Genome-wide probing of RNA structure reveals active unfolding of mRNA structures in vivo

Cryo-EM reveals mechanisms of natural RNA multivalency

Homo-oligomerization of biological macromolecules leads to functional assemblies that are critical to understanding various cellular processes. However, RNA quaternary structures have rarely been reported. Comparative genomics analysis has identified RNA families containing hundreds of sequences that adopt conserved secondary structures and likely fold into complex three-dimensional structures. In this study, we used cryo-electron microscopy (cryo-EM) to determine structures from four RNA families, including ARRPOF and OLE forming dimers and ROOL and GOLLD forming hexameric, octameric, and dodecameric nanostructures, at 2.6- to 4.6-angstrom resolutions. These homo-oligomeric assemblies reveal a plethora of structural motifs that contribute to RNA multivalency, including kissing-loop, palindromic base-pairing, A-stacking, metal ion coordination, pseudoknot, and minor-groove interactions. These results provide the molecular basis of intermolecular interactions driving RNA multivalency with potential functional relevance.

RNA Structure: Function and Application in Plant Biology

RNA orchestrates intricate structures that influence gene expression and protein production in all living organisms, with implications for fundamental biology, medicine, and agriculture. Although extensive research has been conducted on RNA biology, many regulatory mechanisms remain elusive due to the complex and dynamic nature of RNA structures and past technological limitations. Recent advancements in RNA structure technology have revolutionized plant RNA biology research. Here, we review cutting-edge technologies for studying RNA structures in plants and their functional significance in diverse biological processes. Additionally, we highlight the pivotal role of RNA structure in influencing plant growth, development, and responses to environmental stresses. We also discuss the potential evolutionary significance of RNA structure in natural adaptation and crop domestication. Finally, we propose leveraging RNA structure-mediated gene regulation as an innovative strategy to bolster plant resilience against climate change.

Biomolecules Interacting with Long Noncoding RNAs

This review explores the complex interactions between long noncoding RNAs (lncRNAs) and other biomolecules, highlighting their pivotal roles in gene regulation and cellular function. LncRNAs, defined as RNA transcripts exceeding 200 nucleotides without encoding proteins, are involved in diverse biological processes, from embryogenesis to pathogenesis. They interact with DNA through mechanisms like triplex structure formation, influencing chromatin organization and gene expression. LncRNAs also modulate RNA-mediated processes, including mRNA stability, translational control, and splicing regulation. Their versatility stems from their forming of complex structures that enable interactions with various biomolecules. This review synthesizes current knowledge on lncRNA functions, discusses emerging roles in development and disease, and evaluates potential applications in diagnostics and therapeutics. By examining lncRNA interactions, it provides insights into the intricate regulatory networks governing cellular processes, underscoring the importance of lncRNAs in molecular biology. Unlike the majority of previous reviews that primarily focused on individual aspects of lncRNA biology, this comprehensive review uniquely integrates structural, functional, and mechanistic perspectives on lncRNA interactions across diverse biomolecules. Additionally, this review critically evaluates cutting-edge methodologies for studying lncRNA interactions, bridges fundamental molecular mechanisms with potential clinical applications, and highlights their potential.

Rapid folding of nascent RNA regulates eukaryotic RNA biogenesis

RNA's catalytic, regulatory, or coding potential depends on structure formation. Because base pairing occurs during transcription, early structural states can govern RNA processing events and dictate the formation of functional conformations. These co-transcriptional states remain mostly unknown. Here, we develop co-transcriptional structure tracking (CoSTseq), which detects nascent RNA base pairing within and upon exit from RNA polymerases (Pols) transcriptome wide in living yeast cells. Monitoring each nucleotide's base pairing activity during transcription, CoSTseq reveals predominantly rapid pairing-within 25 bp of transcription after addition to the nascent chain. Moreover, ∼23% of rRNA nucleotides attain their final base pairing state near Pol I, while most other nucleotides must undergo changes in pairing status during later steps of ribosome biogenesis. We show that helicases act immediately to remodel structures across the rDNA locus to facilitate ribosome biogenesis. By contrast, nascent pre-mRNAs attain local structures indistinguishable from mature mRNAs, suggesting that refolding behind elongating ribosomes resembles co-transcriptional folding behind Pol II.

Genome-Wide Analysis of Stable RNA Secondary Structures across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics

Small molecules targeting specific RNA-binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, which is an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA secondary structure finder (R2S-Finder) to discover short, stable RNA structural motifs in humans, Escherichia coli (E. coli), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across the organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also validated that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent across different organisms, indicating that stable structure formation is mostly driven by RNA folding, while a larger variation was found between in vitro and in vivo data for certain RNA types, such as human long intergenic noncoding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist under both in vivo and in vitro conditions and can potentially serve as drug targets. All results of stable structures, stem-loops, internal loops, bulges, and n-way junctions have been collated in the R2S-Finder database (https://github.com/JingxinWangLab/R2S-Finder), which is coded in hyperlinked HTML pages and tabulated in CSV files.

