Mapping RNA-RNA interactome and RNA structure in vivo by MARIO

Global in situ profiling of RNA-RNA spatial interactions with RIC-seq

Emerging evidence has demonstrated that RNA-RNA interactions are vital in controlling diverse biological processes, including transcription, RNA splicing and protein translation. RNA in situ conformation sequencing (RIC-seq) is a technique for capturing protein-mediated RNA-RNA proximal interactions globally in living cells at single-base resolution. Cells are first treated with formaldehyde to fix all the protein-mediated RNA-RNA interactions in situ. After cell permeabilization and micrococcal nuclease digestion, the proximally interacting RNAs are 3' end-labeled with pCp-biotin and subsequently ligated using T4 RNA ligase. The chimeric RNAs are then enriched and converted into libraries for paired-end sequencing. After deep sequencing, computational analysis yields interaction strength scores for every base on proximally interacting RNAs in the starting populations. The whole experimental procedure is designed to be completed within 6 d, followed by an additional 8 d for computational analysis. RIC-seq technology can unbiasedly detect intra- and intermolecular RNA-RNA interactions, thereby rendering it useful for reconstructing RNA higher-order structures and identifying direct noncoding RNA targets.

Decoding LncRNAs

Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.

Multiplexed mRNA assembly into ribonucleoprotein particles plays an operon-like role in the control of yeast cell physiology

Prokaryotes utilize polycistronic messages (operons) to co-translate proteins involved in the same biological processes. Whether eukaryotes achieve similar regulation by selectively assembling and translating monocistronic messages derived from different chromosomes is unknown. We employed transcript-specific RNA pulldowns and RNA-seq/RT-PCR to identify yeast mRNAs that co-precipitate as ribonucleoprotein (RNP) complexes. Consistent with the hypothesis of eukaryotic RNA operons, mRNAs encoding components of the mating pathway, heat shock proteins, and mitochondrial outer membrane proteins multiplex in trans, forming discrete messenger ribonucleoprotein (mRNP) complexes (called transperons). Chromatin capture and allele tagging experiments reveal that genes encoding multiplexed mRNAs physically interact; thus, RNA assembly may result from co-regulated gene expression. Transperon assembly and function depends upon histone H4, and its depletion leads to defects in RNA multiplexing, decreased pheromone responsiveness and mating, and increased heat shock sensitivity. We propose that intergenic associations and non-canonical histone H4 functions contribute to transperon formation in eukaryotic cells and regulate cell physiology.

Inter-dependent Centrosomal Co-localization of the cen and ik2 cis-Natural Antisense mRNAs in Drosophila

Overlapping genes are prevalent in most genomes, but the extent to which this organization influences regulatory events operating at the post-transcriptional level remains unclear. Studying the cen and ik2 genes of Drosophila melanogaster, which are convergently transcribed as cis-natural antisense transcripts (cis-NATs) with overlapping 3' UTRs, we found that their encoded mRNAs strikingly co-localize to centrosomes. These transcripts physically interact in a 3' UTR-dependent manner, and the targeting of ik2 requires its 3' UTR sequence and the presence of cen mRNA, which serves as the main driver of centrosomal co-localization. The cen transcript undergoes localized translation in proximity to centrosomes, and its localization is perturbed by polysome-disrupting drugs. By interrogating global fractionation-sequencing datasets generated from Drosophila and human cellular models, we find that RNAs expressed as cis-NATs tend to co-localize to specific subcellular fractions. This work suggests that post-transcriptional interactions between RNAs with complementary sequences can dictate their localization fate in the cytoplasm.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Battle for supremacy: nucleic acid interactions between viruses and cells

Since the COVID-19 pandemic swept across the globe, researchers have been trying to understand its origin, life cycle, and pathogenesis. There is a striking variability in the phenotypic response to infection with SARS-CoV-2 that may reflect differences in host genetics and/or immune response. It is known that the human epigenome is influenced by ethnicity, age, lifestyle, and environmental factors, including previous viral infections. This Review examines the influence of viruses on the host epigenome. We describe general lessons and methodologies that can be used to understand how the virus evades the host immune response. We consider how variation in the epigenome may contribute to heterogeneity in the response to SARS-CoV-2 and may identify a precision medicine approach to treatment.

Functional classification of plant long noncoding RNAs: a transcript is known by the company it keeps

The extraordinary maturation in high-throughput sequencing technologies has revealed the existence of a complex network of transcripts in eukaryotic organisms, including thousands of long noncoding (lnc) RNAs with little or no protein-coding capacity. Subsequent discoveries have shown that lncRNAs participate in a wide range of molecular processes, controlling gene expression and protein activity though direct interactions with proteins, DNA or other RNA molecules. Although significant advances have been achieved in the understanding of lncRNA biology in the animal kingdom, the functional characterization of plant lncRNAs is still in its infancy and remains a major challenge. In this review, we report emerging functional and mechanistic paradigms of plant lncRNAs and partner molecules, and discuss how cutting-edge technologies may help to identify and classify yet uncharacterized transcripts into functional groups.

