Progress and challenges for chemical probing of RNA structure inside living cells

Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes

Accurate identification of the correct, biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological processes. Thus, a plethora of approaches have been developed to predict, identify, or solve RNA structures based on various computational, molecular, genetic, chemical, or physicochemical strategies. Purely computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation, time, speed, cost, and throughput, but they strongly underperform in terms of accuracy that significantly limits their broader application. Nonetheless, the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based programs. Here, we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying complexity. We found that while many programs performed well in relatively simple tasks, their performance varied significantly in more complex RNA folding problems. However, in general, a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures, at least based on the specific class of sequences tested, suggesting that it may represent the future of RNA structure prediction algorithms.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

Massively parallel dissection of RNA in RNA-protein interactions in vivo

Many of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to biology. Here, we present massively parallel RNA assay combined with immunoprecipitation (MPRNA-IP) for in vivo high-throughput dissection of RNA-protein interactions and describe statistical models for identifying RNA domains and parsing the structural contributions of RNA. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, MPRNA-IP is able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest, including the long noncoding RNA NORAD, bacteriophage MS2 RNA, and human telomerase RNA, and we use it to interrogate the hitherto unknown sequence or structural RNA-binding preferences of the DNA-looping factor CTCF. By integrating systematic mutation analysis with crosslinking immunoprecipitation, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA-protein interactions in vivo.

Probing RNA structure and dynamics using nanopore and next generation sequencing

It has become increasingly evident that the structures RNAs adopt are conformationally dynamic; the various structured states that RNAs sample govern their interactions with other nucleic acids, proteins, and ligands to regulate a myriad of biological processes. Although several biophysical approaches have been developed and used to study the dynamic landscape of structured RNAs, technical limitations have limited their application to all classes of RNA due to variable size and flexibility. Recent advances combining chemical probing experiments with next-generation- and direct sequencing have emerged as an alternative approach to exploring the conformational dynamics of RNA. In this review, we provide a methodological overview of the sequencing-based techniques used to study RNA conformational dynamics. We discuss how different techniques have enabled us to better understand the propensity of RNAs from a variety of different classes to sample multiple conformational states. Finally, we present examples of the ways these techniques have reshaped how we think about RNA structure.

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.

Evolutionary Structure Conservation and Covariance Scores

Effective homology search for non-coding RNAs is frequently not possible via sequence similarity alone. Current methods leverage evolutionary information like structure conservation or covariance scores to identify homologs in organisms that are phylogenetically more distant. In this chapter, we introduce the theoretical background of evolutionary structure conservation and covariance score, and we show hands-on how current methods in the field are applied on example datasets.

Capture the in vivo intact RNA structurome by CAP-STRUCTURE-seq

RNA decay serves as a crucial mechanism for maintaining cellular homeostasis and regulating gene expression. Large-scale analyses indicate that altered rates of decay contribute significantly to changes in mRNA levels, with up to half of these changes attributed to decay. The regulation of RNA decay is, at least in part, through structured RNA elements, especially in the non-coding regions of the mRNAs. The development of next-generation sequencing, and in vivo chemical probing techniques has allowed for unprecedented understanding of RNA folding in vivo and genome-wide. To explore the RNA structure elements that are responsible for RNA cleavage, we need to capture the RNA structure before cleavage. In this method, we introduce a new experimental procedure called CAP-STRUCTURE-seq, a modified STRUCTURE-Seq approach combining with the enrichment of in intact mRNAs by the use of terminator exonuclease treatment (5'-Phosphate-Dependent Exonuclease) that digests RNA containing 5-monophosphate ends. This approach is designed to investigate the RNA structure for these intact RNAs, providing a means to study the impact of RNA structure on RNA decay in greater detail. This method can provide insights into the function of RNA structure in RNA decay and help advance our understanding of biological processes.

Single-Molecule Correlated Chemical Probing: A Revolution in RNA Structure Analysis

