The secondary structure of human 28S rRNA: The structure and evolution of a mosaic rRNA gene

Summary We have determined the secondary structure of the human 28S rRNA molecule based on comparative analysis of available eukaryotic cytoplasmic and prokaryotic large-rRNA gene sequences. Examination of large-rRNA sequences of both distantly and closely related species has enabled us to derive a structure that accounts both for highly conserved sequence tracts and for previously unanalyzed variable-sequence tracts that account for the evolutionary differences in size among the large rRNAs.Human 28S rRNA is composed of two different types of sequence tracts: conserved and variable. They differ in composition, degree of conservation, and evolution. The conserved regions demonstrate a striking constancy of size and sequence. We have confirmed that the conserved regions of large-rRNA molecules are capable of forming structures that are superimposable on one another. The variable regions contain the sequences responsible for the 83% increase in size of the human large-rRNA molecule over that ofEscherichia coli. Their locations in the gene are maintained during evolution. They are G+C rich and largely nonhomologous, contain simple repetitive sequences, appear to evolve by frequent recombinational events, and are capable of forming large, stable hairpins.The secondary-structure model presented here is in close agreement with existing prokaryotic 23S rRNA secondary-structure models. The introduction of this model helps resolve differences between previously proposed prokaryotic and eukaryotic large-rRNA secondary-structure models.     

Algorithm independent properties of RNA secondary structure predictions

Algorithms predicting RNA secondary structures based on different folding criteria – minimum free energies (mfe), kinetic folding (kin), maximum matching (mm) – and different parameter sets are studied systematically. Two base pairing alphabets were used: the binary GC and the natural four-letter AUGC alphabet. Computed structures and free energies depend strongly on both the algorithm and the parameter set. Statistical properties, such as mean number of base pairs, mean numbers of stacks, mean loop sizes, etc., are much less sensitive to the choice of parameter set and even of algorithm. Some features of RNA secondary structures, such as structure correlation functions, shape space covering and neutral networks, seem to depend only on the base pairing logic (GC or AUGC alphabet). Received: 16 May 1996 / Accepted: 10 July 1996     

How slippage-derived sequences are incorporated into rRNA variable-region secondary structure: implications for phylogeny reconstruction

We analyzed the type and frequency of mutational changes in hypervariable rRNA regions, using the highly length-variable region V4 of the small subunit rRNA locus of tiger beetles (Cicindelidae) as an example. Phylogenetic analysis of indels in closely related species showed that (1) most indels are single nucleotides (usually A or T and sometimes G) or di-nucleotides of A and T. These occur at numerous foci, and they exhibit a strong bias for duplication of 5' single and di-nucleotide motifs but not 3' motifs. (2) Insertions/deletions in stem-forming regions affected paired and unpaired bases with about equal frequency but they did not disrupt the secondary structure. (3) Recurring mutations involving short repeats of the same bases caused parallel evolution of similar sequence motifs in the rRNA of different lineages. The observed types of change are consistent with the propostion that slippage is the main mutational mechanism. Slippage-derived sequences tend to be self-complementary, and therefore the stem-loop structure could be self-organizing as a consequence of the underlying mutational mechanism. Thus, the secondary structure in the cicindelid V4 region may be conserved due to the dynamics of the mutational mechanism rather than to functional constraints. These processes may also have a tendency to produce similar primary sequences irrespective of phylogenetic associations. The findings have implications for sequence alignment in phylogenetic analysis and should caution against the use of secondary structure to improve the determination of positional homology in hypervariable regions.     

Conservation of secondary structure in 5 S ribosomal RNA: a uniform model for eukaryotic, eubacterial, archaebacterial and organelle sequences is energetically favourable

The most commonly accepted secondary structure models for 5S RNA differ for molecules of eubacterial origin, where the four-helix model of Fox and Woese is generally cited, and those of eukaryotic origin, where a fifth helix is assumed to exist. We have carefully aligned all available sequences from eukaryotes, eubacteria, chloroplasts, archaebacteria and plant mitochondria. We could thus derive a unified secondary structure model applicable to all 5S RNA sequences known to-date. It contains the five helices already present in the eukaryotic model, extended by additional segments that were not previously assumed to be universally present. One of the helices can be written in two equilibrium forms, which could reflect the existence of a flexible, dynamic structure. For the derivation of the model and the estimation of the free energies we followed a set of rules optimized to predict the tRNA cloverleaf. The stability of the unified model is higher than that of nearly all previously proposed sequence-specific and general models.     

