Toward a next-generation atlas of RNA secondary structure

Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes

RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/. We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

RNA therapy: Are we using the right molecules?

Small-molecule and protein/antibody drugs mainly act on genome-derived proteins to exert pharmacological effects. RNA based therapies hold the promise to expand the range of druggable targets from proteins to RNAs and the genome, as evidenced by several RNA drugs approved for clinical practice and many others under active trials. While chemo-engineered RNA mimics have found their success in marketed drugs and continue dominating basic research and drug development, these molecules are usually conjugated with extensive and various modifications. This makes them completely different from cellular RNAs transcribed from the genome that usually consist of unmodified ribonucleotides or just contain a few posttranscriptional modifications. The use of synthetic RNA mimics for RNA research and drug development is also in contrast with the ultimate success of protein research and therapy utilizing biologic or recombinant proteins produced and folded in living cells instead of polypeptides or proteins synthesized in vitro. Indeed, efforts have been made recently to develop RNA bioengineering technologies for cost-effective and large-scale production of biologic RNA molecules that may better capture the structures, functions, and safety profiles of natural RNAs. In this article, we provide an overview on RNA therapeutics for the treatment of human diseases via RNA interference mechanisms. By illustrating the structural differences between natural RNAs and chemo-engineered RNA mimics, we focus on discussion of a novel class of bioengineered/biologic RNA agents produced through fermentation and their potential applications to RNA research and drug development.

Exploring Consensus RNA Substructural Patterns Using Subgraph Mining

Frequently recurring RNA structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks. This article thus proposes a novel labeled graph-based algorithm RnaGraph to uncover frequently RNA substructure patterns. Attribute data and graph data are combined to characterize diverse substructures and their correlations, respectively. Further, a top-k graph pattern mining algorithm is developed to extract interesting substructure motifs by integrating frequency and similarity. The experimental results show that our methods assist in not only modelling complex RNA secondary structures but also identifying hidden but interesting RNA substructure patterns.

The Ebola virus VP35 protein binds viral immunostimulatory and host RNAs identified through deep sequencing

Ebola virus and Marburg virus are members of the Filovirdae family and causative agents of hemorrhagic fever with high fatality rates in humans. Filovirus virulence is partially attributed to the VP35 protein, a well-characterized inhibitor of the RIG-I-like receptor pathway that triggers the antiviral interferon (IFN) response. Prior work demonstrates the ability of VP35 to block potent RIG-I activators, such as Sendai virus (SeV), and this IFN-antagonist activity is directly correlated with its ability to bind RNA. Several structural studies demonstrate that VP35 binds short synthetic dsRNAs; yet, there are no data that identify viral immunostimulatory RNAs (isRNA) or host RNAs bound to VP35 in cells. Utilizing a SeV infection model, we demonstrate that both viral isRNA and host RNAs are bound to Ebola and Marburg VP35s in cells. By deep sequencing the purified VP35-bound RNA, we identified the SeV copy-back defective interfering (DI) RNA, previously identified as a robust RIG-I activator, as the isRNA bound by multiple filovirus VP35 proteins, including the VP35 protein from the West African outbreak strain (Makona EBOV). Moreover, RNAs isolated from a VP35 RNA-binding mutant were not immunostimulatory and did not include the SeV DI RNA. Strikingly, an analysis of host RNAs bound by wild-type, but not mutant, VP35 revealed that select host RNAs are preferentially bound by VP35 in cell culture. Taken together, these data support a model in which VP35 sequesters isRNA in virus-infected cells to avert RIG-I like receptor (RLR) activation.

Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions

BACKGROUND

Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data.

RESULTS

We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy.

CONCLUSIONS

To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.

Do we know whether potential G-quadruplexes actually form in long functional RNA molecules?

The roles of deoxyribonucleic acid (DNA) G-quadruplex structures in gene expression and telomere maintenance have been well characterized. Recent results suggest that such structures could also play pivotal roles in ribonucleic acid (RNA) biology, such as splicing or translation regulation. However, it has been difficult to show that RNA G-quadruplexes (G4s) exist in specific long RNA sequences, such as precursor messenger RNA, in a functional or cellular context. Most current methods for identifying G4s involve the use of short, purified RNA sequences in vitro, in the absence of competition with secondary structures or protein binding. Therefore, novel methods need to be developed to allow the characterization of G4s in long functional RNAs and in a cellular context. This need has in part been met by our recent development of a method based on a comparison of RNA and 7-deaza-RNA that provides a test for identifying RNA G4s in such conditions.

Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE

icSHAPE (in vivo click selective 2-hydroxyl acylation and profiling experiment) captures RNA secondary structure at a transcriptome-wide level by measuring nucleotide flexibility at base resolution. Living cells are treated with the icSHAPE chemical NAI-N3 followed by selective chemical enrichment of NAI-N3-modified RNA, which provides an improved signal-to-noise ratio compared with similar methods leveraging deep sequencing. Purified RNA is then reverse-transcribed to produce cDNA, with SHAPE-modified bases leading to truncated cDNA. After deep sequencing of cDNA, computational analysis yields flexibility scores for every base across the starting RNA population. The entire experimental procedure can be completed in ∼5 d, and the sequencing and bioinformatics data analysis take an additional 4-5 d with no extensive computational skills required. Comparing in vivo and in vitro icSHAPE measurements can reveal in vivo RNA-binding protein imprints or facilitate the dissection of RNA post-transcriptional modifications. icSHAPE reactivities can additionally be used to constrain and improve RNA secondary structure prediction models.