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

Making target sites in large structured RNAs accessible to RNA-cleaving DNAzymes through hybridization with synthetic DNA oligonucleotides

The 10-23 DNAzyme is one of the most active DNA-based enzymes, and in theory, can be designed to target any purine-pyrimidine junction within an RNA sequence for cleavage. However, purine-pyrimidine junctions within a large, structured RNA (lsRNA) molecule of biological origin are not always accessible to 10-23, negating its general utility as an RNA-cutting molecular scissor. Herein, we report a generalizable strategy that allows 10-23 to access any purine-pyrimidine junction within an lsRNA. Using three large SARS-CoV-2 mRNA sequences of 566, 584 and 831 nucleotides in length as model systems, we show that the use of antisense DNA oligonucleotides (ASOs) that target the upstream and downstream regions flanking the cleavage site can restore the activity (kobs) of previously poorly active 10-23 DNAzyme systems by up to 2000-fold. We corroborated these findings mechanistically using in-line probing to demonstrate that ASOs reduced 10-23 DNAzyme target site structure within the lsRNA substrates. This approach represents a simple, efficient, cost-effective, and generalizable way to improve the accessibility of 10-23 to a chosen target site within an lsRNA molecule, especially where direct access to the genomic RNA target is necessary.

Isoform-specific RNA structure determination using Nano-DMS-MaP

RNA structure determination is essential to understand how RNA carries out its diverse biological functions. In cells, RNA isoforms are readily expressed with partial variations within their sequences due, for example, to alternative splicing, heterogeneity in the transcription start site, RNA processing or differential termination/polyadenylation. Nanopore dimethyl sulfate mutational profiling (Nano-DMS-MaP) is a method for in situ isoform-specific RNA structure determination. Unlike similar methods that rely on short sequencing reads, Nano-DMS-MaP employs nanopore sequencing to resolve the structures of long and highly similar RNA molecules to reveal their previously hidden structural differences. This Protocol describes the development and applications of Nano-DMS-MaP and outlines the main considerations for designing and implementing a successful experiment: from bench to data analysis. In cell probing experiments can be carried out by an experienced molecular biologist in 3-4 d. Data analysis requires good knowledge of command line tools and Python scripts and requires a further 3-5 d.

sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure

MOTIVATION

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement.

RESULTS

In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

Navigating the landscape of epitranscriptomics and host immunity

RNA modifications, also termed epitranscriptomic marks, encompass chemical alterations to individual nucleotides, including processes such as methylation and editing. These marks contribute to a wide range of biological processes, many of which are related to host immune system defense. The functions of immune-related RNA modifications can be categorized into three main groups: regulation of immunogenic RNAs, control of genes involved in innate immune response, and facilitation of adaptive immunity. Here, we provide an overview of recent research findings that elucidate the contributions of RNA modifications to each of these processes. We also discuss relevant methods for genome-wide identification of RNA modifications and their immunogenic substrates. Finally, we highlight recent advances in cancer immunotherapies that aim to reduce cancer cell viability by targeting the enzymes responsible for RNA modifications. Our presentation of these dynamic research avenues sets the stage for future investigations in this field.

Global identification of mRNA-interacting circular RNAs by CLiPPR-Seq

Although the functional role of circular RNA (circRNA) interaction with microRNAs and proteins has been studied extensively, circRNA interactions with the protein-coding mRNAs in intact cells remain largely unknown. Here, by employing AMT-mediated proximity ligation of RNA-RNA duplexes followed by circRNA enrichment and deep sequencing, we report a novel Cross-Linking Poly(A) Pulldown RNase R Sequencing (CLiPPR-seq) technology which identified hundreds of mRNA-interacting circRNAs in three different cell types, including βTC6, C2C12 and HeLa cells. Furthermore, CLiPP-seq without RNase R treatment was also performed to identify the mRNA expression in these cells. BLAST analysis of circRNAs in CLiPPR-seq sample with the mRNAs in CLiPP-seq samples determined their potential complementary sequences for circRNA-mRNA interaction. Pulldown of circRNAs and poly(A) RNAs confirmed the direct interaction of circRNAs with target mRNAs. Silencing of mRNA-interacting circRNAs led to the altered expression of target mRNAs in βTC6 cells, suggesting the role of direct interaction of circRNAs with mRNAs in gene expression regulation. CLiPPR-seq thus represents a novel method for illuminating the myriad of uncharacterized circRNA-mRNA hybrids that may regulate gene expression.

