Multiple conformations are a conserved and regulatory feature of the RB1 5' UTR

Structural determinants of inverted Alu-mediated backsplicing revealed by -MaP and -JuMP

Biogenesis of circular RNA usually involves a backsplicing reaction where the downstream donor site is ligated to the upstream acceptor site by the spliceosome. For this reaction to occur, these sites must be in proximity. Inverted repeat sequences, such as Alu elements, if positioned in the upstream and downstream introns, can base pair and represent one mechanism for inducing proximity. Here, we investigate the pre-mRNA structure of the human HIPK3 gene at exon 2, which forms a circular RNA via backsplicing. We leverage multiple chemical probing approaches, including the recently developed SHAPE-JuMP (selective 2'-hydroxyl acylation analyzed by primer extension and juxtaposed merged pairs) strategy, to characterize secondary and tertiary interactions in the pre-mRNA that govern backsplicing. Our data confirm that the antisense Alu elements AluSz(-) and AluSq2(+), in the upstream and downstream introns, form a highly paired interaction. Circularization requires formation of long-range Alu-mediated base pairs but does not require the full-length AluSq2(+). In addition to confirming long-range base pairs, our SHAPE-JuMP data identified multiple long-range interactions between non-pairing nucleotides. Genome-wide analysis of inverted repeats flanking circular RNAs confirms that the presence of these elements favors circularization, but with modest predictive power. Together, our study suggests that secondary structure considerations alone do not fully explain backsplicing and that additional interactions are involved.

Structural determinants of inverted Alu-mediated backsplicing revealed by -MaP and -JuMP

Biogenesis of circular RNA usually involves a backsplicing reaction where the downstream donor site is ligated to the upstream acceptor site by the spliceosome. For this reaction to occur, it is hypothesized that these sites must be in proximity. Inverted repeat sequences, such as Alu elements, in the upstream and downstream introns are predicted to base-pair and represent one mechanism for inducing proximity. Here, we investigate the pre-mRNA structure of the human HIPK3 gene at exon 2, which forms a circular RNA via backsplicing. We leverage multiple chemical probing techniques, including the recently developed SHAPE- JuMP strategy, to characterize secondary and tertiary interactions in the pre- mRNA that govern backsplicing. Our data confirm that the antisense Alu elements, AluSz(-) and AluSq2(+) in the upstream and downstream introns, form a highly- paired interaction. Circularization requires formation of long-range Alu-mediated base pairs but does not require the full-length AluSq2(+). In addition to confirming long-range base pairs, our SHAPE-JuMP data identified multiple long-range interactions between non-pairing nucleotides. Genome-wide analysis of inverted repeats flanking circular RNAs confirm that their presence favors circularization, but the overall effect is modest. Together these results suggest that secondary structure considerations alone cannot fully explain backsplicing and additional interactions are key.

Conformational penalties: New insights into nucleic acid recognition

The energy cost accompanying changes in the structures of nucleic acids when they bind partner molecules is a significant but underappreciated thermodynamic contribution to binding affinity and specificity. This review highlights recent advances in measuring conformational penalties and determining their contribution to the recognition, folding, and regulatory activities of nucleic acids. Notable progress includes methods for measuring and structurally characterizing lowly populated conformational states, obtaining ensemble information in high throughput, for large macromolecular assemblies, and in complex cellular environments. Additionally, quantitative and predictive thermodynamic models have been developed that relate conformational penalties to nucleic acid-protein association and cellular activity. These studies underscore the crucial role of conformational penalties in nucleic acid recognition.

Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli

Protein expression is a critical process in diverse biological systems. For Escherichia coli, a widely employed microbial host in industrial catalysis and healthcare, researchers often face significant challenges in constructing recombinant expression systems. To maximize the potential of E. coli expression systems, it is essential to address problems regarding the low or absent production of certain target proteins. This article presents viable solutions to the main factors posing challenges to heterologous protein expression in E. coli, which includes protein toxicity, the intrinsic influence of gene sequences, and mRNA structure. These strategies include specialized approaches for managing toxic protein expression, addressing issues related to mRNA structure and codon bias, advanced codon optimization methodologies that consider multiple factors, and emerging optimization techniques facilitated by big data and machine learning.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Pressure pushes tRNALys3 into excited conformational states

Conformational dynamics play essential roles in RNA function. However, detailed structural characterization of excited states of RNA remains challenging. Here, we apply high hydrostatic pressure (HP) to populate excited conformational states of tRNALys3, and structurally characterize them using a combination of HP 2D-NMR, HP-SAXS (HP-small-angle X-ray scattering), and computational modeling. HP-NMR revealed that pressure disrupts the interactions of the imino protons of the uridine and guanosine U-A and G-C base pairs of tRNALys3. HP-SAXS profiles showed a change in shape, but no change in overall extension of the transfer RNA (tRNA) at HP. Configurations extracted from computational ensemble modeling of HP-SAXS profiles were consistent with the NMR results, exhibiting significant disruptions to the acceptor stem, the anticodon stem, and the D-stem regions at HP. We propose that initiation of reverse transcription of HIV RNA could make use of one or more of these excited states.

