RNA Structure Analysis Research Articles

Bounds for metric dimension and defensive $k$-alliance of graphs under deleted lexicographic product

‎Metric dimension and defensive $k$-alliance number are two distance-based graph invariants‎ ‎which have applications in robot navigation‎, ‎quantitative analysis of secondary RNA structures‎, ‎national defense and fault-tolerant computing‎. ‎In this paper‎, ‎some bounds for metric‎ ‎dimension and defensive $k$-alliance of deleted lexicographic product of graphs are presented‎. ‎We also show that the bounds are sharp‎.

The Effects of Self-cleavage Intein-ELK16 Tag in the Transcript Steric Hindrance of IFN.

Intervening proteins (Inteins) are identified as protein domains in a precursor protein structure. Inteins can excise itself from precursor protein and join the remaining portions which result in forming an active protein. In this study, the transcript expression level of recombinant human Interferon beta (rhIFNβ) connected to the self-cleavage Intein-ELK16 (LELELKLKLELELKLK) tag was measured by real-time PCR in HEK293T cell line. First, the sequence of Mycobacterium tuberculosis RecA (Mtu recA) was obtained from the InBase database to do appropriate changes including adding the restriction sites, kozak sequence, signal peptide and ELK16 sequence by SnapGene software. The RNA secondary structure were also examined using the online RNA Fold 2.2 web server. Next, the construct was inserted into pUC19 plasmid. The sequence of rhIFNβ was also cloned into pBudCE4.1 vector. In the next step, the rhIFNβ was ligated into the construct (self-cleavage tag of ELK16) using T4 DNA ligase and the recombinant construct was transfected into HEK293T cell line. Finally, expression of the cassette was evaluated by real-time PCR. The analysis of secondary RNA structure indicates a minimum free energy of MEF - 261.10kcal/mol. Our results indicate that IFNβ was upregulated (37.8-fold, p < 0.0001) in cells which transfected by rhIFNβ-ELK16 compared to the mock and un-transfected conditions. Altogether, our results show that the presence of mini self-cleavage Intein-ELK16 tag along with the rhIFNβ had no interference in transcription of rhIFNβ in the HEK293T cell line.

In silico study on RNA structures of intronic mutations of beta-globin gene

Background: Mutation of the beta-globin gene (HBB) interferes with primary mRNA transcription, leading to beta-thalassemia disease. The IVS1nt1 and IVS1nt5 mutations were reported as two of the most prevalent intronic mutations associated with beta-thalassemia major. These mutations may affect the mRNA structure of the human beta-globin (HBB) gene. However, the mechanism by which variation in HBB alters the mRNA structure remains unclear. The objective of this study was to unveil the secondary and tertiary conformation difference of the mutants compared to the wildtype using in silico analysis. Methods: The sequence of HBB was obtained from Ensemble database and mutated manually at nucleotides 143 (IVS1nt1G&gt;T) and 147 (IVS1nt5G&gt;C). The RNA secondary and tertiary structure were performed by ViennaRNA Web Services and RNA Composer, respectively. Results and Discussion: The results revealed the unique folding characteristics of each mutations for the secondary and tertiary structures. Based on the structure, unwanted folding occurred in the IVS1nt1G&gt;T and IVS1nt5G&gt;C mRNA structures compared to the wild-type structure. This finding was supported by the results of centroid-based analysis and RNA structure analysis, indicating that the larger loops in IVS1nt1 and IVS1nt5 result in an unstable structure. Our study found that intronic mutations affect the mRNA structure of HBB by altering its folding mechanism.

From the structure analysis of RNA to the identification of novel drug targets

From the structure analysis of RNA to the identification of novel drug targets

RNA Structure Analysis by Chemical Probing with DMS and CMCT.