Small RNA structural biochemistry in a post-sequencing era

High-throughput sequencing has had an enormous impact on small RNA research during the past decade. However, sequencing only offers a one-dimensional view of the transcriptome and is often highly biased. Additionally, the 'sequence, map and annotate' approach, used widely in small RNA research, can lead to flawed interpretations of the data, lacking biological plausibility, due in part to database issues. Even in the absence of technical biases, the loss of three-dimensional information is a major limitation to understanding RNA stability, turnover and function. For example, noncoding RNA-derived fragments seem to exist mainly as dimers, tetramers or as nicked forms of their parental RNAs, contrary to widespread assumptions. In this perspective, we will discuss main sources of bias during small RNA-sequencing, present several useful bias-reducing strategies and provide guidance on the interpretation of small RNA-sequencing results, with emphasis on RNA fragmentomics. As sequencing offers a one-dimensional projection of a four-dimensional reality, prior structure-level knowledge is often needed to make sense of the data. Consequently, while less-biased sequencing methods are welcomed, integration of orthologous experimental techniques is also strongly recommended.

RNAs undergo phase transitions with lower critical solution temperatures

Co-phase separation of RNAs and RNA-binding proteins drives the biogenesis of ribonucleoprotein granules. RNAs can also undergo phase transitions in the absence of proteins. However, the physicochemical driving forces of protein-free, RNA-driven phase transitions remain unclear. Here we report that various types of RNA undergo phase separation with system-specific lower critical solution temperatures. This entropically driven phase separation is an intrinsic feature of the phosphate backbone that requires Mg2+ ions and is modulated by RNA bases. RNA-only condensates can additionally undergo enthalpically favourable percolation transitions within dense phases. This is enabled by a combination of Mg2+-dependent bridging interactions between phosphate groups and RNA-specific base stacking and base pairing. Phase separation coupled to percolation can cause dynamic arrest of RNAs within condensates and suppress the catalytic activity of an RNase P ribozyme. Our work highlights the need to incorporate RNA-driven phase transitions into models for ribonucleoprotein granule biogenesis.

dsRID: in silico identification of dsRNA regions using long-read RNA-seq data

MOTIVATION

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases.

RESULTS

Here, we report dsRID (double-stranded RNA identifier), a machine-learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

AVAILABILITY AND IMPLEMENTATION

Software implementation of dsRID, and genomic coordinates of regions predicted by dsRID in all samples are available at the GitHub repository: https://github.com/gxiaolab/dsRID.

Nanopore molecular trajectories of a eukaryotic reverse transcriptase reveal a long-range RNA structure sensing mechanism

Eukaryotic reverse transcriptases (RTs) can have essential or deleterious roles in normal human physiology and disease. Compared to well-studied helicases, it remains unclear how RTs overcome the ubiquitous RNA structural barriers during reverse transcription. Herein, we describe the development of a Mycobacterium smegmatis porin A (MspA) nanopore technique to sequence RNA to quantify the single-molecule kinetics of an RT from Bombyx mori with single-nucleotide resolution. By establishing a quadromer map that correlates RNA sequence and MspA ion current, we were able to quantify the RT's dwell time at every single nucleotide step along its RNA template. By challenging the enzyme with various RNA structures, we found that during cDNA synthesis the RT can sense and actively destabilize RNA structures 11-12 nt downstream of its front boundary. The ability to sequence single molecules of RNA with nanopores paves the way to investigate the single-nucleotide activity of other processive RNA translocases.

RNA-RNA competitive interactions: a molecular civil war ruling cell physiology and diseases

The idea that proteins are the main determining factors in the functioning of cells and organisms, and their dysfunctions are the first cause of pathologies, has been predominant in biology and biomedicine until recently. This protein-centered view was too simplistic and failed to explain the physiological and pathological complexity of the cell. About 80% of the human genome is dynamically and pervasively transcribed, mostly as non-protein-coding RNAs (ncRNAs), which competitively interact with each other and with coding RNAs generating a complex RNA network regulating RNA processing, stability, and translation and, accordingly, fine-tuning the gene expression of the cells. Qualitative and quantitative dysregulations of RNA-RNA interaction networks are strongly involved in the onset and progression of many pathologies, including cancers and degenerative diseases. This review will summarize the RNA species involved in the competitive endogenous RNA network, their mechanisms of action, and involvement in pathological phenotypes. Moreover, it will give an overview of the most advanced experimental and computational methods to dissect and rebuild RNA networks.

dsRID: Editing-free in silico identification of dsRNA region using long-read RNA-seq data