RNA molecules convey biological information both in their linear sequence and in their base-paired secondary and tertiary structures. Chemical probing experiments, which involve treating an RNA with a reagent that modifies conformationally dynamic nucleotides, have broadly enabled examination of short- and long-range RNA structure in diverse contexts, including in living cells. For decades, chemical probing experiments have been interpreted in a per-nucleotide way, such that the reactivity measured at each nucleotide reports the average structure at a position over all RNA molecules within a sample. However, there are numerous important cases where per-nucleotide chemical probing falls short, including for RNAs that are bound by proteins, RNAs that form complex higher order structures, and RNAs that sample multiple conformations.Recent experimental and computational innovations have started a revolution in RNA structure analysis by transforming chemical probing into a massively parallel, single-molecule experiment. Enabled by a specialized reverse transcription strategy called mutational profiling (MaP), multiple chemical modification events can be measured within individual RNA molecules. Nucleotides that communicate structurally through direct base pairing or large-scale folding-unfolding transitions will react with chemical probes in a correlated manner, thereby revealing structural complexity hidden to conventional approaches. These single-molecule correlated chemical probing (smCCP) experiments can be interpreted to directly identify nucleotides that base pair (the PAIR-MaP strategy) and to reveal long-range, through-space structural communication (RING-MaP). Correlated probing can also define the thermodynamic populations of complex RNA ensembles (DANCE-MaP). Complex RNA-protein networks can be interrogated by cross-linking proteins to RNA and measuring correlations between cross-linked positions (RNP-MaP).smCCP thus visualizes RNA secondary and higher-order structure with unprecedented accuracy, defining novel structures, RNA-protein interaction networks, time-resolved dynamics, and allosteric structural switches. These strategies are not mutually exclusive; in favorable cases, multiple levels of RNA structure ─ base pairing, through-space structural communication, and equilibrium ensembles ─ can be resolved concurrently. The physical experimentation required for smCCP is profoundly simple, and experiments are readily performed in cells on RNAs of any size, including large noncoding RNAs and mRNAs. Single-molecule correlated chemical probing is paving the way for a new generation of biophysical studies on RNA in living systems.

Probing the dynamic RNA structurome and its functions

RNA is a key regulator of almost every cellular process, and the structures adopted by RNA molecules are thought to be central to their functions. The recent fast-paced evolution of high-throughput sequencing-based RNA structure mapping methods has enabled the rapid in vivo structural interrogation of entire cellular transcriptomes. Collectively, these studies are shedding new light on the long underestimated complexity of the structural organization of the transcriptome - the RNA structurome. Moreover, recent analyses are challenging the view that the RNA structurome is a static entity by revealing how RNA molecules establish intricate networks of alternative intramolecular and intermolecular interactions and that these ensembles of RNA structures are dynamically regulated to finely tune RNA functions in living cells. This new understanding of how RNA can shape cell phenotypes has important implications for the development of RNA-targeted therapeutic strategies.

Discovering riboswitches: the past and the future

Riboswitches are structured noncoding RNA domains used by many bacteria to monitor the concentrations of target ligands and regulate gene expression accordingly. In the past 20 years over 55 distinct classes of natural riboswitches have been discovered that selectively sense small molecules or elemental ions, and thousands more are predicted to exist. Evidence suggests that some riboswitches might be direct descendants of the RNA-based sensors and switches that were likely present in ancient organisms before the evolutionary emergence of proteins. We provide an overview of the current state of riboswitch research, focusing primarily on the discovery of riboswitches, and speculate on the major challenges facing researchers in the field.

Detection of Multiplex NASBA RNA Products Using Colorimetric Split G Quadruplex Probes

Structural RNA is a challenging target for recognition by hybridization probes. This chapter addresses the recognition problem of RNA amplicons in samples obtained by multiplex nucleic acid sequence-based amplification (NASBA). The method describes the design of G-quadruplex binary (split) DNA peroxidase sensors that produces colorimetric signal upon recognition of NASBA amplicons.

Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling

MOTIVATION

Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.

RESULTS

Here, we showed that by integrated deep learning with physics-inferred secondary structures, co-evolutionary information and multiple sequence-alignment sampling, we can achieve RNA contact-map prediction at a level of accuracy similar to that in protein contact-map prediction. More importantly, highly accurate prediction for top L long-range contacts can be assured for those RNAs with a high effective number of homologous sequences (Neff > 50). The initial use of the predicted contact map as distance-based restraints confirmed its usefulness in 3D structure prediction.

AVAILABILITY AND IMPLEMENTATION

SPOT-RNA-2D is available as a web server at https://sparks-lab.org/server/spot-rna-2d/ and as a standalone program at https://github.com/jaswindersingh2/SPOT-RNA-2D.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Affinity-Based Profiling of the Flavin Mononucleotide Riboswitch

Riboswitches are structural RNA elements that control gene expression. These naturally occurring RNA sensors are of continued interest as antibiotic targets, molecular sensors, and functional elements of synthetic circuits. Here, we describe affinity-based profiling of the flavin mononucleotide (FMN) riboswitch to characterize ligand binding and structural folding. We designed and synthesized photoreactive ligands and used them for photoaffinity labeling. We showed selective labeling of the FMN riboswitch and used this covalent interaction to quantitatively measure ligand binding, which we demonstrate with the naturally occurring antibiotic roseoflavin. We measured conditional riboswitch folding as a function of temperature and cation concentration. Furthermore, combining photoaffinity labeling with reverse transcription revealed ligand binding sites within the aptamer domain with single-nucleotide resolution. The photoaffinity probe was applied to cellular extracts of Bacillus subtilis to demonstrate conditional folding of the endogenous low-abundant ribD FMN riboswitch in biologically derived samples using quantitative PCR. Lastly, binding of the riboswitch-targeting antibiotic roseoflavin to the FMN riboswitch was measured in live bacteria using the photoaffinity probe.

