Advances and challenges towards the study of RNA-RNA interactions in a transcriptome-wide scale

Antisense and Functional Nucleic Acids in Rational Drug Development

This review is focused on antisense and functional nucleic acid used for completely rational drug design and drug target assessment, aiming to reduce the time and money spent and increase the successful rate of drug development. Nucleic acids have unique properties that play two essential roles in drug development as drug targets and as drugs. Drug targets can be messenger, ribosomal, non-coding RNAs, ribozymes, riboswitches, and other RNAs. Furthermore, various antisense and functional nucleic acids can be valuable tools in drug discovery. Many mechanisms for RNA-based control of gene expression in both pro-and-eukaryotes and engineering approaches open new avenues for drug discovery with a critical role. This review discusses the design principles, applications, and prospects of antisense and functional nucleic acids in drug delivery and design. Such nucleic acids include antisense oligonucleotides, synthetic ribozymes, and siRNAs, which can be employed for rational antibacterial drug development that can be very efficient. An important feature of antisense and functional nucleic acids is the possibility of using rational design methods for drug development. This review aims to popularize these novel approaches to benefit the drug industry and patients.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

Detecting RNA-RNA interactome

The last decade has seen a robust increase in various types of novel RNA molecules and their complexity in gene regulation. RNA molecules play a critical role in cellular events by interacting with other biomolecules, including protein, DNA, and RNA. It has been established that RNA-RNA interactions play a critical role in several biological processes by regulating the biogenesis and function of RNA molecules. Interestingly, RNA-RNA interactions regulate the biogenesis of diverse RNA molecules, including mRNAs, microRNAs, tRNAs, and circRNAs, through splicing or backsplicing. Structured RNAs like rRNA, tRNA, and snRNAs achieve their functional conformation by intramolecular RNA-RNA interactions. In addition, functional consequences of many intermolecular RNA-RNA interactions have been extensively studied in the regulation of gene expression. Hence, it is essential to understand the mechanism and functions of RNA-RNA interactions in eukaryotes. Conventionally, RNA-RNA interactions have been identified through diverse biochemical methods for decades. The advent of high-throughput RNA-sequencing technologies has revolutionized the identification of global RNA-RNA interactome in cells and their importance in RNA structure and function in gene expression regulation. Although these technologies revealed tens of thousands of intramolecular and intermolecular RNA-RNA interactions, we further look forward to future unbiased and quantitative high-throughput technologies for detecting transcriptome-wide RNA-RNA interactions. With the ability to detect RNA-RNA interactome, we expect that future studies will reveal the higher-order structures of RNA molecules and multi-RNA hybrids impacting human health and diseases. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems.

Role of Nuclear Non-Canonical Nucleic Acid Structures in Organismal Development and Adaptation to Stress Conditions

Over the last decades, numerous examples have involved nuclear non-coding RNAs (ncRNAs) in the regulation of gene expression. ncRNAs can interact with the genome by forming non-canonical nucleic acid structures such as R-loops or DNA:RNA triplexes. They bind chromatin and DNA modifiers and transcription factors and favor or prevent their targeting to specific DNA sequences and regulate gene expression of diverse genes. We review the function of these non-canonical nucleic acid structures in regulating gene expression of multicellular organisms during development and in response to different stress conditions and DNA damage using examples described in several organisms, from plants to humans. We also overview recent techniques developed to study where R-loops or DNA:RNA triplexes are formed in the genome and their interaction with proteins.

Experimental Approaches to Identify Host Factors Important for Influenza Virus

An ever-expanding toolkit of experimental methods provides the means to discover and characterize host factors important for influenza virus. Here, we describe common methods for investigating genetic relationships and physical interactions between virus and host. A comprehensive knowledge of host:virus interactions is key to understanding how influenza virus exploits the host cell and to potentially identify vulnerabilities that may be manipulated to prevent or treat disease.