RNA matchmaking in chromatin regulation

Beyond being the product of gene expression, RNA can also influence the regulation of chromatin. The majority of the human genome has the capacity to be transcribed and the majority of the non-protein-coding transcripts made by RNA Polymerase II are enriched in the nucleus. Many chromatin regulators can bind to these ncRNAs in the nucleus; in some cases, there are clear examples of direct RNA-mediated chromatin regulation mechanisms stemming from these interactions, while others have yet to be determined. Recent studies have highlighted examples of chromatin regulation via RNA matchmaking, a term we use broadly here to describe intermolecular base-pairing interactions between one RNA molecule and an RNA or DNA match. This review provides examples of RNA matchmaking that regulates chromatin processes and summarizes the technical approaches used to capture these events.

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

SARS-CoV-2 is a betacoronavirus with a linear single-stranded, positive-sense RNA genome, whose outbreak caused the ongoing COVID-19 pandemic. The ability of coronaviruses to rapidly evolve, adapt, and cross species barriers makes the development of effective and durable therapeutic strategies a challenging and urgent need. As for other RNA viruses, genomic RNA structures are expected to play crucial roles in several steps of the coronavirus replication cycle. Despite this, only a handful of functionally-conserved coronavirus structural RNA elements have been identified to date. Here, we performed RNA structure probing to obtain single-base resolution secondary structure maps of the full SARS-CoV-2 coronavirus genome both in vitro and in living infected cells. Probing data recapitulate the previously described coronavirus RNA elements (5' UTR and s2m), and reveal new structures. Of these, ∼10.2% show significant covariation among SARS-CoV-2 and other coronaviruses, hinting at their functionally-conserved role. Secondary structure-restrained 3D modeling of these segments further allowed for the identification of putative druggable pockets. In addition, we identify a set of single-stranded segments in vivo, showing high sequence conservation, suitable for the development of antisense oligonucleotide therapeutics. Collectively, our work lays the foundation for the development of innovative RNA-targeted therapeutic strategies to fight SARS-related infections.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Direct Interactions with Nascent Transcripts Is Potentially a Common Targeting Mechanism of Long Non-Coding RNAs

Although thousands of mammalian long non-coding RNAs (lncRNAs) have been reported in the last decade, their functional annotation remains limited. A wet-lab approach to detect functions of a novel lncRNA usually includes its knockdown followed by RNA sequencing and identification of the deferentially expressed genes. However, identification of the molecular mechanism(s) used by the lncRNA to regulate its targets frequently becomes a challenge. Previously, we developed the ASSA algorithm that detects statistically significant inter-molecular RNA-RNA interactions. Here we designed a workflow that uses ASSA predictions to estimate the ability of an lncRNA to function via direct base pairing with the target transcripts (co- or post-transcriptionally). The workflow was applied to 300+ lncRNA knockdown experiments from the FANTOM6 pilot project producing statistically significant predictions for 71 unique lncRNAs (104 knockdowns). Surprisingly, the majority of these lncRNAs were likely to function co-transcriptionally, i.e., hybridize with the nascent transcripts of the target genes. Moreover, a number of the obtained predictions were supported by independent iMARGI experimental data on co-localization of lncRNA and chromatin. We detected an evolutionarily conserved lncRNA CHASERR (AC013394.2 or LINC01578) that could regulate target genes co-transcriptionally via interaction with a nascent transcript by directing CHD2 helicase. The obtained results suggested that this nuclear lncRNA may be able to activate expression of the target genes in trans by base-pairing with the nascent transcripts and directing the CHD2 helicase to the regulated promoters leading to open the chromatin and active transcription. Our study highlights the possible importance of base-pairing between nuclear lncRNAs and nascent transcripts for the regulation of gene expression.

R-chie: a web server and R package for visualizing cis and trans RNA-RNA, RNA-DNA and DNA-DNA interactions

Interactions between biological entities are key to understanding their potential functional roles. Three fields of research have recently made particular progress: the investigation of transRNA-RNA and RNA-DNA transcriptome interactions and of trans DNA-DNA genome interactions. We now have both experimental and computational methods for examining these interactions in vivo and on a transcriptome- and genome-wide scale, respectively. Often, key insights can be gained by visually inspecting figures that manage to combine different sources of evidence and quantitative information. We here present R-chie, a web server and R package for visualizing cis and transRNA-RNA, RNA-DNA and DNA-DNA interactions. For this, we have completely revised and significantly extended an earlier version of R-chie (1) which was initially introduced for visualizing RNA secondary structure features. The new R-chie offers a range of unique features for visualizing cis and transRNA-RNA, RNA-DNA and DNA-DNA interactions. Particularly note-worthy features include the ability to incorporate evolutionary information, e.g. multiple-sequence alignments, to compare two alternative sets of information and to incorporate detailed, quantitative information. R-chie is readily available via a web server as well as a corresponding R package called R4RNA which can be used to run the software locally.

