RNA Primary Sequence Research Articles

SMARTIV: combined sequence and structure de-novo motif discovery for in-vivo RNA binding data.

Gene expression regulation is highly dependent on binding of RNA-binding proteins (RBPs) to their RNA targets. Growing evidence supports the notion that both RNA primary sequence and its local secondary structure play a role in specific Protein-RNA recognition and binding. Despite the great advance in high-throughput experimental methods for identifying sequence targets of RBPs, predicting the specific sequence and structure binding preferences of RBPs remains a major challenge. We present a novel webserver, SMARTIV, designed for discovering and visualizing combined RNA sequence and structure motifs from high-throughput RNA-binding data, generated from in-vivo experiments. The uniqueness of SMARTIV is that it predicts motifs from enriched k-mers that combine information from ranked RNA sequences and their predicted secondary structure, obtained using various folding methods. Consequently, SMARTIV generates Position Weight Matrices (PWMs) in a combined sequence and structure alphabet with assigned P-values. SMARTIV concisely represents the sequence and structure motif content as a single graphical logo, which is informative and easy for visual perception. SMARTIV was examined extensively on a variety of high-throughput binding experiments for RBPs from different families, generated from different technologies, showing consistent and accurate results. Finally, SMARTIV is a user-friendly webserver, highly efficient in run-time and freely accessible via http://smartiv.technion.ac.il/.

Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome.

N6-methyladenosine (m6A) plays important roles in a branch of biological and physiological processes. Accurate identification of m6A sites is especially helpful for understanding their biological functions. Since the wet-lab techniques are still expensive and time-consuming, it's urgent to develop computational methods to identify m6A sites from primary RNA sequences. Although there are some computational methods for identifying m6A sites, no methods whatsoever are available for detecting m6A sites in microbial genomes. In this study, we developed a computational method for identifying m6A sites in Escherichia coli genome. The accuracies obtained by the proposed method are >90% in both 10-fold cross-validation test and independent dataset test, indicating that the proposed method holds the high potential to become a useful tool for the identification of m6A sites in microbial genomes.

Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble

RNA 5-methylcytosine (m5C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m5C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m5C. Due to the difficulty and expensive costs of identifying m5C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m5C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m5C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/.

RNA structure, binding, and coordination in Arabidopsis.

From the moment of transcription, up through degradation, each RNA transcript is bound by an ever-changing cohort of RNA binding proteins. The binding of these proteins is regulated by both the primary RNA sequence, as well as the intramolecular RNA folding, or secondary structure, of the transcript. Thus, RNA secondary structure regulates many post-transcriptional processes. With the advent of next generation sequencing, several techniques have been developed to generate global landscapes of both RNA-protein interactions and RNA secondary structure. In this review, we describe the current state of the field detailing techniques to globally interrogate RNA secondary structure and/or RNA-protein interaction sites, as well as our current understanding of these features in the transcriptome of the model plant Arabidopsis thaliana. WIREs RNA 2017, 8:e1426. doi: 10.1002/wrna.1426 For further resources related to this article, please visit the WIREs website.

In vitrorecapitulation of the site-specific editing (to wild-type) of mutantIDSmRNA transcripts, and the characterization of IDS protein translated from the edited mRNAs

The transfer of genomic information into the primary RNA sequence can be altered by RNA editing. We have previously shown that genomic variants can be RNA-edited to wild-type. The presence of distinct "edited" iduronate 2-sulfatase (IDS) mRNA transcripts ex vivo evidenced the correction of a nonsense and frameshift variant, respectively, in three unrelated Hunter syndrome patients. This phenomenon was confirmed in various patient samples by a variety of techniques, and was quantified by single-nucleotide primer extension. Western blotting also confirmed the presence of IDS protein similar in size to the wild-type. Since preliminary experimental evidence suggested that the "corrected" IDS proteins produced by the patients were similar in molecular weight and net charge to their wild-type counterparts, an in vitro system employing different cell types was established to recapitulate the site-specific editing of IDS RNA (uridine to cytidine conversion and uridine deletion), and to confirm the findings previously observed ex vivo in the three patients. In addition, confocal microscopy and flow cytometry analyses demonstrated the expression and lysosomal localization in HEK293 cells of GFP-labeled proteins translated from edited IDS mRNAs. Confocal high-content analysis of the two patients' cells expressing wild-type or mutated IDS confirmed lysosomal localization and showed no accumulation in the Golgi or early endosomes.

