ScanFold: an approach for genome-wide discovery of local RNA structural elements-applications to Zika virus and HIV

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.

Precursor RNA structural patterns at SF3B1 mutation sensitive cryptic 3' splice sites

SF3B1 is a core component of the spliceosome involved in branch point recognition and 3' splice site selection. SF3B1 mutation is common in myelodysplastic syndrome and other blood disorders. The most common mutation in SF3B1 is K700E, a lysine to glutamic acid change within the pre-mRNA interacting heat repeat domain. A hallmark of SF3B1 mutation is an increased use of cryptic 3' splice sites; however, the properties distinguishing SF3B1-sensitive splice junctions from other alternatively spliced junctions are unknown. We identify a subset of 192 core splice junctions that are mis-spliced with SF3B1 K700E mutation. We use our core set to test whether SF3B1-sensitive splice sites are different from control cryptic 3' splice sites via RNA structural accessibility. As a comparison, we define a set of SF3B1-resistant splice junctions with cryptic splice site use that does not change with SF3B1 K700E mutation. We find sequence differences between SF3B1-sensitive and SF3B1-resistant junctions, particularly at the cryptic sites. SF3B1-sensitive cryptic 3' splice sites are within an extended polypyrimidine tract and have lower splice site strength scores. We develop experimental RNA structure data for 83 SF3B1-sensitive junctions and 39 SF3B1-resistant junctions. We find that the pattern of structural accessibility at the NAG splicing motif in cryptic and canonical 3' splice sites is similar. In addition, this pattern can be found in both SF3B1-resistant and SF3B1-sensitive junctions. However, SF3B1-sensitive junctions have cryptic splice sites that are less structurally distinct from the canonical splice sites. In addition, SF3B1-sensitive splice junctions are overall more flexible than SF3B1-resistant junctions. Our results suggest that the SF3B1-sensitive splice junctions have unique structure and sequence properties, containing poorly differentiated, weak splice sites that lead to altered 3' splice site recognition in the presence of SF3B1 mutation.

The RNA Revolution in the Central Molecular Biology Dogma Evolution

Human genome projects in the 1990s identified about 20,000 protein-coding sequences. We are now in the RNA revolution, propelled by the realization that genes determine phenotype beyond the foundational central molecular biology dogma, stating that inherited linear pieces of DNA are transcribed to RNAs and translated into proteins. Crucially, over 95% of the genome, initially considered junk DNA between protein-coding genes, encodes essential, functionally diverse non-protein-coding RNAs, raising the gene count by at least one order of magnitude. Most inherited phenotype-determining changes in DNA are in regulatory areas that control RNA and regulatory sequences. RNAs can directly or indirectly determine phenotypes by regulating protein and RNA function, transferring information within and between organisms, and generating DNA. RNAs also exhibit high structural, functional, and biomolecular interaction plasticity and are modified via editing, methylation, glycosylation, and other mechanisms, which bestow them with diverse intra- and extracellular functions without altering the underlying DNA. RNA is, therefore, currently considered the primary determinant of cellular to populational functional diversity, disease-linked and biomolecular structural variations, and cell function regulation. As demonstrated by RNA-based coronavirus vaccines' success, RNA technology is transforming medicine, agriculture, and industry, as did the advent of recombinant DNA technology in the 1980s.

Intronic RNA secondary structural information captured for the human MYC pre-mRNA

To address the lack of intronic reads in secondary structure probing data for the human MYC pre-mRNA, we developed a method that combines spliceosomal inhibition with RNA probing and sequencing. Here, the SIRP-seq method was applied to study the secondary structure of human MYC RNAs by chemically probing HeLa cells with dimethyl sulfate in the presence of the small molecule spliceosome inhibitor pladienolide B. Pladienolide B binds to the SF3B complex of the spliceosome to inhibit intron removal during splicing, resulting in retained intronic sequences. This method was used to increase the read coverage over intronic regions of MYC. The purpose for increasing coverage across introns was to generate complete reactivity profiles for intronic sequences via the DMS-MaPseq approach. Notably, depth was sufficient for analysis by the program DRACO, which was able to deduce distinct reactivity profiles and predict multiple secondary structural conformations as well as their suggested stoichiometric abundances. The results presented here provide a new method for intronic RNA secondary structural analyses, as well as specific structural insights relevant to MYC RNA splicing regulation and therapeutic targeting.

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 RNA secondary structure of androgen receptor-FL and V7 transcripts reveals novel regulatory regions

The androgen receptor (AR) is a ligand-dependent nuclear transcription factor belonging to the steroid hormone nuclear receptor family. Due to its roles in regulating cell proliferation and differentiation, AR is tightly regulated to maintain proper levels of itself and the many genes it controls. AR dysregulation is a driver of many human diseases including prostate cancer. Though this dysregulation often occurs at the RNA level, there are many unknowns surrounding post-transcriptional regulation of AR mRNA, particularly the role that RNA secondary structure plays. Thus, a comprehensive analysis of AR transcript secondary structure is needed. We address this through the computational and experimental analyses of two key isoforms, full length (AR-FL) and truncated (AR-V7). Here, a combination of in-cell RNA secondary structure probing experiments (targeted DMS-MaPseq) and computational predictions were used to characterize the static structural landscape and conformational dynamics of both isoforms. Additionally, in-cell assays were used to identify functionally relevant structures in the 5' and 3' UTRs of AR-FL. A notable example is a conserved stem loop structure in the 5'UTR of AR-FL that can bind to Poly(RC) Binding Protein 2 (PCBP2). Taken together, our results reveal novel features that regulate AR expression.