Trapping Transient RNA Complexes by Chemically Reversible Acylation

RNA-RNA interactions are essential for biology, but they can be difficult to study due to their transient nature. While crosslinking strategies can in principle be used to trap such interactions, virtually all existing strategies for crosslinking are poorly reversible, chemically modifying the RNA and hindering molecular analysis. We describe a soluble crosslinker design (BINARI) that reacts with RNA through acylation. We show that it efficiently crosslinks noncovalent RNA complexes with mimimal sequence bias and establish that the crosslink can be reversed by phosphine reduction of azide trigger groups, thereby liberating the individual RNA components for further analysis. The utility of the new approach is demonstrated by reversible protection against nuclease degradation and trapping transient RNA complexes of E. coli DsrA-rpoS derived bulge-loop interactions, which underlines the potential of BINARI crosslinkers to probe RNA regulatory networks.

The New Frontier of Functional Genomics: From Chromatin Architecture and Noncoding RNAs to Therapeutic Targets

Common diseases are complex, multifactorial disorders whose pathogenesis is influenced by the interplay of genetic predisposition and environmental factors. Genome-wide association studies have interrogated genetic polymorphisms across genomes of individuals to test associations between genotype and susceptibility to specific disorders, providing insights into the genetic architecture of several complex disorders. However, genetic variants associated with the susceptibility to common diseases are often located in noncoding regions of the genome, such as tissue-specific enhancers or long noncoding RNAs, suggesting that regulatory elements might play a relevant role in human diseases. Enhancers are cis-regulatory genomic sequences that act in concert with promoters to regulate gene expression in a precise spatiotemporal manner. They can be located at a considerable distance from their cognate target promoters, increasing the difficulty of their identification. Genomes are organized in domains of chromatin folding, namely topologically associating domains (TADs). Identification of enhancer-promoter interactions within TADs has revealed principles of cell-type specificity across several organisms and tissues. The vast majority of mammalian genomes are pervasively transcribed, accounting for a previously unappreciated complexity of the noncoding RNA fraction. Particularly, long noncoding RNAs have emerged as key players for the establishment of chromatin architecture and regulation of gene expression. In this perspective, we describe the new advances in the fields of transcriptomics and genome organization, focusing on the role of noncoding genomic variants in the predisposition of common diseases. Finally, we propose a new framework for the identification of the next generation of pharmacological targets for common human diseases.

Non-coding RNAs in cancer: platforms and strategies for investigating the genomic "dark matter"

The discovery of the role of non-coding RNAs (ncRNAs) in the onset and progression of malignancies is a promising frontier of cancer genetics. It is clear that ncRNAs are candidates for therapeutic intervention, since they may act as biomarkers or key regulators of cancer gene network. Recently, profiling and sequencing of ncRNAs disclosed deep deregulation in human cancers mostly due to aberrant mechanisms of ncRNAs biogenesis, such as amplification, deletion, abnormal epigenetic or transcriptional regulation. Although dysregulated ncRNAs may promote hallmarks of cancer as oncogenes or antagonize them as tumor suppressors, the mechanisms behind these events remain to be clarified. The development of new bioinformatic tools as well as novel molecular technologies is a challenging opportunity to disclose the role of the "dark matter" of the genome. In this review, we focus on currently available platforms, computational analyses and experimental strategies to investigate ncRNAs in cancer. We highlight the differences among experimental approaches aimed to dissect miRNAs and lncRNAs, which are the most studied ncRNAs. These two classes indeed need different investigation taking into account their intrinsic characteristics, such as length, structures and also the interacting molecules. Finally, we discuss the relevance of ncRNAs in clinical practice by considering promises and challenges behind the bench to bedside translation.

