulatory Elements in the SARS‐CoV‐2 Genome

AbstractSARS‐CoV‐2 contains a positive single‐stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS‐CoV and SARS‐CoV‐2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex‐vivo structural probing experiments. These elements contain non‐base‐paired regions that potentially harbor ligand‐binding pockets. Here, we performed an NMR‐based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1H‐based 1D NMR binding assays. The screening identified common as well as RNA‐element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS‐CoV‐2.

Exploring the Druggability of Conserved RNA Regulatory Elements in the SARS-CoV-2 Genome.

SARS‐CoV‐2 contains a positive single‐stranded RNA genome of approximately 30 000 nucleotides. Within this genome, 15 RNA elements were identified as conserved between SARS‐CoV and SARS‐CoV‐2. By nuclear magnetic resonance (NMR) spectroscopy, we previously determined that these elements fold independently, in line with data from in vivo and ex‐vivo structural probing experiments. These elements contain non‐base‐paired regions that potentially harbor ligand‐binding pockets. Here, we performed an NMR‐based screening of a poised fragment library of 768 compounds for binding to these RNAs, employing three different 1H‐based 1D NMR binding assays. The screening identified common as well as RNA‐element specific hits. The results allow selection of the most promising of the 15 RNA elements as putative drug targets. Based on the identified hits, we derive key functional units and groups in ligands for effective targeting of the RNA of SARS‐CoV‐2.

To Knot or Not to Knot: Multiple Conformations of the SARS-CoV-2 Frameshifting RNA Element.

The SARS-CoV-2 frameshifting RNA element (FSE) is an excellent target for therapeutic intervention against Covid-19. This small gene element employs a shifting mechanism to pause and backtrack the ribosome during translation between Open Reading Frames 1a and 1b, which code for viral polyproteins. Any interference with this process has a profound effect on viral replication and propagation. Pinpointing the structures adapted by the FSE and associated structural transformations involved in frameshifting has been a challenge. Using our graph-theory-based modeling tools for representing RNA secondary structures, “RAG” (RNA-As-Graphs), and chemical structure probing experiments, we show that the 3-stem H-type pseudoknot (3_6 dual graph), long assumed to be the dominant structure, has a viable alternative, an HL-type 3-stem pseudoknot (3_3) for longer constructs. In addition, an unknotted 3-way junction RNA (3_5) emerges as a minor conformation. These three conformations share Stems 1 and 3, while the different Stem 2 may be involved in a conformational switch and possibly associations with the ribosome during translation. For full-length genomes, a stem-loop motif (2_2) may compete with these forms. These structural and mechanistic insights advance our understanding of the SARS-CoV-2 frameshifting process and concomitant virus life cycle, and point to three avenues of therapeutic intervention.

RNA Framework for Assaying the Structure of RNAs by High-Throughput Sequencing.

RNA structure is a key player in regulating a plethora of biological processes. A large part of the functions carried out by RNA is mediated by its structure. To this end, in the last decade big effort has been put in the development of new RNA probing methods based on Next-Generation Sequencing (NGS), aimed at the rapid transcriptome-scale interrogation of RNA structures. In this chapter we describe RNA Framework, the to date most comprehensive toolkit for the analysis of NGS-based RNA structure probing experiments. By using two published datasets, we here illustrate how to use the different components of the RNA Framework and how to choose the analysis parameters according to the experimental setup.

RNAthor - fast, accurate normalization, visualization and statistical analysis of RNA probing data resolved by capillary electrophoresis.

RNAs adopt specific structures to perform their functions, which are critical to fundamental cellular processes. For decades, these structures have been determined and modeled with strong support from computational methods. Still, the accuracy of the latter ones depends on the availability of experimental data, for example, chemical probing information that can define pseudo-energy constraints for RNA folding algorithms. At the same time, diverse computational tools have been developed to facilitate analysis and visualization of data from RNA structure probing experiments followed by capillary electrophoresis or next-generation sequencing. RNAthor, a new software tool for the fully automated normalization of SHAPE and DMS probing data resolved by capillary electrophoresis, has recently joined this collection. RNAthor automatically identifies unreliable probing data. It normalizes the reactivity information to a uniform scale and uses it in the RNA secondary structure prediction. Our web server also provides tools for fast and easy RNA probing data visualization and statistical analysis that facilitates the comparison of multiple data sets. RNAthor is freely available at http://rnathor.cs.put.poznan.pl/.