Protein-Encoding Free-Standing RNA Hydrogel for Sub-Compartmentalized Translation

RNA can self-fold into complex structures that can serve as major biological regulators in protein synthesis and in catalysis. Due to the abundance of structural primitives and functional diversity, RNA has been utilized for designing nature-defined goals despite its intrinsic chemical instability and lack of technologies. Here, a robust, free-standing RNA hydrogel is developed through a sequential process involving both ligation and rolling circle transcription to form RNA G-quadruplexes, capable of both catalytic activity and enhancing expression of several proteins in sub-compartmentalized, phase-separated translation environments. The observations suggest that this hydrogel will expand RNA research and impact practical RNA principles and applications.

Thoughts on how to think (and talk) about RNA structure

Recent events have pushed RNA research into the spotlight. Continued discoveries of RNA with unexpected diverse functions in healthy and diseased cells, such as the role of RNA as both the source and countermeasure to a severe acute respiratory syndrome coronavirus 2 infection, are igniting a new passion for understanding this functionally and structurally versatile molecule. Although RNA structure is key to function, many foundational characteristics of RNA structure are misunderstood, and the default state of RNA is often thought of and depicted as a single floppy strand. The purpose of this perspective is to help adjust mental models, equipping the community to better use the fundamental aspects of RNA structural information in new mechanistic models, enhance experimental design to test these models, and refine data interpretation. We discuss six core observations focused on the inherent nature of RNA structure and how to incorporate these characteristics to better understand RNA structure. We also offer some ideas for future efforts to make validated RNA structural information available and readily used by all researchers.

Probing in vivo RNA Structure With Optimized DMS-MaPseq in Rice

RNA transcripts form various secondary and tertiary structures that have a wide range of regulatory functions. Several methods have been developed to profile in vivo RNA secondary structure in prokaryotes and eukaryotes. These methods, such as dimethyl sulfate (DMS) mutational profiling with high-throughput sequencing (DMS-MaPseq), couple small chemical-mediated RNA modifications with next-generation sequencing. DMS-MaPseq, a powerful method for genome-wide and target-specific RNA secondary structure profiling, has been applied in yeast, mammals, Drosophila, and Arabidopsis thaliana, but not in crops. Here, we used DMS-MaPseq to conduct a target-specific and genome-wide profile of in vivo RNA secondary structure in rice (Oryza sativa). The DMS treatment conditions were optimized for rice leaf and root tissues. To increase the sequencing depth and coverage of low-abundance transcripts in genome-wide DMS-MaPseq, we used streptavidin-biotin depletion to reduce the abundance of highly expressed chloroplast transcripts during library construction. The resulting target-specific and genome-wide rice DMS-MaPseq data were of high quality and reproducibility. Furthermore, we used DMS-MaPseq to profile the in vivo RNA secondary structure of an OsmiR399 target region located at 5'UTR of OsPHO2, which participates in rice phosphate homeostasis. An unfolded RNA structure downstream of miRNA target site was observed in predicted in vivo RNA secondary structure, reminiscence of the TAM (Target Adjacent nucleotide Motif) involved in mRNA structure-mediated regulation in miRNA cleavage. Our study optimized DMS-MaPseq for probing in vivo RNA secondary structure in rice, facilitating the study of RNA structure-mediated regulations in crops.

Global mapping of RNA G-quadruplexes (G4-RNAs) using G4RP-seq

Guanine-rich RNAs can fold into four-stranded structures, termed G-quadruplexes (G4-RNAs), and participate in a wide range of biological processes. Here we describe in detail a G4-RNA-specific precipitation (G4RP) protocol, which enables the transcriptomic profiling of G4-RNAs. The G4RP protocol consists of a chemical cross-linking step, followed by affinity capture with a G4-specific probe, BioTASQ. G4RP can be coupled with sequencing to capture a comprehensive global snapshot of folded G4-RNAs. This method can also be used to profile induced changes (i.e., through G4 ligand treatments) within the G4-RNA transcriptome. The entire protocol can be completed in 1-2 weeks and can be scaled up or down depending on the specific experimental goals. In addition to the protocol details, we also provide here a guide for optimization in different laboratory setups.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Diverse functional elements in RNA predicted transcriptome-wide by orthogonal RNA structure probing