Double-stranded RNAs (dsRNAs) are potent triggers of innate immune responses upon recognition by cytosolic dsRNA sensor proteins. Identification of endogenous dsRNAs helps to better understand the dsRNAome and its relevance to innate immunity related to human diseases. Here, we report dsRID (double-stranded RNA identifier), a machine learning-based method to predict dsRNA regions in silico, leveraging the power of long-read RNA-sequencing (RNA-seq) and molecular traits of dsRNAs. Using models trained with PacBio long-read RNA-seq data derived from Alzheimer's disease (AD) brain, we show that our approach is highly accurate in predicting dsRNA regions in multiple datasets. Applied to an AD cohort sequenced by the ENCODE consortium, we characterize the global dsRNA profile with potentially distinct expression patterns between AD and controls. Together, we show that dsRID provides an effective approach to capture global dsRNA profiles using long-read RNA-seq data.

Thirteen dubious ways to detect conserved structural RNAs

Covariation induced by compensatory base substitutions in RNA alignments is a great way to deduce conserved RNA structure, in principle. In practice, success depends on many factors, importantly the quality and depth of the alignment and the choice of covariation statistic. Measuring covariation between pairs of aligned positions is easy. However, using covariation to infer evolutionarily conserved RNA structure is complicated by other extraneous sources of covariation such as that resulting from homologous sequences having evolved from a common ancestor. In order to provide evidence of evolutionarily conserved RNA structure, a method to distinguish covariation due to sources other than RNA structure is necessary. Moreover, there are several sorts of artifactually generated covariation signals that can further confound the analysis. Additionally, some covariation signal is difficult to detect due to incomplete comparative data. Here, we investigate and critically discuss the practice of inferring conserved RNA structure by comparative sequence analysis. We provide new methods on how to approach and decide which of the numerous long non-coding RNAs (lncRNAs) have biologically relevant structures.

Archaeal Kink-Turn Binding Protein Mediates Inhibition of Orthomyxovirus Splicing Biology

Despite lacking a DNA intermediate, orthomyxoviruses complete their replication cycle in the nucleus and generate multiple transcripts by usurping the host splicing machinery. This biology results in dynamic changes of relative viral transcripts over time and dictates the replicative phase of the infection. Here, we demonstrate that the family of archaeal L7Ae proteins uniquely inhibit the splicing biology of influenza A virus, influenza B virus, and Salmon isavirus, revealing a common strategy utilized by Orthomyxoviridae members to achieve this dynamic. L7Ae-mediated inhibition of virus biology was lost with the generation of a splicing-independent strain of influenza A virus and attempts to select for an escape mutant resulted in variants that conformed to host splicing biology at significant cost to their overall fitness. As L7Ae recognizes conventional kink turns in various RNAs, these data implicate the formation of a similar structure as a shared strategy adopted by this virus family to coordinate their replication cycle. IMPORTANCE Here, we demonstrate that a family of proteins from archaea specifically inhibit this splicing biology of all tested members of the Orthomyxoviridae family. We show that this inhibition extends to influenza A virus, influenza B virus, and isavirus genera, while having no significant impact on the mammalian transcriptome or proteome. Attempts to generate an escape mutant against L7Ae-mediated inhibition resulted in mutations surrounding the viral splice sites and a significant loss of viral fitness. Together, these findings reveal a unique biology shared among diverse members of the Orthomyxoviridae family that may serve as a means to generate future universal therapeutics.

A riboswitch separated from its ribosome-binding site still regulates translation

Riboswitches regulate downstream gene expression by binding cellular metabolites. Regulation of translation initiation by riboswitches is posited to occur by metabolite-mediated sequestration of the Shine-Dalgarno sequence (SDS), causing bypass by the ribosome. Recently, we solved a co-crystal structure of a prequeuosine1-sensing riboswitch from Carnobacterium antarcticum that binds two metabolites in a single pocket. The structure revealed that the second nucleotide within the gene-regulatory SDS, G34, engages in a crystal contact, obscuring the molecular basis of gene regulation. Here, we report a co-crystal structure wherein C10 pairs with G34. However, molecular dynamics simulations reveal quick dissolution of the pair, which fails to reform. Functional and chemical probing assays inside live bacterial cells corroborate the dispensability of the C10-G34 pair in gene regulation, leading to the hypothesis that the compact pseudoknot fold is sufficient for translation attenuation. Remarkably, the C. antarcticum aptamer retained significant gene-regulatory activity when uncoupled from the SDS using unstructured spacers up to 10 nucleotides away from the riboswitch-akin to steric-blocking employed by sRNAs. Accordingly, our work reveals that the RNA fold regulates translation without SDS sequestration, expanding known riboswitch-mediated gene-regulatory mechanisms. The results infer that riboswitches exist wherein the SDS is not embedded inside a stable fold.