Structural analysis of MALAT1 long noncoding RNA in cells and in evolution

Although not canonically polyadenylated, the long noncoding RNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is stabilized by a highly conserved 76-nt triple helix structure on its 3' end. The entire MALAT1 transcript is over 8000 nt long in humans. The strongest structural conservation signal in MALAT1 (as measured by covariation of base pairs) is in the triple helix structure. Primary sequence analysis of covariation alone does not reveal the degree of structural conservation of the entire full-length transcript, however. Furthermore, RNA structure is often context dependent; RNA binding proteins that are differentially expressed in different cell types may alter structure. We investigate here the in-cell and cell-free structures of the full-length human and green monkey (Chlorocebus sabaeus) MALAT1 transcripts in multiple tissue-derived cell lines using SHAPE chemical probing. Our data reveal levels of uniform structural conservation in different cell lines, in cells and cell-free, and even between species, despite significant differences in primary sequence. The uniformity of the structural conservation across the entire transcript suggests that, despite seeing covariation signals only in the triple helix junction of the lncRNA, the rest of the transcript's structure is remarkably conserved, at least in primates and across multiple cell types and conditions.

The Functional Meaning of 5'UTR in Protein-Coding Genes

As it is well known, messenger RNA has many regulatory regions along its sequence length. One of them is the 5' untranslated region (5'UTR), which itself contains many regulatory elements such as upstream ORFs (uORFs), internal ribosome entry sites (IRESs), microRNA binding sites, and structural components involved in the regulation of mRNA stability, pre-mRNA splicing, and translation initiation. Activation of the alternative, more upstream transcription start site leads to an extension of 5'UTR. One of the consequences of 5'UTRs extension may be head-to-head gene overlap. This review describes elements in 5'UTR of protein-coding transcripts and the functional significance of protein-coding genes 5' overlap with implications for transcription, translation, and disease.

Disease-associated human genetic variation through the lens of precursor and mature RNA structure

Disease-associated variants (DAVs) are commonly considered either through a genomic lens that describes variant function at the DNA level, or at the protein function level if the variant is translated. Although the genomic and proteomic effects of variation are well-characterized, genetic variants disrupting post-transcriptional regulation is another mechanism of disease that remains understudied. Specific RNA sequence motifs mediate post-transcriptional regulation both in the nucleus and cytoplasm of eukaryotic cells, often by binding to RNA-binding proteins or other RNAs. However, many DAVs map far from these motifs, which suggests deeper layers of post-transcriptional mechanistic control. Here, we consider a transcriptomic framework to outline the importance of post-transcriptional regulation as a mechanism of disease-causing single-nucleotide variation in the human genome. We first describe the composition of the human transcriptome and the importance of abundant yet overlooked components such as introns and untranslated regions (UTRs) of messenger RNAs (mRNAs). We present an analysis of Human Gene Mutation Database variants mapping to mRNAs and examine the distribution of causative disease-associated variation across the transcriptome. Although our analysis confirms the importance of post-transcriptional regulatory motifs, a majority of DAVs do not directly map to known regulatory motifs. Therefore, we review evidence that regions outside these well-characterized motifs can regulate function by RNA structure-mediated mechanisms in all four elements of an mRNA: exons, introns, 5' and 3' UTRs. To this end, we review published examples of riboSNitches, which are single-nucleotide variants that result in a change in RNA structure that is causative of the disease phenotype. In this review, we present the current state of knowledge of how DAVs act at the transcriptome level, both through altering post-transcriptional regulatory motifs and by the effects of RNA structure.

Computational approaches for RNA structure ensemble deconvolution from structure probing data

RNA structure probing experiments have emerged over the last decade as a straightforward way to determine the structure of RNA molecules in a number of different contexts. Although powerful, the ability of RNA to dynamically interconvert between, and to simultaneously populate, alternative structural configurations, poses a nontrivial challenge to the interpretation of data derived from these experiments. Recent efforts aimed at developing computational methods for the reconstruction of coexisting alternative RNA conformations from structure probing data are paving the way to the study of RNA structure ensembles, even in the context of living cells. In this review, we critically discuss these methods, their limitations and possible future improvements.

RNA Secondary Structurome Revealed Distinct Thermoregulation in Plasmodium falciparum

The development of Plasmodium parasites, a causative agent of malaria, requests two hosts and the completion of 11 different parasite stages during development. Therefore, an efficient and fast response of parasites to various complex environmental changes, such as ambient temperature, pH, ions, and nutrients, is essential for parasite development and survival. Among many of these environmental changes, temperature is a decisive factor for parasite development and pathogenesis, including the thermoregulation of rRNA expression, gametogenesis, and parasite sequestration in cerebral malaria. However, the exact mechanism of how Plasmodium parasites rapidly respond and adapt to temperature change remains elusive. As a fundamental and pervasive regulator of gene expression, RNA structure can be a specific mechanism for fine tuning various biological processes. For example, dynamic and temperature-dependent changes in RNA secondary structures can control the expression of different gene programs, as shown by RNA thermometers. In this study, we applied the in vitro and in vivo transcriptomic-wide secondary structurome approach icSHAPE to measure parasite RNA structure changes with temperature alteration at single-nucleotide resolution for ring and trophozoite stage parasites. Among 3,000 probed structures at different temperatures, our data showed structural changes in the global transcriptome, such as S-type rRNA, HRPII gene, and the erythrocyte membrane protein family. When the temperature drops from 37°C to 26°C, most of the genes in the trophozoite stage cause significantly more changes to the RNA structure than the genes in the ring stage. A multi-omics analysis of transcriptome data from RNA-seq and RNA structure data from icSHAPE reveals that the specific RNA secondary structure plays a significant role in the regulation of transcript expression for parasites in response to temperature changes. In addition, we identified several RNA thermometers (RNATs) that responded quickly to temperature changes. The possible thermo-responsive RNAs in Plasmodium falciparum were further mapped. To this end, we identified dynamic and temperature-dependent RNA structural changes in the P. falciparum transcriptome and performed a comprehensive characterization of RNA secondary structures over the course of temperature stress in blood stage development. These findings not only contribute to a better understanding of the function of the RNA secondary structure but may also provide novel targets for efficient vaccines or drugs.