The Druggable Transcriptome Project: From Chemical Probes to Precision Medicines

RNA presents abundant opportunities as a drug target, offering significant potential for small molecule medicine development. The transcriptome, comprising both coding and noncoding RNAs, is a rich area for therapeutic innovation, yet challenges persist in targeting RNA with small molecules. RNA structure can be predicted with or without experimental data, but discrepancies with the actual biological structure can impede progress. Prioritizing RNA targets supported by genetic or evolutionary evidence enhances success. Further, small molecules must demonstrate binding to RNA in cells, not solely in vitro, to validate both the target and compound. Effective small molecule binders modulate functional sites that influence RNA biology, as binding to nonfunctional sites requires recruiting effector mechanisms, for example degradation, to achieve therapeutic outcomes. Addressing these challenges is critical to unlocking RNA's vast potential for small molecule medicines, and a strategic framework is proposed to navigate this promising field, with a focus on targeting human RNAs.

Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments

Small molecule chemical probes that covalently bond atoms of flexible nucleotides are widely employed in RNA structure determination. Atomistic molecular dynamic (MD) simulations recently suggested that RNA-probe binding can be cooperative, leading to measured reactivities that differ from expected trends as probe concentrations are varied. Here, we use selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE), dimethyl sulfate (DMS) chemical probing, and nuclear magnetic resonance (NMR) spectroscopy to explore the relationship between RNA structural dynamics and chemical probe reactivity. Our NMR chemical exchange experiments revealed that SHAPE-reactive base-paired nucleotides exhibit high imino proton exchange rates. Additionally, we find that as the concentration of a probe increases, some nucleotides' modification rates shift unexpectedly. For instance, some base-paired nucleotides that are unreactive at one probe concentration become reactive at another, often corresponding with a shift in the modification rate of the complementary nucleotide. We believe this effect can be harnessed to infer pairing interactions. Lastly, our results suggest that the overmodification of an RNA can impact its conformational dynamics, leading to modulations in the structural ensembles representing the RNA's fold. Our findings suggest an intricate interplay between RNA conformational dynamics and chemical probing reactivity.

A Bright Red Molecular Marker for Super-Resolution Imaging of DNA Nanoscale Organization in Cells

SEZ-NDEA, a long-range emissive molecular marker, can be used in fluorescence microscopy for super-resolution imaging (SRRF) of DNA nanoscale organization in cells. The developed probe stains the cell nucleus with minimum cytoplasmic leakage with good contrast. It showed good photostability in cellular medium.

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning

Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential druggable target must satisfy several criteria, including disease association, cellular accessibility, binding pockets for drug-like molecules, and minimal cross-reactivity. While several methods exist for prediction of druggable proteins, they cannot be repurposed for RNAs due to fundamental differences in their binding modality. Taking all these constraints into account, a new structure-based model, Druggable RNA-Ligand binding Pocket Selector (DRLiPS), is developed here to predict binding site-level druggability of any given RNA target. A novel strategy for sampling negative binding sites in RNA structures using three parallel approaches is demonstrated here to improve model specificity: backbone motif search, exhaustive pocket prediction, and blind docking. An external blind test dataset has also been curated to showcase the model's generalizability to both experimental and modelled apo state RNA structures. DRLiPS has achieved an F1-score of 0.70, precision of 0.61, specificity of 0.89, and recall of 0.73 on this external test dataset, outperforming two existing methods, DrugPred_RNA and RNACavityMiner. Further analysis indicates that the features selected for model-building generalize well to both apo and holo states with a backbone RMSD tolerance of 3 Å. It can also predict the effect of binding site single point mutations on druggability, which can aid in optimizing synthetic RNA aptamers for small molecule recognition. The DRLiPS model is freely accessible at https://web.iitm.ac.in/bioinfo2/DRLiPS/.

Mapping small molecule-RNA binding sites via Chem-CLIP synergized with capillary electrophoresis and nanopore sequencing

Target validation and identification of binding sites are keys to the development of bioactive small molecules that target RNA. Herein, we describe optimized protocols to profile small molecule-RNA interactions and to define binding sites of the small molecules in RNAs using covalent chemistry. Various reactive modules appended to an RNA-binding small molecule were studied for cross-linking to the RNA target. Electrophilic modules, whether N-chloroethyl aniline or diazirine, have reactive profiles consistent with induced proximity; however, probes with N-chloroethyl aniline were more reactive and more specific than those with a diazirine cross-linking moiety. Depending upon the identity of the cross-linking module, covalent adducts with different nucleotides that are proximal to a small molecule's binding site were formed. The nucleotides where cross-linking occurred were elucidated by using two different platforms: (i) automated capillary electrophoresis that identified a binding site by impeding reverse transcriptase, or "RT stops"; and (ii) nanopore sequencing where the cross-link produces mutations in the corresponding complementary DNA formed by reverse transcriptase-polymerase chain reaction amplification of the cross-linked RNA. These approaches are broadly applicable to aid in the advancement of chemical probes targeting RNA, including identifying binding sites and using covalent chemistry to screen for RNA-binding molecules in a high throughput format.