Determinants of Functional MicroRNA Targeting

MicroRNAs (miRNAs) play cardinal roles in regulating biological pathways and processes, resulting in significant physiological effects. To understand the complex regulatory network of miRNAs, previous studies have utilized massivescale datasets of miRNA targeting and attempted to computationally predict the functional targets of miRNAs. Many miRNA target prediction tools have been developed and are widely used by scientists from various fields of biology and medicine. Most of these tools consider seed pairing between miRNAs and their mRNA targets and additionally consider other determinants to improve prediction accuracy. However, these tools exhibit limited prediction accuracy and high false positive rates. The utilization of additional determinants, such as RNA modifications and RNA-binding protein binding sites, may further improve miRNA target prediction. In this review, we discuss the determinants of functional miRNA targeting that are currently used in miRNA target prediction and the potentially predictive but unappreciated determinants that may improve prediction accuracy.

Nucleic acid strand displacement - from DNA nanotechnology to translational regulation

Nucleic acid strand displacement reactions involve the competition of two or more DNA or RNA strands of similar sequence for binding to a complementary strand, and facilitate the isothermal replacement of an incumbent strand by an invader. The process can be biased by augmenting the duplex comprising the incumbent with a single-stranded extension, which can act as a toehold for a complementary invader. The toehold gives the invader a thermodynamic advantage over the incumbent, and can be programmed as a unique label to activate a specific strand displacement process. Toehold-mediated strand displacement processes have been extensively utilized for the operation of DNA-based molecular machines and devices as well as for the design of DNA-based chemical reaction networks. More recently, principles developed initially in the context of DNA nanotechnology have been applied for the de novo design of gene regulatory switches that can operate inside living cells. The article specifically focuses on the design of RNA-based translational regulators termed toehold switches. Toehold switches utilize toehold-mediated strand invasion to either activate or repress translation of an mRNA in response to the binding of a trigger RNA molecule. The basic operation principles of toehold switches will be discussed as well as their applications in sensing and biocomputing. Finally, strategies for their optimization will be described as well as challenges for their operation in vivo.

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.

The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.

An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops

Multiple types of non-canonical nucleic acid structures play essential roles in DNA recombination and replication, transcription, and genomic instability and have been associated with several human diseases. Thus, an increasing number of experimental and bioinformatics methods have been developed to identify these structures. To date, most reviews have focused on the features of non-canonical DNA/RNA structure formation, experimental approaches to mapping these structures, and the association of these structures with diseases. In addition, two reviews of computational algorithms for the prediction of non-canonical nucleic acid structures have been published. One of these reviews focused only on computational approaches for G4 detection until 2020. The other mainly summarized the computational tools for predicting cruciform, H-DNA and Z-DNA, in which the algorithms discussed were published before 2012. Since then, several experimental and computational methods have been developed. However, a systematic review including the conformation, sequencing mapping methods and computational prediction strategies for these structures has not yet been published. The purpose of this review is to provide an updated overview of conformation, current sequencing technologies and computational identification methods for non-canonical nucleic acid structures, as well as their strengths and weaknesses. We expect that this review will aid in understanding how these structures are characterised and how they contribute to related biological processes and diseases.

Detecting RNA-RNA interactome

The last decade has seen a robust increase in various types of novel RNA molecules and their complexity in gene regulation. RNA molecules play a critical role in cellular events by interacting with other biomolecules, including protein, DNA, and RNA. It has been established that RNA-RNA interactions play a critical role in several biological processes by regulating the biogenesis and function of RNA molecules. Interestingly, RNA-RNA interactions regulate the biogenesis of diverse RNA molecules, including mRNAs, microRNAs, tRNAs, and circRNAs, through splicing or backsplicing. Structured RNAs like rRNA, tRNA, and snRNAs achieve their functional conformation by intramolecular RNA-RNA interactions. In addition, functional consequences of many intermolecular RNA-RNA interactions have been extensively studied in the regulation of gene expression. Hence, it is essential to understand the mechanism and functions of RNA-RNA interactions in eukaryotes. Conventionally, RNA-RNA interactions have been identified through diverse biochemical methods for decades. The advent of high-throughput RNA-sequencing technologies has revolutionized the identification of global RNA-RNA interactome in cells and their importance in RNA structure and function in gene expression regulation. Although these technologies revealed tens of thousands of intramolecular and intermolecular RNA-RNA interactions, we further look forward to future unbiased and quantitative high-throughput technologies for detecting transcriptome-wide RNA-RNA interactions. With the ability to detect RNA-RNA interactome, we expect that future studies will reveal the higher-order structures of RNA molecules and multi-RNA hybrids impacting human health and diseases. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

MOTIVATION

In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated.