Advances in RNA 3D Structure Modeling Using Experimental Data

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

Terminus-Associated Non-coding RNAs: Trash or Treasure?

3′ untranslated regions (3′ UTRs) of protein-coding genes are well known for their important roles in determining the fate of mRNAs in diverse processes, including trafficking, stabilization, translation, and RNA–protein interactions. However, non-coding RNAs (ncRNAs) scattered around 3′ termini of the protein-coding genes, here referred to as terminus-associated non-coding RNAs (TANRs), have not attracted wide attention in RNA research. Indeed, whether TANRs are transcriptional noise, degraded mRNA products, alternative 3′ UTRs, or functional molecules has remained unclear for a long time. As a new category of ncRNAs, TANRs are widespread, abundant, and conserved in diverse eukaryotes. The biogenesis of TANRs mainly follows the same promoter model, the RNA-dependent RNA polymerase activity-dependent model, or the independent promoter model. Functional studies of TANRs suggested that they are significantly involved in the versatile regulation of gene expression. For instance, at the transcriptional level, they can lead to transcriptional interference, induce the formation of gene loops, and participate in transcriptional termination. Furthermore, at the posttranscriptional level, they can act as microRNA sponges, and guide cleavage or modification of target RNAs. Here, we review current knowledge of the potential role of TANRs in the modulation of gene expression. In this review, we comprehensively summarize the current state of knowledge about TANRs, and discuss TANR nomenclature, relation to ncRNAs, cross-talk biogenesis pathways and potential functions. We further outline directions of future studies of TANRs, to promote investigations of this emerging and enigmatic category of RNA.

RNA structures in alternative splicing and back-splicing

Alternative splicing greatly expands the transcriptomic and proteomic diversities related to physiological and developmental processes in higher eukaryotes. Splicing of long noncoding RNAs, and back- and trans- splicing further expanded the regulatory repertoire of alternative splicing. RNA structures were shown to play an important role in regulating alternative splicing and back-splicing. Application of novel sequencing technologies made it possible to identify genome-wide RNA structures and interaction networks, which might provide new insights into RNA splicing regulation in vitro to in vivo. The emerging transcription-folding-splicing paradigm is changing our understanding of RNA alternative splicing regulation. Here, we review the insights into the roles and mechanisms of RNA structures in alternative splicing and back-splicing, as well as how disruption of these structures affects alternative splicing and then leads to human diseases. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Natural display of nuclear-encoded RNA on the cell surface and its impact on cell interaction

BACKGROUND

Compared to proteins, glycans, and lipids, much less is known about RNAs on the cell surface. We develop a series of technologies to test for any nuclear-encoded RNAs that are stably attached to the cell surface and exposed to the extracellular space, hereafter called membrane-associated extracellular RNAs (maxRNAs).

RESULTS

We develop a technique called Surface-seq to selectively sequence maxRNAs and validate two Surface-seq identified maxRNAs by RNA fluorescence in situ hybridization. To test for cell-type specificity of maxRNA, we use antisense oligos to hybridize to single-stranded transcripts exposed on the surface of human peripheral blood mononuclear cells (PBMCs). Combining this strategy with imaging flow cytometry, single-cell RNA sequencing, and maxRNA sequencing, we identify monocytes as the major type of maxRNA+ PBMCs and prioritize 11 candidate maxRNAs for functional tests. Extracellular application of antisense oligos of FNDC3B and CTSS transcripts inhibits monocyte adhesion to vascular endothelial cells.

CONCLUSIONS

Collectively, these data highlight maxRNAs as functional components of the cell surface, suggesting an expanded role for RNA in cell-cell and cell-environment interactions.

Long non-coding RNAs and transposable elements: A functional relationship

Long non-coding RNAs (lncRNAs) have become increasingly important in the past decade. They are known to regulate gene expression and to interact with chromatin, proteins and other coding and non-coding RNAs. The study of lncRNAs has been challenging due to their low expression and the lack of tools developed to adapt to their particular features. Studies on lncRNAs performed to date have largely focused on cellular functions, whereas details on the mechanism of action has only been thoroughly investigated for a small number of lncRNAs. Nevertheless, some studies have highlighted the potential of these transcripts to contain functional domains, following the same accepted trend as proteins. Interestingly, many of these identified "domains" are attributed to functional units derived from transposable elements. Here, we review several types of functions of lncRNAs and relate these functions to lncRNA-embedded transposable elements.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