Effects of Refolding on Large-Scale RNA Structure.

Understanding RNA-mediated functions requires a detailed characterization of the underlying RNA structure. In many cases, structure probing experiments are performed on RNA that has been "refolded" in some way, which may cause the conformation to differ from that of the native RNA. We used SHAPE-MaP (selective 2'-hydroxyl acylation analyzed by primer extension, read out by mutational profiling), to probe the structure of the Dengue virus (DENV) RNA genome after gentle extraction of the native RNA from intact virions (ex virion) and after heat denaturation and refolding. Comparison of mutiple SHAPE-informed structural features revealed that refolded RNA is more highly structured and samples fewer conformations than the ex virion RNA. Regions with similar structural features are generally those with low SHAPE reactivity and low Shannon entropy (lowSS regions), which correspond to elements with high levels of well-determined structure. This high-structure and low-entropy analysis framework, previously shown to make possible discovery of functional RNA structures, is thus now shown to allow de novo identification of structural elements in a refolded RNA that are likely to recapitulate RNA structures in the ex virion RNA state. Regions with less well-defined structures, which occurred more frequently in the more native-like ex virion RNA and may contain RNA switches, are challenging to recapitulate using refolded RNA.

The evolution of RNA structural probing methods: From gels to next-generation sequencing.

RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read-out methods. This was superseded by capillary electrophoresis, and more recently by next-generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome-wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells.

RNA Framework: an all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications.

RNA is emerging as a key regulator of a plethora of biological processes. While its study has remained elusive for decades, the recent advent of high-throughput sequencing technologies provided the unique opportunity to develop novel techniques for the study of RNA structure and post-transcriptional modifications. Nonetheless, most of the required downstream bioinformatics analyses steps are not easily reproducible, thus making the application of these techniques a prerogative of few laboratories. Here we introduce RNA Framework, an all-in-one toolkit for the analysis of most NGS-based RNA structure probing and post-transcriptional modification mapping experiments. To prove the extreme versatility of RNA Framework, we applied it to both an in-house generated DMS-MaPseq dataset, and to a series of literature available experiments. Notably, when starting from publicly available datasets, our software easily allows replicating authors' findings. Collectively, RNA Framework provides the most complete and versatile toolkit to date for a rapid and streamlined analysis of the RNA epistructurome. RNA Framework is available for download at: http://www.rnaframework.com.

Identifying the RNA Kinetic Traps in Ribosome Assembly

The ribosome is the cellular machine responsible for protein production in every living cell. While the final structure of the mature ribosome is known from x-ray crystallography studies, the assembly process remains largely unknown. To fulfill the cells’ demand for proteins, ribosome assembly in vivo occurs fast and efficiently. Consequently, intermediates of the assembly process are short lived and do not accumulate in large amounts for investigation. To increase the accumulation of in vivo intermediates of large subunit ribosome assembly in Escherichia coli, we take advantage of the helicase inactive DbpA construct R331A. When R331A DbpA is expressed in cells, three large subunit particles, with sedimentation coefficients of 27S, 35S, and 45S, accumulate. The particles rearrange to form the native 50S large subunit via parallel pathways. We interrogated the RNA structure of the 45S particle using dimethyl sulfate and SHAPE chemical probing reagents combined with Next Generation Sequencing. The structural probing experiments revealed perturbed secondary structures of the P-loop and the helices of the peptidyltransferase center. Additionally, it revealed an incorrect orientation of helix 38, the finger of the A-site, and several critical, broken tertiary RNA contacts between distant regions of the 23S rRNA. The data suggests the peptidyltransferase center's maturation is one of the final events during assembly. This severe misfolding explains the slow rearrangement of the 45S particle to the native 50S subunit. Next, we will characterize the RNA structure of the 27S and 35S particles. The structural probing of the 27S and 35S particles will provide insights into the early steps of ribosome assembly, which remain a complete mystery.

Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments.

