Chemical reversible crosslinking enables measurement of RNA 3D distances and alternative conformations in cells

Second-Generation Chiral Amino Acid Derivatives Afford High Stereoselectivity and Stability in Aqueous RNA Acylation

Activated acyl species have proven versatile in the esterification of 2'-OH groups in RNA, enabling structure mapping, caging, profiling, and labeling of the biopolymer. Nearly all reagents developed for this reaction have been achiral; however, a recent study reported that simple chiral amino acid acylimidazole derivatives could yield diastereoselective reactions at RNA 2'-OH in water, enabling up to 4:1 selectivity in screening. Here, we investigated the effect of steric bulk on the stereoselectivity of RNA reaction and on the stability of adducts with a library of 36 chiral acylimidazole scaffolds with increasing steric demand. The results document the highest stereoselectivity yet achieved in RNA acylation reactions, with as high as >99:1 diastereoselectivity at >70% conversion. Also notably, the bulky adducts were found to have markedly improved stability on RNA.

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.

RNA damage compartmentalization by DHX9 stress granules

Biomolecules incur damage during stress conditions, and damage partitioning represents a vital survival strategy for cells. Here, we identified a distinct stress granule (SG), marked by dsRNA helicase DHX9, which compartmentalizes ultraviolet (UV)-induced RNA, but not DNA, damage. Our FANCI technology revealed that DHX9 SGs are enriched in damaged intron RNA, in contrast to classical SGs that are composed of mature mRNA. UV exposure causes RNA crosslinking damage, impedes intron splicing and decay, and triggers DHX9 SGs within daughter cells. DHX9 SGs promote cell survival and induce dsRNA-related immune response and translation shutdown, differentiating them from classical SGs that assemble downstream of translation arrest. DHX9 modulates dsRNA abundance in the DHX9 SGs and promotes cell viability. Autophagy receptor p62 is activated and important for DHX9 SG disassembly. Our findings establish non-canonical DHX9 SGs as a dedicated non-membrane-bound cytoplasmic compartment that safeguards daughter cells from parental RNA damage.

2'-OH as a universal handle for studying intracellular RNAs

RNA plays pivotal roles in most cellular processes, serving as both the traditional carrier of genetic information and as a key regulator of cellular functions. The advent of chemical technologies has contributed critically to the analysis of cellular RNA structures, functions, and interactions. Many of these methods and molecules involve the utilization of chemically reactive handles in RNAs, either introduced externally or inherent within the polymer itself. Among these handles, the 2'-hydroxyl (2'-OH) group has emerged as an exceptionally well-suited and general chemical moiety for the modification and profiling of RNAs in intracellular studies. In this review, we provide an overview of the recent advancements in intracellular applications of acylation at the 2'-OH group of RNA. We outline progress made in probing RNA structure and interactomes, controlling RNA function, RNA imaging, and analyzing RNA-small molecule interactions, all achieved in living cells through this simple chemical handle on the biopolymer.

Machine learning modeling of RNA structures: methods, challenges and future perspectives

The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.

Progress in 7SK ribonucleoprotein structural biology

The 7SK ribonucleoprotein (RNP) is a dynamic and multifunctional regulator of RNA Polymerase II (RNAPII) transcription in metazoa. Comprised of the non-coding 7SK RNA, core proteins, and numerous accessory proteins, the most well-known 7SK RNP function is the sequestration and inactivation of the positive transcription elongation factor b (P-TEFb). More recently, 7SK RNP has been shown to regulate RNAPII transcription through P-TEFb-independent pathways. Due to its fundamental role in cellular function, dysregulation has been linked with human diseases including cancers, heart disease, developmental disorders, and viral infection. Significant advances in 7SK RNP structural biology have improved our understanding of 7SK RNP assembly and function. Here, we review progress in understanding the structural basis of 7SK RNA folding, biogenesis, and RNP assembly.