Proximity RNA-seq: A Sequencing Method to Identify Co-localization of RNA

RNA localization is an important regulatory layer of gene expression and cell functioning. The protocol guides through the Proximity RNA-seq method, in which RNA molecules are sequenced in their spatial, cellular context to derive RNA co-localization and transcriptome organization. Transcripts in individual subcellular particles from chemically crosslinked cells are tagged with the same, unique DNA barcode in water-in-oil emulsion droplets. First, single DNA barcodes are PCR amplified and immobilized on single, small magnetic beads in droplets. Subsequently, 3' ends of bead-bound barcode copies are tailed with random pentadecamers. Then beads are encapsulated again into droplets together with crosslinked subcellular particles containing RNA. Reverse transcription using random pentadecamers as primers is performed in droplets, which optimally contain one bead and one particle, in order to tag RNAs co-localized to the same particle. Sequencing such cDNA molecules identifies the RNA molecule and the barcode. Subsequent analysis of transcripts that share the same barcode, i.e., co-barcoding, reveals RNA co-localization and interactions. The technique is not restricted to pairs of RNAs but can as well detect groups of transcripts and estimates local RNA density or connectivity for individual transcripts. We provide here a detailed protocol to perform and analyze Proximity RNA-seq on cell nuclei to study spatial, nuclear RNA organization.

Unboxing the T-box riboswitches-A glimpse into multivalent and multimodal RNA-RNA interactions

The T-box riboswitches are widespread bacterial noncoding RNAs that directly bind specific tRNAs, sense aminoacylation on bound tRNAs, and switch conformations to control amino-acid metabolism and to maintain nutritional homeostasis. The core mechanisms of tRNA recognition, amino acid sensing, and conformational switching by the T-boxes have been recently elucidated, providing a wealth of new insights into multivalent and multimodal RNA-RNA interactions. This review dissects the structures and tRNA-recognition mechanisms by the Stem I, Stem II, and Discriminator domains, which collectively compose the T-box riboswitches. It further compares and contrasts the two classes of T-boxes that regulate transcription and translation, respectively, and integrates recent findings to derive general themes, trends, and insights into complex RNA-RNA interactions. Specifically, the T-box paradigm reveals that noncoding RNAs can interact with each other through multiple coordinated contacts, concatenation of stacked helices, and mutually induced fit. Numerous tertiary contacts, especially those emanating from strings of single-stranded purines, act in concert to reinforce long-range base-pairing and stacking interactions. These coordinated, mixed-mode contacts allow the T-box RNA to sterically sense aminoacylation on the tRNA using a bipartite steric sieve, and to couple this readout to a conformational switch mediated by tRNA-T-box stacking. Together, the insights gleaned from the T-box riboswitches inform investigations into other complex RNA structures and assemblies, development of T-box-targeted antimicrobials, and may inspire design and engineering of novel RNA sensors, regulators, and interfaces. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs Regulatory RNAs/RNAi/Riboswitches > Riboswitches.

RIC-seq for global in situ profiling of RNA-RNA spatial interactions

Highly structured RNA molecules usually interact with each other, and associate with various RNA-binding proteins, to regulate critical biological processes. However, RNA structures and interactions in intact cells remain largely unknown. Here, by coupling proximity ligation mediated by RNA-binding proteins with deep sequencing, we report an RNA in situ conformation sequencing (RIC-seq) technology for the global profiling of intra- and intermolecular RNA-RNA interactions. This technique not only recapitulates known RNA secondary structures and tertiary interactions, but also facilitates the generation of three-dimensional (3D) interaction maps of RNA in human cells. Using these maps, we identify noncoding RNA targets globally, and discern RNA topological domains and trans-interacting hubs. We reveal that the functional connectivity of enhancers and promoters can be assigned using their pairwise-interacting RNAs. Furthermore, we show that CCAT1-5L-a super-enhancer hub RNA-interacts with the RNA-binding protein hnRNPK, as well as RNA derived from the MYC promoter and enhancer, to boost MYC transcription by modulating chromatin looping. Our study demonstrates the power and applicability of RIC-seq in discovering the 3D structures, interactions and regulatory roles of RNA.

RNA Conformation Capture by Proximity Ligation

RNA proximity ligation is a set of molecular biology techniques used to analyze the conformations and spatial proximity of RNA molecules within cells. A typical experiment starts with cross-linking of a biological sample using UV light or psoralen, followed by partial fragmentation of RNA, RNA-RNA ligation, library preparation, and high-throughput sequencing. In the past decade, proximity ligation has been used to study structures of individual RNAs, networks of interactions between small RNAs and their targets, and whole RNA-RNA interactomes, in models ranging from bacteria to animal tissues and whole animals. Here, we provide an overview of the field, highlight the main findings, review the recent experimental and computational developments, and provide troubleshooting advice for new users. In the final section, we draw parallels between DNA and RNA proximity ligation and speculate on possible future research directions.