Selection for fitness versus selection for robustness in RNA secondary structure folding

I investigate the competition between two quasispecies residing on two disparate neutral networks. Under the assumption that the two neutral networks have different topologies and fitness levels, it is the mutation rate that determines which quasispecies will eventually be driven to extinction. For small mutation rates, I find that the quasispecies residing on the neutral network with the lower replication rate will disappear. For higher mutation rates, however, the faster replicating sequences may be outcompeted by the slower replicating ones if the connection density on the second neutral network is sufficiently high. The analytical results are in excellent agreement with flow-reactor simulations of replicating RNA sequences.     

Nuclease S1 analysis of eubacterial 5S rRNA secondary structure

Summary Single-strand-specific nuclease S₁ was employed as a structural probe to confirm locations of unpaired nucleotide bases in 5S rRNAs purified from prokaryotic species of rRNA superfamily I. Limited nuclease S₁ digests of 3- and 5-end-labeled [³²P]5S rRNAs were electrophoresed in parallel with reference endoribonuclease digests on thin allel with reference endoribonuclease digests on thin sequencing gels. Nuclease S₁ primary hydrolysis patterns were comparable for 5S rRNAs prepared from all 11 species examined in this study. The locations of base-paired regions determined by enzymatic analysis corroborate the general features of the proposed universal five-helix model for prokaryotic 5S rRNA, although the results of this study suggest a significant difference between prokaryotic and eukaryotic 5S rRNAs in the evolution of helix IV. Furthermore, the extent of base-pairing predicted by helix IV needs to be reevaluated for eubacterial species. Clipping patterns in helices II and IV appear to be consistent with a secondary structural model that undergoes a conformational rearrangement between two (or more) structures. Primary clipping patterns in the helix II region, obtained by S₁ analysis, may provide useful information concerning the tertiary structure of the 5S rRNA molecule.     

General combinatorics of RNA secondary structure

The total number of RNA secondary structures of a given length with minimal hairpin loop length m(m>0) and with minimal stack length l(l>0) is computed, under the assumption that all base pairs can occur. Asymptotics are derived from the determination of recurrence relations of decomposition properties.     

18S rRNA alignments derived from different secondary structure models can produce alternative phylogenies

Molecular sequence data are often aligned on the basis of secondary and/or tertiary structure models. However, these models are regularly updated and sometimes differ depending on the way in which they were constructed. We examined whether the choice of a particular 18S rRNA secondary structure model as alignment basis influences phylogeny inference. We therefore compared 18S rRNA phylogenies derived from alignments based on different models. We used: 1. Maximum parsimony; 2. The neighbour-joining method; 3. The maximum-likelihood approach; and 4. Evolutionary parsimony. This demonstrated that the secondary structure model on which an alignment is based may influence: 1. The tree topologies found by these four methods; 2. The numbers of most parsimonious trees found; and 3. The statistical values calculated by the evolutionary parsimony method.     

Mitochondrial 12S rRNA gene mutations affect RNA secondary structure and lead to variable penetrance in hearing impairment

Mutations in the mitochondrial DNA are one of the most important causes of sensorineural hearing loss, especially in the 12S ribosomal RNA (rRNA) gene. We have analyzed the mtDNA 12S rRNA gene in a cohort of 443 families with hearing impairment, and have identified the A1555G mutation in 69 unrelated cases. A1555G is not a fully penetrant change, since only 63% of subjects with this change have developed hearing impairment. In addition, only 22% of the 183 A1555G deaf subjects were treated with aminoglycosides. Two novel nucleotide changes (T1291C and T1243C) were identified. T1243C was found in five deafness cases and one control sample. Mutation T1291C was detected in all maternally related individuals of a pedigree and in none of 95 control samples. Conservation analysis and comparison of the 12S rRNA structure with the 16S rRNA of Escherichia coli showed that the T at nucleotide 1243 and A at nucleotide 1555 are conserved positions. Prediction of RNA secondary structure showed changes in all 12S rRNA variants, the most severe being for T1291C. The reported data confirm the high prevalence of mutation A1555G in deafness cases and the major role of the 12S rRNA gene in hearing. The two novel changes reported here might have different contributions as deafness-related variants. T1291C fulfills the criteria of a disease-causing change. As in the case of mutation A1555G, the underlying phenotype of T1291C is not homogeneous for all family members, providing evidence for the implication of environmental and/or additional genetic factors.     