A sequence-specific RNA acetylation catalyst

N4-acetylcytidine (ac4C) is a ubiquitous RNA modification incorporated by cytidine acetyltransferase enzymes. Here, we report the biochemical characterization of Thermococcus kodakarensis Nat10 (TkNat10), an RNA acetyltransferase involved in archaeal thermotolerance. We demonstrate that TkNat10's catalytic activity is critical for T. kodakarensis fitness at elevated temperatures. Unlike eukaryotic homologs, TkNat10 exhibits robust stand-alone activity, modifying diverse RNA substrates in a temperature, ATP, and acetyl-CoA-dependent manner. Transcriptome-wide analysis reveals TkNat10 preferentially modifies unstructured RNAs containing a 5'-CCG-3' consensus sequence. Using a high-throughput mutagenesis approach, we define sequence and structural determinants of TkNat10 substrate recognition. We find TkNat10 can be engineered to facilitate use of propionyl-CoA, providing insight into its cofactor specificity. Finally, we demonstrate TkNat10's utility for site-specific acetylation of RNA oligonucleotides, enabling analysis of ac4C-dependent RNA-protein interactions. Our findings establish a framework for understanding archaeal RNA acetylation and a new tool for studying the functional consequences of ac4C in diverse RNA contexts.

GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings

Small nucleolar RNAs (snoRNAs) play essential roles in various cellular processes, and their associations with diseases are increasingly recognized. Identifying these snoRNA-disease relationships is critical for advancing our understanding of their functional roles and potential therapeutic implications. This work presents a novel approach, called GL4SDA, to predict snoRNA-disease associations using Graph Neural Networks (GNN) and Large Language Models. Our methodology leverages the unique strengths of heterogeneous graph structures to model complex biological interactions. Differently from existing methods, we define a set of features able to capture deeper information content related to the inner attributes of both snoRNAs and diseases and design a GNN model based on highly performing layers, which can maximize results on this representation. We consider snoRNA secondary structures and disease embeddings derived from large language models to obtain snoRNAs and disease node features, respectively. By combining structural features of snoRNAs with rich semantic embeddings of diseases, we construct a feature-rich graph representation that improves the predictive performance of our model. We evaluate our approach using different architectures that exploit the capabilities of many graph convolutional layers and compare the results with three other state-of-the-art graph-based predictors. GL4SDA demonstrates improved scores in link prediction tasks and demonstrates its potential implication as a tool for exploring snoRNA-disease relationships. We also validate our findings through biological case studies about cancer diseases, highlighting the practical application of our method in real-world scenarios and obtaining the most important snoRNA features using explainable artificial intelligence methods.

Consistent features observed in structural probing data of eukaryotic RNAs

Understanding RNA structure is crucial for elucidating its regulatory mechanisms. With the recent commercialization of messenger RNA vaccines, the profound impact of RNA structure on stability and translation efficiency has become increasingly evident, underscoring the importance of understanding RNA structure. Chemical probing of RNA has emerged as a powerful technique for investigating RNA structure in living cells. This approach utilizes chemical probes that selectively react with accessible regions of RNA, and by measuring reactivity, the openness and potential of RNA for protein binding or base pairing can be inferred. Extensive experimental data generated using RNA chemical probing have significantly contributed to our understanding of RNA structure in cells. However, it is crucial to acknowledge potential biases in chemical probing data to ensure an accurate interpretation. In this study, we comprehensively analyzed transcriptome-scale RNA chemical probing data in eukaryotes and report common features. Notably, in all experiments, the number of bases modified in probing was small, the bases showing the top 10% reactivity well reflected the known secondary structure, bases with high reactivity were more likely to be exposed to solvent and low reactivity did not reflect solvent exposure, which is important information for the analysis of RNA chemical probing data.

Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data

Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from nascent RNA sequencing data. First, we devise a probabilistic model that predicts nucleotide-specific elongation rates as a generalized linear function of nearby genomic and epigenomic features. We validate this model with simulations and apply it to public PRO-seq (Precision Run-On Sequencing) and epigenomic data for four cell types, finding that reductions in local elongation rate are associated with cytosine nucleotides, DNA methylation, splice sites, RNA stem-loops, CTCF (CCCTC-binding factor) binding sites, and several histone marks, including H3K36me3 and H4K20me1. By contrast, increases in local elongation rate are associated with thymines, A+T-rich and low-complexity sequences, and H3K79me2 marks. We then introduce a convolutional neural network that improves our local rate predictions. Our analysis is the first to permit genome-wide predictions of relative nucleotide-specific elongation rates.

From the genome's perspective: Bearing somatic retrotransposition to leverage the regulatory potential of L1 RNAs

Transposable elements (TEs) are mobile genomic elements constituting a big fraction of eukaryotic genomes. They ignite an evolutionary arms race with host genomes, which in turn evolve strategies to restrict their activity. Despite being tightly repressed, TEs display precisely regulated expression patterns during specific stages of mammalian development, suggesting potential benefits for the host. Among TEs, the long interspersed nuclear element (LINE-1 or L1) has been found to be active in neurons. This activity prompted extensive research into its possible role in cognition. So far, no specific cause-effect relationship between L1 retrotransposition and brain functions has been conclusively identified. Nevertheless, accumulating evidence suggests that interactions between L1 RNAs and RNA/DNA binding proteins encode specific messages that cells utilize to activate or repress entire transcriptional programs. We summarize recent findings highlighting the activity of L1 RNAs at the non-coding level during early embryonic and brain development. We propose a hypothesis suggesting a mutualistic relationship between L1 mRNAs and the host cell. In this scenario, cells tolerate a certain rate of retrotransposition to leverage the regulatory effects of L1s as non-coding RNAs on potentiating their mitotic potential. In turn, L1s benefit from the cell's proliferative state to increase their chance to mobilize.