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

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

A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces cerevisiae

The growing number of RNA-mediated regulation mechanisms identified in the past decades suggests a widespread impact of RNA-RNA interactions. The efficiency of the regulation relies on highly specific and coordinated interactions while simultaneously repressing the formation of opportunistic complexes. However, the analysis of RNA interactomes is highly challenging because of the large number of potential partners, discrepancy of the size of RNA families, and the inherent noise in interaction predictions. We designed a recursive two-step cross-validation pipeline to capture the specificity of noncoding RNA (ncRNA) messenger RNA (mRNA) interactomes. Our method has been designed to detect significant loss or gain of specificity between ncRNA-mRNA interaction profiles. Applied to small nucleolar RNA-mRNA in Saccharomyces cerevisiae, our results suggest the existence of a repression of ncRNA affinities with mRNAs and thus the existence of an evolutionary pressure leveling down such interactions.

Probing the function of long noncoding RNAs in the nucleus

The nucleus is a highly organized and dynamic environment where regulation and coordination of processes such as gene expression and DNA replication are paramount. In recent years, noncoding RNAs have emerged as key participants in the regulation of nuclear processes. There are a multitude of functional roles for long noncoding RNA (lncRNA), mediated through their ability to act as molecular scaffolds bridging interactions with proteins, chromatin, and other RNA molecules within the nuclear environment. In this review, we discuss the diversity of techniques that have been developed to probe the function of nuclear lncRNAs, along with the ways in which those techniques have revealed insights into their mechanisms of action. Foundational observations into lncRNA function have been gleaned from molecular cytology-based, single-cell approaches to illuminate both the localization and abundance of lncRNAs in addition to their potential binding partners. Biochemical, extraction-based approaches have revealed the molecular contacts between lncRNAs and other molecules within the nuclear environment and how those interactions may contribute to nuclear organization and regulation. Using examples of well-studied nuclear lncRNAs, we demonstrate that the emerging functions of individual lncRNAs have been most clearly deduced from combined cytology and biochemical approaches tailored to study specific lncRNAs. As more functional nuclear lncRNAs continue to emerge, the development of additional technologies to study their interactions and mechanisms of action promise to continually expand our understanding of nuclear organization, chromosome architecture, genome regulation, and disease states.

Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution

High-throughput sequencing-based methods and their applications in the study of transcriptomes have revolutionized our understanding of alternative splicing. Networks of functionally coordinated and biologically important alternative splicing events continue to be discovered in an ever-increasing diversity of cell types in the context of physiologically normal and disease states. These studies have been complemented by efforts directed at defining sequence codes governing splicing and their cognate trans-acting factors, which have illuminated important combinatorial principles of regulation. Additional studies have revealed critical roles of position-dependent, multivalent protein-RNA interactions that direct splicing outcomes. Investigations of evolutionary changes in RNA binding proteins, splice variants, and associated cis elements have further shed light on the emergence, mechanisms, and functions of splicing networks. Progress in these areas has emphasized the need for a coordinated, community-based effort to systematically address the functions of individual splice variants associated with normal and disease biology.

Human Long Noncoding RNA Interactome: Detection, Characterization and Function

The application of a new generation of sequencing techniques has revealed that most of the genome has already been transcribed. However, only a small part of the genome codes proteins. The rest of the genome "dark matter" belongs to divergent groups of non-coding RNA (ncRNA), that is not translated into proteins. There are two groups of ncRNAs, which include small and long non-coding RNAs (sncRNA and lncRNA respectively). Over the last decade, there has been an increased interest in lncRNAs and their interaction with cellular components. In this review, we presented the newest information about the human lncRNA interactome. The term lncRNA interactome refers to cellular biomolecules, such as nucleic acids, proteins, and peptides that interact with lncRNA. The lncRNA interactome was characterized in the last decade, however, understanding what role the biomolecules associated with lncRNA play and the nature of these interactions will allow us to better understand lncRNA's biological functions in the cell. We also describe a set of methods currently used for the detection of lncRNA interactome components and the analysis of their interactions. We think that such a holistic and integrated analysis of the lncRNA interactome will help to better understand its potential role in the development of organisms and cancers.

RNA: interactions drive functionalities

RNA is produced from the majority of human genomic sequences, although only a relatively small portion of these transcripts has known functions. Diverse RNA species interact with RNA, DNA, proteins, lipids, and metabolites to form intricate molecular networks. In this review, we attempt to delineate diverse RNA functions by interaction types between RNA and other macromolecules. Through such interactions RNAs participate in essentially every major molecular function and process, including information flow and storage, environment sensing, signal transduction, and gene regulation at transcriptional and posttranscriptional levels. Through such interactions, RNAs promote or inhibit diverse biological processes, and act as catalyzer or quencher to modulate the pace of these progresses. Alterations and personal variations of these interactions are mechanistically coupled with disease etiology and phenotypical variations for clinical use.