RNA structure is important for understanding RNA function and stability within a cell. Chemical probing is a well-established and convenient method to evaluate the structure of an RNA. Several structure-sensitive chemicals can differentiate paired and unpaired nucleotides. This chapter specifically addresses the use of DMS and CMCT. Although exhibiting different affinities, the combination of these two chemical reagents enables screening of all four nucleobases. DMS and CMCT are only reactive with exposed unpaired nucleotides. We have used this method to analyze the effect of the RNA chaperone Hfq on the conformation of the 16S rRNA. The strategy here described may be applied for the study of many other RNA-binding proteins and RNAs.

RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools.

Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use. Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure prediction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA_format, RNA_normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.

Phylogenetic and secondary RNA structure analysis of monogenean gill ectoparasites(Dactylogyrus spp.) parasitizing certain freshwater fishes.

The current study is the first phylogenetic and secondary RNA structure analysis of Dactylogyrus species parasitising gill filaments of Iraqi cyprinid fishes. Most previous phylogenetic studies have targeted on primary DNA sequence data. Nevertheless, RNA secondary configuration is principally helpful in systematics since they comprise features that do not appear in the primary sequence and provide morphological information. The primary objective was molecular-based identification of Dactylogyrids species using evolutionary tree and secondary RNA structure prediction. A total of 681 fish were collected from the Lesser Zab River in the northeast of Iraq in the sub-district of Altun-Kopru from August 2016 to September 2017 and brought to the Zoology Research Laboratory, Salahaddin University-Erbil, Iraq. All fish were classified as 18 cyprinid species. The species of Dactylogyrus were identified by the 28S rDNA subunit using PCR and sequencing methods, and the obtained nucleotide sequences were then compared with the available GenBank sequences. Phylogenetic relationships were concluded using Neighbour-Joining (NJ), Maximum Likelihood (ML), and Minimum Evolution (ME) methods. The results justify the validation of 11 Dactylogyrus species (three of them were newly recorded in Iraq). Additionally, out of nine infected fish species, seven of them were regarded as a new host for Dactylogyrus species. Secondary RNA configuration prediction using minimum free energy was considered as a hopeful tool for species identification. This was considered the first comprehensive phylogenetic study in the area. It was concluded that PCR sequencing, phylogenetic and secondary RNA analysis were proper molecular methods for identifying Dactylogyrids species on the gills of fishes.

RNA structural analysis of the MYC mRNA reveals conserved motifs that affect gene expression.

The MYC gene encodes a human transcription factor and proto-oncogene that is dysregulated in over half of all known cancers. To better understand potential post-transcriptional regulatory features affecting MYC expression, we analyzed secondary structures in the MYC mRNA using a program that is optimized for finding small locally-folded motifs with a high propensity for function. This was accomplished by calculating folding metrics across the MYC sequence using a sliding analysis window and generating unique consensus base pairing models weighted by their lower-than-random predicted folding energy. A series of 30 motifs were identified, primarily in the 5' and 3' untranslated regions, which show evidence of structural conservation and compensating mutations across vertebrate MYC homologs. This analysis was able to recapitulate known elements found within an internal ribosomal entry site, as well as discover a novel element in the 3' UTR that is unusually stable and conserved. This novel motif was shown to affect MYC expression, potentially via the modulation of miRNA target accessibility or other trans-regulatory factors. In addition to providing basic insights into mechanisms that regulate MYC expression, this study provides numerous, potentially druggable RNA targets for the MYC gene, which is considered “undruggable” at the protein level.

Benefits of stable isotope labeling in RNA analysis.