Heterobifunctional small molecules to modulate RNA function

RNA has diverse cellular functionality, including regulating gene expression, protein translation, and cellular response to stimuli, due to its intricate structures. Over the past decade, small molecules have been discovered that target functional structures within cellular RNAs and modulate their function. Simple binding, however, is often insufficient, resulting in low or even no biological activity. To overcome this challenge, heterobifunctional compounds have been developed that can covalently bind to the RNA target, alter RNA sequence, or induce its cleavage. Herein, we review the recent progress in the field of RNA-targeted heterobifunctional compounds using representative case studies. We identify critical gaps and limitations and propose a strategic pathway for future developments of RNA-targeted molecules with augmented functionalities.

Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis

SARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multiple sequence alignment with related coronaviruses as a starting point. We applied a computational pipeline developed for identifying non-coding RNA elements. Our pipeline employed three different RNA structural prediction approaches. We identified forty genomic regions likely to harbor structures, with ten of them showing three-way consensus substructure predictions among our predictive utilities. We conducted intracomparisons of the predictive utilities within the pipeline and intercomparisons with four previously published SARS-CoV-2 structural datasets. While there was limited agreement on the precise structure, different approaches seemed to converge on regions likely to contain structures in the viral genome. By comparing and combining various computational approaches, we can predict regions most likely to form structures, as well as a probable structure or ensemble of structures. These predictions can be used to guide surveillance, prophylactic measures, or therapeutic efforts. Data and scripts employed in this study may be found at https://doi.org/10.5281/zenodo.8298680.

Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis

Major advances in RNA secondary structural motif prediction have been achieved in the last few years; however, few methods harness the predictive power of multiple approaches to deliver in-depth characterizations of local RNA motifs and their potential functionality. Additionally, most available methods do not predict RNA pseudoknots. This work combines complementary bioinformatic systems into one robust discovery pipeline where: •RNA sequences are folded to search for thermodynamically favorable motifs utilizing ScanFold.•Motifs are expanded and refolded into alternate pseudoknot conformations by Knotty/Iterative HFold.•All conformations are evaluated for covariance via the cm-builder pipeline (Infernal and R-scape).

SARS-CoV-2 Orphan Gene ORF10 Contributes to More Severe COVID-19 Disease

The orphan gene of SARS-CoV-2, ORF10, is the least studied gene in the virus responsible for the COVID-19 pandemic. Recent experimentation indicated ORF10 expression moderates innate immunity in vitro. However, whether ORF10 affects COVID-19 in humans remained unknown. We determine that the ORF10 sequence is identical to the Wuhan-Hu-1 ancestral haplotype in 95% of genomes across five variants of concern (VOC). Four ORF10 variants are associated with less virulent clinical outcomes in the human host: three of these affect ORF10 protein structure, one affects ORF10 RNA structural dynamics. RNA-Seq data from 2070 samples from diverse human cells and tissues reveals ORF10 accumulation is conditionally discordant from that of other SARS-CoV-2 transcripts. Expression of ORF10 in A549 and HEK293 cells perturbs immune-related gene expression networks, alters expression of the majority of mitochondrially-encoded genes of oxidative respiration, and leads to large shifts in levels of 14 newly-identified transcripts. We conclude ORF10 contributes to more severe COVID-19 clinical outcomes in the human host.

Broad-spectrum metastasis suppressing compounds and therapeutic uses thereof in human tumors

Previously, we have identified a novel human metastasis-inducing lncRNA (named SKAI1BC), that suppresses the KAI1/CD82 metastasis-suppressing gene and is upregulated in triple negative breast cancer and melanoma derived cell lines. Modeling of the SKAI1BC lncRNA secondary structure and its potential interaction with Inforna compounds, led us to identify several compounds that might bind the SKAI1BC lncRNA. We found that these compounds inhibit metastasis invasion and cell migration in culture, in all eight types of solid human cancers tested: several of which are the most lethal and/or frequent human malignancies. Moreover, in most cases, the mechanism of action of several of our compounds involves enhancement of KAI1/CD82 RNA level depending on the specific compound and the human tumor type. With the epigenetic inactivation of KAI1/CD82 in at least ten additional solid human cancers, this implies a very good chance to broaden the spectrum of human cancers affected by our compounds. This is the first time that modeling of a large lncRNA (> 700 bp) secondary structure followed by its potential interaction with Inforna like compounds database has led to the identification of potential biologically active small molecule drugs.

Programming inactive RNA-binding small molecules into bioactive degraders

Target occupancy is often insufficient to elicit biological activity, particularly for RNA, compounded by the longstanding challenges surrounding the molecular recognition of RNA structures by small molecules. Here we studied molecular recognition patterns between a natural-product-inspired small-molecule collection and three-dimensionally folded RNA structures. Mapping these interaction landscapes across the human transcriptome defined structure-activity relationships. Although RNA-binding compounds that bind to functional sites were expected to elicit a biological response, most identified interactions were predicted to be biologically inert as they bind elsewhere. We reasoned that, for such cases, an alternative strategy to modulate RNA biology is to cleave the target through a ribonuclease-targeting chimera, where an RNA-binding molecule is appended to a heterocycle that binds to and locally activates RNase L1. Overlay of the substrate specificity for RNase L with the binding landscape of small molecules revealed many favourable candidate binders that might be bioactive when converted into degraders. We provide a proof of concept, designing selective degraders for the precursor to the disease-associated microRNA-155 (pre-miR-155), JUN mRNA and MYC mRNA. Thus, small-molecule RNA-targeted degradation can be leveraged to convert strong, yet inactive, binding interactions into potent and specific modulators of RNA function.