RNAInter in 2020: RNA interactome repository with increased coverage and annotation

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

The Emerging Role of Major Regulatory RNAs in Cancer Control

Alterations and personal variations of RNA interactions have been mechanistically coupled with disease etiology and phenotypical variations. RNA biomarkers, RNA mimics, and RNA antagonists have been developed for diagnostic, prognostic, and therapeutic uses. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two major types of RNA molecules with regulatory roles, deregulation of which has been implicated in the initiation and progression of many human malignancies. Accumulating evidence indicated the clinical roles of regulatory RNAs in cancer control, stimulating a surge in exploring the functionalities of regulatory RNAs for improved understanding on disease pathogenesis and management. In this review, we highlight the critical roles of lncRNAs and miRNAs played in tumorigenesis, scrutinize their potential functionalities as diagnostic/prognostic biomarkers and/or therapeutic targets in clinics, outline opportunities that ncRNAs may bring to complement current clinical practice for improved cancer management and identify challenges faced by translating frontier knowledge on non-coding RNAs (ncRNAs) to bedside clinics as well as possible solutions.

Interpreting and integrating big data in non-coding RNA research

In the last two decades, we have witnessed an impressive crescendo of non-coding RNA studies, due to both the development of high-throughput RNA-sequencing strategies and an ever-increasing awareness of the involvement of newly discovered ncRNA classes in complex regulatory networks. Together with excitement for the possibility to explore previously unknown layers of gene regulation, these advancements led to the realization of the need for shared criteria of data collection and analysis and for novel integrative perspectives and tools aimed at making biological sense of very large bodies of molecular information. In the last few years, efforts to respond to this need have been devoted mainly to the regulatory interactions involving ncRNAs as direct or indirect regulators of protein-coding mRNAs. Such efforts resulted in the development of new computational tools, allowing the exploitation of the information spread in numerous different ncRNA data sets to interpret transcriptome changes under physiological and pathological cell responses. While experimental validation remains essential to identify key RNA regulatory interactions, the integration of ncRNA big data, in combination with systematic literature mining, is proving to be invaluable in identifying potential new players, biomarkers and therapeutic targets in cancer and other diseases.

RISE: a database of RNA interactome from sequencing experiments

We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.

Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools

The genomes of mammalian species are pervasively transcribed producing as many noncoding as protein-coding RNAs. There is a growing body of evidence supporting their functional role. Long noncoding RNA (lncRNA) can bind both nucleic acids and proteins through several mechanisms. A reliable computational prediction of the most probable mechanism of lncRNA interaction can facilitate experimental validation of its function. In this study, we benchmarked computational tools capable to discriminate lncRNA from mRNA and predict lncRNA interactions with other nucleic acids. We assessed the performance of 9 tools for distinguishing protein-coding from noncoding RNAs, as well as 19 tools for prediction of RNA-RNA and RNA-DNA interactions. Our conclusions about the considered tools were based on their performances on the entire genome/transcriptome level, as it is the most common task nowadays. We found that FEELnc and CPAT distinguish between coding and noncoding mammalian transcripts in the most accurate manner. ASSA, RIBlast and LASTAL, as well as Triplexator, turned out to be the best predictors of RNA-RNA and RNA-DNA interactions, respectively. We showed that the normalization of the predicted interaction strength to the transcript length and GC content may improve the accuracy of inferring RNA interactions. Yet, all the current tools have difficulties to make accurate predictions of short-trans RNA-RNA interactions-stretches of sparse contacts. All over, there is still room for improvement in each category, especially for predictions of RNA interactions.

In vivo analysis of influenza A mRNA secondary structures identifies critical regulatory motifs

The influenza A virus (IAV) is a continuous health threat to humans as well as animals due to its recurring epidemics and pandemics. The IAV genome is segmented and the eight negative-sense viral RNAs (vRNAs) are transcribed into positive sense complementary RNAs (cRNAs) and viral messenger RNAs (mRNAs) inside infected host cells. A role for the secondary structure of IAV mRNAs has been hypothesized and debated for many years, but knowledge on the structure mRNAs adopt in vivo is currently missing. Here we solve, for the first time, the in vivo secondary structure of IAV mRNAs in living infected cells. We demonstrate that, compared to the in vitro refolded structure, in vivo IAV mRNAs are less structured but exhibit specific locally stable elements. Moreover, we show that the targeted disruption of these high-confidence structured domains results in an extraordinary attenuation of IAV replicative capacity. Collectively, our data provide the first comprehensive map of the in vivo structural landscape of IAV mRNAs, hence providing the means for the development of new RNA-targeted antivirals.

High-throughput determination of RNA structures

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

RNA proximity sequencing reveals the spatial organization of the transcriptome in the nucleus

The global, three-dimensional organization of RNA molecules in the nucleus is difficult to determine using existing methods. Here we introduce Proximity RNA-seq, which identifies colocalization preferences for pairs or groups of nascent and fully transcribed RNAs in the nucleus. Proximity RNA-seq is based on massive-throughput RNA barcoding of subnuclear particles in water-in-oil emulsion droplets, followed by cDNA sequencing. Our results show RNAs of varying tissue-specificity of expression, speed of RNA polymerase elongation and extent of alternative splicing positioned at varying distances from nucleoli. The simultaneous detection of multiple RNAs in proximity to each other distinguishes RNA-dense from sparse compartments. Application of Proximity RNA-seq will facilitate study of the spatial organization of transcripts in the nucleus, including non-coding RNAs, and its functional relevance.