RNAs are key players in life as they connect the genetic code (DNA) with all cellular processes dominated by proteins. They contain a variety of chemical modifications and many RNAs fold into complex structures. Here, we review recent progress in the analysis of RNA modification and structure on the basis of stable isotope labeling techniques. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the key tools and many breakthrough developments were made possible by the analysis of stable isotope labeled RNA. Therefore, we discuss current stable isotope labeling techniques such as metabolic labeling, enzymatic labeling and chemical synthesis. RNA structure analysis by NMR is challenging due to two major problems that become even more salient when the size of the RNA increases, namely chemical shift overlaps and line broadening leading to complete signal loss. Several isotope labeling strategies have been developed to provide solutions to these major issues, such as deuteration, segmental isotope labeling or site-specific labeling. Quantification of modified nucleosides in RNA by MS is only possible through the application of stable isotope labeled internal standards. With nucleic acid isotope labeling coupled mass spectrometry (NAIL-MS), it is now possible to analyze the dynamic processes of post-transcriptional RNA modification and demodification. The trend, in both NMR and MS RNA analytics, is without doubt shifting from the analysis of snapshot moments towards the development and application of tools capable of analyzing the dynamics of RNA structure and modification profiles.

Structural and phenotypic analysis of Chikungunya Virus RNA structures during viral genome replication and translation

Chikungunya virus (CHIKV) is a pathogenic, positive-sense RNA virus of the Alphavirus genus, which causes fever and debilitating joint pain in humans. CHIKV is transmitted by Aedes mosquitoes and is currently re-emerging in the absence of approved vaccines or antiviral therapies. One approach to vaccine production is to attenuate CHIKV replication through disruption of genomic RNA secondary structures. We have mapped the RNA structure of the 5’ region of the CHIKV genome using selective 2’-hydroxyl acylation analysed by primer extension (SHAPE) to investigate intramolecular base-pairing at single-nucleotide resolution. We have identified five highly-conserved RNA structures within the nsP1-coding region of ORF-1 and, using a reverse genetics approach, determined their impact on virus replication in infectious virus and luciferase-reporter subgenomic replicon systems. Our results suggest that RNA structures within the nsP1-coding region are required for efficient CHIKV genome replication in mammalian and mosquito cells, potentially via vertebrate/invertebrate host-specific mechanisms. For example, disruption of one of the stem-loops inhibits CHIKV replication in mammalian cell lines, while having no significant effect in mosquito cells. Restoration of the structure via compensatory silent mutations restores replication – indicating RNA structure-dependent enhancement of CHIKV replication. Conversely, disruption of an adjacent stem-loop inhibits CHIKV replication in mosquito cells but not in mammalian cell lines. Our structural data also suggests that higher-order interactions within this region impact CHIKV replication. As arboviruses continue to re-emerge, it is critical that we improve our understanding of their replication cycles. RNA structures may constitute novel targets for vaccine attenuation or drug design.

ReactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction

BackgroundRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.ResultsHere, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures.ConclusionsWe have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR.

Functional polymorphism within miR-23a∼27a∼24-2 cluster confers clinical outcome of breast cancer in Pakistani cohort.

MicroRNAs (miRNAs) are small regulatory RNA molecules that control gene activity by base pairing with target messenger RNA leading to their cleavage or translational repression. Previous studies show an involvement of miRNAs in various diseases including cancer. Members of the Mir-23a cluster (MIR23A, MIR24-2and MIR27A) are involved in breast cancer (BC). In the present study, miR-23a/24-2/27a cluster was screened for genetic mutation in BC patients. Heterozygous (A/G allele) as well as homozygous (G/G allele) variants were found in mir-27a gene in screened BC patients. RNA structural analysis revealed that the single nucleotide polymorphism (SNP) effects the size of the terminal loop in the precursor miRNA (pre-miRNA). The altered (G allele) hairpin structure observed was two bases longer than the reference (A allele) hairpin.

Sensitive detection of structural features and rearrangements in long, structured RNA molecules.

Technical innovations in structural probing have drastically advanced the field of RNA structure analysis. These advances have led to parallel approaches developed in separate labs for analyzing RNA structure and dynamics. With the wealth of methodologies available, it can be difficult to determine which is best suited for a given application. Here, using a long, highly structured viral RNA as an example (the positive strand genome of Hepatitis C Virus), we present a semi-comprehensive analysis and describe the major approaches for analyzing the architecture of RNA that is modified with structure-sensitive probes. Additionally, we present an updated method for generating in vitro transcribed and folded RNA that maintains native secondary structures in long RNA molecules. We anticipate that the methods described here will streamline the use of current approaches and help investigators who are unfamiliar with structure probing, obviating the need for time-consuming and expensive optimization.