Structure probing coupled with high-throughput sequencing could revolutionize our understanding of the role of RNA structure in regulation of gene expression. Despite recent technological advances, intrinsic noise and high sequence coverage requirements greatly limit the applicability of these techniques. Here we describe a probabilistic modeling pipeline that accounts for biological variability and biases in the data, yielding statistically interpretable scores for the probability of nucleotide modification transcriptome wide. Using two yeast data sets, we demonstrate that our method has increased sensitivity, and thus our pipeline identifies modified regions on many more transcripts than do existing pipelines. Our method also provides confident predictions at much lower sequence coverage levels than those recommended for reliable structural probing. Our results show that statistical modeling extends the scope and potential of transcriptome-wide structure probing experiments.

Metrics for rapid quality control in RNA structure probing experiments.

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. 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. 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.

Combining structure probing data on RNA mutants with evolutionary information reveals RNA-binding interfaces.

Systematic structure probing experiments (e.g. SHAPE) of RNA mutants such as the mutate-and-map (MaM) protocol give us a direct access into the genetic robustness of ncRNA structures. Comparative studies of homologous sequences provide a distinct, yet complementary, approach to analyze structural and functional properties of non-coding RNAs. In this paper, we introduce a formal framework to combine the biochemical signal collected from MaM experiments, with the evolutionary information available in multiple sequence alignments. We apply neutral theory principles to detect complex long-range dependencies between nucleotides of a single stranded RNA, and implement these ideas into a software called aRNhAck. We illustrate the biological significance of this signal and show that the nucleotides networks calculated with aRNhAck are correlated with nucleotides located in RNA–RNA, RNA–protein, RNA–DNA and RNA–ligand interfaces. aRNhAck is freely available at http://csb.cs.mcgill.ca/arnhack.

The reverse transcription signature of N-1-methyladenosine in RNA-Seq is sequence dependent.

The combination of Reverse Transcription (RT) and high-throughput sequencing has emerged as a powerful combination to detect modified nucleotides in RNA via analysis of either abortive RT-products or of the incorporation of mismatched dNTPs into cDNA. Here we simultaneously analyze both parameters in detail with respect to the occurrence of N-1-methyladenosine (m1A) in the template RNA. This naturally occurring modification is associated with structural effects, but it is also known as a mediator of antibiotic resistance in ribosomal RNA. In structural probing experiments with dimethylsulfate, m1A is routinely detected by RT-arrest. A specifically developed RNA-Seq protocol was tailored to the simultaneous analysis of RT-arrest and misincorporation patterns. By application to a variety of native and synthetic RNA preparations, we found a characteristic signature of m1A, which, in addition to an arrest rate, features misincorporation as a significant component. Detailed analysis suggests that the signature depends on RNA structure and on the nature of the nucleotide 3′ of m1A in the template RNA, meaning it is sequence dependent. The RT-signature of m1A was used for inspection and confirmation of suspected modification sites and resulted in the identification of hitherto unknown m1A residues in trypanosomal tRNA.

Automated band annotation for RNA structure probing experiments with numerous capillary electrophoresis profiles.

Capillary electrophoresis (CE) is a powerful approach for structural analysis of nucleic acids, with recent high-throughput variants enabling three-dimensional RNA modeling and the discovery of new rules for RNA structure design. Among the steps composing CE analysis, the process of finding each band in an electrophoretic trace and mapping it to a position in the nucleic acid sequence has required significant manual inspection and remains the most time-consuming and error-prone step. The few available tools seeking to automate this band annotation have achieved limited accuracy and have not taken advantage of information across dozens of profiles routinely acquired in high-throughput measurements. We present a dynamic-programming-based approach to automate band annotation for high-throughput capillary electrophoresis. The approach is uniquely able to define and optimize a robust target function that takes into account multiple CE profiles (sequencing ladders, different chemical probes, different mutants) collected for the RNA. Over a large benchmark of multi-profile datasets for biological RNAs and designed RNAs from the EteRNA project, the method outperforms prior tools (QuSHAPE and FAST) significantly in terms of accuracy compared with gold-standard manual annotations. The amount of computation required is reasonable at a few seconds per dataset. We also introduce an 'E-score' metric to automatically assess the reliability of the band annotation and show it to be practically useful in flagging uncertainties in band annotation for further inspection. The implementation of the proposed algorithm is included in the HiTRACE software, freely available as an online server and for download at http://hitrace.stanford.edu. sryoon@snu.ac.kr or rhiju@stanford.edu Supplementary data are available at Bioinformatics online.

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.