RESULTS

In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.

AVAILABILITY

Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding.

CONTACT

lbugnon@sinc.unl.edu.ar.

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-ligand molecular docking: advances and challenges

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

An assessment of the mutational load caused by various reactions used in DNA encoded libraries

DNA encoded libraries have become an essential hit-finding tool in early drug discovery. Recent advances in synthetic methods for DNA encoded libraries have expanded the available chemical space, but precisely how each type of chemistry affects the DNA is unstudied. Available assays to quantify the damage are limited to write efficiency, where the ability to ligate DNA onto a working encoded library strand is measured, or qPCR is performed to measure the amplifiability of the DNA. These measures read signal quantity and overall integrity, but do not report on specific damages in the encoded information. Herein, we use next generation sequencing (NGS) to measure the quality of the read signal in order to quantify the truthfulness of the retrieved information. We identify CuAAC to be the worst offender in terms of DNA damage amongst commonly used reactions in DELs, causing an increase of G → T transversions. Furthermore, we show that the analysis provides useful information even in fully elaborated DELs; indeed we see that vestiges of the synthetic history, both chemical and biochemical, are written into the mutational spectra of NGS datasets.

Long non-coding RNAs as possible therapeutic targets in protozoa, and in Schistosoma and other helminths

Long non-coding RNAs (lncRNAs) emerged in the past 20 years due to massive amounts of scientific data regarding transcriptomic analyses. They have been implicated in a plethora of cellular processes in higher eukaryotes. However, little is known about lncRNA possible involvement in parasitic diseases, with most studies only detecting their presence in parasites of human medical importance. Here, we review the progress on lncRNA studies and their functions in protozoans and helminths. In addition, we show an example of knockdown of one lncRNA in Schistosoma mansoni, SmLINC156349, which led to in vitro parasite adhesion, motility, and pairing impairment, with a 20% decrease in parasite viability and 33% reduction in female oviposition. Other observed phenotypes were a decrease in the proliferation rate of both male and female worms and their gonads, and reduced female lipid and vitelline droplets that are markers for well-developed vitellaria. Impairment of female worms' vitellaria in SmLINC156349-silenced worms led to egg development deficiency. All those results demonstrate the great potential of the tools and methods to characterize lncRNAs as potential new therapeutic targets. Further, we discuss the challenges and limitations of current methods for studying lncRNAs in parasites and possible solutions to overcome them, and we highlight the future directions of this exciting field.

Chemical and Enzymatic Probing of Viral RNAs: From Infancy to Maturity and Beyond

RNA molecules are key players in a variety of biological events, and this is particularly true for viral RNAs. To better understand the replication of those pathogens and try to block them, special attention has been paid to the structure of their RNAs. Methods to probe RNA structures have been developed since the 1960s; even if they have evolved over the years, they are still in use today and provide useful information on the folding of RNA molecules, including viral RNAs. The aim of this review is to offer a historical perspective on the structural probing methods used to decipher RNA structures before the development of the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) methodology and to show how they have influenced the current probing techniques. Actually, these technological breakthroughs, which involved advanced detection methods, were made possible thanks to the development of next-generation sequencing (NGS) but also to the previous works accumulated in the field of structural RNA biology. Finally, we will also discuss how high-throughput SHAPE (hSHAPE) paved the way for the development of sophisticated RNA structural techniques.

A genetic variant alters the secondary structure of the lncRNA H19 and is associated with dilated cardiomyopathy