Beyond CLIP: advances and opportunities to measure RBP-RNA and RNA-RNA interactions

RNA is an essential player in almost all biological processes, and has an ever-growing number of roles in regulating cellular growth and organization. RNA functions extend far beyond just coding for proteins and RNA has been shown to function in signaling events, chromatin organization and transcriptional regulation. Dissecting how the complex network of RNA-binding proteins (RBPs) and regulatory RNAs interact with their substrates within the cell is a real, but exciting, challenge for the RNA community. Investigating these biological questions has fueled the development of new quantitative technologies to measure how RNA and RBPs interact both locally and on a global scale. In this review, we provide an assessment of available approaches to enable researchers to select the protocol most applicable for their experimental question.

Accurate and Efficient Mapping of the Cross-Linked microRNA-mRNA Duplex Reads

MicroRNA (miRNA) trans-regulates the stability of many mRNAs and controls their expression levels. Reconstruction of the miRNA-mRNA interactome is key to the understanding of the miRNA regulatory network and related biological processes. However, existing miRNA target prediction methods are limited to canonical miRNA-mRNA interactions and have high false prediction rates. Other experimental methods are low throughput and cannot be used to probe genome-wide interactions. To address this challenge, the Cross-linking Ligation and Sequencing of Hybrids (CLASH) technology was developed for high-throughput probing of transcriptome-wide microRNA-mRNA interactions in vivo. The mapping of duplex reads, chimeras of two ultra-short RNA strands, poses computational challenges to current mapping and alignment methods. To address this issue, we developed CLAN (CrossLinked reads ANalysis toolkit). CLAN generated a comparable mapping of singular reads to other tools, and significantly outperformed in mapping simulated and real CLASH duplex reads, offering a potential application to other next-generation sequencing-based duplex-read-generating technologies.

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Cross-linked miRNA-mRNA read may contain artificial sequences and is difficult to map

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We developed CLAN for miRNA-mRNA duplex read mapping

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CLAN aims at maximizing the total length of the mapped segments of the read

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CLAN was benchmarked with other mapping tools and showed improved performances

LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and Subcellular Localization Data

Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed “LncRRIsearch,” which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA interaction. The prediction was conducted using RIblast, which is a fast and accurate RNA-RNA interaction prediction tool. Users can investigate interaction target RNAs of a particular lncRNA through a web interface. In addition, we integrated tissue-specific expression and subcellular localization data for the lncRNAs with the web server. These data enable users to examine tissue-specific or subcellular localized lncRNA interactions. LncRRIsearch is publicly accessible at http://rtools.cbrc.jp/LncRRIsearch/.

Teaching an Old Virus New Tricks: A Review on New Approaches to Study Age-Old Questions in Influenza Biology

Influenza viruses have been studied for over 80 years, yet much about the basic viral lifecycle remain unknown. However, new imaging, biochemical, and sequencing techniques have revealed significant insight into many age-old questions of influenza virus biology. In this review, we will cover the role of imaging techniques to describe unique aspects of influenza virus assembly, biochemical techniques to study viral genomic organization, and next-generation sequencing to explore influenza genomic evolution. Our goal is to provide a brief overview of how emerging techniques are being used to answer basic questions about influenza viruses. This is not a comprehensive list of emerging techniques, rather ones that we feel will continue to make significant contributions to field of influenza biology.

Multi-Omics Approaches to Study Long Non-coding RNA Function in Atherosclerosis

Atherosclerosis is a complex inflammatory disease of the vessel wall involving the interplay of multiple cell types including vascular smooth muscle cells, endothelial cells, and macrophages. Large-scale genome-wide association studies (GWAS) and the advancement of next generation sequencing technologies have rapidly expanded the number of long non-coding RNA (lncRNA) transcripts predicted to play critical roles in the pathogenesis of the disease. In this review, we highlight several lncRNAs whose functional role in atherosclerosis is well-documented through traditional biochemical approaches as well as those identified through RNA-sequencing and other high-throughput assays. We describe novel genomics approaches to study both evolutionarily conserved and divergent lncRNA functions and interactions with DNA, RNA, and proteins. We also highlight assays to resolve the complex spatial and temporal regulation of lncRNAs. Finally, we summarize the latest suite of computational tools designed to improve genomic and functional annotation of these transcripts in the human genome. Deep characterization of lncRNAs is fundamental to unravel coronary atherosclerosis and other cardiovascular diseases, as these regulatory molecules represent a new class of potential therapeutic targets and/or diagnostic markers to mitigate both genetic and environmental risk factors.