RNA molecules can fold into complex structures and interact with trans-acting factors to control their biology. Recent methods have been focused on developing novel tools to measure RNA structure transcriptome-wide, but their utility to study and predict RNA-protein interactions or RNA processing has been limited thus far. Here, we extend these studies with the first transcriptome-wide mapping method for cataloging RNA solvent accessibility, icLASER. By combining solvent accessibility (icLASER) with RNA flexibility (icSHAPE) data, we efficiently predict RNA-protein interactions transcriptome-wide and catalog RNA polyadenylation sites by RNA structure alone. These studies showcase the power of designing novel chemical approaches to studying RNA biology. Further, our study exemplifies merging complementary methods to measure RNA structure inside cells and its utility for predicting transcriptome-wide interactions that are critical for control of and regulation by RNA structure. We envision such approaches can be applied to studying different cell types or cells under varying conditions, using RNA structure and footprinting to characterize cellular interactions and processing involving RNA.

A noncoding RNA modulator potentiates phenylalanine metabolism in mice

The functional role of long noncoding RNAs (lncRNAs) in inherited metabolic disorders, including phenylketonuria (PKU), is unknown. Here, we demonstrate that the mouse lncRNA Pair and human HULC associate with phenylalanine hydroxylase (PAH). Pair-knockout mice exhibited excessive blood phenylalanine (Phe), musty odor, hypopigmentation, growth retardation, and progressive neurological symptoms including seizures, which faithfully models human PKU. HULC depletion led to reduced PAH enzymatic activities in human induced pluripotent stem cell-differentiated hepatocytes. Mechanistically, HULC modulated the enzymatic activities of PAH by facilitating PAH-substrate and PAH-cofactor interactions. To develop a therapeutic strategy for restoring liver lncRNAs, we designed GalNAc-tagged lncRNA mimics that exhibit liver enrichment. Treatment with GalNAc-HULC mimics reduced excessive Phe in Pair -/- and Pah R408W/R408W mice and improved the Phe tolerance of these mice.

B-CePs as cross-linking probes for the investigation of RNA higher-order structure

Elucidating the structure of RNA and RNA ensembles is essential to understand biological functions. In this work, we explored the previously uncharted reactivity of bis-chloropiperidines (B-CePs) towards RNA. We characterized at the molecular level the different adducts induced by the fast reacting compound B-CeP 1 with RNA. Following an approach based on solution thermal melting coupled with ESI mass spectrometry (STHEM-ESI), we proved the ability of B-CePs to induce inter-molecular cross-links between guanines in double stranded RNA. These results open the possibility of using B-CePs as structural probes for investigating higher-order structures, such as the kissing loop complex established by the dimerization initiation site (DIS) of the HIV-1 genome. We confirmed the potential of B-CePs to reveal the identity of RNA structures involved in long-range interactions, expecting to benefit the characterization of samples that are not readily amenable to traditional high-resolution techniques, and thus promoting the elucidation of pertinent RNA systems associated with old and new diseases.

Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning

MOTIVATION

The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling.

RESULTS

The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, noncanonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences.

AVAILABILITY

Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

RASP: an atlas of transcriptome-wide RNA secondary structure probing data

RNA molecules fold into complex structures that are important across many biological processes. Recent technological developments have enabled transcriptome-wide probing of RNA secondary structure using nucleases and chemical modifiers. These approaches have been widely applied to capture RNA secondary structure in many studies, but gathering and presenting such data from very different technologies in a comprehensive and accessible way has been challenging. Existing RNA structure probing databases usually focus on low-throughput or very specific datasets. Here, we present a comprehensive RNA structure probing database called RASP (RNA Atlas of Structure Probing) by collecting 161 deduplicated transcriptome-wide RNA secondary structure probing datasets from 38 papers. RASP covers 18 species across animals, plants, bacteria, fungi, and also viruses, and categorizes 18 experimental methods including DMS-seq, SHAPE-Seq, SHAPE-MaP, and icSHAPE, etc. Specially, RASP curates the up-to-date datasets of several RNA secondary structure probing studies for the RNA genome of SARS-CoV-2, the RNA virus that caused the on-going COVID-19 pandemic. RASP also provides a user-friendly interface to query, browse, and visualize RNA structure profiles, offering a shortcut to accessing RNA secondary structures grounded in experimental data. The database is freely available at http://rasp.zhanglab.net.