lncRNAs are at the core of many regulatory processes and have also been recognized to be involved in various complex diseases. They affect gene regulation through direct interactions with RNA, DNA or proteins. Accordingly, lncRNA structure is likely to be essential for their regulatory function. Point mutations, which manifest as SNPs (single nucleotide polymorphisms) in genome screens, can substantially alter their function and, subsequently, the expression of their downstream regulated genes. To test the effect of SNPs on structure, we investigated lncRNAs associated with dilated cardiomyopathy. Among 322 human candidate lncRNAs, we demonstrate first the significant association of an SNP located in lncRNA H19 using data from 1084 diseased and 751 control patients. H19 is generally highly expressed in the heart, with a complex expression pattern during heart development. Next, we used MFE (minimum free energy) folding to demonstrate a significant refolding in the secondary structure of this 861 nt long lncRNA. Since MFE folding may overlook the importance of sub-optimal structures, we showed that this refolding also manifests in the overall Boltzmann structure ensemble. There, the composition of structures is tremendously affected in their thermodynamic probabilities through the genetic variant. Finally, we confirmed these results experimentally, using SHAPE-Seq, corroborating that SNPs affecting such structures may explain hidden genetic variance not accounted for through genome wide association studies. Our results suggest that structural changes in lncRNAs, and lncRNA H19 in particular, affect regulatory processes and represent optimal targets for further in-depth studies probing their molecular interactions.

diffBUM-HMM: a robust statistical modeling approach for detecting RNA flexibility changes in high-throughput structure probing data

Advancing RNA structural probing techniques with next-generation sequencing has generated demands for complementary computational tools to robustly extract RNA structural information amidst sampling noise and variability. We present diffBUM-HMM, a noise-aware model that enables accurate detection of RNA flexibility and conformational changes from high-throughput RNA structure-probing data. diffBUM-HMM is widely compatible, accounting for sampling variation and sequence coverage biases, and displays higher sensitivity than existing methods while robust against false positives. Our analyses of datasets generated with a variety of RNA probing chemistries demonstrate the value of diffBUM-HMM for quantitatively detecting RNA structural changes and RNA-binding protein binding sites.

The nuclear functions of long noncoding RNAs come into focus

Long noncoding RNAs (lncRNAs), defined as untranslated and tightly-regulated transcripts with a length exceeding 200 nt, are common outputs of the eukaryotic genome. It is becoming increasingly apparent that many lncRNAs likely serve as important regulators in a variety of biological processes. In particular, some of them accumulate in the nucleus and function in diverse nuclear events, including chromatin remodeling, transcriptional regulation, RNA processing, DNA damage repair, etc. Here, we unite recent progresses on the functions of nuclear lncRNAs and provide insights into the future research directions of this field.

Evolutionary conservation of RNA sequence and structure

An RNA structure prediction from a single‐sequence RNA folding program is not evidence for an RNA whose structure is important for function. Random sequences have plausible and complex predicted structures not easily distinguishable from those of structural RNAs. How to tell when an RNA has a conserved structure is a question that requires looking at the evolutionary signature left by the conserved RNA. This question is important not just for long noncoding RNAs which usually lack an identified function, but also for RNA binding protein motifs which can be single stranded RNAs or structures. Here we review recent advances using sequence and structural analysis to determine when RNA structure is conserved or not. Although covariation measures assess structural RNA conservation, one must distinguish covariation due to RNA structure from covariation due to independent phylogenetic substitutions. We review a statistical test to measure false positives expected under the null hypothesis of phylogenetic covariation alone (specificity). We also review a complementary test that measures power, that is, expected covariation derived from sequence variation alone (sensitivity). Power in the absence of covariation signals the absence of a conserved RNA structure. We analyze artifacts that falsely identify conserved RNA structure such as the misuse of programs that do not assess significance, the use of inappropriate statistics confounded by signals other than covariation, or misalignments that induce spurious covariation. Among artifacts that obscure the signal of a conserved RNA structure, we discuss the inclusion of pseudogenes in alignments which increase power but destroy covariation.

This article is categorized under:

RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry

RNA Evolution and Genomics > Computational Analyses of RNA

RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution

Machine learning a model for RNA structure prediction

RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and ensemble populations. These models sometimes fail in identifying native structures unless complemented by auxiliary experimental data. Here, we build a set of models that combine thermodynamic parameters, chemical probing data (DMS and SHAPE) and co-evolutionary data (direct coupling analysis) through a network that outputs perturbations to the ensemble free energy. Perturbations are trained to increase the ensemble populations of a representative set of known native RNA structures. In the chemical probing nodes of the network, a convolutional window combines neighboring reactivities, enlightening their structural information content and the contribution of local conformational ensembles. Regularization is used to limit overfitting and improve transferability. The most transferable model is selected through a cross-validation strategy that estimates the performance of models on systems on which they are not trained. With the selected model we obtain increased ensemble populations for native structures and more accurate predictions in an independent validation set. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.

Sieving RNA 3D Structures with SHAPE and Evaluating Mechanisms Driving Sequence-Dependent Reactivity Bias

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.