Melting temperature highlights functionally important RNA structure and sequence elements in yeast mRNA coding regions.

Secondary structure elements in the coding regions of mRNAs play an important role in gene expression and regulation, but distinguishing functional from non-functional structures remains challenging. Here we investigate the dependence of sequence–structure relationships in the coding regions on temperature based on the recent PARTE data by Wan et al. Our main finding is that the regions with high and low thermostability (high Tm and low Tm regions) are under evolutionary pressure to preserve RNA secondary structure and primary sequence, respectively. Sequences of low Tm regions display a higher degree of evolutionary conservation compared to high Tm regions. Low Tm regions are under strong synonymous constraint, while high Tm regions are not. These findings imply that high Tm regions contain thermo-stable functionally important RNA structures, which impose relaxed evolutionary constraint on sequence as long as the base-pairing patterns remain intact. By contrast, low thermostability regions contain single-stranded functionally important conserved RNA sequence elements accessible for binding by other molecules. We also find that theoretically predicted structures of paralogous mRNA pairs become more similar with growing temperature, while experimentally measured structures tend to diverge, which implies that the melting pathways of RNA structures cannot be fully captured by current computational approaches.

A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data.

RNA binding proteins (RBPs) play an important role in regulating many processes in the cell. RBPs often recognize their RNA targets in a specific manner. In addition to the RNA primary sequence, the structure of the RNA has been shown to play a central role in RNA recognition by RBPs. In recent years, many experimental approaches, both in vitro and in vivo, were developed and employed to identify and characterize RBP targets and extract their binding specificities. In vivo binding techniques, such as CrossLinking and ImmunoPrecipitation (CLIP)-based methods, enable the characterization of protein binding sites on RNA targets. However, these methods do not provide information regarding the structural preferences of the protein. While methods to obtain the structure of RNA are available, inferring both the sequence and the structure preferences of RBPs remains a challenge. Here we present SMARTIV, a novel computational tool for discovering combined sequence and structure binding motifs from in vivo RNA binding data relying on the sequences of the target sites, the ranking of their binding scores and their predicted secondary structure. The combined motifs are provided in a unified representation that is informative and easy for visual perception. We tested the method on CLIP-seq data from different platforms for a variety of RBPs. Overall, we show that our results are highly consistent with known binding motifs of RBPs, offering additional information on their structural preferences.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

BackgroundRNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.ResultsAn alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.ConclusionRNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/.

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

As one of the most abundant RNA post-transcriptional modifications, N6-methyladenosine (m6A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m6A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m6A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m6A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m6A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/.

Genome recoding by tRNA modifications.

RNA modifications are emerging as an additional regulatory layer on top of the primary RNA sequence. These modifications are particularly enriched in tRNAs where they can regulate not only global protein translation, but also protein translation at the codon level. Modifications located in or in the vicinity of tRNA anticodons are highly conserved in eukaryotes and have been identified as potential regulators of mRNA decoding. Recent studies have provided novel insights into how these modifications orchestrate the speed and fidelity of translation to ensure proper protein homeostasis. This review highlights the prominent modifications in the tRNA anticodon loop: queuosine, inosine, 5-methoxycarbonylmethyl-2-thiouridine, wybutosine, threonyl–carbamoyl–adenosine and 5-methylcytosine. We discuss the functional relevance of these modifications in protein translation and their emerging role in eukaryotic genome recoding during cellular adaptation and disease.

TargetM6A: Identifying N6-Methyladenosine Sites From RNA Sequences via Position-Specific Nucleotide Propensities and a Support Vector Machine.