Targeting Noncoding RNA Domains to Genomic Loci with CRISPR-Display: Guidelines for Designing, Building, and Testing sgRNA-ncRNA Expression Constructs

CRISPR-Display uses the S. pyogenes Cas9 protein to posttranscriptionally localize noncoding RNA (ncRNA) domains to any genomic site, by directly fusing these domains to the Cas9 sgRNA cofactor. This versatile technology enables numerous applications for interrogating natural chromatin-regulatory ncRNAs, or for utilizing artificial ncRNA and ribonucleoprotein (RNP) devices at individual chromatin loci. To achieve these, a successful CRISPR-Display experiment requires that chimeric sgRNA-ncRNA fusions are stably expressed and incorporated into Cas9 complexes, and that they retain their ncRNA "cargo" domains at the targeted genomic sites. Here, I describe a workflow for designing, building, and testing such chimeric sgRNA-ncRNA expression constructs. I detail strategies for choosing expression systems and sgRNA insertion topologies, for assaying the incorporation of sgRNA-ncRNA fusions into functional Cas9 complexes, and for surveying the activities of ncRNA domains at targeted genomic loci. This establishes an initial set of "best practices" for the design and implementation of CRISPR-Display experiments.

Integrated approaches to reveal mechanisms by which RNA viruses reprogram the cellular environment

RNA viruses are major threats to global society and mass outbreaks can cause long-lasting damage to international economies. RNA and related retro viruses represent a large and diverse family that contribute to the onset of human diseases such as AIDS; certain cancers like T cell lymphoma; severe acute respiratory illnesses as seen with COVID-19; and others. The hallmark of this viral family is the storage of genetic material in the form of RNA, and upon infecting host cells, their RNA genomes reprogram the cellular environment to favor productive viral replication. RNA is a multifunctional biomolecule that not only stores and transmits heritable information, but it also has the capacity to catalyze complex biochemical reactions. It is therefore no surprise that RNA viruses use this functional diversity to their advantage to sustain chronic or lifelong infections. Efforts to subvert RNA viruses therefore requires a deep understanding of the mechanisms by which these pathogens usurp cellular machinery. Here, we briefly summarize several experimental techniques that individually inform on key physicochemical features of viral RNA genomes and their interactions with proteins. Each of these techniques provide important vantage points to understand the complexities of virus-host interactions, but we attempt to make the case that by integrating these and similar methods, more vivid descriptions of how viruses reprogram the cellular environment emerges. These vivid descriptions should expedite the identification of novel therapeutic targets.

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.

On the Way to Understanding the Interplay between the RNA Structure and Functions in Cells: A Genome-Wide Perspective

RNAs adopt specific structures in order to perform their biological activities. The structure of RNA is an important layer of gene expression regulation, and can impact a plethora of cellular processes, starting with transcription, RNA processing, and translation, and ending with RNA turnover. The development of high-throughput technologies has enabled a deeper insight into the sophisticated interplay between the structure of the cellular transcriptome and the living cells environment. In this review, we present the current view on the RNA structure in vivo resulting from the most recent transcriptome-wide studies in different organisms, including mammalians, yeast, plants, and bacteria. We focus on the relationship between the mRNA structure and translation, mRNA stability and degradation, protein binding, and RNA posttranscriptional modifications.

Challenges and perspectives for structural biology of lncRNAs-the example of the Xist lncRNA A-repeats

Following the discovery of numerous long non-coding RNA (lncRNA) transcripts in the human genome, their important roles in biology and human disease are emerging. Recent progress in experimental methods has enabled the identification of structural features of lncRNAs. However, determining high-resolution structures is challenging as lncRNAs are expected to be dynamic and adopt multiple conformations, which may be modulated by interaction with protein binding partners. The X-inactive specific transcript (Xist) is necessary for X inactivation during dosage compensation in female placental mammals and one of the best-studied lncRNAs. Recent progress has provided new insights into the domain organization, molecular features, and RNA binding proteins that interact with distinct regions of Xist. The A-repeats located at the 5′ end of the transcript are of particular interest as they are essential for mediating silencing of the inactive X chromosome. Here, we discuss recent progress with elucidating structural features of the Xist lncRNA, focusing on the A-repeats. We discuss the experimental and computational approaches employed that have led to distinct structural models, likely reflecting the intrinsic dynamics of this RNA. The presence of multiple dynamic conformations may also play an important role in the formation of the associated RNPs, thus influencing the molecular mechanism underlying the biological function of the Xist A-repeats. We propose that integrative approaches that combine biochemical experiments and high-resolution structural biology in vitro with chemical probing and functional studies in vivo are required to unravel the molecular mechanisms of lncRNAs.