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.

Hierarchical natural move Monte Carlo refines flexible RNA structures into cryo-EM densities

Ribonucleic acids (RNAs) play essential roles in living cells. Many of them fold into defined three-dimensional (3D) structures to perform functions. Recent advances in single-particle cryo-electron microscopy (cryo-EM) have enabled structure determinations of RNA to atomic resolutions. However, most RNA molecules are structurally flexible, limiting the resolution of their structures solved by cryo-EM. In modeling these molecules, several computational methods are limited by the requirement of massive computational resources and/or the low efficiency in exploring large-scale structural variations. Here we use hierarchical natural move Monte Carlo (HNMMC), which takes advantage of collective motions for groups of nucleic acid residues, to refine RNA structures into their cryo-EM maps, preserving atomic details in the models. After validating the method on a simulated density map of tRNA, we applied it to objectively obtain the model of the folding intermediate for the specificity domain of ribonuclease P from Bacillus subtilis and refine a flexible ribosomal RNA (rRNA) expansion segment from the Mycobacterium tuberculosis (Mtb) ribosome in different conformational states. Finally, we used HNMMC to model atomic details and flexibility for two distinct conformations of the complete genomic RNA (gRNA) inside MS2, a single-stranded RNA virus, revealing multiple pathways for its capsid assembly.

Strategies and technologies for exploring long noncoding RNAs in heart failure

Long non-coding RNA (lncRNA) was once considered to be the "noise" of genome transcription without biological function. However, increasing evidence shows that lncRNA is dynamically expressed in developmental stage or disease status, playing a regulatory role in the process of gene expression and translation. In recent years, lncRNA is considered to be a core node of functional regulatory networks that controls cardiac and also involves in multiple process of heart failure such as myocardial hypertrophy, fibrosis, angiogenesis, etc., which would be a therapeutic target for diseases. In fact, it is the development of technology that has improved our understanding of lncRNAs and broadened our perspective on heart failure. From transcriptional "noise" to star molecule, progress of lncRNAs can't be achieved without the combination of multidisciplinary technologies, especially the emergence of high-throughput approach. Thus, here, we review the strategies and technologies available for the exploration lncRNAs and try to yield insights into the prospect of lncRNAs in clinical diagnosis and treatment in heart failure.

Advances in RNA 3D Structure Modeling Using Experimental Data

RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.

Analytical ultracentrifuge: an ideal tool for characterization of non-coding RNAs

Analytical ultracentrifugation (AUC) has emerged as a robust and reliable technique for biomolecular characterization with extraordinary sensitivity. AUC is widely used to study purity, conformational changes, biomolecular interactions, and stoichiometry. Furthermore, AUC is used to determine the molecular weight of biomolecules such as proteins, carbohydrates, and DNA and RNA. Due to the multifaceted role(s) of non-coding RNAs from viruses, prokaryotes, and eukaryotes, research aimed at understanding the structure-function relationships of non-coding RNAs is rapidly increasing. However, due to their large size, flexibility, complicated secondary structures, and conformations, structural studies of non-coding RNAs are challenging. In this review, we are summarizing the application of AUC to evaluate the homogeneity, interactions, and conformational changes of non-coding RNAs from adenovirus as well as from Murray Valley, Powassan, and West Nile viruses. We also discuss the application of AUC to characterize eukaryotic long non-coding RNAs, Xist, and HOTAIR. These examples highlight the significant role AUC can play in facilitating the structural determination of non-coding RNAs and their complexes.

rG4-seeker enables high-confidence identification of novel and non-canonical rG4 motifs from rG4-seq experiments

We recently developed the rG4-seq method to detect and map in vitro RNA G-quadruplex (rG4s) structures on a transcriptome-wide scale. rG4-seq of purified human HeLa RNA has revealed many non-canonical rG4s and the effects adjacent sequences have on rG4 formation. In this study, we aimed to improve the outcomes and false-positive discrimination in rG4-seq experiments using a bioinformatic approach. By establishing connections between rG4-seq library preparation chemistry and the underlying properties of sequencing data, we identified how to mitigate indigenous sampling errors and background noise in rG4-seq. We applied these findings to develop a novel bioinformatics pipeline named rG4-seeker (https://github.com/TF-Chan-Lab/rG4-seeker), which uses tailored noise models to autonomously assess and optimize rG4 detections in a replicate-independent manner. Compared with previous methods, rG4-seeker exhibited better false-positive discrimination and improved sensitivity for non-canonical rG4s. Using rG4-seeker, we identified novel features in rG4 formation that were missed previously. rG4-seeker provides a reliable and sensitive approach for rG4-seq investigations, laying the foundations for further elucidation of rG4 biology.