Mechanisms and consequences of subcellular RNA localization across diverse cell types

Essentially all cells contain a variety of spatially restricted regions that are important for carrying out specialized functions. Often, these regions contain specialized transcriptomes that facilitate these functions by providing transcripts for localized translation. These transcripts play a functional role in maintaining cell physiology by enabling a quick response to changes in the cellular environment. Here, we review how RNA molecules are trafficked within cells, with a focus on the subcellular locations to which they are trafficked, mechanisms that regulate their transport and clinical disorders associated with misregulation of the process.

CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments

In silico RNA-RNA interaction prediction is widely applied to identify putative interaction partners and to assess interaction details in base pair resolution. To verify specific interactions, in vitro evidence can be obtained via compensatory mutation experiments. Unfortunately, the selection of compensatory mutations is non-trivial and typically based on subjective ad hoc decisions. To support the decision process, we introduce our COmPensatOry MUtation Selector CopomuS. CopomuS evaluates the effects of mutations on RNA-RNA interaction formation using a set of objective criteria, and outputs a reliable ranking of compensatory mutation candidates. For RNA-RNA interaction assessment, the state-of-the-art IntaRNA prediction tool is applied. We investigate characteristics of successfully verified RNA-RNA interactions from the literature, which guided the design of CopomuS. Finally, we evaluate its performance based on experimentally validated compensatory mutations of prokaryotic sRNAs and their target mRNAs. CopomuS predictions highly agree with known results, making it a valuable tool to support the design of verification experiments for RNA-RNA interactions. It is part of the IntaRNA package and available as stand-alone webserver for ad hoc application.

HNADOCK: a nucleic acid docking server for modeling RNA/DNA-RNA/DNA 3D complex structures

Interactions between nuclide acids (RNA/DNA) play important roles in many basic cellular activities like transcription regulation, RNA processing, and protein synthesis. Therefore, determining the complex structures between RNAs/DNAs is crucial to understand the molecular mechanism of related RNA/DNA-RNA/DNA interactions. Here, we have presented HNADOCK, a user-friendly web server for nucleic acid (NA)-nucleic acid docking to model the 3D complex structures between two RNAs/DNAs, where both sequence and structure inputs are accepted for RNAs, while only structure inputs are supported for DNAs. HNADOCK server was tested through both unbound structure and sequence inputs on the benchmark of 60 RNA-RNA complexes and compared with the state-of-the-art algorithm SimRNA. For structure input, HNADOCK server achieved a high success rate of 71.7% for top 10 predictions, compared to 58.3% for SimRNA. For sequence input, HNADOCK server also obtained a satisfactory performance and gave a success rate of 83.3% when the bound RNA templates are included or 53.3% when excluding those bound RNA templates. It was also found that inclusion of the inter-RNA base-pairing information from RNA-RNA interaction prediction can significantly improve the docking accuracy, especially for the top prediction. HNADOCK is fast and can normally finish a job in about 10 minutes. The HNADOCK web server is available at http://huanglab.phys.hust.edu.cn/hnadock/.

Molecular shape as a key source of prebiotic information

One of the most striking features of a living system is the self-sustaining functional inner organization, which is only possible when a source of internal references is available from which the system is able to self-organize components and processes. Internal references are intrinsically related to biological information, which is typically understood as genetic information. However, the organization in living systems supports a diversity of intricate processes that enable life to endure, adapt and reproduce because of this organization. In a biological context, information refers to a complex relationship between internal architecture and system functionality. Nongenetic processes, such as conformational recognition, are not considered biological information, although they exert important control over cell processes. In this contribution, we discuss the informational nature in the recognition of molecular shape in living systems. Thus, we highlight supramolecular matching as having a theoretical key role in the origin of life. Based on recent data, we demonstrate that the transfer of molecular conformation is a very likely dynamic of prebiotic information, which is closely related to the origin of biological homochirality and biogenic systems. In light of the current hypothesis, we also revisit the central dogma of molecular biology to assess the consistency of the proposal presented here. We conclude that both spatial (molecular shape) and sequential (genetic) information must be represented in this biological paradigm.