Structure Of Ribonucleic Acid Research Articles

Deep IntronicGBE1Mutation in Manifesting Heterozygous Patients With Adult Polyglucosan Body Disease

We describe a deep intronic mutation in adult polyglucosan body disease. Similar mechanisms can also explain manifesting heterozygous cases in other inborn metabolic diseases. To explain the genetic change consistently associated with manifesting heterozygous patients with adult polyglucosan body disease. This retrospective study took place from November 8, 2012, to November 7, 2014. We studied 35 typical patients with adult polyglucosan body disease, of whom 16 were heterozygous for the well-known c.986A>C mutation in the glycogen branching enzyme gene (GBE1) but harbored no other known mutation in 16 exons. All 16 manifesting heterozygous patients had lower glycogen branching activity compared with homozygous patients, which showed inactivation of the apparently normal allele. We studied the messenger ribonucleic acid (mRNA) structure and the genetic change due to the elusive second mutation. When we reverse transcribed and sequenced the mRNA of GBE1, we found that all manifesting heterozygous patients had the c.986A>C mutant mRNA and complete lack of mRNA encoded by the second allele. We identified a deep intronic mutation in this allele, GBE1-IVS15+5289_5297delGTGTGGTGGinsTGTTTTTTACATGACAGGT, which acts as a gene trap, creating an ectopic last exon. The mRNA transcript from this allele missed the exon 16 and 3'UTR and encoded abnormal GBE causing further decrease of enzyme activity from 18% to 8%. We identified the deep intronic mutation, which acts as a gene trap. This second-most common adult polyglucosan body disease mutation explains another founder effect in all Ashkenazi-Jewish cases.

Aldolase as a chirality intersection of L-amino acids and D-sugars.

Aldolase plays an important role in glycolysis and gluconeogenesis to produce D-fructose-1,6-bisphosphate (D-FBP) from dihydroxyacetone phosphate (DHP) and D-glyceraldehyde-3-phosphate (D-GAP). This reaction is stereoselective and retains the D-GAP 2R configuration and yields D-FBP (with the configuration: 3S, 4S, 5R). The 3- and 4-position carbons are the newly formed chiral carbons because the 5-position carbon of D-FBP comes from the 2-position of D-GAP. Although four diastereomeric products, (3S, 4R, 5R), (3R, 4R, 5R), (3R, 4S, 5R), (3S, 4S, 5R), are expected in the nonenzymatic reaction, only the (3S, 4S, 5R) diastereomer (D-FBP) is obtained. Therefore, the chirality in the 3- and 4-positions is induced by the chirality of the enzyme composed of L-amino acid residues. D-Glucose-6-phosphate (D-G6P), which is generated from D-FBP in the gluconeogenesis pathway, produces D-ribose-5-phosphate (D-R5P) in the pentose phosphate pathway. D-R5P is converted to PRPP (5-phosphoribosyl-α-pyrophosphate), which is used for the de novo synthesis of nucleotides. Ribonucleic acid (RNA) uses the nucleotides as building blocks. The configurations of the 4R-carbon and of the 3S-carbon are retained. The stereochemical structure of RNA is based on 3S as well as 4R (D). The consideration above suggests that aldolase is a key enzyme that determines the 3S configuration in D-R5P. It is thus a chirality intersection between amino acids and sugars, because the sugar chirality is determined by the chiral environment of an L-amino acid protein, aldolase, to produce D-FBP.