As one of the most ubiquitous post-transcriptional modifications of RNA, N6-methyladenosine ( [Formula: see text]) plays an essential role in many vital biological processes. The identification of [Formula: see text] sites in RNAs is significantly important for both basic biomedical research and practical drug development. In this study, we designed a computational-based method, called TargetM6A, to rapidly and accurately target [Formula: see text] sites solely from the primary RNA sequences. Two new features, i.e., position-specific nucleotide/dinucleotide propensities (PSNP/PSDP), are introduced and combined with the traditional nucleotide composition (NC) feature to formulate RNA sequences. The extracted features are further optimized to obtain a much more compact and discriminative feature subset by applying an incremental feature selection (IFS) procedure. Based on the optimized feature subset, we trained TargetM6A on the training dataset with a support vector machine (SVM) as the prediction engine. We compared the proposed TargetM6A method with existing methods for predicting [Formula: see text] sites by performing stringent jackknife tests and independent validation tests on benchmark datasets. The experimental results show that the proposed TargetM6A method outperformed the existing methods for predicting [Formula: see text] sites and remarkably improved the prediction performances, with MCC = 0.526 and AUC = 0.818. We also provided a user-friendly web server for TargetM6A, which is publicly accessible for academic use at http://csbio.njust.edu.cn/bioinf/TargetM6A.

The prediction of virus mutation using neural networks and rough set techniques.

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

An autoregulatory network between menin and pri-miR-24-1 is required for the processing of its specific modulator miR-24-1 in BON1 cells.

Multiple endocrine neoplasia type 1 (MEN1) is a rare hereditary cancer complex syndrome manifesting a variety of endocrine and non-endocrine neoplasms and lesions. MEN1 is characterized by tumours of the parathyroids, of the neuroendocrine cells of the gastroenteropancreatic tract, and of the anterior pituitary. The MEN1 gene, a tumour suppressor gene, encodes the menin protein. Loss of heterozygosity (LOH) at 11q13 is typical of MEN1 tumours in agreement with Knudson's two-hit hypothesis. We previously showed that the MEN1 parathyroid tumorigenesis is under the control of an "incoherent feedback loop" between miR-24-1 and the menin protein that generates a "Gene Regulatory Network" (GRN) that mimics the second hit of Knudson's hypothesis and that could buffer the effect of the stochastic factors that contribute to the onset and progression of this disease. Here we show, in the BON1 cell line derived from lymphnode metastasis of a human carcinoid tumour of the pancreas, that menin binds specifically to the primary RNA sequence pri-miR-24-1 by promoting the miR-24-1 biogenesis. Network simulation showed a new feed-forward loop between menin, microRNA-24-1 and Musashi-1 proteins. This result shows a novel mechanism whereby menin, a RNA-binding protein, facilitates the processing of its specific miRNA by regulating the dynamics of the menin-miR-24 Gene Regulatory Network at the level of pri-miRNA processing.

Sequence-Specific Modifications Enhance the Broad-Spectrum Antiviral Response Activated by RIG-I Agonists.