RASP v2.0: an updated atlas for RNA structure probing data

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

RNA Structure: Past, Future, and Gene Therapy Applications

First believed to be a simple intermediary between the information encoded in deoxyribonucleic acid and that functionally displayed in proteins, ribonucleic acid (RNA) is now known to have many functions through its abundance and intricate, ubiquitous, diverse, and dynamic structure. About 70-90% of the human genome is transcribed into protein-coding and noncoding RNAs as main determinants along with regulatory sequences of cellular to populational biological diversity. From the nucleotide sequence or primary structure, through Watson-Crick pairing self-folding or secondary structure, to compaction via longer distance Watson-Crick and non-Watson-Crick interactions or tertiary structure, and interactions with RNA or other biopolymers or quaternary structure, or with metabolites and biomolecules or quinary structure, RNA structure plays a critical role in RNA's lifecycle from transcription to decay and many cellular processes. In contrast to the success of 3-dimensional protein structure prediction using AlphaFold, RNA tertiary and beyond structures prediction remains challenging. However, approaches involving machine learning and artificial intelligence, sequencing of RNA and its modifications, and structural analyses at the single-cell and intact tissue levels, among others, provide an optimistic outlook for the continued development and refinement of RNA-based applications. Here, we highlight those in gene therapy.

Protocol for mapping RNA G-quadruplex for chromatin-bound RNA using d-rG4-seq

Here, we present a protocol for using d-rG4-seq, a technique for mapping RNA G-quadruplex (rG4) for chromatin-bound RNA. We describe steps for identifying in vivo rG4 structures based on differential sensitivity of rG4 to dimethyl sulfate (DMS) modification, folding in the presence of monovalent cations, K+ versus Li+, and reverse transcriptase (RT) readthrough when folded. We then detail procedures for isolating RNA from the chromatin-bound fractions to enrich for epigenetic regulators and comparing in vitro versus in vivo profiles. For complete details on the use and execution of this protocol, please refer to Lee et al.1.

RNA tertiary structure and conformational dynamics revealed by BASH MaP

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

RNA ensembles from in vitro to in vivo: Toward predictive models of RNA cellular function

Deepening our understanding of RNA biology and accelerating development of RNA-based therapeutics go hand-in-hand-both requiring a transition from qualitative descriptions of RNA structure to quantitative models capable of predicting RNA behaviors, and from a static to an ensemble view. Ensembles are determined from their free energy landscapes, which define the relative populations of conformational states and the energetic barriers separating them. Experimental determination of RNA ensembles over the past decade has led to powerful predictive models of RNA behavior in vitro. It has also been shown during this time that the cellular environment redistributes RNA ensembles, changing the abundances of functionally relevant conformers relative to in vitro contexts with subsequent functional RNA consequences. However, recent studies have demonstrated that testing models built from in vitro ensembles with highly quantitative measurements of RNA cellular function, aided by emerging computational methodologies, enables predictive modelling of cellular activity and biological discovery.

Novel Biallelic Synonymous Exonic Variant in VPS13A Affecting mRNA Splicing: Case Report

Objectives

Chorea-acanthocytosis is an autosomal recessively inherited condition caused by loss-of-function pathogenic variants in VPS13A. We identified a novel synonymous exonic variant leading to abnormal mRNA splicing in a patient with chorea-acanthocytosis.

Methods

A patient with focal epilepsy developed generalized chorea with orolingual dystonia, cognitive decline, and peripheral neuropathy, consistent with chorea-acanthocytosis. Her parents were first cousins, but there was otherwise no family history. Targeted gene sequencing for variants in VPS13A, mRNA splicing analysis, and Western blot for chorein were performed.

Results

A homozygous synonymous variant in exon 41 of VPS13A (NM_033305.3): c.5157C>T; p.Gly1719 = was identified; this was previously classified as a variant of uncertain significance. SpliceAI predicted a splice donor gain with a score of 0.75 2 base pairs upstream of the reported variant. RNA splicing analysis revealed the creation of a type III splice variant, resulting in a frameshift and a premature termination codon. Western blot showed absent chorein/VPS13A protein.

Discussion

The variant is reclassified as likely pathogenic based on the American College of Medical Genetics criteria. This is the first reported case of ChAc caused by a synonymous variant in VPS13A proven to affect splicing. Our report further expands the spectrum of variants known to cause ChAc.