Rock, scissors, paper: How RNA structure informs function

RNA can fold back on itself to adopt a wide range of structures. These range from relatively simple hairpins to intricate 3D folds and can be accompanied by regulatory interactions with both metabolites and macromolecules. The last 50 yr have witnessed elucidation of an astonishing array of RNA structures including transfer RNAs, ribozymes, riboswitches, the ribosome, the spliceosome, and most recently entire RNA structuromes. These advances in RNA structural biology have deepened insight into fundamental biological processes including gene editing, transcription, translation, and structure-based detection and response to temperature and other environmental signals. These discoveries reveal that RNA can be relatively static, like a rock; that it can have catalytic functions of cutting bonds, like scissors; and that it can adopt myriad functional shapes, like paper. We relate these extraordinary discoveries in the biology of RNA structure to the plant way of life. We trace plant-specific discovery of ribozymes and riboswitches, alternative splicing, organellar ribosomes, thermometers, whole-transcriptome structuromes and pan-structuromes, and conclude that plants have a special set of RNA structures that confer unique types of gene regulation. We finish with a consideration of future directions for the RNA structure-function field.

Fragment-Based Approaches to Identify RNA Binders

Although fragment-based drug discovery (FBDD) has been successfully implemented and well-explored for protein targets, its feasibility for RNA targets is emerging. Despite the challenges associated with the selective targeting of RNA, efforts to integrate known methods of RNA binder discovery with fragment-based approaches have been fruitful, as a few bioactive ligands have been identified. Here, we review various fragment-based approaches implemented for RNA targets and provide insights into experimental design and outcomes to guide future work in the area. Indeed, investigations surrounding the molecular recognition of RNA by fragments address rather important questions such as the limits of molecular weight that confer selective binding and the physicochemical properties favorable for RNA binding and bioactivity.

The Functional RNA Identification (FRID) Pipeline: Identification of Potential Pseudoknot-Containing RNA Elements as Therapeutic Targets for SARS-CoV-2

The COVID-19 pandemic persists despite the development of effective vaccines. As such, it remains crucial to identify new targets for antiviral therapies. The causative virus of COVID-19, SARS-CoV-2, is a positive-sense RNA virus with RNA structures that could serve as therapeutic targets. One such RNA with established function is the frameshift stimulatory element (FSE), which promotes programmed ribosomal frameshifting. To accelerate identification of additional functional RNA elements, we introduce a novel computational approach termed the Functional RNA Identification (FRID) pipeline. The guiding principle of our pipeline, which uses established component programs as well as customized component programs, is that functional RNA elements have conserved secondary and pseudoknot structures that facilitate function. To assess the presence and conservation of putative functional RNA elements in SARS-CoV-2, we compared over 6,000 SARS-CoV-2 genomic isolates. We identified 22 functional RNA elements from the SARS-CoV-2 genome, 14 of which have conserved pseudoknots and serve as potential targets for small molecule or antisense oligonucleotide therapeutics. The FRID pipeline is general and can be applied to identify pseudoknotted RNAs for targeted therapeutics in genomes or transcriptomes from any virus or organism.

An effective live-attenuated Zika vaccine candidate with a modified 5' untranslated region

Zika virus (ZIKV) is a mosquito-transmitted flavivirus that has caused devastating congenital Zika syndrome (CZS), including microcephaly, congenital malformation, and fetal demise in human newborns in recent epidemics. ZIKV infection can also cause Guillain-Barré syndrome (GBS) and meningoencephalitis in adults. Despite intensive research in recent years, there are no approved vaccines or antiviral therapeutics against CZS and adult Zika diseases. In this report, we developed a novel live-attenuated ZIKV strain (named Z7) by inserting 50 RNA nucleotides (nt) into the 5' untranslated region (UTR) of a pre-epidemic ZIKV Cambodian strain, FSS13025. We used this particular ZIKV strain as it is attenuated in neurovirulence, immune antagonism, and mosquito infectivity compared with the American epidemic isolates. Our data demonstrate that Z7 replicates efficiently and produces high titers without causing apparent cytopathic effects (CPE) in Vero cells or losing the insert sequence, even after ten passages. Significantly, Z7 induces robust humoral and cellular immune responses that completely prevent viremia after a challenge with a high dose of an American epidemic ZIKV strain PRVABC59 infection in type I interferon (IFN) receptor A deficient (Ifnar1-/-) mice. Moreover, adoptive transfer of plasma collected from Z7 immunized mice protects Ifnar1-/- mice from ZIKV (strain PRVABC59) infection. These results suggest that modifying the ZIKV 5' UTR is a novel strategy to develop live-attenuated vaccine candidates for ZIKV and potentially for other flaviviruses.

Genome-Wide RNA Secondary Structure Prediction

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

Discovery of RNA-targeted small molecules through the merging of experimental and computational technologies

INTRODUCTION

The field of RNA-targeted small molecules is rapidly evolving, owing to the advances in experimental and computational technologies. With the identification of several bioactive small molecules that target RNA, including the FDA-approved risdiplam, the biopharmaceutical industry is gaining confidence in the field. This review, based on the literature obtained from PubMed, aims to disseminate information about the various technologies developed for targeting RNA with small molecules and propose areas for improvement to develop drugs more efficiently, particularly those linked to diseases with unmet medical needs.

AREAS COVERED

The technologies for the identification of RNA targets, screening of chemical libraries against RNA, assessing the bioactivity and target engagement of the hit compounds, structure determination, and hit-to-lead optimization are reviewed. Along with the description of the technologies, their strengths, limitations, and examples of how they can impact drug discovery are provided.

EXPERT OPINION

Many existing technologies employed for protein targets have been repurposed for use in the discovery of RNA-targeted small molecules. In addition, technologies tailored for RNA targets have been developed. Nevertheless, more improvements are necessary, such as artificial intelligence to dissect important RNA structures and RNA-small-molecule interactions and more powerful chemical probing and structure prediction techniques.