The cytosolic RIG-I (retinoic acid-inducible gene I) receptor plays a pivotal role in the initiation of the immune response against RNA virus infection by recognizing short 5'-triphosphate (5'ppp)-containing viral RNA and activating the host antiviral innate response. In the present study, we generated novel 5'ppp RIG-I agonists of varieous lengths, structures, and sequences and evaluated the generation of the antiviral and inflammatory responses in human epithelial A549 cells, human innate immune primary cells, and murine models of influenza and chikungunya viral pathogenesis. A 99-nucleotide, uridine-rich hairpin 5'pppRNA termed M8 stimulated an extensive and robust interferon response compared to other modified 5'pppRNA structures, RIG-I aptamers, or poly(I·C). Interestingly, manipulation of the primary RNA sequence alone was sufficient to modulate antiviral activity and inflammatory response, in a manner dependent exclusively on RIG-I and independent of MDA5 and TLR3. Both prophylactic and therapeutic administration of M8 effectively inhibited influenza virus and dengue virus replication in vitro. Furthermore, multiple strains of influenza virus that were resistant to oseltamivir, an FDA-approved therapeutic treatment for influenza, were highly sensitive to inhibition by M8. Finally, prophylactic M8 treatment in vivo prolonged survival and reduced lung viral titers of mice challenged with influenza virus, as well as reducing chikungunya virus-associated foot swelling and viral load. Altogether, these results demonstrate that 5'pppRNA can be rationally designed to achieve a maximal RIG-I-mediated protective antiviral response against human-pathogenic RNA viruses. The development of novel therapeutics to treat human-pathogenic RNA viral infections is an important goal to reduce spread of infection and to improve human health and safety. This study investigated the design of an RNA agonist with enhanced antiviral and inflammatory properties against influenza, dengue, and chikungunya viruses. A novel, sequence-dependent, uridine-rich RIG-I agonist generated a protective antiviral response in vitro and in vivo and was effective at concentrations 100-fold lower than prototype sequences or other RNA agonists, highlighting the robust activity and potential clinical use of the 5'pppRNA against RNA virus infection. Altogether, the results identify a novel, sequence-specific RIG-I agonist as an attractive therapeutic candidate for the treatment of a broad range of RNA viruses, a pressing issue in which a need for new and more effective options persists.

Toward a next-generation atlas of RNA secondary structure

RNA structure plays a crucial role in gene maturation, regulation and function. Determining the form and frequency of RNA folds is essential for a better understanding of how RNA exerts its functions. Low-throughput studies have focused on RNA primary sequences and expression levels, but with an emphasis on relatively small numbers of transcripts. However, with the recent advent of high-throughput technologies, it is realistic to begin analyzing RNA secondary structures on a genome-wide scale. Here, we review genome-wide RNA secondary structure profiles as well as advances in computational structure predictions. We further discuss the novel characteristics of RNA secondary structure across messenger RNAs. Probing RNA secondary structure by high-throughput sequencing will enable us to build atlases of RNA secondary structures, an important step in helping us to understand the versatility of RNA functions in diverse cellular processes.

Looking for the key to secrets of RNA under the lamp-post.

Looking for the key to secrets of RNA under the lamp-post.

Computational methods toward accurate RNA structure prediction using coarse-grained and all-atom models.

Computational methods can provide significant insights into RNA structure and dynamics, bridging the gap in our understanding of the relationship between structure and biological function. Simulations enrich and enhance our understanding of data derived on the bench, as well as provide feasible alternatives to costly or technically challenging experiments. Coarse-grained computational models of RNA are especially important in this regard, as they allow analysis of events occurring in timescales relevant to RNA biological function, which are inaccessible through experimental methods alone. We have developed a three-bead coarse-grained model of RNA for discrete molecular dynamics simulations. This model is efficient in de novo prediction of short RNA tertiary structure, starting from RNA primary sequences of less than 50 nucleotides. To complement this model, we have incorporated additional base-pairing constraints and have developed a bias potential reliant on data obtained from hydroxyl probing experiments that guide RNA folding to its correct state. By introducing experimentally derived constraints to our computer simulations, we are able to make reliable predictions of RNA tertiary structures up to a few hundred nucleotides. Our refined model exemplifies a valuable benefit achieved through integration of computation and experimental methods.

A simple protocol for the inference of RNA global pairwise alignments.

RNA alignment is an important step in the annotation and characterization of unknown RNAs, and several methods have been developed to meet the need of fast and accurate alignments. Being the performances of the aligning methods affected by the input RNA features, finding the most suitable method is not trivial. Indeed, no available method clearly outperforms the others. Here we present a simple workflow to help choosing the more suitable method for RNA pairwise alignment. We tested the performances of six algorithms, based on different approaches, on datasets created by merging publicly available datasets of known or curated RNA secondary structure annotations with datasets of curated RNA alignments. Then, we simulated the frequent case where the secondary structure is unknown by using the same alignment datasets but ignoring the known structure and instead predicting it. In conclusion, the proposed workflow for pairwise RNA alignment depends on the input RNA primary sequence identity and the availability of reliable secondary structures.