ALS-associated FUS mutation reshapes the RNA and protein composition of stress granules

Stress granules (SG) are part of a cellular protection mechanism where untranslated messenger RNAs and RNA-binding proteins are stored upon conditions of cellular stress. Compositional variations due to qualitative or quantitative protein changes can disrupt their functionality and alter their structure. This is the case of different forms of amyotrophic lateral sclerosis (ALS) where a causative link has been proposed between the cytoplasmic de-localization of mutant proteins, such as FUS (Fused in Sarcoma), and the formation of cytotoxic inclusions. Here, we describe the SG transcriptome in neuroblastoma cells and define several features for RNA recruitment in these condensates. We demonstrate that SG dynamics and RNA content are strongly modified by the incorporation of mutant FUS, switching to a more unstructured, AU-rich SG transcriptome. Moreover, we show that mutant FUS, together with its protein interactors and their target RNAs, are responsible for the reshaping of the mutant SG transcriptome with alterations that can be linked to neurodegeneration. Our data describe the molecular differences between physiological and pathological SG in ALS-FUS conditions, showing how FUS mutations impact the RNA and protein composition of these condensates.

Rapid folding of nascent RNA regulates eukaryotic RNA biogenesis

An RNA's catalytic, regulatory, or coding potential depends on RNA structure formation. Because base pairing occurs during transcription, early structural states can govern RNA processing events and dictate the formation of functional conformations. These co-transcriptional states remain unknown. Here, we develop CoSTseq, which detects nascent RNA base pairing within and upon exit from RNA polymerases (Pols) transcriptome-wide in living yeast cells. By monitoring each nucleotide's base pairing activity during transcription, we identify distinct classes of behaviors. While 47% of rRNA nucleotides remain unpaired, rapid and delayed base pairing - with rates of 48.5 and 13.2 kb -1 of transcribed rDNA, respectively - typically completes when Pol I is only 25 bp downstream. We show that helicases act immediately to remodel structures across the rDNA locus and facilitate ribosome biogenesis. In contrast, nascent pre-mRNAs attain local structures indistinguishable from mature mRNAs, suggesting that refolding behind elongating ribosomes resembles co-transcriptional folding behind Pol II.

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

The role of structure in regulatory RNA elements

Regulatory RNA elements fulfill functions such as translational regulation, control of transcript levels, and regulation of viral genome replication. Trans-acting factors (i.e., RNA-binding proteins) bind the so-called cis elements and confer functionality to the complex. The specificity during protein-RNA complex (RNP) formation often exploits the structural plasticity of RNA. Functional integrity of cis-trans pairs depends on the availability of properly folded RNA elements, and RNA conformational transitions can cause diseases. Knowledge of RNA structure and the conformational space is needed for understanding complex formation and deducing functional effects. However, structure determination of RNAs under in vivo conditions remains challenging. This review provides an overview of structured eukaryotic and viral RNA cis elements and discusses the effect of RNA structural equilibria on RNP formation. We showcase implications of RNA structural changes for diseases, outline strategies for RNA structure-based drug targeting, and summarize the methodological toolbox for deciphering RNA structures.

Exonuclease-assisted enrichment and base resolution analysis of pseudouridine in single-stranded RNA

Pseudouridine (Ψ) is one of the most abundant RNA modifications, playing crucial roles in various biological processes. Identifying Ψ sites is vital for understanding their functions. In this study, we proposed a novel method for identifying Ψ sites with an improved signal-to-noise ratio. This method, called RNA exonuclease-assisted identification of pseudouridine sites (RIPS), combines specific CMC-labeling of Ψ sites with an exonuclease-assisted digestion strategy for the detection of Ψ sites. Utilizing exonuclease XRN1 to digest RNA strands not labeled by CMC, RIPS significantly reduces the background signal from unlabeled strands and enhances the positive signal of Ψ sites labeled by CMC, which terminates exonuclease digestion. As a result, we can enrich Ψ sites and identify them at single-base resolution. Considering the unique functions of single-stranded RNA (ssRNA), we employed RIPS to distinguish Ψ sites in single-stranded and double-stranded regions of RNA. Our results indicated that CMC could specifically label Ψ sites in ssRNA under natural conditions, enabling RIPS to selectively identify Ψ sites in ssRNA, which may facilitate the study on the functions of Ψ sites.

RNA architecture of porcine deltacoronavirus genome inside virions detected by vRIC-seq

Porcine deltacoronavirus (PDCoV) is a newly emerging and special delta coronavirus, which infect mammals such as pigs, cattle and humans, as well as chickens and birds. Exploring RNA structures in the viral genome benefits the understanding of the role of RNA in the lifecycle of viruses. In this study, vRIC-seq is employed to analyze the RNA-RNA interaction in the whole genome structure of PDCoV in virions. About 12.87 and 13.52 million paired reads are obtained in two biological replicates, respectively, with 17.9% and 14.8% of them are identified as valid chimeric reads. These are employed to predict the RNA secondary structure, which is compact and highly structured. A twisted-cyclized conformation is observed in the RNA-RNA interaction map of PDCoV for the first time. 77 multi-way junctions are evenly distributed in the PDCoV genome. Our work provides fundamental structural insights that are essential for understanding the genomic structure and function, genetic evolution, and packaging characteristics of PDCoV.