Examples of Structural Motifs in Viral Genomes and Approaches for RNA Structure Characterization

The relationship between conserved structural motifs and their biological function in the virus replication cycle is the interest of many researchers around the world. RNA structure is closely related to RNA function. Therefore, technological progress in high-throughput approaches for RNA structure analysis and the development of new ones are very important. In this mini review, we discuss a few perspectives on the structural elements of viral genomes and some methods used for RNA structure prediction and characterization. Based on the recent literature, we describe several examples of studies concerning the viral genomes, especially severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (IAV). Herein, we emphasize that a better understanding of viral genome architecture allows for the discovery of the structure-function relationship, and as a result, the discovery of new potential antiviral therapeutics.

Thermodynamic and structural characterization of an EBV infected B-cell lymphoma transcriptome

Epstein-Barr virus (EBV) is a widely prevalent human herpes virus infecting over 95% of all adults and is associated with a variety of B-cell cancers and induction of multiple sclerosis. EBV accomplishes this in part by expression of coding and noncoding RNAs and alteration of the host cell transcriptome. To better understand the structures which are forming in the viral and host transcriptomes of infected cells, the RNA structure probing technique Structure-seq2 was applied to the BJAB-B1 cell line (an EBV infected B-cell lymphoma). This resulted in reactivity profiles and secondary structural analyses for over 10000 human mRNAs and lncRNAs, along with 19 lytic and latent EBV transcripts. We report in-depth structural analyses for the human MYC mRNA and the human lncRNA CYTOR. Additionally, we provide a new model for the EBV noncoding RNA EBER2 and provide the first reported model for the EBV tandem terminal repeat RNA. In-depth thermodynamic and structural analyses were carried out with the motif discovery tool ScanFold and RNAfold prediction tool; subsequent covariation analyses were performed on resulting models finding various levels of support. ScanFold results for all analyzed transcripts are made available for viewing and download on the user-friendly RNAStructuromeDB.

Metagenomic identification of novel viruses of maize and teosinte in North America

Background

Maize-infecting viruses are known to inflict significant agronomic yield loss throughout the world annually. Identification of known or novel causal agents of disease prior to outbreak is imperative to preserve food security via future crop protection efforts. Toward this goal, a large-scale metagenomic approach utilizing high throughput sequencing (HTS) was employed to identify novel viruses with the potential to contribute to yield loss of graminaceous species, particularly maize, in North America.

Results

Here we present four novel viruses discovered by HTS and individually validated by Sanger sequencing. Three of these viruses are RNA viruses belonging to either the Betaflexiviridae or Tombusviridae families. Additionally, a novel DNA virus belonging to the Geminiviridae family was discovered, the first Mastrevirus identified in North American maize.

Conclusions

Metagenomic studies of crop and crop-related species such as this may be useful for the identification and surveillance of known and novel viral pathogens of crops. Monitoring related species may prove useful in identifying viruses capable of infecting crops due to overlapping insect vectors and viral host-range to protect food security.

ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes

A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.

Analysis of key genes in Mycobacterium ulcerans reveals conserved RNA structural motifs and regions with apparent pressure to remain unstructured

Buruli Ulcer is a neglected tropical disease that results in disfiguring and dangerous lesions in affected persons across a wide geographic area, including much of West Africa. The causative agent of Buruli Ulcer is Mycobacterium ulcerans, a relative of the bacterium that causes tuberculosis and leprosy. Few therapeutic options exist for the treatment of this disease beyond antibiotics in the early stages, which are frequently ineffective, and surgical removal in the later stage. In this study we analyze six genes in Mycobacterium ulcerans that have high potential of therapeutic targeting. We focus our analysis on a combined in silico and comparative sequence study of potential RNA secondary structure across these genes. The result of this work was the comprehensive local RNA structural landscape across each of these significant genes. This revealed multiple sites of ordered and evolved RNA structure interspersed between sequences that either have no bias for structure or, indeed, appear to be ordered to be unstructured and (potentially) accessible. In addition to providing data that could be of interest to basic biology, our results provide guides for efforts aimed at targeting this pathogen at the RNA level. We explore this latter possibility through the in silico analysis of antisense oligonucleotides that could potentially be used to target pathogen RNA.

Targeting RNA structures with small molecules

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.

Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome

RNA plays vital functional roles in almost every component of biology, and these functional roles are often influenced by its folding into secondary and tertiary structures. An important role of RNA secondary structure is in maintaining proper gene regulation; therefore, making accurate predictions of the structures involved in these processes is important. In this study, we have expanded on our previous work that led to the creation of the RNAStructuromeDB. Unlike this previous study that analyzed the human genome at low resolution, we have now scanned the protein-coding human transcriptome at high (single nt) resolution. This provides more robust structure predictions for over 100,000 isoforms of known protein-coding genes. Notably, we also utilize the motif identification tool, ScanFold, to model structures with high propensity for ordered/evolved stability. All data have been uploaded to the RNAStructuromeDB, allowing for easy searching of transcripts, visualization of data tracks (via the Integrative Genomics Viewer or IGV), and download of ScanFold data—including unique highly-ordered motifs. Herein, we provide an example analysis of MAT2A to demonstrate the utility of ScanFold at finding known and novel secondary structures, highlighting regions of potential functionality, and guiding generation of functional hypotheses through use of the data.

Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses

Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world.