Nucleobase mutants of a bacterial preQ1-II riboswitch that uncouple metabolite sensing from gene regulation

Riboswitches are a class of nonprotein-coding RNAs that directly sense cellular metabolites to regulate gene expression. They are model systems for analyzing RNA-ligand interactions and are established targets for antibacterial agents. Many studies have analyzed the ligand-binding properties of riboswitches, but this work has outpaced our understanding of the underlying chemical pathways that govern riboswitch-controlled gene expression. To address this knowledge gap, we prepared 15 mutants of the preQ1-II riboswitch-a structurally and biochemically well-characterized HLout pseudoknot that recognizes the metabolite prequeuosine1 (preQ1). The mutants span the preQ1-binding pocket through the adjoining Shine-Dalgarno sequence (SDS) and include A-minor motifs, pseudoknot-insertion helix P4, U·A-U base triples, and canonical G-C pairs in the anti-SDS. As predicted-and confirmed by in vitro isothermal titration calorimetry measurements-specific mutations ablated preQ1 binding, but most aberrant binding effects were corrected by compensatory mutations. In contrast, functional analysis in live bacteria using a riboswitch-controlled GFPuv-reporter assay revealed that each mutant had a deleterious effect on gene regulation, even when compensatory changes were included. Our results indicate that effector binding can be uncoupled from gene regulation. We attribute loss of function to defects in a chemical interaction network that links effector binding to distal regions of the fold that support the gene-off RNA conformation. Our findings differentiate effector binding from biological function, which has ramifications for riboswitch characterization. Our results are considered in the context of synthetic ligands and drugs that bind tightly to riboswitches without eliciting a biological response.

Thioguanosine Conversion Enables mRNA-Lifetime Evaluation by RNA Sequencing Using Double Metabolic Labeling (TUC-seq DUAL)

Temporal information about cellular RNA populations is essential to understand the functional roles of RNA. We have developed the hydrazine/NH₄ Cl/OsO₄ -based conversion of 6-thioguanosine (6sG) into A', where A' constitutes a 6-hydrazino purine derivative. A' retains the Watson-Crick base-pair mode and is efficiently decoded as adenosine in primer extension assays and in RNA sequencing. Because 6sG is applicable to metabolic labeling of freshly synthesized RNA and because the conversion chemistry is fully compatible with the conversion of the frequently used metabolic label 4-thiouridine (4sU) into C, the combination of both modified nucleosides in dual-labeling setups enables high accuracy measurements of RNA decay. This approach, termed TUC-seq DUAL, uses the two modified nucleosides in subsequent pulses and their simultaneous detection, enabling mRNA-lifetime evaluation with unprecedented precision.

Keth-seq for transcriptome-wide RNA structure mapping

RNA secondary structure is critical to RNA regulation and function. We report a new N₃-kethoxal reagent that allows fast and reversible labeling of single-stranded guanine bases in live cells. This N₃-kethoxal-based chemistry allows efficient RNA labeling under mild conditions and transcriptome-wide RNA secondary structure mapping.

R-BIND: An Interactive Database for Exploring and Developing RNA-Targeted Chemical Probes

While the opportunities available for targeting RNA with small molecules have been widely appreciated, the challenges associated with achieving specific RNA recognition in biological systems have hindered progress and prevented many researchers from entering the field. To facilitate the discovery of RNA-targeted chemical probes and their subsequent applications, we curated the RNA-targeted BIoactive ligaNd Database (R-BIND). This collection contains an array of information on reported chemical probes that target non-rRNA and have biological activity, and analysis has led to the discovery of RNA-privileged properties. Herein, we developed an online platform to make this information freely available to the community, offering search options, a suite of tools for probe development, and an updated R-BIND data set with detailed experimental information for each probe. We repeated the previous cheminformatics analysis on the updated R-BIND list and found that the distinguishing physicochemical, structural, and spatial properties remained unchanged, despite an almost 50% increase in the database size. Further, we developed several user-friendly tools, including queries based on cheminformatic parameters, experimental details, functional groups, and substructures. In addition, a nearest neighbor algorithm can assess the similarity of user-uploaded molecules to R-BIND ligands. These tools and resources can be used to design small molecule libraries, optimize lead ligands, or select targets, probes, assays, and control experiments. Chemical probes are critical to the study and discovery of novel functions for RNA, and we expect this resource to greatly assist researchers in exploring and developing successful RNA-targeted probes.

RNAdt: An online tutorial and data portal for the RNA structurome era

RNA is not only a passive transporter of genetic information, but also a pivotal player in all domains of life. RNA can regulate gene expression because of its involvement in transcription, mRNA modification and processing, and translation. RNA also possesses other intricate functions such as catalysis, ligand sensing, interaction with biomolecules, response to environment stresses, and information storage. The primary structure of RNA is single stranded, but it always folds into complex secondary and tertiary structures owing to base pairing and effects from the cellular environment. The importance of structure has been increasingly recognized in understanding the myriad functions of RNA. After decades of development, there is a wide range of RNA structure probing techniques. The marriage between structure probing and high-throughput sequencing (HTS) especially enables the measurement of RNA structure on a transcriptomic scale, advancing the advent of the RNA structurome era. Dozens of HTS-associated RNA structure probing methods have been published, so it is urgent to provide a user-friendly and easy-to-use resource for users who are perplexed by selecting the most suitable method for their experiments. Motivated by this demand, we collected currently available HTS-associated RNA structure probing methods and then developed RNAdt (freely accessible at http://www.zhounan.org/rnadt). RNAdt can be used as a web-based tutorial to learn fundamental knowledge of HTS-associated RNA structure probing methods. RNAdt can also be used as a data portal to access HTS data sets from previous RNA structurome studies. At the end of this work, we also provided perspectives on future development of RNA structure probing methods. Our study is expected to facilitate RNA structure probing and ultimately elucidate the connection between RNA structure and biological functions.