Genome-Wide Identification of Stable RNA Secondary Structures Across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics

Small molecules targeting specific RNA binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA Secondary Structure Finder (R2S-Finder) to discover short, stable RNA structural motifs for humans, Escherichia coli ( E. coli ), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also found that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans, SARS-CoV-2, and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent in humans, E. coli , and SARS-CoV-2, indicating that most stable structure formation were driven by RNA folding alone, while a larger variation was found between in vitro and in vivo data with certain RNA types, such as human long intergenic non-coding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist both in vivo and in vitro conditions, which can potentially serve as drug targets. All results of stable sequences, stem-loops, internal loops, bulges, and three- and four-way junctions have been collated in the R2S-Finder database ( https://github.com/JingxinWangLab/R2S-Finder ), which is coded in hyperlinked HTML pages and tabulated in CSV files.

Technological advancements in deciphering RNA-RNA interactions

RNA-RNA interactions (RRIs) can dictate RNA molecules to form intricate higher-order structures and bind their RNA substrates in diverse biological processes. To elucidate the function, binding specificity, and regulatory mechanisms of various RNA molecules, especially the vast repertoire of non-coding RNAs, advanced technologies and methods that globally map RRIs are extremely valuable. In the past decades, many state-of-the-art technologies have been developed for this purpose. This review focuses on those high-throughput technologies for the global mapping of RRIs. We summarize the key concepts and the pros and cons of different technologies. In addition, we highlight the novel biological insights uncovered by these RRI mapping methods and discuss the future challenges for appreciating the crucial roles of RRIs in gene regulation across bacteria, viruses, archaea, and mammals.

Untangling the pseudoknots of SARS-CoV-2: Insights into structural heterogeneity and plasticity

Since the onset of the COVID-19 pandemic, one productive area of research has focused on the intricate two- and three-dimensional structures taken on by SARS-CoV-2's RNA genome. These structures control essential viral processes, making them tempting targets for therapeutic intervention. This review focuses on two such structured regions, the frameshift stimulation element (FSE), which controls the translation of viral protein, and the 3' untranslated region (3' UTR), which is thought to regulate genome replication. For the FSE, we discuss its canonical pseudoknot's threaded and unthreaded topologies, as well as the diversity of competing two-dimensional structures formed by local and long-distance base pairing. For the 3' UTR, we review the evidence both for and against the formation of its replication-enabling pseudoknot.

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

SEISMICgraph: a web-based tool for RNA structure data visualization

In recent years, RNA has been increasingly recognized for its essential roles in biology, functioning not only as a carrier of genetic information but also as a dynamic regulator of gene expression through its interactions with other RNAs, proteins, and itself. Advances in chemical probing techniques have significantly enhanced our ability to identify RNA secondary structures and understand their regulatory roles. These developments, alongside improvements in experimental design and data processing, have greatly increased the resolution and throughput of structural analyses. Here, we introduce SEISMICgraph, a web-based tool designed to support RNA structure research by offering data visualization and analysis capabilities for a variety of chemical probing modalities. SEISMICgraph enables simultaneous comparison of data across different sequences and experimental conditions through a user-friendly interface that requires no programming expertise. We demonstrate its utility by investigating known and putative riboswitches and exploring how RNA modifications influence their structure and binding. SEISMICgraph's ability to rapidly visualize adenine-dependent structural changes and assess the impact of pseudouridylation on these transitions provides novel insights and establishes a roadmap for numerous future applications.

Identification of leader-trailer helices of precursor ribosomal RNA in all phyla of bacteria and archaea

Ribosomal RNAs are transcribed as part of larger precursor molecules. In Escherichia coli, complementary RNA segments flank each rRNA and form long leader-trailer (LT) helices, which are crucial for subunit biogenesis in the cell. A previous study of 15 representative species suggested that most but not all prokaryotes contain LT helices. Here, we use a combination of in silico folding and covariation methods to identify and characterize LT helices in 4464 bacterial and 260 archaeal organisms. Our results suggest that LT helices are present in all phyla, including Deinococcota, which had previously been suspected to lack LT helices. In very few organisms, our pipeline failed to detect LT helices for both 16S and 23S rRNA. However, a closer case-by-case look revealed that LT helices are indeed present but escaped initial detection. Over 3600 secondary structure models, many well supported by nucleotide covariation, were generated. These structures show a high degree of diversity. Yet, all exhibit extensive base-pairing between the leader and trailer strands, in line with a common and essential function.

A systematic search for RNA structural switches across the human transcriptome

RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3' untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites

RNA secondary structures play essential roles in the formation of the tertiary structure and function of a transcript. Recent genome-wide studies highlight significant potential for RNA structures in the mammalian genome. However, a major challenge is assigning functional roles to these structured RNAs. In this study, we conduct a guilt-by-association analysis of clusters of computationally predicted conserved RNA structure (CRSs) in human untranslated regions (UTRs) to associate them with gene functions. We filtered a broad pool of ∼500 000 human CRSs for UTR overlap, resulting in 4734 and 24 754 CRSs from the 5' and 3' UTR of protein-coding genes, respectively. We separately clustered these CRSs for both sets using RNAscClust, obtaining 793 and 2403 clusters, each containing an average of five CRSs per cluster. We identified overrepresented binding sites for 60 and 43 RNA-binding proteins co-localizing with the clustered CRSs. Furthermore, 104 and 441 clusters from the 5' and 3' UTRs, respectively, showed enrichment for various Gene Ontologies, including biological processes such as 'signal transduction', 'nervous system development', molecular functions like 'transferase activity' and the cellular components such as 'synapse' among others. Our study shows that significant functional insights can be gained by clustering RNA structures based on their structural characteristics.