Prediction of secondary structures of 16S and 23S rRNA fromEscherichia coli

Small and large subunits ofEscherichia coli ribosome have three different rRNAs, the sequences of which are known. However, attempts by three groups to predict secondary structures of 16S and 23S rRNAs have certain common limitations namely, these structures are predicted assuming no interactions among various domains of the molecule and only 40% residues are involved in base pairing as against the experimental observation of 60 % residues in base paired state. Recent experimental studies have shown that there is a specific interaction between naked 16S and 23S rRNA molecules. This is significant because we have observed that the regions (oligonucleotides of length 9–10 residues), in 16S rRNA which are complementary to those in 23S rRNA do not have internal complementary sequences. Therefore, we have developed a simple graph theoretical approach to predict secondary structures of 16S and 23S rRNAs. Our method for model building not only uses complete sequence of 16S or 23S rRNA molecule along with other experimental observations but also takes into account the observation that specific recognition is possible through the complementary sequences between 16S and 23S rRNA molecules and, therefore, these parts of the molecules are not used for internal base pairing. The method used to predict secondary structures is discussed. A typical secondary structure of the complex between 16S and 23S rRNA molecules, obtained using our method, is presented and compared Briefly with earlier model Building studies.     

Measuring the thermodynamics of RNA secondary structure formation

The thermodynamics of RNA secondary structure formation in small model systems provides a database for predicting RNA structure from sequence. Methods for making these measurements are reviewed with emphasis on optical methods and treatment of experimental errors. Analysis of experimental results in terms of simple nearest-neighbor models is presented. Some measured sequence dependences of non-Watson-Crick motifs are discussed. © 1998 John Wiley & Sons, Inc. Biopoly 44: 309–319, 1997     

Improved free energy parameters for RNA pseudoknotted secondary structure prediction

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.     

Dynalign: an algorithm for finding the secondary structure common to two RNA sequences

With the rapid increase in the size of the genome sequence database, computational analysis of RNA will become increasingly important in revealing structure-function relationships and potential drug targets. RNA secondary structure prediction for a single sequence is 73 % accurate on average for a large database of known secondary structures. This level of accuracy provides a good starting point for determining a secondary structure either by comparative sequence analysis or by the interpretation of experimental studies. Dynalign is a new computer algorithm that improves the accuracy of structure prediction by combining free energy minimization and comparative sequence analysis to find a low free energy structure common to two sequences without requiring any sequence identity. It uses a dynamic programming construct suggested by Sankoff. Dynalign, however, restricts the maximum distance, M, allowed between aligned nucleotides in the two sequences. This makes the calculation tractable because the complexity is simplified to O(M(3)N(3)), where N is the length of the shorter sequence.The accuracy of Dynalign was tested with sets of 13 tRNAs, seven 5 S rRNAs, and two R2 3' UTR sequences. On average, Dynalign predicted 86.1 % of known base-pairs in the tRNAs, as compared to 59.7 % for free energy minimization alone. For the 5 S rRNAs, the average accuracy improves from 47.8 % to 86.4 %. The secondary structure of the R2 3' UTR from Drosophila takahashii is poorly predicted by standard free energy minimization. With Dynalign, however, the structure predicted in tandem with the sequence from Drosophila melanogaster nearly matches the structure determined by comparative sequence analysis.     

Sequence and secondary structure of the central domain ofDrosophila 26S rRNA: A universal model for the central domain of the large rRNA containing the region in which the central break may happen

Summary An 890-bp sequence from the central region ofDrosophila melanogaster 26S ribosomal DNA (rDNA) has been determined and used in an extensive comparative analysis of the central domain of the large subunit ribosomal RNA (lrRNA) from prokaryotes, organelles, and eukaryotes. An alignment of these different sequences has allowed us to precisely map the regions of the central domain that have highly diverged during evolution. Using this sequence comparison, we have derived a secondary structure model of the central domain ofDrosophila 26S ribosomal RNA (rRNA). We show that a large part of this model can be applied to the central domain of lrRNA from prokaryotes, eukaryotes, and organelles, therefore defining a universal common structural core. Likewise, a comparative study of the secondary structure of the divergent regions has been performed in several organisms. The results show that, despite a nearly complete divergence in their length and sequence, a common structural core is also present in divergent regions. In some organisms, one or two of the divergent regions of the central domain are removed by processing events. The sequence and structure of these regions (fragmentation spacers) have been compared to those of the corresponding divergent regions that remain part of the mature rRNA in other species.     