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."

Integrative Analysis of Zika Virus Genome RNA Structure Reveals Critical Determinants of Viral Infectivity

Zika virus (ZIKV) strains can be classified into the ancestral African and contemporary Asian lineages, with the latter responsible for recent epidemics associated with neurological conditions. To understand how Asian strains lead to exacerbated disease, a crucial step is identifying genomic variations that affect infectivity and pathogenicity. Here we use two high-throughput sequencing approaches to assess RNA secondary structures and intramolecular RNA-RNA interactions invivo for the RNA genomes of Asian and African ZIKV lineages. Our analysis identified functional RNA structural elements and a functional long-range intramolecular interaction specific for the Asian epidemic strains. Mutants that disrupt this extended RNA interaction between the 5' UTR and the E protein coding region reduce virus infectivity, which is partially rescued with compensatory mutants, restoring this RNA-RNA interaction. These findings illuminate the structural basis of ZIKV regulation and provide a resource for the discovery of RNA structural elements important for ZIKV infection.

A deep neural network approach for learning intrinsic protein-RNA binding preferences.

The complexes formed by binding of proteins to RNAs play key roles in many biological processes, such as splicing, gene expression regulation, translation and viral replication. Understanding protein-RNA binding may thus provide important insights to the functionality and dynamics of many cellular processes. This has sparked substantial interest in exploring protein-RNA binding experimentally, and predicting it computationally. The key computational challenge is to efficiently and accurately infer protein-RNA binding models that will enable prediction of novel protein-RNA interactions to additional transcripts of interest. We developed DLPRB (Deep Learning for Protein-RNA Binding), a new deep neural network (DNN) approach for learning intrinsic protein-RNA binding preferences and predicting novel interactions. We present two different network architectures: a convolutional neural network (CNN), and a recurrent neural network (RNN). The novelty of our network hinges upon two key aspects: (i) the joint analysis of both RNA sequence and structure, which is represented as a probability vector of different RNA structural contexts; (ii) novel features in the architecture of the networks, such as the application of RNNs to RNA-binding prediction, and the combination of hundreds of variable-length filters in the CNN. Our results in inferring accurate RNA-binding models from high-throughput in vitro data exhibit substantial improvements, compared to all previous approaches for protein-RNA binding prediction (both DNN and non-DNN based). A more modest, yet statistically significant, improvement is achieved for in vivo binding prediction. When incorporating experimentally-measured RNA structure, compared to predicted one, the improvement on in vivo data increases. By visualizing the binding specificities, we can gain biological insights underlying the mechanism of protein RNA-binding. The source code is publicly available at https://github.com/ilanbb/dlprb. Supplementary data are available at Bioinformatics online.

Dual Graph Partitioning Highlights a Small Group of Pseudoknot-Containing RNA Submotifs.

RNA molecules are composed of modular architectural units that define their unique structural and functional properties. Characterization of these building blocks can help interpret RNA structure/function relationships. We present an RNA secondary structure motif and submotif library using dual graph representation and partitioning. Dual graphs represent RNA helices as vertices and loops as edges. Unlike tree graphs, dual graphs can represent RNA pseudoknots (intertwined base pairs). For a representative set of RNA structures, we construct dual graphs from their secondary structures, and apply our partitioning algorithm to identify non-separable subgraphs (or blocks) without breaking pseudoknots. We report 56 subgraph blocks up to nine vertices; among them, 22 are frequently occurring, 15 of which contain pseudoknots. We then catalog atomic fragments corresponding to the subgraph blocks to define a library of building blocks that can be used for RNA design, which we call RAG-3Dual, as we have done for tree graphs. As an application, we analyze the distribution of these subgraph blocks within ribosomal RNAs of various prokaryotic and eukaryotic species to identify common subgraphs and possible ancestry relationships. Other applications of dual graph partitioning and motif library can be envisioned for RNA structure analysis and design.