Methods to identify and optimize small molecules interacting with RNA (SMIRNAs)

RNAs, particularly noncoding RNAs (ncRNAs), are becoming increasingly important therapeutic targets, because they are causative and antagonists of human disease. Indeed, aberrant RNA structural elements and expression deregulate biological processes. In this review, we describe methodologies to discover and optimize small molecules interacting with RNA (SMIRNAs), including the evaluation of direct target engagement and the rescue of RNA-mediated phenotypes in vitro and in vivo. Such studies are essential to fully characterize the mode of action of SMIRNAs and advance our understanding of rationally and efficiently drugging RNAs for therapeutic benefit.

Probing In Vivo Structure of Individual mRNA 3' Isoforms Using Dimethyl Sulfate

The DMS region extraction and deep sequencing (DREADS) procedure was designed to probe RNA structure in vivo and to link this structural information to specific 3' isoforms. Growing cells are treated with the alkylating agent dimethyl sulfate (DMS), which enters easily into cells and modifies RNA molecules at solvent-exposed A and C residues. RNA is isolated, and sequencing libraries are constructed in a manner that preserves the identities of individual mRNA isoforms arising from alternative cleavage/polyadenylation sites. During the cDNA synthesis step of library construction, the progress of reverse transcriptase (RT) is blocked when it encounters a DMS modification on the RNA, leading to disproportionate cDNA termination adjacent to DMS-modified positions. After paired-end deep sequencing, the downstream end of each sequenced fragment is mapped to a specific cleavage/poly(A) site representing an individual mRNA 3' isoform. The upstream mapped end of the sequenced fragment defines where the RT reaction stopped. Over the population of all sequenced fragments derived from a particular isoform, A and C positions that are overrepresented next to the upstream endpoints in the DMS sample (relative to a parallel untreated control) are inferred to have been DMS modified, and hence solvent exposed. This method thus allows in vivo structural information obtained using DMS to be linked to individual mRNA 3' isoforms. © 2019 by John Wiley & Sons, Inc.

Insights into the development of chemical probes for RNA

Over the past decade, the RNA revolution has revealed thousands of non-coding RNAs that are essential for cellular regulation and are misregulated in disease. While the development of methods and tools to study these RNAs has been challenging, the power and promise of small molecule chemical probes is increasingly recognized. To harness existing knowledge, we compiled a list of 116 ligands with reported activity against RNA targets in biological systems (R-BIND). In this survey, we examine the RNA targets, design and discovery strategies, and chemical probe characterization techniques of these ligands. We discuss the applicability of current tools to identify and evaluate RNA-targeted chemical probes, suggest criteria to assess the quality of RNA chemical probes and targets, and propose areas where new tools are particularly needed. We anticipate that this knowledge will expedite the discovery of RNA-targeted ligands and the next phase of the RNA revolution.

Computational approaches for the discovery of splicing regulatory RNA structures

Global RNA structure and local functional motifs mediate interactions important in determining the rates and patterns of mRNA splicing. In this review, we overview approaches for the computational prediction of RNA secondary structure with a special emphasis on the discovery of motifs important to RNA splicing. The process of identifying and modeling potential splicing regulatory structures is illustrated using a recently-developed approach for RNA structural motif discovery, the ScanFold pipeline, which is applied to the identification of a known splicing regulatory structure in influenza virus.

Carbodiimide reagents for the chemical probing of RNA structure in cells

Deciphering the conformations of RNAs in their cellular environment allows identification of RNA elements with potentially functional roles within biological contexts. Insight into the conformation of RNA in cells has been achieved using chemical probes that were developed to react specifically with flexible RNA nucleotides, or the Watson-Crick face of single-stranded nucleotides. The most widely used probes are either selective SHAPE (2'-hydroxyl acylation and primer extension) reagents that probe nucleotide flexibility, or dimethyl sulfate (DMS), which probes the base-pairing at adenine and cytosine but is unable to interrogate guanine or uracil. The constitutively charged carbodiimide N-cyclohexyl-N'-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate (CMC) is widely used for probing G and U nucleotides, but has not been established for probing RNA in cells. Here, we report the use of a smaller and conditionally charged reagent, 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC), as a chemical probe of RNA conformation, and the first reagent validated for structure probing of unpaired G and U nucleotides in intact cells. We showed that EDC demonstrates similar reactivity to CMC when probing transcripts in vitro. We found that EDC specifically reacted with accessible nucleotides in the 7SK noncoding RNA in intact cells. We probed structured regions within the Xist lncRNA with EDC and integrated these data with DMS probing data. Together, EDC and DMS allowed us to refine predicted structure models for the 3' extension of repeat C within Xist. These results highlight how complementing DMS probing experiments with EDC allows the analysis of Watson-Crick base-pairing at all four nucleotides of RNAs in their cellular context.

