Sequence and structure analysis of noncoding RNAs

Functional Analysis of RNA Motifs Essential for BC200 RNA-mediated Translational Regulation

Brain cytoplasmic 200 RNA (BC200 RNA) is proposed to act as a local translational modulator by inhibiting translation after being targeted to neuronal dendrites. However, the mechanism by which BC200 RNA inhibits translation is not fully understood. Although a detailed functional analysis of RNA motifs is essential for understanding the BC200 RNA-mediated translation-inhibition mechanism, there is little relevant research on the subject. Here, we performed a systematic domain-dissection analysis of BC200 RNA to identify functional RNA motifs responsible for its translational-inhibition activity. Various RNA variants were assayed for their ability to inhibit translation of luciferase mRNA in vitro. We found that the 111–200-nucleotide region consisting of part of the Alu domain as well as the A/C-rich domain (consisting of both the A-rich and C-rich domains) is most effective for translation inhibition. Surprisingly, we also found that individual A-rich, A/C-rich, and Alu domains can enhance translation but at different levels for each domain, and that these enhancing effects manifest as cap-dependent translation.

Sequencing and Functional Annotation of the Whole Genome of Shiraia bambusicola

Shiraia bambusicola is a rare medicinal fungus found in China that causes bamboo plants to decay and die with severe infection. Hypocrellin, its main active ingredient, is widely used in several fields, such as medicine, agriculture, and food industry. In this study, to clarify the genomic components, taxonomic status, pathogenic genes, secondary metabolite synthesis pathways, and regulatory mechanisms of S. bambusicola, whole-genome sequencing, assembly, and functional annotation were performed using high-throughput sequencing and bioinformatics approaches. It was observed that S. bambusicola has 33 Mb genome size, 48.89% GC content, 333 scaffolds, 2590 contigs, 10,703 genes, 82 tRNAs, and 21 rRNAs. The total length of the repeat sequence is 2,151,640 bp. The annotation of 5945 proteins was obtained from InterProScan hits based on the Gene Ontology database. Phylogenetic analysis showed that S. bambusicola belongs to Shiraiaceae, a new family of Pleosporales. It was speculated that there are more than two species or genus in Shiraiaceae. According to the annotation, 777 secreted proteins were associated with virulence or detoxification, including 777 predicted by the PHI database, 776 by the CAZY and Fungal CytochromeP450 database, and 441 by the Proteases database. The 252 genes associated with the secondary metabolism of S. bambusicola were screened and enriched into 28 pathways, among which the terpenoids, staurosporine, aflatoxin, and folate synthesis pathways have not been reported in S. bambusicola. The T1PKS was the main gene cluster among the 28 secondary metabolite synthesis gene clusters in S. bambusicola. The analysis of the T3PKS gene cluster related to the synthesis of hypocrellin showed that there was some similarity between S. bambusicola and 10 other species of fungi; however, the similarity was very low wherein the highest similarity was 17%. The genomic information of S. bambusicola obtained in this study was valuable to understand its genetic function and pathogenicity. The genomic information revealed that several enzyme genes and secreted proteins might be related to their host interactions and pathogenicity. The annotation and analysis of its secondary metabolite synthesis genes and gene clusters will be an important reference for future studies on the biosynthesis and regulation mechanism of the secondary metabolites, contributing to the discovery of new metabolites and accelerating drug development and application.

Predicting RNA Structure with Vfold

In order to carry out biological functions, RNA molecules must fold into specific three-dimensional (3D) structures. Current experimental methods to determine RNA 3D structures are expensive and time consuming. With the recent advances in computational biology, RNA structure prediction is becoming increasingly reliable. This chapter describes a recently developed RNA structure prediction software, Vfold, a virtual bond-based RNA folding model. The main features of Vfold are the physics-based loop free energy calculations for various RNA structure motifs and a template-based assembly method for RNA 3D structure prediction. For illustration, we use the yybP-ykoY Orphan riboswitch as an example to show the implementation of the Vfold model in RNA structure prediction from the sequence.

Multiple 3D RNA Structure Superposition Using Neighbor Joining

Recent advances in RNA research and the steady growth of available RNA structures call for bioinformatics methods for handling and analyzing RNA structural data. Recently, we introduced SETTER-a fast and accurate method for RNA pairwise structure alignment. In this paper, we describe MultiSETTER, SETTER extension for multiple RNA structure alignment. MultiSETTER combines SETTER's decomposition of RNA structures into non-overlapping structural subunits with the multiple sequence alignment algorithm ClustalW adapted for the structure alignment. The accuracy of MultiSETTER was assessed by the automatic classification of RNA structures and its comparison to SCOR annotations. In addition, MultiSETTER classification was also compared to multiple sequence alignment-based and secondary structure alignment-based classifications provided by LocARNA and RNADistance tools, respectively. MultiSETTER precompiled Windows libraries, as well as the C++ source code, are freely available from http://siret.cz/multisetter.

