Rtools: a web server for various secondary structural analyses on single RNA sequences

M6A-methylated circPOLR2B forms an R-loop and regulates the biological behavior of glioma stem cells through positive feedback loops

Glioma is the most common primary brain tumor, and targeting glioma stem cells (GSCs) has become a key aspect of glioma treatment. In this study, we discovered a molecular network in which circRNA forms an R-loop structure with its parental gene to regulate the biological behavior of GSCs. Genes with abnormal expression in GSCs were screened using RNA-seq and circRNA microarray analyses. The study results showed that high expression of YTHDC1 in GSCs promoted the transportation of N6-methyladenosine (m6A)-modified circPOLR2B from the nucleus to the cytoplasm. Decreased circPOLR2B levels in the nucleus resulted in fewer R-loop structures formed with its parental gene POLR2B. This reduction in R-loop structures relieved the inhibitory effect on POLR2B transcription and upregulated PBX1 expression through alternative polyadenylation (APA) action, thereby promoting the malignant biological behavior of GSCs. Knockdown of YTHDC1, POLR2B, and PBX1 reduced xenograft tumor volume and prolonged the survival of nude mice. The YTHDC1/circPOLR2B/POLR2B/PBX1 axis plays a regulatory role in the biological behavior of GSCs, offering potential targets and novel strategies for the treatment of glioma.

Diagnostic and Therapeutic Aptamers: A Promising Pathway to Improved Cardiovascular Disease Management

Despite advances in care, cardiovascular diseases remain the leading cause of death worldwide. As a result, identifying suitable biomarkers for early diagnosis and improving therapeutic and diagnostic strategies is crucial. Because of their significant advantages over other therapeutic approaches, nucleic-based therapies, particularly aptamers, are gaining increased attention. Aptamers are innovative synthetic polymers or oligomers of single-stranded DNA (ssDNA) or RNA molecules that can form 3-dimensional structures and thus interact with their targets with high specificity and affinity. Furthermore, they outperform classical protein-based antibodies in terms of in vitro selection, production, ease of modification and conjugation, high stability, low immunogenicity, and suitability for nanoparticle functionalization for targeted drug delivery. This work aims to review the advances made in the aptamers' field in biomarker detection, diagnosis, imaging, and targeted therapy, which highlight their huge potential in the management of cardiovascular diseases.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Rtools: A Web Server for Various Secondary Structural Analyses on Single RNA Sequences

Predicting the secondary structures of RNA molecules is an essential step to characterize their functions, but the thermodynamic probability of any prediction is generally small. On the other hand, there are a few tools for calculating and visualizing various secondary structural information from RNA sequences. We implemented a web server that calculates in parallel various features of secondary structures: different types of secondary structure predictions, the marginal probabilities for local structural contexts, accessibilities of the subsequences, the energy changes by arbitrary base mutations, and the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp , which integrates software tools, CentroidFold, CentroidHomfold, IPknot, CapR, Raccess, Rchange, RintD, and RintW.

Generative aptamer discovery using RaptGen

Nucleic acid aptamers are generated by an in vitro molecular evolution method known as systematic evolution of ligands by exponential enrichment (SELEX). Various candidates are limited by actual sequencing data from an experiment. Here we developed RaptGen, which is a variational autoencoder for in silico aptamer generation. RaptGen exploits a profile hidden Markov model decoder to represent motif sequences effectively. We showed that RaptGen embedded simulation sequence data into low-dimensional latent space on the basis of motif information. We also performed sequence embedding using two independent SELEX datasets. RaptGen successfully generated aptamers from the latent space even though they were not included in high-throughput sequencing. RaptGen could also generate a truncated aptamer with a short learning model. We demonstrated that RaptGen could be applied to activity-guided aptamer generation according to Bayesian optimization. We concluded that a generative method by RaptGen and latent representation are useful for aptamer discovery.

Aptamers targeting amyloidogenic proteins and their emerging role in neurodegenerative diseases

Aptamers are oligonucleotides selected from large pools of random sequences based on their affinity for bioactive molecules and are used in similar ways to antibodies. Aptamers provide several advantages over antibodies, including their small size, facile, large-scale chemical synthesis, high stability, and low immunogenicity. Amyloidogenic proteins, whose aggregation is relevant to neurodegenerative diseases, such as Alzheimer's, Parkinson's, and prion diseases, are among the most challenging targets for aptamer development due to their conformational instability and heterogeneity, the same characteristics that make drug development against amyloidogenic proteins difficult. Recently, chemical tethering of aptagens (equivalent to antigens) and advances in high-throughput sequencing-based analysis have been used to overcome some of these challenges. In addition, internalization technologies using fusion to cellular receptors and extracellular vesicles have facilitated central nervous system (CNS) aptamer delivery. In view of the development of these techniques and resources, here we review antiamyloid aptamers, highlighting preclinical application to CNS therapy.

Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

Binding patterns of RNA-binding proteins to repeat-derived RNA sequences reveal putative functional RNA elements

Recent reports have revealed that repeat-derived sequences embedded in introns or long noncoding RNAs (lncRNAs) are targets of RNA-binding proteins (RBPs) and contribute to biological processes such as RNA splicing or transcriptional regulation. These findings suggest that repeat-derived RNAs are important as scaffolds of RBPs and functional elements. However, the overall functional sequences of the repeat-derived RNAs are not fully understood. Here, we show the putative functional repeat-derived RNAs by analyzing the binding patterns of RBPs based on ENCODE eCLIP data. We mapped all eCLIP reads to repeat sequences and observed that 10.75 % and 7.04 % of reads on average were enriched (at least 2-fold over control) in the repeats in K562 and HepG2 cells, respectively. Using these data, we predicted functional RNA elements on the sense and antisense strands of long interspersed element 1 (LINE1) sequences. Furthermore, we found several new sets of RBPs on fragments derived from other transposable element (TE) families. Some of these fragments show specific and stable secondary structures and are found to be inserted into the introns of genes or lncRNAs. These results suggest that the repeat-derived RNA sequences are strong candidates for the functional RNA elements of endogenous noncoding RNAs.

Estimating Energy Parameters for RNA Secondary Structure Predictions Using Both Experimental and Computational Data

Computational RNA secondary structure prediction depends on a large number of nearest-neighbor free-energy parameters, including 10 parameters for Watson-Crick stacked base pairs that were estimated from experimental measurements of the free energies of 90 RNA duplexes. These experimental data are provided by time-consuming and cost-intensive experiments. In contrast, various modified nucleotides in RNAs, which would affect not only their structures but also functions, have been found, and rapid determination of energy parameters for a such modified nucleotides is needed. To reduce the high cost of determining energy parameters, we propose a novel method to estimate energy parameters from both experimental and computational data, where the computational data are provided by a recently developed molecular dynamics simulation protocol. We evaluate our method for Watson-Crick stacked base pairs, and show that parameters estimated from 10 experimental data items and 10 computational data items can predict RNA secondary structures with accuracy comparable to that using conventional parameters. The results indicate that the combination of experimental free-energy measurements and molecular dynamics simulations is capable of estimating the thermodynamic properties of RNA secondary structures at lower cost.

SilentMutations (SIM): A tool for analyzing long-range RNA-RNA interactions in viral genomes and structured RNAs

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We developed a tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs.

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Our tool simulates destructive and compensatory mutants of two key regions from a single-stranded RNA.

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The simulated mutants can be utilized for the combinatorial in vitro analysis of RNA–RNA interactions.

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We predicted potential mutants for in vitro validation experiments of influenza A virus and hepatitis C virus interactions.

A single nucleotide change in the coding region can alter the amino acid sequence of a protein. In consequence, natural or artificial sequence changes in viral RNAs may have various effects not only on protein stability, function and structure but also on viral replication.

In recent decades, several tools have been developed to predict the effect of mutations in structured RNAs such as viral genomes or non-coding RNAs. Some tools use multiple point mutations and also take coding regions into account. However, none of these tools was designed to specifically simulate the effect of mutations on viral long-range interactions.

Here, we developed SilentMutations (SIM), an easy-to-use tool to analyze the effect of multiple point mutations on the secondary structures of two interacting viral RNAs. The tool can simulate disruptive and compensatory mutants of two interacting single-stranded RNAs. This allows a fast and accurate assessment of key regions potentially involved in functional long-range RNA–RNA interactions and will eventually help virologists and RNA-experts to design appropriate experiments.

SIM only requires two interacting single-stranded RNA regions as input. The output is a plain text file containing the most promising mutants and a graphical representation of all interactions.

We applied our tool on two experimentally validated influenza A virus and hepatitis C virus interactions and we were able to predict potential double mutants for in vitro validation experiments.

The source code and documentation of SIM are freely available at github.com/desiro/silentMutations.