Pathogenic noncoding variants in the neurofibromatosis and schwannomatosis predisposition genes

Neurofibromatosis type 1 (NF1), type 2 (NF2), and schwannomatosis are a group of autosomal dominant disorders that predispose to the development of nerve sheath tumors. Pathogenic variants (PVs) that cause NF1 and NF2 are located in the NF1 and NF2 loci, respectively. To date, most variants associated with schwannomatosis have been identified in the SMARCB1 and LZTR1 genes, and a missense variant in the DGCR8 gene was recently reported to predispose to schwannomas. In spite of the high detection rate for PVs in NF1 and NF2 (over 90% of non-mosaic germline variants can be identified by routine genetic screening) underlying PVs for a proportion of clinical cases remain undetected. A higher proportion of non-NF2 schwannomatosis cases have no detected PV, with PVs currently only identified in around 70%-86% of familial cases and 30%-40% of non-NF2 sporadic schwannomatosis cases. A number of variants of uncertain significance have been observed for each disorder, many of them located in noncoding, regulatory, or intergenic regions. Here we summarize noncoding variants in this group of genes and discuss their established or potential role in the pathogenesis of NF1, NF2, and schwannomatosis.

Multiplexed functional genomic analysis of 5' untranslated region mutations across the spectrum of prostate cancer

The functional consequences of genetic variants within 5’ untranslated regions (UTRs) on a genome-wide scale are poorly understood in disease. Here we develop a high-throughput multi-layer functional genomics method called PLUMAGE (Pooled full-length UTR Multiplex Assay on Gene Expression) to quantify the molecular consequences of somatic 5’ UTR mutations in human prostate cancer. We show that 5’ UTR mutations can control transcript levels and mRNA translation rates through the creation of DNA binding elements or RNA-based cis-regulatory motifs. We discover that point mutations can simultaneously impact transcript and translation levels of the same gene. We provide evidence that functional 5’ UTR mutations in the MAP kinase signaling pathway can upregulate pathway-specific gene expression and are associated with clinical outcomes. Our study reveals the diverse mechanisms by which the mutational landscape of 5’ UTRs can co-opt gene expression and demonstrates that single nucleotide alterations within 5’ UTRs are functional in cancer.

Mutations in 5’ untranslated regions (UTRs) have a functional role in gene expression in cancer. Here, the authors develop a sequencing-based high throughput functional assay named PLUMAGE and show the effects of these mutations on gene expression and their association with clinical outcomes in prostate cancer.

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.

Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures

The discovery and design of biologically important RNA molecules is outpacing three-dimensional structural characterization. Here, we demonstrate that cryo-electron microscopy can routinely resolve maps of RNA-only systems and that these maps enable subnanometer-resolution coordinate estimation when complemented with multidimensional chemical mapping and Rosetta DRRAFTER computational modeling. This hybrid 'Ribosolve' pipeline detects and falsifies homologies and conformational rearrangements in 11 previously unknown 119- to 338-nucleotide protein-free RNA structures: full-length Tetrahymena ribozyme, hc16 ligase with and without substrate, full-length Vibrio cholerae and Fusobacterium nucleatum glycine riboswitch aptamers with and without glycine, Mycobacterium SAM-IV riboswitch with and without S-adenosylmethionine, and the computer-designed ATP-TTR-3 aptamer with and without AMP. Simulation benchmarks, blind challenges, compensatory mutagenesis, cross-RNA homologies and internal controls demonstrate that Ribosolve can accurately resolve the global architectures of RNA molecules but does not resolve atomic details. These tests offer guidelines for making inferences in future RNA structural studies with similarly accelerated throughput.

Linkage disequilibrium and haplotype analysis of Src and Yes1 genes in thyroid cancer

Purpose: This study was planned to examine the effects of Src and Yes1 single nucleotide polymorphism (SNPs) on the risk of thyroid cancer in 499 patients and 500 controls. Materials & methods: Three SNPs of Src gene and three SNPs of Yes1 gene were analyzed using Tetra-primer ARMS-PCR followed by sequencing. Results: rs121913314 of Src gene genotype TT showed 32-fold increased risk of thyroid cancer and rs2305994 of Yes1 genotypes TT and CT showed 2.7-fold and 16-fold increased risk in thyroid cancer (p < 0.0001). Haplotype analysis revealed that CATGCC, CATGCT, CATGTC, CATGTT, TATGCC and TATGTTA haplotypes are associated with thyroid cancer risk. Conclusion: Results showed that genotypes and allele distribution of Src and Yes1 genes are significantly linked with increased risk of thyroid cancer.