Towards Long-Range RNA Structure Prediction in Eukaryotic Genes.

The ability to form an intramolecular structure plays a fundamental role in eukaryotic RNA biogenesis. Proximate regions in the primary transcripts fold into a local secondary structure, which is then hierarchically assembled into a tertiary structure that is stabilized by RNA-binding proteins and long-range intramolecular base pairings. While the local RNA structure can be predicted reasonably well for short sequences, long-range structure at the scale of eukaryotic genes remains problematic from the computational standpoint. The aim of this review is to list functional examples of long-range RNA structures, to summarize current comparative methods of structure prediction, and to highlight their advances and limitations in the context of long-range RNA structures. Most comparative methods implement the “first-align-then-fold” principle, i.e., they operate on multiple sequence alignments, while functional RNA structures often reside in non-conserved parts of the primary transcripts. The opposite “first-fold-then-align” approach is currently explored to a much lesser extent. Developing novel methods in both directions will improve the performance of comparative RNA structure analysis and help discover novel long-range structures, their higher-order organization, and RNA–RNA interactions across the transcriptome.

RNA Framework: an all-in-one toolkit for the analysis of RNA structures and post-transcriptional modifications.

RNA is emerging as a key regulator of a plethora of biological processes. While its study has remained elusive for decades, the recent advent of high-throughput sequencing technologies provided the unique opportunity to develop novel techniques for the study of RNA structure and post-transcriptional modifications. Nonetheless, most of the required downstream bioinformatics analyses steps are not easily reproducible, thus making the application of these techniques a prerogative of few laboratories. Here we introduce RNA Framework, an all-in-one toolkit for the analysis of most NGS-based RNA structure probing and post-transcriptional modification mapping experiments. To prove the extreme versatility of RNA Framework, we applied it to both an in-house generated DMS-MaPseq dataset, and to a series of literature available experiments. Notably, when starting from publicly available datasets, our software easily allows replicating authors' findings. Collectively, RNA Framework provides the most complete and versatile toolkit to date for a rapid and streamlined analysis of the RNA epistructurome. RNA Framework is available for download at: http://www.rnaframework.com.

In-cell RNA structure probing with SHAPE-MaP.

This protocol is an extension to: Nat. Protoc. 10, 1643-1669 (2015); doi:10.1038/nprot.2015.103; published online 01 October 2015RNAs play key roles in many cellular processes. The underlying structure of RNA is an important determinant of how transcripts function, are processed, and interact with RNA-binding proteins and ligands. RNA structure analysis by selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) takes advantage of the reactivity of small electrophilic chemical probes that react with the 2'-hydroxyl group to assess RNA structure at nucleotide resolution. When coupled with mutational profiling (MaP), in which modified nucleotides are detected as internal miscodings during reverse transcription and then read out by massively parallel sequencing, SHAPE yields quantitative per-nucleotide measurements of RNA structure. Here, we provide an extension to our previous in vitro SHAPE-MaP protocol with detailed guidance for undertaking and analyzing SHAPE-MaP probing experiments in live cells. The MaP strategy works for both abundant-transcriptome experiments and for cellular RNAs of low to moderate abundance, which are not well examined by whole-transcriptome methods. In-cell SHAPE-MaP, performed in roughly 3 d, can be applied in cell types ranging from bacteria to cultured mammalian cells and is compatible with a variety of structure-probing reagents. We detail several strategies by which in-cell SHAPE-MaP can inform new biological hypotheses and emphasize downstream analyses that reveal sequence or structure motifs important for RNA interactions in cells.