Genome-wide colocalization of RNA-DNA interactions and fusion RNA pairs

Fusion transcripts are used as biomarkers in companion diagnoses. Although more than 15,000 fusion RNAs have been identified from diverse cancer types, few common features have been reported. Here, we compared 16,410 fusion transcripts detected in cancer (from a published cohort of 9,966 tumor samples of 33 cancer types) with genome-wide RNA-DNA interactions mapped in two normal, noncancerous cell types [using iMARGI, an enhanced version of the mapping of RNA-genome interactions (MARGI) assay]. Among the top 10 most significant RNA-DNA interactions in normal cells, 5 colocalized with the gene pairs that formed fusion RNAs in cancer. Furthermore, throughout the genome, the frequency of a gene pair to exhibit RNA-DNA interactions is positively correlated with the probability of this gene pair to present documented fusion transcripts in cancer. To test whether RNA-DNA interactions in normal cells are predictive of fusion RNAs, we analyzed these in a validation cohort of 96 lung cancer samples using RNA sequencing (RNA-seq). Thirty-seven of 42 fusion transcripts in the validation cohort were found to exhibit RNA-DNA interactions in normal cells. Finally, by combining RNA-seq, single-molecule RNA FISH, and DNA FISH, we detected a cancer sample with EML4-ALK fusion RNA without forming the EML4-ALK fusion gene. Collectively, these data suggest an RNA-poise model, where spatial proximity of RNA and DNA could poise for the creation of fusion transcripts.

The RNA Base-Pairing Problem and Base-Pairing Solutions

RNA molecules are folded into structures and complexes to perform a wide variety of functions. Determination of RNA structures and their interactions is a fundamental problem in RNA biology. Most RNA molecules in living cells are large and dynamic, posing unique challenges to structure analysis. Here we review progress in RNA structure analysis, focusing on methods that use the "cross-link, proximally ligate, and sequence" principle for high-throughput detection of base-pairing interactions in living cells. Beginning with a comparison of commonly used methods in structure determination and a brief historical account of psoralen cross-linking studies, we highlight the important features of cross-linking methods and new biological insights into RNA structures and interactions from recent studies. Further improvement of these cross-linking methods and application to previously intractable problems will shed new light on the mechanisms of the "modern RNA world."

The evolution of RNA structural probing methods: From gels to next-generation sequencing

RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read-out methods. This was superseded by capillary electrophoresis, and more recently by next-generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome-wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells.

RNA interactomics: recent advances and remaining challenges

Tight regulation of cellular processes is key to the development of complex organisms but also vital for simpler ones. During evolution, different regulatory systems have emerged, among them RNA-based regulation that is carried out mainly by intramolecular and intermolecular RNA-RNA interactions. However, methods for the transcriptome-wide detection of these interactions were long unavailable. Recently, three publications described high-throughput methods to directly detect RNA duplexes in living cells. This promises to enable in-depth studies of RNA-based regulation and will narrow the gaps in our understanding of RNA structure and function. In this review, we highlight the benefits of these methods and their commonalities and differences and, in particular, point to methodological shortcomings that hamper their wider application. We conclude by presenting ideas for how to overcome these problems and commenting on the prospects we see in this area of research.

Revealing lncRNA Structures and Interactions by Sequencing-Based Approaches

Long noncoding RNAs (lncRNAs) have emerged as significant players in almost every level of gene function and regulation. Thus, characterizing the structures and interactions of lncRNAs is essential for understanding their mechanistic roles in cells. Through a combination of (bio)chemical approaches and automated capillary and high-throughput sequencing (HTS), the complexity and diversity of RNA structures and interactions has been revealed in the transcriptomes of multiple species. These methods have uncovered important biological insights into the mechanistic and functional roles of lncRNA in gene expression and RNA metabolism, as well as in development and disease. In this review, we summarize the latest sequencing strategies to reveal RNA structure, RNA-RNA, RNA-DNA, and RNA-protein interactions, and highlight the recent applications of these approaches to map functional lncRNAs. We discuss the advantages and limitations of these strategies, and provide recommendations to further advance methodologies capable of mapping RNA structure and interactions in order to discover new biology of lncRNAs and decipher their molecular mechanisms and implication in diseases.

Investigation of RNA-RNA Interactions Using the RISE Database

RNA-RNA interactions (RRIs) are essential to understanding the regulatory mechanisms of RNAs. Mapping RRIs in vivo in a transcriptome-wide manner remained challenging until the recent development of several sequencing-based technologies. However, RRIs generated from large-scale studies had not been systematically collected and analyzed before. This article introduces RISE, a database of the RNA Interactome from Sequencing Experiments. RISE provides a comprehensive collection of RRIs in human, mouse, and yeast, derived from transcriptome-wide sequencing experiments, as well as targeted sequencing studies and other public databases/datasets. To facilitate better understanding of the biological roles of these RRIs, RISE also offers rich functional annotations involving RNAs, and an interactive interface to explore the analysis results. Here, we provide a brief description of the RISE website and a step-by-step protocol for using RISE to study RRIs. © 2018 by John Wiley & Sons, Inc.