The roles of structural dynamics in the cellular functions of RNAs

RNAs fold into 3D structures that range from simple helical elements to complex tertiary structures and quaternary ribonucleoprotein assemblies. The functions of many regulatory RNAs depend on how their 3D structure changes in response to a diverse array of cellular conditions. In this Review, we examine how the structural characterization of RNA as dynamic ensembles of conformations, which form with different probabilities and at different timescales, is improving our understanding of RNA function in cells. We discuss the mechanisms of gene regulation by microRNAs, riboswitches, ribozymes, post-transcriptional RNA modifications and RNA-binding proteins, and how the cellular environment and processes such as liquid-liquid phase separation may affect RNA folding and activity. The emerging RNA-ensemble-function paradigm is changing our perspective and understanding of RNA regulation, from in vitro to in vivo and from descriptive to predictive.

Alternate RNA Structures

RNA molecules fold into complex three-dimensional structures that sample alternate conformations ranging from minor differences in tertiary structure dynamics to major differences in secondary structure. This allows them to form entirely different substructures with each population potentially giving rise to a distinct biological outcome. The substructures can be partitioned along an existing energy landscape given a particular static cellular cue or can be shifted in response to dynamic cues such as ligand binding. We review a few key examples of RNA molecules that sample alternate conformations and how these are capitalized on for control of critical regulatory functions.

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.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes

RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/. We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

Characterizing micro-to-millisecond chemical exchange in nucleic acids using off-resonance R1ρ relaxation dispersion

This review describes off-resonance R1ρ relaxation dispersion NMR methods for characterizing microsecond-to-millisecond chemical exchange in uniformly 13C/15N labeled nucleic acids in solution. The review opens with a historical account of key developments that formed the basis for modern R1ρ techniques used to study chemical exchange in biomolecules. A vector model is then used to describe the R1ρ relaxation dispersion experiment, and how the exchange contribution to relaxation varies with the amplitude and frequency offset of an applied spin-locking field, as well as the population, exchange rate, and differences in chemical shifts of two exchanging species. Mathematical treatment of chemical exchange based on the Bloch-McConnell equations is then presented and used to examine relaxation dispersion profiles for more complex exchange scenarios including three-state exchange. Pulse sequences that employ selective Hartmann-Hahn cross-polarization transfers to excite individual 13C or 15N spins are then described for measuring off-resonance R1ρ(13C) and R1ρ(15N) in uniformly 13C/15N labeled DNA and RNA samples prepared using commercially available 13C/15N labeled nucleotide triphosphates. Approaches for analyzing R1ρ data measured at a single static magnetic field to extract a full set of exchange parameters are then presented that rely on numerical integration of the Bloch-McConnell equations or the use of algebraic expressions. Methods for determining structures of nucleic acid excited states are then reviewed that rely on mutations and chemical modifications to bias conformational equilibria, as well as structure-based approaches to calculate chemical shifts. Applications of the methodology to the study of DNA and RNA conformational dynamics are reviewed and the biological significance of the exchange processes is briefly discussed.

Nuclear Magnetic Resonance Reveals That GU Base Pairs Flanking Internal Loops Can Adopt Diverse Structures

RNA thermodynamics play an important role in determining the two- and three-dimensional structures of RNA. Internal loops of the sequence 5'-GMNU/3'-UNMG are relatively unstable thermodynamically. Here, five duplexes with GU-flanked 2 × 2 nucleotide internal loops were structurally investigated to reveal determinants of their instability. The following internal loops were investigated: 5'-GCAU/3'-UACG, 5'-UUCG/3'-GCUU, 5'-GCUU/3'-UUCG, 5'-GUCU/3'-UCUG, and 5'-GCCU/3'-UCCG. Two-dimensional nuclear magnetic resonance spectra indicate the absence of GU wobble base pairing in 5'-GCUU/3'-UUCG, 5'-GUCU/3'-UCUG, and 5'-GCCU/3'-UCCG. The 5'-GCUU/3'-UUCG loop has an unusual conformation of the GU base pairs, in which U's O2 carbonyl forms a bifurcated hydrogen bond with G's amino and imino protons. The internal loop of 5'-GUCU/3'-UCUG displays a shifted configuration in which GC pairs flank a U-U pair and several U's are in fast exchange between positions inside and outside the helix. In contrast, 5'-GCAU/3'-UACG and 5'-UUCG/3'-GCUU both have the expected GU wobble base pairs flanking the internal loop. Evidently, GU base pairs flanking internal loops are more likely to display atypical structures relative to Watson-Crick base pairs flanking internal loops. This appears to be more likely when the G of the GU pair is 5' to the loop. Such unusual structures could serve as recognition elements for biological function and as benchmarks for structure prediction methods.