Functions and properties of nuclear lncRNAs-from systematically mapping the interactomes of lncRNAs

Protein and DNA have been considered as the major components of chromatin. But beyond that, an increasing number of studies show that RNA occupies a large amount of chromatin and acts as a regulator of nuclear architecture. A significant fraction of long non-coding RNAs (lncRNAs) prefers to stay in the nucleus and cooperate with protein complexes to modulate epigenetic regulation, phase separation, compartment formation, and nuclear organization. An RNA strand also can invade into double-stranded DNA to form RNA:DNA hybrids (R-loops) in living cells, contributing to the regulation of gene expression and genomic instability. In this review, we discuss how nuclear lncRNAs orchestrate cellular processes through their interactions with proteins and DNA and summarize the recent genome-wide techniques to study the functions of lncRNAs by revealing their interactomes in vivo.

Conserved novel ORFs in the mitochondrial genome of the ctenophore Beroe forskalii

To date, five ctenophore species' mitochondrial genomes have been sequenced, and each contains open reading frames (ORFs) that if translated have no identifiable orthologs. ORFs with no identifiable orthologs are called unidentified reading frames (URFs). If truly protein-coding, ctenophore mitochondrial URFs represent a little understood path in early-diverging metazoan mitochondrial evolution and metabolism. We sequenced and annotated the mitochondrial genomes of three individuals of the beroid ctenophore Beroe forskalii and found that in addition to sharing the same canonical mitochondrial genes as other ctenophores, the B. forskalii mitochondrial genome contains two URFs. These URFs are conserved among the three individuals but not found in other sequenced species. We developed computational tools called pauvre and cuttlery to determine the likelihood that URFs are protein coding. There is evidence that the two URFs are under negative selection, and a novel Bayesian hypothesis test of trinucleotide frequency shows that the URFs are more similar to known coding genes than noncoding intergenic sequence. Protein structure and function prediction of all ctenophore URFs suggests that they all code for transmembrane transport proteins. These findings, along with the presence of URFs in other sequenced ctenophore mitochondrial genomes, suggest that ctenophores may have uncharacterized transmembrane proteins present in their mitochondria.

lncRNAs: function and mechanism in cartilage development, degeneration, and regeneration

With the increasing incidence of cartilage-related diseases such as osteoarthritis (OA) and intervertebral disc degeneration (IDD), heavier financial and social burdens need to be faced. Unfortunately, there is no satisfactory clinical method to target the pathophysiology of cartilage-related diseases. Many gene expressions, signaling pathways, and biomechanical dysregulations were involved in cartilage development, degeneration, and regeneration. However, the underlying mechanism was not clearly understood. Recently, lots of long non-coding RNAs (lncRNAs) were identified in the biological processes, including cartilage development, degeneration, and regeneration. It is clear that lncRNAs were important in regulating gene expression and maintaining chondrocyte phenotypes and homeostasis. In this review, we summarize the recent researches studying lncRNAs’ expression and function in cartilage development, degeneration, and regeneration and illustrate the potential mechanism of how they act in the pathologic process. With continued efforts, regulating lncRNA expression in the cartilage regeneration may be a promising biological treatment approach.

Physical and Functional Analysis of Viral RNA Genomes by SHAPE

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

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.

High-throughput determination of RNA structures

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

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

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

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

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

Advances in engineered trans-acting regulatory RNAs and their application in bacterial genome engineering

Small noncoding RNAs, a large class of ancient posttranscriptional regulators, are increasingly recognized and utilized as key modulators of gene expression in a broad range of microorganisms. Owing to their small molecular size and the central role of Watson-Crick base pairing in defining their interactions, structure and function, numerous diverse types of trans-acting RNA regulators that are functional at the DNA, mRNA and protein levels have been experimentally characterized. It has become increasingly clear that most small RNAs play critical regulatory roles in many processes and are, therefore, considered to be powerful tools for genetic engineering and synthetic biology. The trans-acting regulatory RNAs accelerate this ability to establish potential framework for genetic engineering and genome-scale engineering, which allows RNA structure characterization, easier to design and model compared to DNA or protein-based systems. In this review, we summarize recent advances in engineered trans-acting regulatory RNAs that are used in bacterial genome-scale engineering and in novel cellular capabilities as well as their implementation in wide range of biotechnological, biological and medical applications.