The ViennaRNA web services

The ViennaRNA package is a widely used collection of programs for thermodynamic RNA secondary structure prediction. Over the years, many additional tools have been developed building on the core programs of the package to also address issues related to noncoding RNA detection, RNA folding kinetics, or efficient sequence design considering RNA-RNA hybridizations. The ViennaRNA web services provide easy and user-friendly web access to these tools. This chapter describes how to use this online platform to perform tasks such as prediction of minimum free energy structures, prediction of RNA-RNA hybrids, or noncoding RNA detection. The ViennaRNA web services can be used free of charge and can be accessed via http://rna.tbi.univie.ac.at.

A Method to Predict the 3D Structure of an RNA Scaffold

The ever increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a recently developed RNA structure prediction method based on the virtual bond-based coarse-grained folding model (Vfold). The main emphasis in the Vfold method is placed on the loop entropy calculations, the treatment of noncanonical (mismatch) interactions and the 3D structure assembly from motif-based template library. As case studies, we use the glycine riboswitch and the G310-U376 domain of MLV RNA to illustrate the Vfold-based prediction of RNA 3D structures from the sequences.

Long intergenic non-coding RNAs (LincRNAs) identified by RNA-seq in breast cancer

In an attempt to find the correlation of aberrant expression of long intergenic noncoding RNAs (lincRNAs) with cancer, twenty-five samples of breast cancer tissue and respective adjacent normal tissue were studied for the expression of lincRNAs by RNA-seq. Among the 538 lincRNAs studied, 124 lincRNAs were exclusively expressed in cancer adjacent tissues and 62 lincRNAs were exclusively expressed in the cancer tissues. Furthermore, the expression of 134 lincRNAs was higher while 272 lower in breast cancer tissue compared with adjacent tissue. The expression of four selected lincRNAs (BC2, BC4, BC5, and BC8) was validated by semi-quantitative and real-time PCR. It was revealed that expression of lincRNA-BC5 was positively correlated with patients' age, pathological stage, and progesterone receptor concentration, while lincRNA-BC8 was negatively correlated with progesterone receptor expression. Higher expression of lincRNA-BC4 was seen in advanced breast cancer grade. LincRNA-BC2 showed no specific changes in the pathological features studied. Interactions between selected lincRNAs and breast cancer associated proteins were highly suggested by RPIseq based on the specific secondary structure. The results demonstrated that this group of lincRNAs was aberrantly expressed in breast cancer. They might play important roles in the function of oncogenes or tumor suppressors affecting the development and progression of breast cancer.

Widespread purifying selection on RNA structure in mammals

Evolutionarily conserved RNA secondary structures are a robust indicator of purifying selection and, consequently, molecular function. Evaluating their genome-wide occurrence through comparative genomics has consistently been plagued by high false-positive rates and divergent predictions. We present a novel benchmarking pipeline aimed at calibrating the precision of genome-wide scans for consensus RNA structure prediction. The benchmarking data obtained from two refined structure prediction algorithms, RNAz and SISSIz, were then analyzed to fine-tune the parameters of an optimized workflow for genomic sliding window screens. When applied to consistency-based multiple genome alignments of 35 mammals, our approach confidently identifies >4 million evolutionarily constrained RNA structures using a conservative sensitivity threshold that entails historically low false discovery rates for such analyses (5-22%). These predictions comprise 13.6% of the human genome, 88% of which fall outside any known sequence-constrained element, suggesting that a large proportion of the mammalian genome is functional. As an example, our findings identify both known and novel conserved RNA structure motifs in the long noncoding RNA MALAT1. This study provides an extensive set of functional transcriptomic annotations that will assist researchers in uncovering the precise mechanisms underlying the developmental ontologies of higher eukaryotes.