Towards an understanding of RNA structural modalities: a riboswitch case study

A riboswitch is a type of RNA molecule that regulates important biological functions by changing structure, typically under ligand-binding. We assess the extent that these ligand-bound structural alternatives are present in the Boltzmann sample, a standard RNA secondary structure prediction method, for three riboswitch test cases. We use the cluster analysis tool RNAStructProfiling to characterize the different modalities present among the suboptimal structures sampled. We compare these modalities to the putative base pairing models obtained from independent experiments using NMR or fluorescence spectroscopy. We find, somewhat unexpectedly, that profiling the Boltzmann sample captures evidence of ligand-bound conformations for two of three riboswitches studied. Moreover, this agreement between predicted modalities and experimental models is consistent with the classification of riboswitches into thermodynamic versus kinetic regulatory mechanisms. Our results support cluster analysis of Boltzmann samples by RNAStructProfiling as a possible basis for de novo identification of thermodynamic riboswitches, while highlighting the challenges for kinetic ones.

Genome-wide RNA structurome reprogramming by acute heat shock globally regulates mRNA abundance

The heat shock response is crucial for organism survival in natural environments. RNA structure is known to influence numerous processes related to gene expression, but there have been few studies on the global RNA structurome as it prevails in vivo. Moreover, how heat shock rapidly affects RNA structure genome-wide in living systems remains unknown. We report here in vivo heat-regulated RNA structuromes. We applied Structure-seq chemical [dimethyl sulfate (DMS)] structure probing to rice (Oryza sativa L.) seedlings with and without 10 min of 42 °C heat shock and obtained structural data on >14,000 mRNAs. We show that RNA secondary structure broadly regulates gene expression in response to heat shock in this essential crop species. Our results indicate significant heat-induced elevation of DMS reactivity in the global transcriptome, revealing RNA unfolding over this biological temperature range. Our parallel Ribo-seq analysis provides no evidence for a correlation between RNA unfolding and heat-induced changes in translation, in contrast to the paradigm established in prokaryotes, wherein melting of RNA thermometers promotes translation. Instead, we find that heat-induced DMS reactivity increases correlate with significant decreases in transcript abundance, as quantified from an RNA-seq time course, indicating that mRNA unfolding promotes transcript degradation. The mechanistic basis for this outcome appears to be mRNA unfolding at both 5' and 3'-UTRs that facilitates access to the RNA degradation machinery. Our results thus reveal unexpected paradigms governing RNA structural changes and the eukaryotic RNA life cycle.

mRNAs and lncRNAs intrinsically form secondary structures with short end-to-end distances

The 5' and 3' termini of RNA play important roles in many cellular processes. Using Förster resonance energy transfer (FRET), we show that mRNAs and lncRNAs have an intrinsic propensity to fold in the absence of proteins into structures in which the 5' end and 3' end are ≤7 nm apart irrespective of mRNA length. Computational estimates suggest that the inherent proximity of the ends is a universal property of most mRNA and lncRNA sequences. Only guanosine-depleted RNA sequences with low sequence complexity are unstructured and exhibit end-to-end distances expected for the random coil conformation of RNA. While the biological implications remain to be explored, short end-to-end distances could facilitate the binding of protein factors that regulate translation initiation by bridging mRNA 5' and 3' ends. Furthermore, our studies provide the basis for measuring, computing and manipulating end-to-end distances and secondary structure in RNA in research and biotechnology.

Structural divergence creates new functional features in alphavirus genomes

Alphaviruses are mosquito-borne pathogens that cause human diseases ranging from debilitating arthritis to lethal encephalitis. Studies with Sindbis virus (SINV), which causes fever, rash, and arthralgia in humans, and Venezuelan equine encephalitis virus (VEEV), which causes encephalitis, have identified RNA structural elements that play key roles in replication and pathogenesis. However, a complete genomic structural profile has not been established for these viruses. We used the structural probing technique SHAPE-MaP to identify structured elements within the SINV and VEEV genomes. Our SHAPE-directed structural models recapitulate known RNA structures, while also identifying novel structural elements, including a new functional element in the nsP1 region of SINV whose disruption causes a defect in infectivity. Although RNA structural elements are important for multiple aspects of alphavirus biology, we found the majority of RNA structures were not conserved between SINV and VEEV. Our data suggest that alphavirus RNA genomes are highly divergent structurally despite similar genomic architecture and sequence conservation; still, RNA structural elements are critical to the viral life cycle. These findings reframe traditional assumptions about RNA structure and evolution: rather than structures being conserved, alphaviruses frequently evolve new structures that may shape interactions with host immune systems or co-evolve with viral proteins.

Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure

The impact of inherited and somatic mutations on messenger RNA (mRNA) structure remains poorly understood. Recent technological advances that leverage next-generation sequencing to obtain experimental structure data, such as SHAPE-MaP, can reveal structural effects of mutations, especially when these data are incorporated into structure modeling. Here, we analyze the ability of SHAPE-MaP to detect the relatively subtle structural changes caused by single-nucleotide mutations. We find that allele-specific sorting greatly improved our detection ability. Thus, we used SHAPE-MaP with a novel combination of clone-free robotic mutagenesis and allele-specific sorting to perform a rapid, comprehensive survey of noncoding somatic and inherited riboSNitches in two cancer-associated mRNAs, TPT1 and LCP1 Using rigorous thermodynamic modeling of the Boltzmann suboptimal ensemble, we identified a subset of mutations that change TPT1 and LCP1 RNA structure, with approximately 14% of all variants identified as riboSNitches. To confirm that these in vitro structures were biologically relevant, we tested how dependent TPT1 and LCP1 mRNA structures were on their environments. We performed SHAPE-MaP on TPT1 and LCP1 mRNAs in the presence or absence of cellular proteins and found that both mRNAs have similar overall folds in all conditions. RiboSNitches identified within these mRNAs in vitro likely exist under biological conditions. Overall, these data reveal a robust mRNA structural landscape where differences in environmental conditions and most sequence variants do not significantly alter RNA structural ensembles. Finally, predicting riboSNitches in mRNAs from sequence alone remains particularly challenging; these data will provide the community with benchmarks for further algorithmic development.