Matrix models with Penner interaction inspired by interacting ribonucleic acid

The Penner interaction known in studies of moduli space of punctured Riemann surfaces is introduced and studied in the context of random matrix model of homo RNA. An analytic derivation of the generating function is given and the corresponding partition function is derived numerically. An additional dependence of the structure combinatorics factor on N (related to the size of the matrix and the interaction strength) is obtained. This factor has a strong effect on the structure combinatorics in the low N regime. Databases are scanned for real ribonucleic acid (RNA) structures and pairing information for these RNA structures is computationally extracted. Then the genus is calculated for every structure and plotted as a function of length. The genus distribution function is compared with the prediction from the nonlinear (NL) model. The specific heat and distribution of structure with temperature calculated from the NL model shows that the NL interaction is biased towards planar structures. The second derivative of specific heat changes phase from a double peaked function for small N to a single peak for large N. Detailed analysis reveals the presence of the double peak only for genus 0 structures, the higher genii behave normally with N. Comparable behaviour is found in studies involving interactions of RNA with osmolytes and monovalent cations in unfolding experiments.

In vivo, large-scale preparation of uniformly (15)N- and site-specifically (13)C-labeled homogeneous, recombinant RNA for NMR studies.

Knowledge of how ribonucleic acid (RNA) structures fold to form intricate, three-dimensional structures has provided fundamental insights into understanding the biological functions of RNA. Nuclear magnetic resonance (NMR) spectroscopy is a particularly useful high-resolution technique to investigate the dynamic structure of RNA. Effective study of RNA by NMR requires enrichment with isotopes of (13)C or (15)N or both. Here, we present a method to produce milligram quantities of uniformly (15)N- and site-specifically (13)C-labeled RNAs using wild-type K12 and mutant tktA Escherichia coli in combination with a tRNA-scaffold approach. The method includes a double selection protocol to obtain an E. coli clone with consistently high expression of the recombinant tRNA-scaffold. We also present protocols for the purification of the tRNA-scaffold from a total cellular RNA extract and the excision of the RNA of interest from the tRNA-scaffold using DNAzymes. Finally, we showcase NMR applications to demonstrate the benefit of using in vivo site-specifically (13)C-labeled RNA.

G-quadruplexes within prion mRNA: the missing link in prion disease?

Cellular ribonucleic acid (RNA) plays a crucial role in the initial conversion of cellular prion protein PrPC to infectious PrPSc or scrapie. The nature of this RNA remains elusive. Previously, RNA aptamers against PrPC have been isolated and found to form G-quadruplexes (G4s). PrPC binding to G4 RNAs destabilizes its structure and is thought to trigger its conversion to PrPSc. Here it is shown that PrP messenger RNA (mRNA) itself contains several G4 motifs, located in the octarepeat region. Investigation of the RNA structure in one of these repeats by circular dichroism, nuclear magnetic resonance and ultraviolet melting studies shows evidence of G4 formation. In vitro translation of full-length PrP mRNA, naturally harboring five consecutive G4 motifs, was specifically affected by G4-binding ligands, lending support to G4 formation in PrP mRNA. A possible role of PrP binding to its own mRNA and the role of anti-prion drugs, many of which are G4-binding ligands, in prion disease are discussed.

RASS: a web server for RNA alignment in the joint sequence-structure space.

Comparison of ribonucleic acid (RNA) molecules is important for revealing their evolutionary relationships, predicting their functions and predicting their structures. Many methods have been developed for comparing RNAs using either sequence or three-dimensional (3D) structure (backbone geometry) information. Sequences and 3D structures contain non-overlapping sets of information that both determine RNA functions. When comparing RNA 3D structures, both types of information need to be taken into account. However, few methods compare RNA structures using both sequence and 3D structure information. Recently, we have developed a new method based on elastic shape analysis (ESA) that compares RNA molecules by combining both sequence and 3D structure information. ESA treats RNA structures as 3D curves with sequence information encoded on additional coordinates so that the alignment can be performed in the joint sequence-structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. In this study, we implement a web server for the method, called RASS, to make it publicly available to research community. The web server is located at http://cloud.stat.fsu.edu/RASS/.

Surprising base pairing and structural properties of 2'-trifluoromethylthio-modified ribonucleic acids.