Hfq mediates transcriptome-wide RNA structurome reprogramming under virulence-inducing conditions in a phytopathogen

Although RNA structures play important roles in regulating gene expression, the mechanism and function of mRNA folding in plant bacterial pathogens remain elusive. Therefore, we perform dimethyl sulfate sequencing (DMS-seq) on the Pseudomonas syringae under nutrition-rich and -deficient conditions, revealing that the mRNA structure changes substantially in the minimal medium (MM) that tunes global translation efficiency (TE), thereby inducing virulence. This process is led by the increased expression of hfq, which is directly activated by transcription regulators RpoS and CysB. The co-occurrence of Hfq and RpoS in diverse bacteria and the deep conservation of Hfq Y25 is critical for RNA-mediated regulation and implicates the wider biological importance of mRNA structure and feedback loops in the control of global gene expression.

RNA tertiary structure and conformational dynamics revealed by BASH MaP

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

Transcriptome-Wide, Unbiased Profiling of Ribonuclease Targeting Chimeras

Various approaches have been developed to target RNA and modulate its function with modes of action including binding and cleavage. Herein, we explored how small molecule binding is correlated with cleavage induced by heterobifunctional ribonuclease targeting chimeras (RiboTACs), where RNase L is recruited to cleave the bound RNA target, in a transcriptome-wide, unbiased fashion. Only a fraction of bound targets was cleaved by RNase L, induced by RiboTAC binding. Global analysis suggested that (i) cleaved targets generally form a region of stable structure that encompasses the small molecule binding site; (ii) cleaved targets have preferred RNase L cleavage sites nearby small molecule binding sites; (iii) RiboTACs facilitate a cellular interaction between cleaved targets and RNase L; and (iv) the expression level of the target influences the extent of cleavage observed. In one example, we converted a binder of LGALS1 (galectin-1) mRNA into a RiboTAC. In MDA-MB-231 cells, the binder had no effect on galectin-1 protein levels, while the RiboTAC cleaved LGALS1 mRNA, reduced galectin-1 protein abundance, and affected galectin-1-associated oncogenic cellular phenotypes. Using LGALS1, we further assessed additional factors including the length of the linker that tethers the two components of the RiboTAC, cellular uptake, and the RNase L-recruiting module on RiboTAC potency. Collectively, these studies may facilitate triangulation of factors to enable the design of RiboTACs.

The human mitochondrial mRNA structurome reveals mechanisms of gene expression

The human mitochondrial genome encodes crucial oxidative phosphorylation system proteins, pivotal for aerobic energy transduction. They are translated from nine monocistronic and two bicistronic transcripts whose native structures remain unexplored, posing a gap in understanding mitochondrial gene expression. In this work, we devised the mitochondrial dimethyl sulfate mutational profiling with sequencing (mitoDMS-MaPseq) method and applied detection of RNA folding ensembles using expectation-maximization (DREEM) clustering to unravel the native mitochondrial messenger RNA (mt-mRNA) structurome in wild-type (WT) and leucine-rich pentatricopeptide repeat-containing protein (LRPPRC)-deficient cells. Our findings elucidate LRPPRC's role as a holdase contributing to maintaining mt-mRNA folding and efficient translation. mt-mRNA structural insights in WT mitochondria, coupled with metabolic labeling, unveil potential mRNA-programmed translational pausing and a distinct programmed ribosomal frameshifting mechanism. Our data define a critical layer of mitochondrial gene expression regulation. These mt-mRNA folding maps provide a reference for studying mt-mRNA structures in diverse physiological and pathological contexts.

The West Nile virus genome harbors essential riboregulatory elements with conserved and host-specific functional roles

West Nile virus (WNV) is an arthropod-borne, positive-sense RNA virus that poses an increasing global threat due to warming climates and lack of effective therapeutics. Like other enzootic viruses, little is known about how host context affects the structure of the full-length RNA genome. Here, we report a complete secondary structure of the entire WNV genome within infected mammalian and arthropod cell lines. Our analysis affords structural insights into multiple, conserved aspects of flaviviral biology. We show that the WNV genome folds with minimal host dependence, and we prioritize well-folded regions for functional validation using structural homology between hosts as a guide. Using structure-disrupting, antisense locked nucleic acids, we then demonstrate that the WNV genome contains riboregulatory structures with conserved and host-specific functional roles. These results reveal promising RNA drug targets within flaviviral genomes, and they highlight the therapeutic potential of ASO-LNAs as both WNV-specific and pan-flaviviral therapeutic agents.