RNA editing, epitranscriptomics, and processing in cancer progression

The transcriptome is extensively and dynamically regulated by a network of RNA modifying factors. RNA editing enzymes APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and ADAR (adenosine deaminase, RNA-specific) irreversibly recode primary RNA sequences, whereas newly described methylases (writers) and de-methylases (erasers) dynamically alter RNA molecules in response to environmental conditions. RNA modifications can affect RNA splicing, nuclear-cytoplasmic transport, translation, and regulation of gene expression by RNA interference. In addition, tRNA base modifications, processing, and regulated cleavage have been shown to alter global patterns of mRNA translation in response to cellular stress pathways. Recent studies, some of which were discussed at this workshop, have rekindled interest in the emerging roles of RNA modifications in health and disease. On September 10th, 2014, the Division of Cancer Biology, NCI sponsored a workshop to explore the role of epitranscriptomic RNA modifications and tRNA processing in cancer progression. The workshop attendees spanned a scientific range including chemists, virologists, and RNA and cancer biologists. The goal of the workshop was to explore the interrelationships between RNA editing, epitranscriptomics, and RNA processing and the enzymatic pathways that regulate these activities in cancer initiation and progression. At the conclusion of the workshop, a general discussion focused on defining the major challenges and opportunities in this field, as well as identifying the tools, technologies, resources and community efforts required to accelerate research in this emerging area.

Hepatitis delta antigen requires a flexible quasi-double-stranded RNA structure to bind and condense hepatitis delta virus RNA in a ribonucleoprotein complex.

The circular genome and antigenome RNAs of hepatitis delta virus (HDV) form characteristic unbranched, quasi-double-stranded RNA secondary structures in which short double-stranded helical segments are interspersed with internal loops and bulges. The ribonucleoprotein complexes (RNPs) formed by these RNAs with the virus-encoded protein hepatitis delta antigen (HDAg) perform essential roles in the viral life cycle, including viral replication and virion formation. Little is understood about the formation and structure of these complexes and how they function in these key processes. Here, the specific RNA features required for HDAg binding and the topology of the complexes formed were investigated. Selective 2'OH acylation analyzed by primer extension (SHAPE) applied to free and HDAg-bound HDV RNAs indicated that the characteristic secondary structure of the RNA is preserved when bound to HDAg. Notably, the analysis indicated that predicted unpaired positions in the RNA remained dynamic in the RNP. Analysis of the in vitro binding activity of RNAs in which internal loops and bulges were mutated and of synthetically designed RNAs demonstrated that the distinctive secondary structure, not the primary RNA sequence, is the major determinant of HDAg RNA binding specificity. Atomic force microscopy analysis of RNPs formed in vitro revealed complexes in which the HDV RNA is substantially condensed by bending or wrapping. Our results support a model in which the internal loops and bulges in HDV RNA contribute flexibility to the quasi-double-stranded structure that allows RNA bending and condensing by HDAg. RNA-protein complexes (RNPs) formed by the hepatitis delta virus RNAs and protein, HDAg, perform critical roles in virus replication. Neither the structures of these RNPs nor the RNA features required to form them have been characterized. HDV RNA is unusual in that it forms an unbranched quasi-double-stranded structure in which short base-paired segments are interspersed with internal loops and bulges. We analyzed the role of the HDV RNA sequence and secondary structure in the formation of a minimal RNP and visualized the structure of this RNP using atomic force microscopy. Our results indicate that HDAg does not recognize the primary sequence of the RNA; rather, the principle contribution of unpaired bases in HDV RNA to HDAg binding is to allow flexibility in the unbranched quasi-double-stranded RNA structure. Visualization of RNPs by atomic force microscopy indicated that the RNA is significantly bent or condensed in the complex.