The chemical synthesis of ribonucleic acids (RNA) with novel chemical modifications is largely driven by the motivation to identify eligible functional probes for the various applications in life sciences. To this end, we have a strong focus on the development of novel fluorinated RNA derivatives that are powerful in NMR spectroscopic analysis of RNA folding and RNA ligand interactions. Here, we report on the synthesis of 2′-SCF3 pyrimidine nucleoside containing oligoribonucleotides and the comprehensive investigation of their structure and base pairing properties. While this modification has a modest impact on thermodynamic stability when it resides in single-stranded regions, it was found to be destabilizing to a surprisingly high extent when located in double helical regions. Our NMR spectroscopic investigations on short single-stranded RNA revealed a strong preference for C2′-endo conformation of the 2′-SCF3 ribose unit. Together with a recent computational study (L. Li, J. W. Szostak, J. Am. Chem. Soc. 2014, 136, 2858–2865) that estimated the extent of destabilization caused by a single C2′-endo nucleotide within a native RNA duplex to amount to 6 kcal mol−1 because of disruption of the planar base pair structure, these findings support the notion that the intrinsic preference for C2′-endo conformation of 2′-SCF3 nucleosides is most likely responsible for the pronounced destabilization of double helices. Importantly, we were able to crystallize 2′-SCF3 modified RNAs and solved their X-ray structures at atomic resolution. Interestingly, the 2′-SCF3 containing nucleosides that were engaged in distinct mismatch arrangements, but also in a standard Watson–Crick base pair, adopted the same C3′-endo ribose conformations as observed in the structure of the unmodified RNA. Likely, strong crystal packing interactions account for this observation. In all structures, the fluorine atoms made surprisingly close contacts to the oxygen atoms of the corresponding pyrimidine nucleobase (O2), and the 2′-SCF3 moieties participated in defined water-bridged hydrogen-bonding networks in the minor groove. All these features allow a rationalization of the structural determinants of the 2′-SCF3 nucleoside modification and correlate them to base pairing properties.

Predicting scheme of RNA folding structure including pseudoknots

The problem of predicting ribonucleic acid (RNA) structure with pseudoknots makes it NP–hard. To find optimal RNA pseudoknotted structure, we investigate the RNA pseudoknotted structure based on computational methods and models with minimum free energy (MFE). The contribution of this paper is to obtain an efficient algorithm for predicting RNA pseudoknotted structure with pseudoknots, and the algorithm takes O(n³) time and O(n²) space. The experimental test in PseudoBase and Rfam10.1 shows that the proposed algorithm is more effective and precise than other compared algorithms, and can predict arbitrary pseudoknots. Furthermore, we prove that there exists 1 + ε (ε > 0) polynomial time approximation scheme (PTAS) in searching maximum number of stackings. We also present a 2–approximation algorithm and analyse the approximation algorithm.

Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.

Evaluating the reproducibility of quantifying modified nucleosides from ribonucleic acids by LC–UV–MS

Post-transcriptional chemical covalent modification of adenosine, guanosine, uridine and cytidine occurs frequently in all types of ribonucleic acids (RNAs). In ribosomal RNA (rRNA) and transfer RNA (tRNA) these modifications make important contributions to RNA structure and stability and to the accuracy and efficiency of protein translation. The functional dynamics, synergistic nature and regulatory roles of these posttranscriptional nucleoside modifications within the cell are not well characterized. These modifications are present at very low levels and isolation of individual nucleosides for analysis requires a complex multi-step approach. The focus of this study is to characterize the reproducibility of a liquid chromatography method used to isolate and quantitatively characterize modified nucleosides in tRNA and rRNA when nucleoside detection is performed using ultraviolet and mass spectrometric detection (UV and MS, respectively). Despite the analytical challenges of sample isolation and dynamic range, quantitative profiling of modified nucleosides obtained from bacterial tRNAs and rRNAs is feasible at relative standard deviations of 5% RSD or less.