Discovering functional motifs in long noncoding RNAs

Long noncoding RNAs (lncRNAs) are products of pervasive transcription that closely resemble messenger RNAs on the molecular level, yet function through largely unknown modes of action. The current model is that the function of lncRNAs often relies on specific, typically short, conserved elements, connected by linkers in which specific sequences and/or structures are less important. This notion has fueled the development of both computational and experimental methods focused on the discovery of functional elements within lncRNA genes, based on diverse signals such as evolutionary conservation, predicted structural elements, or the ability to rescue loss-of-function phenotypes. In this review, we outline the main challenges that the different methods need to overcome, describe the recently developed approaches, and discuss their respective limitations. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Structural Context of a Critical Exon of Spinal Muscular Atrophy Gene

Humans contain two nearly identical copies of Survival Motor Neuron genes, SMN1 and SMN2. Deletion or mutation of SMN1 causes spinal muscular atrophy (SMA), one of the leading genetic diseases associated with infant mortality. SMN2 is unable to compensate for the loss of SMN1 due to predominant exon 7 skipping, leading to the production of a truncated protein. Antisense oligonucleotide and small molecule-based strategies aimed at the restoration of SMN2 exon 7 inclusion are approved therapies of SMA. Many cis-elements and transacting factors have been implicated in regulation of SMN exon 7 splicing. Also, several structural elements, including those formed by a long-distance interaction, have been implicated in the modulation of SMN exon 7 splicing. Several of these structures have been confirmed by enzymatic and chemical structure-probing methods. Additional structures formed by inter-intronic interactions have been predicted by computational algorithms. SMN genes generate a vast repertoire of circular RNAs through inter-intronic secondary structures formed by inverted Alu repeats present in large number in SMN genes. Here, we review the structural context of the exonic and intronic cis-elements that promote or prevent exon 7 recognition. We discuss how structural rearrangements triggered by single nucleotide substitutions could bring drastic changes in SMN2 exon 7 splicing. We also propose potential mechanisms by which inter-intronic structures might impact the splicing outcomes.

RNAvigator: A Pipeline to Identify Candidates for Functional RNA Structure Elements

Identifying structural elements in long and complex RNAs, such as long non-coding and RNA viruses, can shed light on the functionality and mechanisms of such RNAs. Here we present RNAvigator, a tool able to identify elements of structural importance by using experimental SHAPE data or SHAPE-like predictions in conjunction with stability and entropy assessments. RNAvigator recognizes regions that are the most stable, unambiguous, and structured on RNA molecules, and thus potentially functional. When relying on predictions, RNAvigator uses the CROSS algorithm, a neural network trained on experimental data that achieved an AUC of 0.74 on hepatitis C virus SHAPE-MaP data and which was able to improve the predictive power of Superfold. By using RNAvigator, we can identify known functional regions on the complete hepatitis C virus genome, including the regulatory regions CRE and IRES, and the 3’ UTR of dengue virus, a region known for the presence of structural elements essential for its replication, and functional regions of long non-coding RNAs such as XIST and HOTAIR. We envision that RNAvigator will be a useful tool for studying long and complex RNA molecules using known chemical probing data or, if they are not available, by employing predicted profiles.

Analysis of estrogen-regulated enhancer RNAs identifies a functional motif required for enhancer assembly and gene expression

To better understand the functions of non-coding enhancer RNAs (eRNAs), we annotated the estrogen-regulated eRNA transcriptome in estrogen receptor α (ERα)-positive breast cancer cells using PRO-cap and RNA sequencing. We then cloned a subset of the eRNAs identified, fused them to single guide RNAs, and targeted them to their ERα enhancers of origin using CRISPR/dCas9. Some of the eRNAs tested modulated the expression of cognate, but not heterologous, target genes after estrogen treatment by increasing ERα recruitment and stimulating p300-catalyzed H3K27 acetylation at the enhancer. We identified a ∼40 nucleotide functional eRNA regulatory motif (FERM) present in many eRNAs that was necessary and sufficient to modulate gene expression, but not the specificity of activation, after estrogen treatment. The FERM interacted with BCAS2, an RNA-binding protein amplified in breast cancers. The ectopic expression of a targeted eRNA controlling the expression of an oncogene resulted in increased cell proliferation, demonstrating the regulatory potential of eRNAs in breast cancer.

Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control

Experimental breakthroughs have provided unprecedented insights into the genes involved in cancer. The identification of such cancer driver genes is a major step in gaining a fuller understanding of oncogenesis and provides novel lists of potential therapeutic targets. A key area that requires additional study is the posttranscriptional control mechanisms at work in cancer driver genes. This is important not only for basic insights into the biology of cancer, but also to advance new therapeutic modalities that target RNA—an emerging field with great promise toward the treatment of various cancers. In the current study we performed an in silico analysis on the transcripts associated with 800 cancer driver genes (10,390 unique transcripts) that identified 179,190 secondary structural motifs with evidence of evolutionarily ordered structures with unusual thermodynamic stability. Narrowing to one transcript per gene, 35,426 predicted structures were subjected to phylogenetic comparisons of sequence and structural conservation. This identified 7,001 RNA secondary structures embedded in transcripts with evidence of covariation between paired sites, supporting structure models and suggesting functional significance. A select set of seven structures were tested in vitro for their ability to regulate gene expression; all were found to have significant effects. These results indicate potentially widespread roles for RNA structure in posttranscriptional control of human cancer driver genes.

Prediction and analysis of functional RNA structures within the integrative genomics viewer

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results—in conjunction with probing data and other annotations—in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast ‘omics’ resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

In silico analysis of local RNA secondary structure in influenza virus A, B and C finds evidence of widespread ordered stability but little evidence of significant covariation

Influenza virus is a persistent threat to human health; indeed, the deadliest modern pandemic was in 1918 when an H1N1 virus killed an estimated 50 million people globally. The intent of this work is to better understand influenza from an RNA-centric perspective to provide local, structural motifs with likely significance to the influenza infectious cycle for therapeutic targeting. To accomplish this, we analyzed over four hundred thousand RNA sequences spanning three major clades: influenza A, B and C. We scanned influenza segments for local secondary structure, identified/modeled motifs of likely functionality, and coupled the results to an analysis of evolutionary conservation. We discovered 185 significant regions of predicted ordered stability, yet evidence of sequence covariation was limited to 7 motifs, where 3-found in influenza C-had higher than expected amounts of sequence covariation.