Proof of concept web application for understanding the energetic basis of oligonucleotide unfolding

Measuring and quantifying thermodynamic parameters that determine both the stability of and interactions between biological macromolecules are an essential and necessary complement to structural studies. Although basic thermodynamic parameters for an observed process can be readily obtained, the data interpretation is often slow and analysis quality can be extremely variable. We have started to develop a web application that will help users to perform thermodynamic characterizations of oligonucleotide unfolding. The application can perform global fitting of calorimetric and spectroscopic data, and uses a three-state equilibrium model to obtain thermodynamic parameters for each transition step – namely, the Gibbs energy, the enthalpy, and the heat capacity. In addition, the application can define the number of K⁺ ions and the number of water molecules being released or taken up during unfolding. To test our application, we used UV spectroscopy, circular dichroism, and differential scanning calorimetry to monitor folding and unfolding of a model 22-nucleotide-long sequence of a human 3′-telomeric overhang, known as Tel22. The obtained data were uploaded to the web application and the global fit revealed that unfolding of Tel22 involves at least one intermediate state, and that K⁺ ions are released during the unfolding, whereas water molecules are taken up.

A novel web application: performing global fitting of oligonucleotide unfolding experimental data in style.

Computational approaches for alternative and transient secondary structures of ribonucleic acids

Transient and alternative structures of ribonucleic acids (RNAs) play essential roles in various regulatory processes, such as translation regulation in living cells. Because experimental analyses for RNA structures are difficult and time-consuming, computational approaches based on RNA secondary structures are promising. In this article, we review computational methods for detecting and analyzing transient/alternative secondary structures of RNAs, including static approaches based on probabilistic distributions of RNA secondary structures and dynamic approaches such as kinetic folding and folding pathway predictions.

Advances in therapeutic bacterial antisense biotechnology

Antisense therapeutics are a biotechnological form of antibiotic therapy using chemical analogues of short single-stranded nucleic acid sequences modified to form stable oligomers. These molecules are termed antisense oligonucleotides (ASOs) because their sequence is complementary, via Watson-Crick specific base pairing, to their target messenger RNA (mRNA). ASOs modify gene expression in this sequence-dependent manner by binding to its complementary mRNA and inhibiting its translation into protein through steric blockage and/or through RNase degradation of the ASO/RNA duplex. The widespread use of conventional antibiotics has led to the increasing emergence of multiple drug-resistant pathogenic bacteria. There is an urgent need to develop alternative therapeutic strategies to reduce the morbidity and mortality associated with bacterial infections, and until recently, the use of ASOs as therapeutic agents has been essentially limited to eukaryotic cells, with ASOs as antibacterials having been largely unexplored primarily due to the poor uptake efficiency of antisense molecules by bacteria. There are conceptual advantages to bacterial antisense antibiotic therapies, including a sequence-dependent approach that allows for a rational design to multiple specific molecular targets. This review summarizes the current knowledge of antisense bacterial biotechnology and highlights the recent progress and the current obstacles in their development for therapeutic applications.

In silico approaches to RNA aptamer design

RNA aptamers are ribonucleic acids that bind to specific target molecules. An RNA aptamer for a disease-related protein has great potential for development into a new drug. However, huge time and cost investments are required to develop an RNA aptamer into a pharmaceutical. Recently, SELEX combined with high-throughput sequencers (i.e., HT-SELEX) has been widely used to select candidate RNA aptamers that bind to a target protein with high affinity and specificity. After candidate selection, further optimizations such as shortening and modifying candidate sequences are performed. In these steps, in silico approaches are expected to reduce the time and cost associated with aptamer drug development. In this article, we review existing in silico approaches to RNA aptamer development, including a method for ranking the candidates of RNA aptamers from HT-SELEX data, clustering a huge number of aptamer sequences, and finding motifs amidst a set of significant RNA aptamers. It is expected that further studies in addition to these methods will be utilized for in silico RNA aptamer design, permitting a minimal number of experiments to be performed through the utilization of sophisticated computational methods.

Transcription-associated mutational pressure in the Parvovirus B19 genome: Reactivated genomes contribute to the variability of viral populations

In this study we used non-overlapping parts of the two long open reading frames coding for nonstructural (NS) and capsid (VP) proteins of all available sequences of the Parvovirus B19 subgenotype 1a genome and found out that the rates of A to G, C to T and A to T mutations are higher in the first long reading frame (NS) of the virus than in the second one (VP). This difference in mutational pressure directions for two parts of the same viral genome can be explained by the fact of transcription of just the first long reading frame during the lifelong latency in nonerythroid cells. Adenine deamination (producing A to G and A to T mutations) and cytosine deamination (producing C to T mutations) occur more frequently in transcriptional bubbles formed by DNA "plus" strand of the first open reading frame. These mutations can be inherited only in case of reactivation of the infectious virus due to the help of Adenovirus that allows latent Parvovirus B19 to start transcription of the second reading frame and then to replicate its genome by the rolling circle mechanism using the specific origin. Results of this study provide evidence that the genomes reactivated from latency make significant contributions to the variability of Parvovirus B19.