Influence of Surface Chemistry and Charge on Mineral–RNA Interactions

We present the results of large-scale molecular simulations, run over several tens of nanoseconds, of 25-mer sequences of single-stranded ribonucleic acid (RNA) in bulk water and at the surface of three hydrated positively charged MgAl layered double hydroxide (LDH) minerals. The three LDHs differ in surface charge density, through varying the number of isomorphic Al substitutions. Over the course of the simulations, RNA adsorbs tightly to the LDH surface through electrostatic interactions between the charged RNA phosphate groups and the alumina charge sites present in the LDH sheet. The RNA strands arrange parallel to the surface with the base groups aligning normal to the surface and exposed to the bulk aqueous region. This templating effect makes LDH a candidate for amplifying the population of a known RNA sequence from a small number of RNAs. The structure and interactions of RNA at a positively charged, hydroxylated LDH surface were compared with those of RNA at a positively charged calcium montmorillonite surface, allowing us to establish the comparative effect of complexation and water structure at hydroxide and silicate surfaces. The systems were studied by computing radial distribution functions, atom density plots, and radii of gyration, as well as visualization. An observation pertinent to the role of these minerals in prebiotic chemistry is that, for a given charge density on the mineral surface, different genetic sequences of RNA adopt different configurations.

205 Ultrathin nanopores for structural analysis of small nucleic acids

Pinpointing the mechanisms behind function in biological macromolecules is essential for understanding the emerging and evolving nature of life. Biological macromolecules have evolved over billions of years to function efficiently in the highly heterogeneous, crowded environment of a cell. In particular, non-coding ribonucleic acids (RNAs) and deoxyribonucleic acids (DNAs) are omnipresent in cells, preforming both regulatory and catalytic functions by virtue of their structure. One class of such nucleic acids, ironically termed “junk DNA,”1 is significantly involved in orchestrating gene regulation. The RNA subunits of the ribosome2 are an integral part of the translational machinery for protein synthesis. Many ribozymes and other viral RNAs such as the hammerhead ribozyme and the canonical internal ribosome entry site3 exhibit enzymatic activity. Although the exact mechanisms remain unclear, the uniting theme in these non-coding nucleic acids is that their tertiary (spatial) structure yields specific chemical activities and functions. While existing techniques (e.g. nuclear magnetic resonance, X-ray crystallography) provide detailed structural information, inherent drawbacks such as ensemble averaging errors, crystallization artifacts, low time resolutions, and the need for ample amounts of material limit the availability and relevancy of the obtained structural information. Bioinformatics-based tools aim to bridge the gap in knowledge by proposing a homology-based approach to structural prediction, although viable experimental techniques are required in order to unequivocally support and improve such predictions. In this talk, I will present our group's efforts to fabricate nanoscale pore devices for extracting useful structural information about nucleic acids that display in vivo function. Electron-beam irradiation of various thin freestanding membranes affords nanopores with controlled dimensions and interfacial properties, to a quality level that allows highly sensitive analysis of individual nucleic acids in solution at high-throughput. I will describe the properties of our nanopores, as well as some of our recent explorations that have permitted the analysis of DNA and RNA structures.

Choi-Williams Distribution to Describe Coding and Non-coding Regions in Primary Transcript Pre-mRNA

Deoxyribonucleic acid (DNA) information is discrete in both ”time” (sequence positions) and ”amplitude” (nucleotide values). This permits the use of signal processing techniques for its characterization. The conversion of DNA nucleotide symbols into discrete numerical values enables signal processing to be employed to solve problems related to sequence analysis, such as finding coding sequences. In this work, a numerical conversion method was chosen based on the thermodynamic data of free energy changes (∆G°) of the formation of a duplex structure of DNA or ribonucleic acid (RNA), associated with the nucleotide sequence pre-mRNA (messenger RNA). The aim of this work was to characterize coding regions (exons) from non-coding regions (introns) using a methodology based on time-frequency representation (TFR). This permits the observation of the evolution of the periodicity and frequency components with time, introducing more variables related to the gene sequences compared to those used in traditional fast Fourier transform analysis. The parameters calculated from TFR are instantaneous frequency and instantaneous power. It was found that instantaneous frequency and power variables in different frequency bands allowed the correct classification between exons and introns with a prediction accuracy of more than 85%.

PREDICTION OF SECONDARY STRUCTURE OF RNAs WITH PSEUDOKNOTS USING MATCHED FILTERS

Prediction of ribonucleic acid (RNA) secondary structure is an important task in bioinformatics. The RNA structure is known to influence its biological functionality. RNA secondary structure contains many substructures such as stems, loops and pseudoknots. The substructure pseudoknot occurs in several classes of RNAs, and plays a vital role in many biological processes. Prediction of pseudoknots in RNA is challenging and still an open research problem. Several computational methods based on dynamic programming, genetic algorithms, statistical models, etc., have been proposed with varying success. In this paper, we employ matched filtering approach to determine the RNA secondary structure containing pseudoknots. The central idea is to use a matched filter to identify the longest possible stem patterns in the base-pairing matrix of an RNA. The stem patterns obtained are then used to determine the locations of the other substructures such as loops and pseudoknots present in the RNA. Comparison of the prediction results, for RNA sequences derived from PseudoBase, illustrate the effectiveness and the accuracy of our proposed approach as compared to some of the existing popular RNA secondary structure prediction methods.

Evaluating our ability to predict the structural disruption of RNA by SNPs

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.

EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS

Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting - a commonly taken approach - to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.

Classification and Identification of Non-canonical Base Pairs and Structural Motifs.

The 3D structures of many ribonucleic acid (RNA) loops are characterized by highly organized networks of non-canonical interactions. Multiple computational methods have been developed to annotate structures with those interactions or automatically identify recurrent interaction networks. By contrast, the reverse problem that aims to retrieve the geometry of a look from its sequence or ensemble of interactions remains much less explored. In this chapter, we will describe how to retrieve and build families of conserved structural motifs using their underlying network of non-canonical interactions. Then, we will show how to assign sequence alignments to those families and use the software BayesPairing to build statistical models of structural motifs with their associated sequence alignments. From this model, we will apply BayesPairing to identify in new sequences regions where those loop geometries can occur.

Fundamental Approaches in Molecular Biology for Communication Sciences and Disorders

This contemporary tutorial will introduce general principles of molecular biology, common deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein assays and their relevance in the field of communication sciences and disorders. Over the past 2 decades, knowledge of the molecular pathophysiology of human disease has increased at a remarkable pace. Most of this progress can be attributed to concomitant advances in basic molecular biology and, specifically, the development of an ever-expanding armamentarium of technologies for analysis of DNA, RNA, and protein structure and function. Details of these methodologies, their limitations, and examples from the communication sciences and disorders literature are presented. Results/Conclusions The use of molecular biology techniques in the fields of speech, language, and hearing sciences is increasing, facilitating the need for an understanding of molecular biology fundamentals and common experimental assays.

An RNA Base Discrete State Model toward Tertiary Structure Prediction

We report a new ribonucleic acid (RNA) base discrete state model, which was first developed in our lab and designed to provide an efficient and accurate way of representing RNA structures toward RNA three-dimensional structure predictions. Since RNA free energy is largely determined by base pairs and base stackings instead of backbone trajectories, we directly model the RNA base configurations with respect to its previous one along the sequence. This is in sharp contrast with all previous works where the backbone trace was represented. To test how faithfully the discrete model can reproduce the chain trace in continuous space, we randomly select partial chains from the native structure of 23S ribosome RNA and re-grow them. The rms distance of the re-grown structures from the native ones is ∼ 1.7 Å for an optimized 16-state discrete model and gradually increases to ∼ 3.3 Å for long chains of length 50. The efficiency is also good, e.g. the program will finish within several tens of second for long loops of length 50. Our model may facilitate the RNA three-dimensional structure predictions in the near future when combined with appropriate free energy evaluation methods.

SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2).