Targeting genomic SARS-CoV-2 RNA with siRNAs allows efficient inhibition of viral replication and spread

A promising approach to tackle the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) could be small interfering (si)RNAs. So far it is unclear, which viral replication steps can be efficiently inhibited with siRNAs. Here, we report that siRNAs can target genomic RNA (gRNA) of SARS-CoV-2 after cell entry, and thereby terminate replication before start of transcription and prevent virus-induced cell death. Coronaviruses replicate via negative sense RNA intermediates using a unique discontinuous transcription process. As a result, each viral RNA contains identical sequences at the 5′ and 3′ end. Surprisingly, siRNAs were not active against intermediate negative sense transcripts. Targeting common sequences shared by all viral transcripts allowed simultaneous suppression of gRNA and subgenomic (sg)RNAs by a single siRNA. The most effective suppression of viral replication and spread, however, was achieved by siRNAs that targeted open reading frame 1 (ORF1) which only exists in gRNA. In contrast, siRNAs that targeted the common regions of transcripts were outcompeted by the highly abundant sgRNAs leading to an impaired antiviral efficacy. Verifying the translational relevance of these findings, we show that a chemically modified siRNA that targets a highly conserved region of ORF1, inhibited SARS-CoV-2 replication ex vivo in explants of the human lung. Our work encourages the development of siRNA-based therapies for COVID-19 and suggests that early therapy start, or prophylactic application, together with specifically targeting gRNA, might be key for high antiviral efficacy.

Potential Achilles heels of SARS-CoV-2 are best displayed by the base order-dependent component of RNA folding energy

The base order-dependent component of folding energy has revealed a highly conserved region in HIV-1 genomes that associates with RNA structure. This corresponds to a packaging signal that is recognized by the nucleocapsid domain of the Gag polyprotein. Long viewed as a potential HIV-1 "Achilles heel," the signal can be targeted by a new antiviral compound. Although SARS-CoV-2 differs in many respects from HIV-1, the same technology displays regions with a high base order-dependent folding energy component, which are also highly conserved. This indicates structural invariance (SI) sustained by natural selection. While the regions are often also protein-encoding (e. g. NSP3, ORF3a), we suggest that their nucleic acid level functions can be considered potential "Achilles heels" for SARS-CoV-2, perhaps susceptible to therapies like those envisaged for AIDS. The ribosomal frameshifting element scored well, but higher SI scores were obtained in other regions, including those encoding NSP13 and the nucleocapsid (N) protein.

Comprehensive mapping of SARS-CoV-2 interactions in vivo reveals functional virus-host interactions

SARS-CoV-2 is a major threat to global health. Here, we investigate the RNA structure and RNA-RNA interactions of wildtype (WT) and a mutant (Δ382) SARS-CoV-2 in cells using Illumina and Nanopore platforms. We identify twelve potentially functional structural elements within the SARS-CoV-2 genome, observe that subgenomic RNAs can form different structures, and that WT and Δ382 virus genomes fold differently. Proximity ligation sequencing identify hundreds of RNA-RNA interactions within the virus genome and between the virus and host RNAs. SARS-CoV-2 genome binds strongly to mitochondrial and small nucleolar RNAs and is extensively 2'-O-methylated. 2'-O-methylation sites are enriched in viral untranslated regions, associated with increased virus pair-wise interactions, and are decreased in host mRNAs upon virus infection, suggesting that the virus sequesters methylation machinery from host RNAs towards its genome. These studies deepen our understanding of the molecular and cellular basis of SARS-CoV-2 pathogenicity and provide a platform for targeted therapy.

Novel Nested-Seq Approach for SARS-CoV-2 Real-Time Epidemiology and In-Depth Mutational Profiling in Wastewater

Considering the lack of effective treatments against COVID-19, wastewater-based epidemiology (WBE) is emerging as a cost-effective approach for real-time population-wide SARS-CoV-2 monitoring. Here, we report novel molecular assays for sensitive detection and mutational/variant analysis of SARS-CoV-2 in wastewater. Highly stable regions of SARS-CoV-2 RNA were identified by RNA stability analysis and targeted for the development of novel nested PCR assays. Targeted DNA sequencing (DNA-seq) was applied for the analysis and quantification of SARS-CoV-2 mutations/variants, following hexamers-based reverse transcription and nested PCR-based amplification of targeted regions. Three-dimensional (3D) structure models were generated to examine the predicted structural modification caused by genomic variants. WBE of SARS-CoV-2 revealed to be assay dependent, and significantly improved sensitivity achieved by assay combination (94%) vs. single-assay screening (30%-60%). Targeted DNA-seq allowed the quantification of SARS-CoV-2 mutations/variants in wastewater, which agreed with COVID-19 patients' sequencing data. A mutational analysis indicated the prevalence of D614G (S) and P323L (RdRP) variants, as well as of the Β.1.1.7/alpha variant of concern, in agreement with the frequency of Β.1.1.7/alpha variant in clinical samples of the same period of the third pandemic wave at the national level. Our assays provide an innovative cost-effective platform for real-time monitoring and early-identification of SARS-CoV-2 variants at community/population levels.