Genetic variants in mRNA untranslated regions

Genome Wide Association Studies (GWAS) have mapped thousands of genetic variants associated with complex disease risk and regulating quantitative traits, thus exploiting an unprecedented high-resolution genetic characterization of the human genome. A small fraction (3.7%) of the identified associations is located in untranslated regions (UTRs), and the molecular mechanism has been elucidated for few of them. Genetic variations at UTRs may modify regulatory elements affecting the interaction of the UTRs with proteins and microRNAs. The overall functional consequences include modulation of messenger RNA (mRNA) transcription, secondary structure, stability, localization, translation, and access to regulators like microRNAs (miRNAs) and RNA-binding proteins (RBPs). Alterations of these regulatory mechanisms are known to modify molecular pathways and cellular processes, potentially leading to disease processes. Here, we analyze some examples of genetic risk variants mapping in the UTR regulatory elements. We describe a recently identified genetic variant localized in the 3'UTR of the TNFSF13B gene, associated with autoimmunity risk and responsible of an increased stability and translation of TNFSF13B mRNA. We discuss how the correct use and interpretation of public GWAS repositories could lead to a better understanding of etiopathogenetic mechanisms and the generation of robust biological hypothesis as starting point for further functional studies. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

Classification of RNA structure change by 'gazing' at experimental data

Motivation

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2' Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by 'gel gazing.' SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human 'gazing' by identifying features of the SHAPE profile that human experts agree 'looks' like a riboSNitch.

Results

We find strong quantitative agreement between experts when RNA scientists 'gaze' at SHAPE data and identify riboSNitches. We identify dynamic time warping and seven other features predictive of the human consensus. The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8. When we analyze 2019 mutant traces for 17 different RNAs, we find that features of the WT SHAPE reactivity allow us to improve thermodynamic structure predictions of riboSNitches. This is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine.

Availability and Implementation

The classSNitch R package is freely available at http://classsnitch.r-forge.r-project.org .

Contact

alain@email.unc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Post-transcriptional regulation of 5'-untranslated regions of human Transient Receptor Potential Vanilloid type-1 (TRPV-1) channels: role in the survival of glioma patients

The Transient Receptor Potential Vanilloid type-1 (TRPV1) channel is a non-selective cation channel belonging to the Transient Receptor Potential family; variation of its expression has been correlated to glioma progression. In human, TRPV1 transcripts display a remarkable homogeneity differing only for the 5'-untranslated region (5'UTR) sequence that generates four variants encoding the same protein. Herein, we investigated the role of the 5'UTR sequences in TRPV1 transcripts stability, regulation of translation, expression in glioma cells and tissues. In addition, the expression of 5'UTR TRPV1 variants as prognostic factor in the survival of glioblastoma patients was evaluated. The expression level for each 5'UTR and their stability was evaluated by RT-PCR analysis. The effect of rapamycin and interferon-gamma in 5'UTR-regulating TRPV1 translation was determined by western blot analysis in glioma cell lines. We demonstrated that the 5'UTR influences the stability and translation efficacy of TRPV1 transcripts, and that TRPV1 variant three (TRPV1_v3) was the most stable and the only variant expressed in GBM samples and in glioma stem-like cells. Furthermore, we found that TRPV1_v3 expression levels correlate with patient's survival, suggesting that it may represent a potential prognostic marker for patients with glioma.

Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes

RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies.

Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.

Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

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.

Swellix: a computational tool to explore RNA conformational space

Background

The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods.

Results

The conceptual advance in Swellix is to count helices and generate all possible combinations of helices rather than counting and combining base pairs. Swellix bundles similar helices and includes improvements in memory use and efficient parallelization. Biological applications of Swellix are demonstrated by computing the reduction in conformational space and entropy due to naturally modified nucleotides in tRNA sequences and by motif searches in Human Endogenous Retroviral (HERV) RNA sequences. The Swellix motif search reveals occurrences of protein and drug binding motifs in the HERV RNA ensemble that do not occur in minimum free energy or centroid predicted structures.

Conclusions

Swellix presents significant improvements over Crumple in terms of efficiency and memory use. The efficient parallelization of Swellix enables the computation of sequences as long as 418 nucleotides with sufficient experimental constraints. Thus, Swellix provides a practical alternative to free energy minimization tools when multiple structures, kinetically determined structures, or complex RNA-RNA and RNA-protein interactions are present in an RNA folding problem.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1910-7) contains supplementary material, which is available to authorized users.