Integrating chemical footprinting data into RNA secondary structure prediction

Chemical and enzymatic footprinting experiments, such as shape (selective 2′-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

The role of RNA structure in posttranscriptional regulation of gene expression

As more information is gathered on the mechanisms of transcription and translation, it is becoming apparent that these processes are highly regulated. The formation of mRNA secondary and tertiary structures is one such regulatory process that until recently it has not been analysed in depth. Formation of these mRNA structures has the potential to enhance and inhibit alternative splicing of transcripts, and regulate rates and amount of translation. As this regulatory mechanism potentially impacts at both the transcriptional and translational level, while also potentially utilising the vast array of non-coding RNAs, it warrants further investigation. Currently, a variety of high-throughput sequencing techniques including parallel analysis of RNA structure (PARS), fragmentation sequencing (FragSeq) and selective 2-hydroxyl acylation analysed by primer extension (SHAPE) lead the way in the genome-wide identification and analysis of mRNA structure formation. These new sequencing techniques highlight the diversity and complexity of the transcriptome, and demonstrate another regulatory mechanism that could become a target for new therapeutic approaches.

Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease

Novel classes of small and long non-coding RNAs (ncRNAs) are being characterized at a rapid pace, driven by recent paradigm shifts in our understanding of genomic architecture, regulation and transcriptional output, as well as by innovations in sequencing technologies and computational and systems biology. These ncRNAs can interact with DNA, RNA and protein molecules; engage in diverse structural, functional and regulatory activities; and have roles in nuclear organization and transcriptional, post-transcriptional and epigenetic processes. This expanding inventory of ncRNAs is implicated in mediating a broad spectrum of processes including brain evolution, development, synaptic plasticity and disease pathogenesis.

Conservation of a triple-helix-forming RNA stability element in noncoding and genomic RNAs of diverse viruses

Abundant expression of the long noncoding (lnc) PAN (polyadenylated nuclear) RNA by the human oncogenic gammaherpesvirus Kaposi's sarcoma-associated herpesvirus (KSHV) depends on a cis-element called the expression and nuclear retention element (ENE). The ENE upregulates PAN RNA by inhibiting its rapid nuclear decay through triple-helix formation with the poly(A) tail. Using structure-based bioinformatics, we identified six ENE-like elements in evolutionarily diverse viral genomes. Five are in double-stranded DNA viruses, including mammalian herpesviruses, insect polydnaviruses, and a protist mimivirus. One is in an insect picorna-like positive-strand RNA virus, suggesting that the ENE can counteract cytoplasmic as well as nuclear RNA decay pathways. Functionality of four of the ENEs was demonstrated by increased accumulation of an intronless polyadenylated reporter transcript in human cells. Identification of these ENEs enabled the discovery of PAN RNA homologs in two additional gammaherpesviruses, RRV and EHV2. Our findings demonstrate that searching for structural elements can lead to rapid identification of lncRNAs.

Annotation of primate miRNAs by high throughput sequencing of small RNA libraries

Background

In addition to genome sequencing, accurate functional annotation of genomes is required in order to carry out comparative and evolutionary analyses between species. Among primates, the human genome is the most extensively annotated. Human miRNA gene annotation is based on multiple lines of evidence including evidence for expression as well as prediction of the characteristic hairpin structure. In contrast, most miRNA genes in non-human primates are annotated based on homology without any expression evidence. We have sequenced small-RNA libraries from chimpanzee, gorilla, orangutan and rhesus macaque from multiple individuals and tissues. Using patterns of miRNA expression in conjunction with a model of miRNA biogenesis we used these high-throughput sequencing data to identify novel miRNAs in non-human primates.

Results

We predicted 47 new miRNAs in chimpanzee, 240 in gorilla, 55 in orangutan and 47 in rhesus macaque. The algorithm we used was able to predict 64% of the previously known miRNAs in chimpanzee, 94% in gorilla, 61% in orangutan and 71% in rhesus macaque. We therefore added evidence for expression in between one and five tissues to miRNAs that were previously annotated based only on homology to human miRNAs. We increased from 60 to 175 the number miRNAs that are located in orthologous regions in humans and the four non-human primate species studied here.

Conclusions

In this study we provide expression evidence for homology-based annotated miRNAs and predict de novo miRNAs in four non-human primate species. We increased the number of annotated miRNA genes and provided evidence for their expression in four non-human primates. Similar approaches using different individuals and tissues would improve annotation in non-human primates and allow for further comparative studies in the future.

Evaluation of online miRNA resources for biomedical applications

MicroRNAs (miRNAs) are endogenous single-stranded, 22-nt (nucleotide) RNAs which complement mRNA to initiate post-transcriptional regulation. This review presents updates and evaluations of the public domain resources available for miRNA identification and target prediction toward their utilization in the biomedical research approach. This study discusses the basic principles of miRNA computational studies based on the nature and mechanism of action of miRNAs. Furthermore, we have explored fifty-nine current online miRNA tools that can be categorized into three classes in this paper: (i) miRNA identification; (ii) miRNA target prediction; and (iii) specialized miRNA tools.