Novel Nested-Seq Approach for SARS-CoV-2 Real-Time Epidemiology and In-Depth Mutational Profiling in Wastewater

Considering the lack of effective treatments against COVID-19, wastewater-based epidemiology (WBE) is emerging as a cost-effective approach for real-time population-wide SARS-CoV-2 monitoring. Here, we report novel molecular assays for sensitive detection and mutational/variant analysis of SARS-CoV-2 in wastewater. Highly stable regions of SARS-CoV-2 RNA were identified by RNA stability analysis and targeted for the development of novel nested PCR assays. Targeted DNA sequencing (DNA-seq) was applied for the analysis and quantification of SARS-CoV-2 mutations/variants, following hexamers-based reverse transcription and nested PCR-based amplification of targeted regions. Three-dimensional (3D) structure models were generated to examine the predicted structural modification caused by genomic variants. WBE of SARS-CoV-2 revealed to be assay dependent, and significantly improved sensitivity achieved by assay combination (94%) vs. single-assay screening (30%-60%). Targeted DNA-seq allowed the quantification of SARS-CoV-2 mutations/variants in wastewater, which agreed with COVID-19 patients' sequencing data. A mutational analysis indicated the prevalence of D614G (S) and P323L (RdRP) variants, as well as of the Β.1.1.7/alpha variant of concern, in agreement with the frequency of Β.1.1.7/alpha variant in clinical samples of the same period of the third pandemic wave at the national level. Our assays provide an innovative cost-effective platform for real-time monitoring and early-identification of SARS-CoV-2 variants at community/population levels.

To Knot or Not to Knot: Multiple Conformations of the SARS-CoV-2 Frameshifting RNA Element

The SARS-CoV-2 frameshifting RNA element (FSE) is an excellent target for therapeutic intervention against Covid-19. This small gene element employs a shifting mechanism to pause and backtrack the ribosome during translation between Open Reading Frames 1a and 1b, which code for viral polyproteins. Any interference with this process has a profound effect on viral replication and propagation. Pinpointing the structures adapted by the FSE and associated structural transformations involved in frameshifting has been a challenge. Using our graph-theory-based modeling tools for representing RNA secondary structures, "RAG" (RNA-As-Graphs), and chemical structure probing experiments, we show that the 3-stem H-type pseudoknot (3_6 dual graph), long assumed to be the dominant structure, has a viable alternative, an HL-type 3-stem pseudoknot (3_3) for longer constructs. In addition, an unknotted 3-way junction RNA (3_5) emerges as a minor conformation. These three conformations share Stems 1 and 3, while the different Stem 2 may be involved in a conformational switch and possibly associations with the ribosome during translation. For full-length genomes, a stem-loop motif (2_2) may compete with these forms. These structural and mechanistic insights advance our understanding of the SARS-CoV-2 frameshifting process and concomitant virus life cycle, and point to three avenues of therapeutic intervention.

To knot or not to knot: Multiple conformations of the SARS-CoV-2 frameshifting RNA element

The SARS-CoV-2 frameshifting RNA element (FSE) is an excellent target for therapeutic intervention against Covid-19. This small gene element employs a shifting mechanism to pause and backtrack the ribosome during translation between Open Reading Frames 1a and 1b, which code for viral polyproteins. Any interference with this process has profound effect on viral replication and propagation. Pinpointing the structures adapted by the FSE and associated structural transformations involved in frameshifting has been a challenge. Using our graph-theory-based modeling tools for representing RNA secondary structures, "RAG" (RNA-As-Graphs), and chemical structure probing experiments, we show that the 3-stem H-type pseudoknot (3_6 dual graph), long assumed to be the dominant structure has a viable alternative, an HL-type 3-stem pseudoknot (3_3) for longer constructs. In addition, an unknotted 3-way junction RNA (3_5) emerges as a minor conformation. These three conformations share Stems 1 and 3, while the different Stem 2 may be involved in a conformational switch and possibly associations with the ribosome during translation. For full-length genomes, a stem-loop motif (2_2) may compete with these forms. These structural and mechanistic insights advance our understanding of the SARS-CoV-2 frameshifting process and concomitant virus life cycle, and point to three avenues of therapeutic intervention.

A database of flavivirus RNA structures with a search algorithm for pseudoknots and triple base interactions

Motivation

The Flavivirus genus includes several important pathogens, such as Zika, dengue and yellow fever virus. Flavivirus RNA genomes contain a number of functionally important structures in their 3′ untranslated regions (3′UTRs). Due to the diversity of sequences and topologies of these structures, their identification is often difficult. In contrast, predictions of such structures are important for understanding of flavivirus replication cycles and development of antiviral strategies.

Results

We have developed an algorithm for structured pattern search in RNA sequences, including secondary structures, pseudoknots and triple base interactions. Using the data on known conserved flavivirus 3′UTR structures, we constructed structural descriptors which covered the diversity of patterns in these motifs. The descriptors and the search algorithm were used for the construction of a database of flavivirus 3′UTR structures. Validating this approach, we identified a number of domains matching a general pattern of exoribonuclease Xrn1-resistant RNAs in the growing group of insect-specific flaviviruses.

Availability and implementation

The Leiden Flavivirus RNA Structure Database is available at https://rna.liacs.nl. The search algorithm is available at https://github.com/LeidenRNA/SRHS.

Supplementary information

Supplementary data are available at Bioinformatics online.

A map of the SARS-CoV-2 RNA structurome

SARS-CoV-2 has exploded throughout the human population. To facilitate efforts to gain insights into SARS-CoV-2 biology and to target the virus therapeutically, it is essential to have a roadmap of likely functional regions embedded in its RNA genome. In this report, we used a bioinformatics approach, ScanFold, to deduce the local RNA structural landscape of the SARS-CoV-2 genome with the highest likelihood of being functional. We recapitulate previously-known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a large reservoir of potential drug targets for RNA-binding small molecules. Results are enhanced via the re-analyses of publicly-available genome-wide biochemical structure probing datasets that are broadly in agreement with our models. Additionally, ScanFold was updated to incorporate experimental data as constraints in the analysis to facilitate comparisons between ScanFold and other RNA modelling approaches. Ultimately, ScanFold was able to identify eight highly structured/conserved motifs in SARS-CoV-2 that agree with experimental data, without explicitly using these data. All results are made available via a public database (the RNAStructuromeDB: https://structurome.bb.iastate.edu/sars-cov-2) and model comparisons are readily viewable at https://structurome.bb.iastate.edu/sars-cov-2-global-model-comparisons.