RNA secondary structure and in vitro translation efficiency

Cell-free protein synthesis is a promising technology featuring many advantages compared to in vivo expression techniques. However, most proteins are still synthesized in vivo due to relatively low protein yields commonly achieved in vitro, especially in the batch mode of reaction. In Escherichia coli S30 extract-based cell-free systems protein yields are supposed to be partially limited by a secondary structure formation of the mRNA. In this study we checked promising members of various classes of RNA chaperones and several different RNA helicases on their ability to enhance in vitro translation. The data clearly show that the addition of none of these factors provides a general solution to the problem. However, protein yields can be increased in presence of a microRNA hybridizing with the 5′ untranslated region of mRNAs, possibly by inducing structural changes improving accessibility of the Shine Dalgarno sequence for the ribosomes.     

Secondary structure model for the ITS-2 precursor rRNA of strongyloid nematodes of equids: implications for phylogenetic inference

In order to maximise the positional homology in the primary sequence alignment of the second internal transcribed spacer for 30 species of equine strongyloid nematodes, the secondary structures of the precursor ribosomal RNA were predicted using an approach combining an energy minimisation method and comparative sequence analysis. The results indicated that a common secondary structure model of the second internal transcribed spacer of these nematodes was maintained, despite significant interspecific differences (2–56%) in primary sequences. The secondary structure model was then used to refine the primary second internal transcribed spacer sequence alignment. The “manual” and “structure” alignments were both subjected to phylogenetic analysis using three different tree-building methods to compare the effect of using different sequence alignments on phylogenetic inference. The topologies of the phylogenetic trees inferred from the manual second internal transcribed spacer alignment were usually different to those derived from the structure second internal transcribed spacer alignment. The results suggested that the positional homology in the second internal transcribed spacer primary sequence alignment was maximised when the secondary structure model was taken into consideration.     

Semiautomated improvement of RNA alignments

We have developed a semiautomated RNA sequence editor (SARSE) that integrates tools for analyzing RNA alignments. The editor highlights different properties of the alignment by color, and its integrated analysis tools prevent the introduction of errors when doing alignment editing. SARSE readily connects to external tools to provide a flexible semiautomatic editing environment. A new method, Pcluster, is introduced for dividing the sequences of an RNA alignment into subgroups with secondary structure differences. Pcluster was used to evaluate 574 seed alignments obtained from the Rfam database and we identified 71 alignments with significant prediction of inconsistent base pairs and 102 alignments with significant prediction of novel base pairs. Four RNA families were used to illustrate how SARSE can be used to manually or automatically correct the inconsistent base pairs detected by Pcluster: the mir-399 RNA, vertebrate telomase RNA (vert-TR), bacterial transfer-messenger RNA (tmRNA), and the signal recognition particle (SRP) RNA. The general use of the method is illustrated by the ability to accommodate pseudoknots and handle even large and divergent RNA families. The open architecture of the SARSE editor makes it a flexible tool to improve all RNA alignments with relatively little human intervention. Online documentation and software are available at (http://sarse.ku.dk).     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.     

Visualizing the global secondary structure of a viral RNA genome with cryo-electron microscopy

The lifecycle, and therefore the virulence, of single-stranded (ss)-RNA viruses is regulated not only by their particular protein gene products, but also by the secondary and tertiary structure of their genomes. The secondary structure of the entire genomic RNA of satellite tobacco mosaic virus (STMV) was recently determined by selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE). The SHAPE analysis suggested a single highly extended secondary structure with much less branching than occurs in the ensemble of structures predicted by purely thermodynamic algorithms. Here we examine the solution-equilibrated STMV genome by direct visualization with cryo-electron microscopy (cryo-EM), using an RNA of similar length transcribed from the yeast genome as a control. The cryo-EM data reveal an ensemble of branching patterns that are collectively consistent with the SHAPE-derived secondary structure model. Thus, our results both elucidate the statistical nature of the secondary structure of large ss-RNAs and give visual support for modern RNA structure determination methods. Additionally, this work introduces cryo-EM as a means to distinguish between competing secondary structure models if the models differ significantly in terms of the number and/or length of branches. Furthermore, with the latest advances in cryo-EM technology, we suggest the possibility of developing methods that incorporate restraints from cryo-EM into the next generation of algorithms for the determination of RNA secondary and tertiary structures.     

芹菜(Apium graveolens L.)叶细胞质5SrRNA的序列测定

应用Gupta等和Tanaka等建立的RNA序列双向直读技术,并辅以部分酶解法、化学法等,测定了芹菜叶细胞质的5SrRNA的全序列:与菠菜和蕃茄细胞质已知5SrRNA序列进行了比较,发现它们之间在序列上有高度的保守性。