Robo-Therm, a pipeline to RNA thermometer discovery and validation

RNA thermometers are highly structured noncoding RNAs located in the 5'-untranslated regions (UTRs) of genes that regulate expression by undergoing conformational changes in response to temperature. The discovery of RNA thermometers through bioinformatics is difficult because there is little sequence conservation among their structural elements. Thus, the abundance of these thermosensitive regulatory structures remains unclear. Herein, to advance the discovery and validation of RNA thermometers, we developed Robo-Therm, a pipeline that combines an adaptive and user-friendly in silico motif search with a well-established reporter system. Through our application of Robo-Therm, we discovered two novel RNA thermometers in bacterial and bacteriophage genomes found in the human gut. One of these thermometers is present in the 5'-UTR of a gene that codes for σ 70 RNA polymerase subunit in the bacteria Mediterraneibacter gnavus and Bacteroides pectinophilus, and in the bacteriophage Caudoviricetes, which infects B. pectinophilus The other thermometer is in the 5'-UTR of a tetracycline resistance gene (tetR) in the intestinal bacteria Escherichia coli and Shigella flexneri Our Robo-Therm pipeline can be applied to discover multiple RNA thermometers across various genomes.

A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS

A key to understanding the roles of RNA in regulating gene expression is knowing their structures in vivo. One way to obtain this information is through probing the structures of RNA with chemicals. To probe RNA structure directly in cells, membrane-permeable reagents that modify the Watson-Crick (WC) face of unpaired nucleotides can be used. Although dimethyl sulfate (DMS) has led to substantial insight into RNA structure, it has limited nucleotide specificity in vivo, with WC face reactivity only at adenine (A) and cytosine (C) at neutral pH. The reagent 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) was recently shown to modify the WC face of guanine (G) and uracil (U). Although useful at lower concentrations in experiments that measure chemical modifications by reverse transcription (RT) stops, at higher concentrations necessary for detection by mutational profiling (MaP), EDC treatment leads to degradation of RNA. Here, we demonstrate EDC-stimulated degradation of RNA in Gram-negative and Gram-positive bacteria. In an attempt to overcome these limitations, we developed a new carbodiimide reagent, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide methiodide (ETC), which we show specifically modifies unpaired Gs and Us in vivo without substantial degradation of RNA. We establish ETC as a probe for MaP and optimize the RT conditions and computational analysis in Escherichia coli Importantly, we demonstrate the utility of ETC as a probe for improving RNA structure prediction both alone and with DMS.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

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.

G-quadruplex folding in Xist RNA antagonizes PRC2 activity for stepwise regulation of X chromosome inactivation

How Polycomb repressive complex 2 (PRC2) is regulated by RNA remains an unsolved problem. Although PRC2 binds G-tracts with the potential to form RNA G-quadruplexes (rG4s), whether rG4s fold extensively in vivo and whether PRC2 binds folded or unfolded rG4 are unknown. Using the X-inactivation model in mouse embryonic stem cells, here we identify multiple folded rG4s in Xist RNA and demonstrate that PRC2 preferentially binds folded rG4s. High-affinity rG4 binding inhibits PRC2's histone methyltransferase activity, and stabilizing rG4 in vivo antagonizes H3 at lysine 27 (H3K27me3) enrichment on the inactive X chromosome. Surprisingly, mutagenizing the rG4 does not affect PRC2 recruitment but promotes its release and catalytic activation on chromatin. H3K27me3 marks are misplaced, however, and gene silencing is compromised. Xist-PRC2 complexes become entrapped in the S1 chromosome compartment, precluding the required translocation into the S2 compartment. Thus, Xist rG4 folding controls PRC2 activity, H3K27me3 enrichment, and the stepwise regulation of chromosome-wide gene silencing.

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences. Recently, deep networks have been investigated to help predict RNA structures directly from their sequences. However, existing deep-learning-based tools are more or less suffering from model overfitting due to their complicated problem formulation and defective model training processes, limiting their applications across sequences from different structural families. In this research, we designed a two-stage RNA structure prediction strategy called DEBFold (deep ensemble boosting and folding) based on convolution encoding/decoding and self-attention mechanisms to enhance the existing thermodynamic structure models. Moreover, the model training process followed rigorous steps to achieve an acceptable prediction generalization. On the family-wise reserved test sets and the PDB-derived test set, DEBFold achieves better structure prediction performance over traditional tools and existing deep-learning methods. In summary, we obtained a cutting-edge deep-learning-based structure prediction tool with supreme across-family generalization performance. The DEBFold tool can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.

Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase or decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression.

New insights into the interaction between m6A modification and lncRNA in cancer drug resistance

Drug resistance is perhaps the greatest obstacle in improving outcomes for cancer patients, leading to recurrence, progression and metastasis of various cancers. Exploring the underlying mechanism worth further study. N6-methyladenosine (m6A) is the most common RNA modification found in eukaryotes, playing a vital role in RNA translation, transportation, stability, degradation, splicing and processing. Long noncoding RNA (lncRNA) refers to a group of transcripts that are longer than 200 nucleotides (nt) and typically lack the ability to code for proteins. LncRNA has been identified to play a significant role in regulating multiple aspects of tumour development and progression, including proliferation, metastasis, metabolism, and resistance to treatment. In recent years, a growing body of evidence has emerged, highlighting the crucial role of the interplay between m6A modification and lncRNA in determining the sensitivity of cancer cells to chemotherapeutic agents. In this review, we focus on the recent advancements in the interaction between m6A modification and lncRNA in the modulation of cancer drug resistance. Additionally, we aim to explore the underlying mechanisms involved in this process. The objective of this review is to provide valuable insights and suggest potential future directions for the reversal of chemoresistance in cancer.