Chemical crosslinking and ligation methods for in vivo analysis of RNA structures and interactions

RNA structures and interactions in living cells drive a variety of biological processes and play critical roles in physiology and disease states. However, studies of RNA structures and interactions have been challenging due to limitations in available technologies. Direct determination of structures in vitro has been only possible to a small number of RNAs with limited sizes and conformations. We recently introduced two chemical crosslink-ligation techniques that enabled studies of transcriptome-wide secondary and tertiary structures and their dynamics. In a dramatically improved version of the psoralen analysis of RNA interactions and structures (PARIS2) method, we detailed the synthesis and use of amotosalen, a highly soluble psoralen analogue, and enhanced enzymology for higher efficiency duplex capture. We also introduced spatial 2'-hydroxyl acylation reversible crosslinking (SHARC) with exonuclease (exo) trimming, a method which utilizes a novel crosslinker class that targets the 2'-OH to capture three-dimensional (3D) structures. Both are powerful orthogonal approaches for solving in vivo RNA structure and interactions, integrating crosslinking, exo trimming, proximity ligation, and high throughput sequencing. In this chapter, we present a detailed protocol for the methods and highlight steps that outperform existing crosslink-ligation approaches.

Measuring intramolecular connectivity in long RNA molecules using two-dimensional DNA patch-probe arrays

We describe a simple method to infer intramolecular connections in a population of long RNA molecules in vitro. First we add DNA oligonucleotide "patches" that perturb the RNA connections, then we use a microarray containing a complete set of DNA oligonucleotide "probes" to record where perturbations occur. The pattern of perturbations reveals couplings between different regions of the RNA sequence, from which we infer connections as well as their prevalences in the population. We validate this patch-probe method using the 1,058-nucleotide RNA genome of satellite tobacco mosaic virus (STMV), which has previously been shown to have multiple long-range connections. Our results not only indicate long duplexes that agree with previous structures but also reveal the prevalence of competing connections. Together, these results suggest that globally-folded and locally-folded structures coexist in solution. We show that the prevalence of connections changes when pseudouridine, an important component of natural and synthetic RNA molecules, is substituted for uridine in STMV RNA.

Chemical RNA Cross-Linking: Mechanisms, Computational Analysis, and Biological Applications

In recent years, RNA has emerged as a multifaceted biomolecule that is involved in virtually every function of the cell and is critical for human health. This has led to a substantial increase in research efforts to uncover the many chemical and biological aspects of RNA and target RNA for therapeutic purposes. In particular, analysis of RNA structures and interactions in cells has been critical for understanding their diverse functions and druggability. In the last 5 years, several chemical methods have been developed to achieve this goal, using chemical cross-linking combined with high-throughput sequencing and computational analysis. Applications of these methods resulted in important new insights into RNA functions in a variety of biological contexts. Given the rapid development of new chemical technologies, a thorough perspective on the past and future of this field is provided. In particular, the various RNA cross-linkers and their mechanisms, the computational analysis and challenges, and illustrative examples from recent literature are discussed.

High-throughput biochemistry in RNA sequence space: predicting structure and function

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

C-terminal determinants for RNA binding motif 7 protein stability and RNA recognition

The 7SK ribonucleoprotein (RNP) is a critical regulator of eukaryotic transcription. Recently, RNA binding motif 7 (RBM7) containing an RNA recognition motif (RRM) was reported to associate with 7SK RNA and core 7SK RNP protein components in response to DNA damage. However, little is known about the mode of RBM7-7SK RNA recognition. Here, we found that RRM constructs containing extended C-termini have increased solubility compared to a minimal RRM construct, although these constructs aggregate in a temperature and concentration-dependent manner. Using solution NMR dynamics experiments, we identified additional structural features observed previously in crystal but not in solution structures. To identify potential RBM7-7SK RNA binding sites, we analyzed deposited data from in cellulo crosslinking experiments and found that RBM7 primarily crosslinks to the distal region of 7SK stem-loop 3 (SL3). Electrophoretic mobility shift assays and NMR chemical shift perturbation experiments showed weak binding to 7SK SL3 constructs in vitro. Together, these results provide new insights into RBM7 RRM folding and recognition of 7SK RNA.

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.

Advances and opportunities in RNA structure experimental determination and computational modeling

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.

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.

Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers

The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here, we introduce a set of computational tools for the systematic analysis of data from a wide variety of crosslink-ligation methods, specifically focusing on read mapping, alignment classification, and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover eight types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and intertwined gapped alignments, we develop a network/graph-based tool Crosslinked RNA Secondary Structure Analysis using Network Techniques (CRSSANT), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multisegment alignments to report complex high-level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.