Affecting RNA biology genome-wide by binding small molecules and chemically induced proximity

The ENCODE and genome-wide association projects have shown that much of the genome is transcribed into RNA and much less is translated into protein. These and other functional studies suggest that the druggable transcriptome is much larger than the druggable proteome. This review highlights approaches to define druggable RNA targets and structure-activity relationships across genomic RNA. Binding compounds can be identified and optimized into structure-specific ligands by using sequence-based design with various modes of action, for example, inhibiting translation or directing pre-mRNA splicing outcomes. In addition, strategies to direct protein activity against an RNA of interest via chemically induced proximity is a burgeoning area that has been validated both in cells and in preclinical animal models, and we describe that it may allow rapid access to new avenues to affect RNA biology. These approaches and the unique modes of action suggest that more RNAs are potentially amenable to targeting than proteins.

Neutralism versus selectionism: Chargaff's second parity rule, revisited

Of Chargaff's four "rules" on DNA base frequencies, the functional interpretation of his second parity rule (PR2) is the most contentious. Thermophile base compositions (GC%) were taken by Galtier and Lobry (1997) as favoring Sueoka's neutral PR2 hypothesis over Forsdyke's selective PR2 hypothesis, namely that mutations improving local within-species recombination efficiency had generated a genome-wide potential for the strands of duplex DNA to separate and initiate recombination through the "kissing" of the tips of stem-loops. However, following Chargaff's GC rule, base composition mainly reflects a species-specific, genome-wide, evolutionary pressure. GC% could not have consistently followed the dictates of temperature, since it plays fundamental roles in both sustaining species integrity and, through primarily neutral genome-wide mutation, fostering speciation. Evidence for a local within-species recombination-initiating role of base order was obtained with a novel technology that masked the contribution of base composition to nucleic acid folding energy. Forsdyke's results were consistent with his PR2 hypothesis, appeared to resolve some root problems in biology and provided a theoretical underpinning for alignment-free taxonomic analyses using relative oligonucleotide frequencies (k-mer analysis). Moreover, consistent with Chargaff's cluster rule, discovery of the thermoadaptive role of the "purine-loading" of open reading frames made less tenable the Galtier-Lobry anti-selectionist arguments.

The global and local distribution of RNA structure throughout the SARS-CoV-2 genome

SARS-CoV-2 is the causative viral agent of COVID-19, the disease at the center of the current global pandemic. While knowledge of highly structured regions is integral for mechanistic insights into the viral infection cycle, very little is known about the location and folding stability of functional elements within the massive, ∼30kb SARS-CoV-2 RNA genome. In this study, we analyze the folding stability of this RNA genome relative to the structural landscape of other well-known viral RNAs. We present an in-silico pipeline to predict regions of high base pair content across long genomes and to pinpoint hotspots of well-defined RNA structures, a method that allows for direct comparisons of RNA structural complexity within the several domains in SARS-CoV-2 genome. We report that the SARS-CoV-2 genomic propensity for stable RNA folding is exceptional among RNA viruses, superseding even that of HCV, one of the most structured viral RNAs in nature. Furthermore, our analysis suggests varying levels of RNA structure across genomic functional regions, with accessory and structural ORFs containing the highest structural density in the viral genome. Finally, we take a step further to examine how individual RNA structures formed by these ORFs are affected by the differences in genomic and subgenomic contexts, which given the technical difficulty of experimentally separating cellular mixtures of sgRNA from gRNA, is a unique advantage of our in-silico pipeline. The resulting findings provide a useful roadmap for planning focused empirical studies of SARS-CoV-2 RNA biology, and a preliminary guide for exploring potential SARS-CoV-2 RNA drug targets.Importance The RNA genome of SARS-CoV-2 is among the largest and most complex viral genomes, and yet its RNA structural features remain relatively unexplored. Since RNA elements guide function in most RNA viruses, and they represent potential drug targets, it is essential to chart the architectural features of SARS-CoV-2 and pinpoint regions that merit focused study. Here we show that RNA folding stability of SARS-CoV-2 genome is exceptional among viral genomes and we develop a method to directly compare levels of predicted secondary structure across SARS-CoV-2 domains. Remarkably, we find that coding regions display the highest structural propensity in the genome, forming motifs that differ between the genomic and subgenomic contexts. Our approach provides an attractive strategy to rapidly screen for candidate structured regions based on base pairing potential and provides a readily interpretable roadmap to guide functional studies of RNA viruses and other pharmacologically relevant RNA transcripts.

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.

Design of small molecules targeting RNA structure from sequence

The design and discovery of small molecule medicines has largely been focused on a small number of druggable protein families. A new paradigm is emerging, however, in which small molecules exert a biological effect by interacting with RNA, both to study human disease biology and provide lead therapeutic modalities. Due to this potential for expanding target pipelines and treating a larger number of human diseases, robust platforms for the rational design and optimization of small molecules interacting with RNAs (SMIRNAs) are in high demand. This review highlights three major pillars in this area. First, the transcriptome-wide identification and validation of structured RNA elements, or motifs, within disease-causing RNAs directly from sequence is presented. Second, we provide an overview of high-throughput screening approaches to identify SMIRNAs as well as discuss the lead identification strategy, Inforna, which decodes the three-dimensional (3D) conformation of RNA motifs with small molecule binding partners, directly from sequence. An emphasis is placed on target validation methods to study the causality between modulating the RNA motif in vitro and the phenotypic outcome in cells. Third, emergent modalities that convert occupancy-driven mode of action SMIRNAs into event-driven small molecule chemical probes, such as RNA cleavers and degraders, are presented. Finally, the future of the small molecule RNA therapeutics field is discussed, as well as hurdles to overcome to develop potent and selective RNA-centric chemical probes.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.