An RNA structure-mediated, posttranscriptional model of human α-1-antitrypsin expression

Protein and mRNA expression are in most cases poorly correlated, which suggests that the posttranscriptional regulatory program of a cell is an important component of gene expression. This regulatory network is still poorly understood, including how RNA structure quantitatively contributes to translational control. We present here a series of structural and functional experiments that together allow us to derive a quantitative, structure-dependent model of translation that accurately predicts translation efficiency in reporter assays and primary human tissue for a complex and medically important protein, α-1-antitrypsin. Our model demonstrates the importance of accurate, experimentally derived RNA structural models partnered with Kozak sequence information to explain protein expression and suggests a strategy by which α-1-antitrypsin expression may be increased in diseased individuals.

Chronic obstructive pulmonary disease (COPD) affects over 65 million individuals worldwide, where α-1-antitrypsin deficiency is a major genetic cause of the disease. The α-1-antitrypsin gene, SERPINA1, expresses an exceptional number of mRNA isoforms generated entirely by alternative splicing in the 5′-untranslated region (5′-UTR). Although all SERPINA1 mRNAs encode exactly the same protein, expression levels of the individual mRNAs vary substantially in different human tissues. We hypothesize that these transcripts behave unequally due to a posttranscriptional regulatory program governed by their distinct 5′-UTRs and that this regulation ultimately determines α-1-antitrypsin expression. Using whole-transcript selective 2′-hydroxyl acylation by primer extension (SHAPE) chemical probing, we show that splicing yields distinct local 5′-UTR secondary structures in SERPINA1 transcripts. Splicing in the 5′-UTR also changes the inclusion of long upstream ORFs (uORFs). We demonstrate that disrupting the uORFs results in markedly increased translation efficiencies in luciferase reporter assays. These uORF-dependent changes suggest that α-1-antitrypsin protein expression levels are controlled at the posttranscriptional level. A leaky-scanning model of translation based on Kozak translation initiation sequences alone does not adequately explain our quantitative expression data. However, when we incorporate the experimentally derived RNA structure data, the model accurately predicts translation efficiencies in reporter assays and improves α-1-antitrypsin expression prediction in primary human tissues. Our results reveal that RNA structure governs a complex posttranscriptional regulatory program of α-1-antitrypsin expression. Crucially, these findings describe a mechanism by which genetic alterations in noncoding gene regions may result in α-1-antitrypsin deficiency.

Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations

Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones.

Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo

When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.

The roles of RNA processing in translating genotype to phenotype

A goal of human genetics studies is to determine the mechanisms by which genetic variation produces phenotypic differences that affect human health. Efforts in this respect have previously focused on genetic variants that affect mRNA levels by altering epigenetic and transcriptional regulation. Recent studies show that genetic variants that affect RNA processing are at least equally as common as, and are largely independent from, those variants that affect transcription. We highlight the impact of genetic variation on pre-mRNA splicing and polyadenylation, and on the stability, translation and structure of mRNAs as mechanisms that produce phenotypic traits. These results emphasize the importance of including RNA processing signals in analyses to identify functional variants.

The roles of RNA processing in translating genotype to phenotype

A goal of human genetics studies is to determine the mechanisms by which genetic variation produces phenotypic differences that affect human health. Efforts in this respect have previously focused on genetic variants that affect mRNA levels by altering epigenetic and transcriptional regulation. Recent studies show that genetic variants that affect RNA processing are at least equally as common as, and are largely independent from, those variants that affect transcription. We highlight the impact of genetic variation on pre-mRNA splicing and polyadenylation, and on the stability, translation and structure of mRNAs as mechanisms that produce phenotypic traits. These results emphasize the importance of including RNA processing signals in analyses to identify functional variants.

Reconstruction of ancestral RNA sequences under multiple structural constraints

BACKGROUND

Secondary structures form the scaffold of multiple sequence alignment of non-coding RNA (ncRNA) families. An accurate reconstruction of ancestral ncRNAs must use this structural signal. However, the inference of ancestors of a single ncRNA family with a single consensus structure may bias the results towards sequences with high affinity to this structure, which are far from the true ancestors.

METHODS

In this paper, we introduce achARNement, a maximum parsimony approach that, given two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree, simultaneously calculates ancestral RNA sequences for these two families.

RESULTS

We test our methodology on simulated data sets, and show that achARNement outperforms classical maximum parsimony approaches in terms of accuracy, but also reduces by several orders of magnitude the number of candidate sequences. To conclude this study, we apply our algorithms on the Glm clan and the FinP-traJ clan from the Rfam database.

CONCLUSIONS

Our results show that our methods reconstruct small sets of high-quality candidate ancestors with better agreement to the two target structures than with classical approaches. Our program is freely available at: http://csb.cs.mcgill.ca/acharnement .

The ribosome-engaged landscape of alternative splicing

High-throughput RNA sequencing (RNA-seq) has revealed an enormous complexity of alternative splicing (AS) across diverse cell and tissue types. However, it is currently unknown to what extent repertoires of splice-variant transcripts are translated into protein products. Here, we surveyed AS events engaged by the ribosome. Notably, at least 75% of human exon-skipping events detected in transcripts with medium-to-high abundance in RNA-seq data were also detected in ribosome profiling data. Furthermore, relatively small subsets of functionally related splice variants are engaged by ribosomes at levels that do not reflect their absolute abundance, thus indicating a role for AS in modulating translational output. This mode of regulation is associated with control of the mammalian cell cycle. Our results thus suggest that a major fraction of splice variants is translated and that specific cellular functions including cell-cycle control are subject to AS-dependent modulation of translation output.