Extensive Structural Differences of Closely Related 3' mRNA Isoforms: Links to Pab1 Binding and mRNA Stability

Alternative polyadenylation generates numerous 3' mRNA isoforms that can vary in biological properties, such as stability and localization. We developed methods to obtain transcriptome-scale structural information and protein binding on individual 3' mRNA isoforms in vivo. Strikingly, near-identical mRNA isoforms can possess dramatically different structures throughout the 3' UTR. Analyses of identical mRNAs in different species or refolded in vitro indicate that structural differences in vivo are often due to trans-acting factors. The level of Pab1 binding to poly(A)-containing isoforms is surprisingly variable, and differences in Pab1 binding correlate with the extent of structural variation for closely spaced isoforms. A pattern encompassing single-strandedness near the 3' terminus, double-strandedness of the poly(A) tail, and low Pab1 binding is associated with mRNA stability. Thus, individual 3' mRNA isoforms can be remarkably different physical entities in vivo. Sequences responsible for isoform-specific structures, differential Pab1 binding, and mRNA stability are evolutionarily conserved, indicating biological function.

Genome-wide probing RNA structure with the modified DMS-MaPseq in Arabidopsis

Transcripts have intrinsic propensity to form stable secondary structure that is fundamental to regulate RNA transcription, splicing, translation, RNA localization and turnover. Numerous methods that integrate chemical reactions with next-generation sequencing (NGS) have been applied to study in vivo RNA structure, providing new insights into RNA biology. Dimethyl sulfate (DMS) probing coupled with mutational profiling through NGS (DMS-MaPseq) is a newly developed method for revealing genome-wide or target-specific RNA structure. Herein, we present our experimental protocol of a modified DMS-MaPseq method for plant materials. The DMS treatment condition was optimized, and library preparation procedures were simplified. We also provided custom scripts for bioinformatic analysis of genome-wide DMS-MaPseq data. Bioinformatic results showed that our method could generate high-quality and reproducible data. Further, we assessed sequencing depth and coverage for genome-wide RNA structure profiling in Arabidopsis, and provided two examples of in vivo structure of mobile RNAs. We hope that our modified DMS-MaPseq method will serve as a powerful tool for analyzing in vivo RNA structurome in plants.

Coupling Green Fluorescent Protein Expression with Chemical Modification to Probe Functionally Relevant Riboswitch Conformations in Live Bacteria

Noncoding RNAs engage in numerous biological activities including gene regulation. To fully understand RNA function it is necessary to probe biologically relevant conformations in living cells. To address this challenge, we coupled RNA-mediated regulation of the green fluorescent protein (GFP)uv-reporter gene to icSHAPE (in cell Selective 2'-Hydroxyl Acylation analyzed by Primer Extension). Our transcript-specific approach provides sensitive, fluorescence-based readout of the regulatory-RNA status as a means to coordinate chemical modification experiments. We chose a plasmid-based reporter compatible with Escherichia coli to allow use of knockout strains that eliminate endogenous effector biosynthesis. The approach was piloted using the Lactobacillus rhamnosus ( Lrh) preQ₁-II riboswitch, which senses the pyrrolopyrimidine metabolite preQ₁. Using an E. coli Δ queF strain incapable of preQ₁ anabolism, the Lrh riboswitch yielded nearly one log unit of GFPuv-gene repression resulting from exogenously added preQ₁. We then subjected cells in gene "on" and "off" states to icSHAPE. The resulting differential analysis indicated reduction in Lrh riboswitch flexibility in the P3 helix of the pseudoknot, which comprises the ribosome-binding site (RBS) paired with the anti-RBS. Such expression platform modulation was not observed by in vitro chemical probing and demonstrates that the crowded cellular environment does not preclude detection of compact and loose RNA-regulatory conformations. Here we describe the design, methods, interpretation, and caveats of Reporter Coupled (ReCo) icSHAPE. We also describe mapping of the differential ReCo-icSHAPE results onto the Lrh riboswitch-preQ₁ cocrystal structure. The approach should be readily applicable to functional RNAs triggered by effectors or environmental variations.

Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures

High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA. We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements.