Advances in RNA structure prediction from sequence: new tools for generating hypotheses about viral RNA structure-function relationships

Isolation and characterization of novel hydrocarbon-degrading bacteria from oil polluted soil near Nacharam, Hyderabad, India

Petroleum is a vital and strategic energy resource for boosting a country's GDP. Despite its high economic value, it is considered a primary factor in environmental deterioration. Bioremediation strategies employ indigenous microbial strains to propose an economical and sustainable alternative to conventional remediation practices. The current study investigates the isolation, identification, and characterization of five novel biosurfactant-producing and petroleum hydrocarbon-degrading bacterial species: Rhodococcus indonesiensis strain SARSHI1, Pseudomonas aeruginosa strain SARSHI2, Pseudomonas argentinensis strain SARSHI3, Acinetobacter baumannii strain SARSHI4, and Rhodococcus qingshengii strain SARSHI5. Molecular identification was determined via 16S rRNA sequencing, and their taxonomic identities were validated through biochemical assessments. Their partial sequences were deposited in NCBI with accession numbers: 'PV034287', 'OP597529', 'OP584476', 'OQ711779', and 'OQ711775' respectively. Amongst them, R. indonesiensis exhibited the highest biosurfactant and hydrocarbon-degrading potential with a critical micelle concentration of 70 mg/L, reduced surface tension of 27 mN/m, an emulsification index (E24) of 85.34%, and hydrocarbon-degrading potency of up to 90%. Gravimetric analysis revealed up to 84% hydrocarbon degradation when supplemented with glycerol, and GC-MS analysis confirmed the selective degradation of n-alkanes (C18-C24). Structural studies employing NMR established the biosurfactant as a lipopeptide. Statistical optimization utilizing RSM - Box-Behnken design obtained the optimized conditions for enhanced biosurfactant and biodegradation activity. Microcosm studies further assessed SARSHI1's bioremediation potential under field-simulated treatments, achieving up to 95% degradation rates under the combined treatment of Bioaugmentation + Biostimulation + Biosurfactant (BA + BS + B), signifying the amplified bioavailability of hydrocarbons. Phytotoxicity tests confirmed the environmental impact of the bacterial strain. The results govern a robust framework for advancing microbial applications in environmental remediation and further support R. indonesiensis SARSHI1 for large-scale biotechnological paradigms.

Machine learning modeling of RNA structures: methods, challenges and future perspectives

The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.

Genome-Wide RNA Secondary Structure Prediction

The information of RNA secondary structure has been widely applied to the inference of RNA function. However, a classical prediction method is not feasible to long RNAs such as mRNA due to the problems of computational time and numerical errors. To overcome those problems, sliding window methods have been applied while their results are not directly comparable to global RNA structure prediction. In this chapter, we introduce ParasoR, a method designed for parallel computation of genome-wide RNA secondary structures. To enable genome-wide prediction, ParasoR distributes dynamic programming (DP) matrices required for structure prediction to multiple computational nodes. Using the database of not the original DP variable but the ratio of variables, ParasoR can locally compute the structure scores such as stem probability or accessibility on demand. A comprehensive analysis of local secondary structures by ParasoR is expected to be a promising way to detect the statistical constraints on long RNAs.

Identification, genetic variation, and structural analysis of 18S rRNA of Theileria orientalis and Theileria velifera-like isolates from Myanmar

Ribosomal RNA genes have been widely used for the identification and phylogenetic analysis of various organisms, including parasitic protozoa. Here, we report nine near full-length Theileria orientalis 18S rRNA gene sequences from cattle from different areas of Myanmar. Phylogenetic analysis of the 18S rRNA genes revealed a considerably close genetic relationship among T. orientalis isolates from Australia, China, Japan, Korea, Myanmar, and Pakistan. We also obtained four Theileria velifera-like (Theileria cf. velifera) 18S rRNA gene sequences from two cattle and two water buffaloes from the northernmost area of Myanmar. The phylogenetic analysis of T. cf. velifera isolates from Myanmar along with T. velifera and T. cf. velifera isolates from African countries suggested an evolutionary lineage of greater complexity in T. velifera-related parasites. DNA alignment analysis indicated the presence of 51 and 55 nucleotide variation positions within the 18S rRNA genes from 15 T. orientalis and 11 T. velifera-related isolates, respectively. Alignment entropy analysis of the 18S rRNA sequences indicated that both T. orientalis and T. velifera-related isolates had three hyper variable regions, corresponding to V2, V4, and V7 regions in eukaryotes. The degree of variation was prominent in the V2 in T. orientalis and V4 in T. velifera-related isolates. The secondary structure analysis of the 18S rRNA predicted using minimum free energy algorism revealed that the structure of V4 region differed most significantly between T. orientalis and T. velifera. These results provide novel insights into common structures, variations and functions of small subunit rRNA in Theileria species.

Challenges and approaches to predicting RNA with multiple functional structures

The revolution in sequencing technology demands new tools to interpret the genetic code. As in vivo transcriptome-wide chemical probing techniques advance, new challenges emerge in the RNA folding problem. The emphasis on one sequence folding into a single minimum free energy structure is fading as a new focus develops on generating RNA structural ensembles and identifying functional structural features in ensembles. This review describes an efficient combinatorially complete method and three free energy minimization approaches to predicting RNA structures with more than one functional fold, as well as two methods for analysis of a thermodynamics-based Boltzmann ensemble of structures. The review then highlights two examples of viral RNA 3'-UTR regions that fold into more than one conformation and have been characterized by single molecule fluorescence energy resonance transfer or NMR spectroscopy. These examples highlight the different approaches and challenges in predicting structure and function from sequence for RNA with multiple biological roles and folds. More well-defined examples and new metrics for measuring differences in RNA structures will guide future improvements in prediction of RNA structure and function from sequence.

The evolution of RNA structural probing methods: From gels to next-generation sequencing

RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read-out methods. This was superseded by capillary electrophoresis, and more recently by next-generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome-wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells.

Swellix: a computational tool to explore RNA conformational space

Background

The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods.

Results

The conceptual advance in Swellix is to count helices and generate all possible combinations of helices rather than counting and combining base pairs. Swellix bundles similar helices and includes improvements in memory use and efficient parallelization. Biological applications of Swellix are demonstrated by computing the reduction in conformational space and entropy due to naturally modified nucleotides in tRNA sequences and by motif searches in Human Endogenous Retroviral (HERV) RNA sequences. The Swellix motif search reveals occurrences of protein and drug binding motifs in the HERV RNA ensemble that do not occur in minimum free energy or centroid predicted structures.

Conclusions

Swellix presents significant improvements over Crumple in terms of efficiency and memory use. The efficient parallelization of Swellix enables the computation of sequences as long as 418 nucleotides with sufficient experimental constraints. Thus, Swellix provides a practical alternative to free energy minimization tools when multiple structures, kinetically determined structures, or complex RNA-RNA and RNA-protein interactions are present in an RNA folding problem.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1910-7) contains supplementary material, which is available to authorized users.

Is Pichia pastoris a realistic platform for industrial production of recombinant human interferon gamma?

Human interferon gamma (hIFNγ) is an important cytokine in the innate and adaptive immune system, produced commercially in Escherichia coli. Efficient expression of hIFNγ has been reported once for Pichia pastoris (Wang et al., 2014) - a proven heterologous expression system. This study investigated hIFNγ expression in P. pastoris replicating the previous study and expanding by using four different strains (X33: wild type; GS115: HIS^-Mut⁺; KM71H: Arg⁺, Mut^- and CBS7435: Mut^S) and three different vectors (pPICZαA, pPIC9 and pPpT4αS). In addition, the native sequence (NS) and two codon-optimised sequences (COS1 and COS2) for P. pastoris were used. Methanol induction yielded no expression/secretion of hIFNγ in X33, highest levels were recorded for CBS7435: Mut^S (∼16 μg. L^-1). mRNA copy number calculations acquired from RT-qPCR for GS115-pPIC9-COS1 proved low abundance of mRNA. A 10-fold increase in expression of hIFNγ was achieved by lowering the minimal free energy of the mRNA and 100-fold by Mut^S phenotypes, substantially lower than reported by Wang et al. (2014). We conclude that commercial production of low cost, eukaryotic recombinant hIFNγ is not an economically viable in P. pastoris. Further research is required to unravel the cause of low expression in P. pastoris to achieve economic viability.

Changes in the Plasticity of HIV-1 Nef RNA during the Evolution of the North American Epidemic

Because of a high mutation rate, HIV exists as a viral swarm of many sequence variants evolving under various selective pressures from the human immune system. Although the Nef gene codes for the most immunogenic of HIV accessory proteins, which alone makes it of great interest to HIV research, it also encodes an RNA structure, whose contribution to HIV virulence has been largely unexplored. Nef RNA helps HIV escape RNA interference (RNAi) through nucleotide changes and alternative folding. This study examines Historic and Modern Datasets of patient HIV-1 Nef sequences during the evolution of the North American epidemic for local changes in RNA plasticity. By definition, RNA plasticity refers to an RNA molecule’s ability to take alternative folds (i.e., alternative conformations). Our most important finding is that an evolutionarily conserved region of the HIV-1 Nef gene, which we denote by R2, recently underwent a statistically significant increase in its RNA plasticity. Thus, our results indicate that Modern Nef R2 typically accommodates an alternative fold more readily than Historic Nef R2. Moreover, the increase in RNA plasticity resides mostly in synonymous nucleotide changes, which cannot be a response to selective pressures on the Nef protein. R2 may therefore be of interest in the development of antiviral RNAi therapies.

Potential siRNA Molecules for Nucleoprotein and M2/L Overlapping Region of Respiratory Syncytial Virus: In Silico Design

Background

Human respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract disease in the pediatric population, elderly and in immunosuppressed individuals. Respiratory syncytial virus is also responsible for bronchiolitis, pneumonia, and chronic obstructive pulmonary infections in all age groups. With this high disease burden and the lack of an effective RSV treatment and vaccine, there is a clear need for discovery and development of novel, effective and safe drugs to prevent and treat RSV disease. The most innovative approach is the use of small interfering RNAs (siRNAs) which represent a revolutionary new concept in human therapeutics. The nucleoprotein gene of RSV which is known as the most conserved gene and the M2/L mRNA, which encompass sixty-eight overlapping nucleotides, were selected as suitable targets for siRNA design.

Objectives

The present study is aimed to design potential siRNAs for silencing nucleoprotein and an overlapping region of M2-L coding mRNAs by computational analysis.

Materials and Methods

Various computational methods (target alignment, similarity search, secondary structure prediction, and RNA interaction calculation) have been used for siRNA designing against different strains of RSV.

Results

In this study, seven siRNA molecules were rationally designed against the nucleoprotein gene and validated using various computational methods for silencing different strains of RSV. Additionally, three effective siRNA molecules targeting the overlapping region of M2/L mRNA were designed.

Conclusions

This approach provides insight and a validated strategy for chemical synthesis of an antiviral RNA molecule which meets many sequence features for efficient silencing and treatment at the genomic level.

Crumple: An Efficient Tool to Explore Thoroughly the RNA Folding Landscape

The folding landscape for an RNA sequence contains many diverse structures and motifs, which are often sampled rather than completely explored. Today's supercomputers make the complete enumeration of all possible folds for an RNA and a detailed description of the RNA folding landscape a more feasible task. This chapter provides protocols for using the Crumple folding algorithm, an efficient tool to generate all possible non-pseudoknotted folds for an RNA sequence. Crumple in conjunction with Sliding Windows and Assembly can incorporate experimental constraints on the global features of an RNA, such as the minimum number and lengths of helices, which may be determined by crystallography or cryo-electron microscopy. This complete enumeration method is independent of free-energy minimization and allows the user to incorporate experimental data such as chemical probing, SELEX data on RNA-protein binding motifs, and phylogenetic covariation.

RNA Viruses: RNA Roles in Pathogenesis, Coreplication and Viral Load

The review intends to present and recapitulate the current knowledge on the roles and importance of regulatory RNAs, such as microRNAs and small interfering RNAs, RNA binding proteins and enzymes processing RNAs or activated by RNAs, in cells infected by RNA viruses. The review focuses on how non-coding RNAs are involved in RNA virus replication, pathogenesis and host response, especially in retroviruses HIV, with examples of the mechanisms of action, transcriptional regulation, and promotion of increased stability of their targets or their degradation.

StructureFold: genome-wide RNA secondary structure mapping and reconstruction in vivo

MOTIVATION

RNAs fold into complex structures that are integral to the diverse mechanisms underlying RNA regulation of gene expression. Recent development of transcriptome-wide RNA structure profiling through the application of structure-probing enzymes or chemicals combined with high-throughput sequencing has opened a new field that greatly expands the amount of in vitro and in vivo RNA structural information available. The resultant datasets provide the opportunity to investigate RNA structural information on a global scale. However, the analysis of high-throughput RNA structure profiling data requires considerable computational effort and expertise.

RESULTS

We present a new platform, StructureFold, that provides an integrated computational solution designed specifically for large-scale RNA structure mapping and reconstruction across any transcriptome. StructureFold automates the processing and analysis of raw high-throughput RNA structure profiling data, allowing the seamless incorporation of wet-bench structural information from chemical probes and/or ribonucleases to restrain RNA secondary structure prediction via the RNAstructure and ViennaRNA package algorithms. StructureFold performs reads mapping and alignment, normalization and reactivity derivation, and RNA structure prediction in a single user-friendly web interface or via local installation. The variation in transcript abundance and length that prevails in living cells and consequently causes variation in the counts of structure-probing events between transcripts is accounted for. Accordingly, StructureFold is applicable to RNA structural profiling data obtained in vivo as well as to in vitro or in silico datasets. StructureFold is deployed via the Galaxy platform.

AVAILABILITY AND IMPLEMENTATION

StructureFold is freely available as a component of Galaxy available at: https://usegalaxy.org/.

CONTACT

yxt148@psu.edu or sma3@psu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

hiCLIP reveals the in vivo atlas of mRNA secondary structures recognized by Staufen 1

The structure of messenger RNA is important for post-transcriptional regulation, mainly because it affects binding of trans-acting factors. However, little is known about the in vivo structure of full-length mRNAs. Here we present hiCLIP, a biochemical technique for transcriptome-wide identification of RNA secondary structures interacting with RNA-binding proteins (RBPs). Using this technique to investigate RNA structures bound by Staufen 1 (STAU1) in human cells, we uncover a dominance of intra-molecular RNA duplexes, a depletion of duplexes from coding regions of highly translated mRNAs, an unexpected prevalence of long-range duplexes in 3' untranslated regions (UTRs), and a decreased incidence of single nucleotide polymorphisms in duplex-forming regions. We also discover a duplex spanning 858 nucleotides in the 3' UTR of the X-box binding protein 1 (XBP1) mRNA that regulates its cytoplasmic splicing and stability. Our study reveals the fundamental role of mRNA secondary structures in gene expression and introduces hiCLIP as a widely applicable method for discovering new, especially long-range, RNA duplexes.

A parallel implementation of the Wuchty algorithm with additional experimental filters to more thoroughly explore RNA conformational space

We present new modifications to the Wuchty algorithm in order to better define and explore possible conformations for an RNA sequence. The new features, including parallelization, energy-independent lonely pair constraints, context-dependent chemical probing constraints, helix filters, and optional multibranch loops, provide useful tools for exploring the landscape of RNA folding. Chemical probing alone may not necessarily define a single unique structure. The helix filters and optional multibranch loops are global constraints on RNA structure that are an especially useful tool for generating models of encapsidated viral RNA for which cryoelectron microscopy or crystallography data may be available. The computations generate a combinatorially complete set of structures near a free energy minimum and thus provide data on the density and diversity of structures near the bottom of a folding funnel for an RNA sequence. The conformational landscapes for some RNA sequences may resemble a low, wide basin rather than a steep funnel that converges to a single structure.

Probing viral genomic structure: alternative viewpoints and alternative structures for satellite tobacco mosaic virus RNA

Viral RNA structure prediction is a valuable tool for development of drugs against viral disease. This work discusses different approaches to predicting encapsidated viral RNA and highlights satellite tobacco mosaic virus (STMV) RNA as a model system with excellent crystallography data. Fundamentally important issues for debate include thermodynamic versus kinetic control of virus assembly and the possible consequences of quasi-species in the primary structure on RNA secondary structure prediction of a single structure or an ensemble of structures. Multiple computational tools and chemical reagents are now available for improved viral RNA structure prediction. Two different predicted structures for encapsidated STMV RNA result from differences in three main areas: a different approach and philosophy to studying encapsidated viral RNA, an emphasis on different RNA motifs, and technical differences in computational methods and chemical reagents. The experiments with traditional chemical probing and SHAPE reagents are compared in terms of chemistry, results, and interpretation for STMV RNA as well as other RNA protein assemblies, such as the 5'UTR of HIV and the ribosome. This discussion of the challenges of viral RNA structure prediction will lead to new experiments and improved future predictions for viral RNA.

Virtual screening of gene expression regulatory sites in non-coding regions of the infectious salmon anemia virus

BACKGROUND

Members of the Orthomyxoviridae family, which contains an important fish pathogen called the infectious salmon anemia virus (ISAV), have a genome consisting of eight segments of single-stranded RNA that encode different viral proteins. Each of these segments is flanked by non-coding regions (NCRs). In other Orthomyxoviruses, sequences have been shown within these NCRs that regulate gene expression and virulence; however, only the sequences of these regions are known in ISAV, and a biological role has not yet been attributed to these regions. This study aims to determine possible functions of the NCRs of ISAV.

RESULTS

The results suggested an association between the molecular architecture of NCR regions and their role in the viral life cycle. The available NCR sequences from ISAV isolates were compiled, alignments were performed to obtain a consensus sequence, and conserved regions were identified in this consensus sequence. To determine the molecular structure adopted by these NCRs, various bioinformatics tools, including RNAfold, RNAstructure, Sfold, and Mfold, were used. This hypothetical structure, together with a comparison with influenza, yielded reliable secondary structure models that lead to the identification of conserved nucleotide positions on an intergenus level. These models determined which nucleotide positions are involved in the recognition of the vRNA/cRNA by RNA-dependent RNA polymerase (RdRp) or mRNA by the ribosome.

CONCLUSIONS

The information obtained in this work allowed the proposal of previously unknown sites that are involved in the regulation of different stages of the viral cycle, leading to the identification of new viral targets that may assist future antiviral strategies.

Structural dynamics of a mitochondrial tRNA possessing weak thermodynamic stability

Folding dynamics are ubiquitously involved in controlling the multivariate functions of RNAs. While the high thermodynamic stabilities of some RNAs favor purely native states at equilibrium, it is unclear whether weakly stable RNAs exist in random, partially folded states or sample well-defined, globally folded conformations. Using a folding assay that precisely tracks the formation of native aminoacylable tRNA, we show that the folding of a weakly stable human mitochondrial (hmt) leucine tRNA is hierarchical with a distinct kinetic folding intermediate. The stabilities of the native and intermediate conformers are separated by only about 1.2 kcal/mol, and the species are readily interconvertible. Comparison of folding dynamics between unmodified and fully modified tRNAs reveals that post-transcriptional modifications produce a more constrained native structure that does not sample intermediate conformations. These structural dynamics may thus be crucial for recognition by some modifying enzymes in vivo, especially those targeting the globular core region, by allowing access to pretransition state conformers. Reduced conformational sampling of the native, modified tRNAs could then permit improved performance in downstream processes of translation. More generally, weak stabilities of small RNAs that fold in the absence of chaperone proteins may facilitate conformational switching that is central to biological function.

Identification of conserved RNA secondary structures at influenza B and C splice sites reveals similarities and differences between influenza A, B, and C

BACKGROUND

Influenza B and C are single-stranded RNA viruses that cause yearly epidemics and infections. Knowledge of RNA secondary structure generated by influenza B and C will be helpful in further understanding the role of RNA structure in the progression of influenza infection.

FINDINGS

All available protein-coding sequences for influenza B and C were analyzed for regions with high potential for functional RNA secondary structure. On the basis of conserved RNA secondary structure with predicted high thermodynamic stability, putative structures were identified that contain splice sites in segment 8 of influenza B and segments 6 and 7 of influenza C. The sequence in segment 6 also contains three unused AUG start codon sites that are sequestered within a hairpin structure.

CONCLUSIONS

When added to previous studies on influenza A, the results suggest that influenza splicing may share common structural strategies for regulation of splicing. In particular, influenza 3' splice sites are predicted to form secondary structures that can switch conformation to regulate splicing. Thus, these RNA structures present attractive targets for therapeutics aimed at targeting one or the other conformation.

Tackling structures of long noncoding RNAs

RNAs are important catalytic machines and regulators at every level of gene expression. A new class of RNAs has emerged called long non-coding RNAs, providing new insights into evolution, development and disease. Long non-coding RNAs (lncRNAs) predominantly found in higher eukaryotes, have been implicated in the regulation of transcription factors, chromatin-remodeling, hormone receptors and many other processes. The structural versatility of RNA allows it to perform various functions, ranging from precise protein recognition to catalysis and metabolite sensing. While major housekeeping RNA molecules have long been the focus of structural studies, lncRNAs remain the least characterized class, both structurally and functionally. Here, we review common methodologies used to tackle RNA structure, emphasizing their potential application to lncRNAs. When considering the complexity of lncRNAs and lack of knowledge of their structure, chemical probing appears to be an indispensable tool, with few restrictions in terms of size, quantity and heterogeneity of the RNA molecule. Probing is not constrained to in vitro analysis and can be adapted to high-throughput sequencing platforms. Significant efforts have been applied to develop new in vivo chemical probing reagents, new library construction protocols for sequencing platforms and improved RNA prediction software based on the experimental evidence.

RNA-SSPT: RNA Secondary Structure Prediction Tools

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

Genome-wide analyses of Epstein-Barr virus reveal conserved RNA structures and a novel stable intronic sequence RNA

BACKGROUND

Epstein-Barr virus (EBV) is a human herpesvirus implicated in cancer and autoimmune disorders. Little is known concerning the roles of RNA structure in this important human pathogen. This study provides the first comprehensive genome-wide survey of RNA and RNA structure in EBV.

RESULTS

Novel EBV RNAs and RNA structures were identified by computational modeling and RNA-Seq analyses of EBV. Scans of the genomic sequences of four EBV strains (EBV-1, EBV-2, GD1, and GD2) and of the closely related Macacine herpesvirus 4 using the RNAz program discovered 265 regions with high probability of forming conserved RNA structures. Secondary structure models are proposed for these regions based on a combination of free energy minimization and comparative sequence analysis. The analysis of RNA-Seq data uncovered the first observation of a stable intronic sequence RNA (sisRNA) in EBV. The abundance of this sisRNA rivals that of the well-known and highly expressed EBV-encoded non-coding RNAs (EBERs).

CONCLUSION

This work identifies regions of the EBV genome likely to generate functional RNAs and RNA structures, provides structural models for these regions, and discusses potential functions suggested by the modeled structures. Enhanced understanding of the EBV transcriptome will guide future experimental analyses of the discovered RNAs and RNA structures.

Crumple: a method for complete enumeration of all possible pseudoknot-free RNA secondary structures

The diverse landscape of RNA conformational space includes many canyons and crevices that are distant from the lowest minimum free energy valley and remain unexplored by traditional RNA structure prediction methods. A complete description of the entire RNA folding landscape can facilitate identification of biologically important conformations. The Crumple algorithm rapidly enumerates all possible non-pseudoknotted structures for an RNA sequence without consideration of thermodynamics while filtering the output with experimental data. The Crumple algorithm provides an alternative approach to traditional free energy minimization programs for RNA secondary structure prediction. A complete computation of all non-pseudoknotted secondary structures can reveal structures that would not be predicted by methods that sample the RNA folding landscape based on thermodynamic predictions. The free energy minimization approach is often successful but is limited by not considering RNA tertiary and protein interactions and the possibility that kinetics rather than thermodynamics determines the functional RNA fold. Efficient parallel computing and filters based on experimental data make practical the complete enumeration of all non-pseudoknotted structures. Efficient parallel computing for Crumple is implemented in a ring graph approach. Filters for experimental data include constraints from chemical probing of solvent accessibility, enzymatic cleavage of paired or unpaired nucleotides, phylogenetic covariation, and the minimum number and lengths of helices determined from crystallography or cryo-electron microscopy. The minimum number and length of helices has a significant effect on reducing conformational space. Pairing constraints reduce conformational space more than single nucleotide constraints. Examples with Alfalfa Mosaic Virus RNA and Trypanosome brucei guide RNA demonstrate the importance of evaluating all possible structures when pseduoknots, RNA-protein interactions, and metastable structures are important for biological function. Crumple software is freely available at http://adenosine.chem.ou.edu/software.html.

Site-specific platinum(II) cross-linking in a ribozyme active site

The function of RNA depends on its ability to adopt complex and dynamic structures, and the incorporation of site-specific cross-linking probes is a powerful method for providing distance constraints that are valuable in RNA structural biology. Here we describe a new RNA-RNA cross-linking strategy based on Pt(II) targeting of specific phosphorothioate substitutions. In this strategy cis-diammine Pt(II) complexes are kinetically recruited and anchored to a phosphorothioate substitution embedded within a structured RNA. Substitution of the remaining exchangeable Pt(II) ligand with a nucleophile supplied by a nearby RNA nucleobase results in metal-mediated cross-links that are stable during isolation. This type of cross-linking strategy was explored within the catalytic core of the Hammerhead ribozyme (HHRz). When a phosphorothioate substitution is installed at the scissile bond normally cleaved by the HHRz, Pt(II) cross-linking takes place to nucleotides G8 and G10 in the ribozyme active site. Both of these positions are predicted to be within ~8 Å of a phosphorothioate-bound Pt(II) metal center. Cross-linking depends on Mg(2+) ion concentration, reaching yields as high as 30%, with rates that indicate cation competition within the RNA three-helix junction. Cross-linking efficiency depends on accurate formation of the HHRz tertiary structure, and cross-links are not observed for RNA helices. Combined, these results show promise for using kinetically inert Pt(II) complexes as new site-specific cross-linking tools for exploring RNA structure and dynamics.

Ensemble of secondary structures for encapsidated satellite tobacco mosaic virus RNA consistent with chemical probing and crystallography constraints

Viral genomic RNA adopts many conformations during its life cycle as the genome is replicated, translated, and encapsidated. The high-resolution crystallographic structure of the satellite tobacco mosaic virus (STMV) particle reveals 30 helices of well-ordered RNA. The crystallographic data provide global constraints on the possible secondary structures for the encapsidated RNA. Traditional free energy minimization methods of RNA secondary structure prediction do not generate structures consistent with the crystallographic data, and to date no complete STMV RNA basepaired secondary structure has been generated. RNA-protein interactions and tertiary interactions may contribute a significant degree of stability, and the kinetics of viral assembly may dominate the folding process. The computational tools, Helix Find & Combine, Crumple, and Sliding Windows and Assembly, evaluate and explore the possible secondary structures for encapsidated STMV RNA. All possible hairpins consistent with the experimental data and a cotranscriptional folding and assembly hypothesis were generated, and the combination of hairpins that was most consistent with experimental data is presented as the best representative structure of the ensemble. Multiple solutions to the genome packaging problem could be an evolutionary advantage for viruses. In such cases, an ensemble of structures that share favorable global features best represents the RNA fold.

Identification of potential conserved RNA secondary structure throughout influenza A coding regions

Influenza A is a negative sense RNA virus of significant public health concern. While much is understood about the life cycle of the virus, knowledge of RNA secondary structure in influenza A virus is sparse. Predictions of RNA secondary structure can focus experimental efforts. The present study analyzes coding regions of the eight viral genome segments in both the (+) and (-) sense RNA for conserved secondary structure. The predictions are based on identifying regions of unusual thermodynamic stabilities and are correlated with studies of suppression of synonymous codon usage (SSCU). The results indicate that secondary structure is favored in the (+) sense influenza RNA. Twenty regions with putative conserved RNA structure have been identified, including two previously described structured regions. Of these predictions, eight have high thermodynamic stability and SSCU, with five of these corresponding to current annotations (e.g., splice sites), while the remaining 12 are predicted by the thermodynamics alone. Secondary structures with high conservation of base-pairing are proposed within the five regions having known function. A combination of thermodynamics, amino acid and nucleotide sequence comparisons along with SSCU was essential for revealing potential secondary structures.

Key genomic changes necessary for an in vivo lethal mouse marburgvirus variant selection process

Marburgvirus (MARV) infections are generally lethal in humans and nonhuman primates but require in vivo lethal mouse variant selection by the serial transfer (passage) of the nonlethal virus into naïve mice to propagate a lethal infection. The passage of progenitor (wild-type) MARV or Ravn virus (RAVV) from infected scid BALB/c mouse liver homogenates into immunocompetent BALB/c mice results in the selection of lethal mouse viruses from within the quasispecies sufficient to establish lethality in immunocompetent mice. Genomic analysis in conjunction with the passage history of each mutation detailed the altered primary and secondary structures of the viral genomic RNA throughout the process. Key findings included the following: (i) a VP40:D184N mutation previously identified in the lethal guinea pig MARV genome was the first mutation to occur during the passage of both the MARV and RAVV variants; (ii) there was biased hypermutagenesis in the RAVV variant genome; (iii) there were two identical mutations in lethal mouse MARV and RAVV variants, VP40:Y19H in the PPPY motif and VP40:D184N in a loop structure between the two VP40 domains; (iv) the passage of wild-type MARV and RAVV in mice resulted in the selection of viral variants from among the quasispecies with different genotypes than those of the wild-type viruses; and (v) a lethal mouse RAVV variant had different tissue tropisms distinct from those of its wild-type virus. These studies provide insights into how marburgviruses manipulate the host for enzymes, metabolites, translation regulators, and effectors of the innate immune response to serve as potential viral countermeasures.

Generalized centroid estimators in bioinformatics

In a number of estimation problems in bioinformatics, accuracy measures of the target problem are usually given, and it is important to design estimators that are suitable to those accuracy measures. However, there is often a discrepancy between an employed estimator and a given accuracy measure of the problem. In this study, we introduce a general class of efficient estimators for estimation problems on high-dimensional binary spaces, which represent many fundamental problems in bioinformatics. Theoretical analysis reveals that the proposed estimators generally fit with commonly-used accuracy measures (e.g. sensitivity, PPV, MCC and F-score) as well as it can be computed efficiently in many cases, and cover a wide range of problems in bioinformatics from the viewpoint of the principle of maximum expected accuracy (MEA). It is also shown that some important algorithms in bioinformatics can be interpreted in a unified manner. Not only the concept presented in this paper gives a useful framework to design MEA-based estimators but also it is highly extendable and sheds new light on many problems in bioinformatics.

Identification of novel conserved functional motifs across most Influenza A viral strains

Background

Influenza A virus poses a continuous threat to global public health. Design of novel universal drugs and vaccine requires a careful analysis of different strains of Influenza A viral genome from diverse hosts and subtypes. We performed a systematic in silico analysis of Influenza A viral segments of all available Influenza A viral strains and subtypes and grouped them based on host, subtype, and years isolated, and through multiple sequence alignments we extrapolated conserved regions, motifs, and accessible regions for functional mapping and annotation.

Results

Across all species and strains 87 highly conserved regions (conservation percentage > = 90%) and 19 functional motifs (conservation percentage = 100%) were found in PB2, PB1, PA, NP, M, and NS segments. The conservation percentage of these segments ranged between 94 - 98% in human strains (the most conserved), 85 - 93% in swine strains (the most variable), and 91 - 94% in avian strains. The most conserved segment was different in each host (PB1 for human strains, NS for avian strains, and M for swine strains). Target accessibility prediction yielded 324 accessible regions, with a single stranded probability > 0.5, of which 78 coincided with conserved regions. Some of the interesting annotations in these regions included sites for protein-protein interactions, the RNA binding groove, and the proton ion channel.

Conclusions

The influenza virus has evolved to adapt to its host through variations in the GC content and conservation percentage of the conserved regions. Nineteen universal conserved functional motifs were discovered, of which some were accessible regions with interesting biological functions. These regions will serve as a foundation for universal drug targets as well as universal vaccine design.

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy

Background

Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.

Results

Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the pseudo-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.

Conclusions

This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.

Influenza virus RNA structure: unique and common features

The influenza A virus genome consists of eight negative-sense RNA segments. Here we review the currently available data on structure-function relationships in influenza virus RNAs. Various ideas and hypotheses about the roles of influenza virus RNA folding in the virus replication are also discussed in relation to other viruses.

Improving the accuracy of predicting secondary structure for aligned RNA sequences

Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms.

Computational approaches to 3D modeling of RNA

Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

SHAPE-directed RNA secondary structure prediction

The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach >or=95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence.

Conservative secondary structure motif of streptavidin-binding aptamers generated by different laboratories

Aptamers that are selected in vitro from random pools of DNA or RNA molecules by SELEX (Systematic evolution of ligands by exponential enrichment) technique have been extensively explored for analytical and biomedical applications. Although many aptamers with high affinity and specificity against specific ligands have been reported, there is still a lack of well characterized DNA aptamers. Here we report the selection of a group of aptamer candidates (85 mer) against streptavidin. Through comparing the predicted secondary structures of all the candidates, a conservative bulge-hairpin structure section (about 29 mer) was found, and then it was determined to be the binding motif to streptavidin. This binding motif was further discovered to also exist in streptavidin-binding aptamers (SBAs) selected by three other laboratories using different methods. The primary sequences of this secondary structure motif are very different, only several nucleotides in the loop and bulge area are critical for binding and other nucleotides are variable. The streptavidin binding of all the SBAs could be competed by biotin implying that they bind to the same site on streptavidin. These results suggest that the evolution of SBA is predominated by specific groups on streptavidin. The highly variable sequence composition of streptavidin-binding aptamer would make the design of aptameric sensor or device based on streptavidin more flexible and easy.

Natural insertions within the N-terminal region of the coat protein of Maize dwarf mosaic potyvirus (MDMV) have an effect on the RNA stability

A 13 amino acid residue insertion was found in the N-terminal region of the coat protein of several Maize dwarf mosaic virus isolates (MDMV). These insertions seem to be the result of a direct duplication event, but differ in some positions. In order to evaluate the influence of the insertion on the RNA secondary structure and stability, the RNA secondary structures and minimum free energies (MFE) of all existing MDMV coat protein sequences were estimated using three different softwares, the Vienna RNA Package, NUPACK, and UNAFold, and compared to the secondary structure and MFE of various random sequence collections preserving the nucleotide distribution of MDMV. The bioinformatic analysis showed that the insertion stabilizes the RNA structure of the coat protein gene.

Dengue virus markers of virulence and pathogenicity

The increased spread of dengue fever and its more severe form, dengue hemorrhagic fever, have made the study of the mosquito-borne dengue viruses that cause these diseases a public health priority. Little is known about how or why the four different (serotypes 1-4) dengue viruses cause pathology in humans only, and there have been no animal models of disease to date. Therefore, there are no vaccines or antivirals to prevent or treat infection and mortality rates of dengue hemorrhagic fever patients can reach up to 20%. Cases occur mainly in tropical zones within developing countries worldwide, and control measures have been limited to the elimination of the mosquito vectors. Thus, it is imperative that we develop new methods of studying dengue virus pathogenicity. This article presents new approaches that may help us to understand dengue virus virulence and the specific mechanisms that lead to dengue fever and severe disease.