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.

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.

Bridging the gap between in vitro and in vivo RNA folding

Deciphering the folding pathways and predicting the structures of complex three-dimensional biomolecules is central to elucidating biological function. RNA is single-stranded, which gives it the freedom to fold into complex secondary and tertiary structures. These structures endow RNA with the ability to perform complex chemistries and functions ranging from enzymatic activity to gene regulation. Given that RNA is involved in many essential cellular processes, it is critical to understand how it folds and functions in vivo. Within the last few years, methods have been developed to probe RNA structures in vivo and genome-wide. These studies reveal that RNA often adopts very different structures in vivo and in vitro, and provide profound insights into RNA biology. Nonetheless, both in vitro and in vivo approaches have limitations: studies in the complex and uncontrolled cellular environment make it difficult to obtain insight into RNA folding pathways and thermodynamics, and studies in vitro often lack direct cellular relevance, leaving a gap in our knowledge of RNA folding in vivo. This gap is being bridged by biophysical and mechanistic studies of RNA structure and function under conditions that mimic the cellular environment. To date, most artificial cytoplasms have used various polymers as molecular crowding agents and a series of small molecules as cosolutes. Studies under such in vivo-like conditions are yielding fresh insights, such as cooperative folding of functional RNAs and increased activity of ribozymes. These observations are accounted for in part by molecular crowding effects and interactions with other molecules. In this review, we report milestones in RNA folding in vitro and in vivo and discuss ongoing experimental and computational efforts to bridge the gap between these two conditions in order to understand how RNA folds in the cell.

RNA Duplex Map in Living Cells Reveals Higher-Order Transcriptome Structure

RNA has the intrinsic property to base pair, forming complex structures fundamental to its diverse functions. Here, we develop PARIS, a method based on reversible psoralen crosslinking for global mapping of RNA duplexes with near base-pair resolution in living cells. PARIS analysis in three human and mouse cell types reveals frequent long-range structures, higher-order architectures, and RNA-RNA interactions in trans across the transcriptome. PARIS determines base-pairing interactions on an individual-molecule level, revealing pervasive alternative conformations. We used PARIS-determined helices to guide phylogenetic analysis of RNA structures and discovered conserved long-range and alternative structures. XIST, a long noncoding RNA (lncRNA) essential for X chromosome inactivation, folds into evolutionarily conserved RNA structural domains that span many kilobases. XIST A-repeat forms complex inter-repeat duplexes that nucleate higher-order assembly of the key epigenetic silencing protein SPEN. PARIS is a generally applicable and versatile method that provides novel insights into the RNA structurome and interactome. VIDEO ABSTRACT.

The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations

Cancer is a disease of the genome caused by oncogene activation and tumor suppressor gene inhibition. Deep sequencing studies including large consortia such as TCGA and ICGC identified numerous tumor‐specific mutations not only in protein‐coding sequences but also in non‐coding sequences. Although 98% of the genome is not translated into proteins, most studies have neglected the information hidden in this “dark matter” of the genome. Malignancy‐driving mutations can occur in all genetic elements outside the coding region, namely in enhancer, silencer, insulator, and promoter as well as in 5′‐UTR and 3′‐UTR. Intron or splice site mutations can alter the splicing pattern. Moreover, cancer genomes contain mutations within non‐coding RNA, such as microRNA, lncRNA, and lincRNA. A synonymous mutation changes the coding region in the DNA and RNA but not the protein sequence. Importantly, oncogenes such as TERT or miR‐21 as well as tumor suppressor genes such as TP53/p53,APC,BRCA1, or RB1 can be affected by these alterations. In summary, coding‐independent mutations can affect gene regulation from transcription, splicing, mRNA stability to translation, and hence, this largely neglected area needs functional studies to elucidate the mechanisms underlying tumorigenesis. This review will focus on the important role and novel mechanisms of these non‐coding or allegedly silent mutations in tumorigenesis.

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.

New insights from cluster analysis methods for RNA secondary structure prediction

A widening gap exists between the best practices for RNA secondary structure prediction developed by computational researchers and the methods used in practice by experimentalists. Minimum free energy predictions, although broadly used, are outperformed by methods which sample from the Boltzmann distribution and data mine the results. In particular, moving beyond the single structure prediction paradigm yields substantial gains in accuracy. Furthermore, the largest improvements in accuracy and precision come from viewing secondary structures not at the base pair level but at lower granularity/higher abstraction. This suggests that random errors affecting precision and systematic ones affecting accuracy are both reduced by this 'fuzzier' view of secondary structures. Thus experimentalists who are willing to adopt a more rigorous, multilayered approach to secondary structure prediction by iterating through these levels of granularity will be much better able to capture fundamental aspects of RNA base pairing. WIREs RNA 2016, 7:278-294. doi: 10.1002/wrna.1334 For further resources related to this article, please visit the WIREs website.