Electron microscopic mapping of secondary structures in bacterial 16S and 23S ribosomal ribonucleic acid and 30S precursor ribosomal ribonucleic acid

Electron microscopy revealed reproducible secondary structure patterns within partially denatured 16S and 23S ribosomal ribonucleic acid (rRNA) from Escherichia coli. When prepared with 50% formamide-100 mM ammonium acetate, 16S rRNA included two small hairpins that appeared in over 50% of all molecules. Three open loops were observed with frequencies of less than 25%. In contrast, 23S rRNA included a terminal open loop and two additional large structures in over 75% of all molecules. These secondary structure patterns were conserved in the 16S and 23S rRNA from Pseudomonas aeruginosa. The secondary structure of the 30S precursor rRNA from the ribonclease III-deficient E. coli mutant AB105 was mapped after partial denaturation in 70% formamide-100 mM ammonium acetate. Two large open loops were superimposed on the 16S and 23S rRNA secondary structure patterns. These loops were the most frequent structures found on the precursor, and their stems coincided with ribonuclease III cleavage sites. A tentative 5'-3 orientation was determined for the secondary structure patterns of 16S and 23S rRNA from their relative locations within 30S precursor rRNA. The relation of secondary structure to ribosomal protein binding and ribonuclease III cleavage is discussed.     

Secondary structure of the large subunit ribosomal RNA from Escherichia coli, Zea mays chloroplast, and human and mouse mitochondrial ribosomes.

Short base-paired RNA fragments, and fragments containing intra-RNA cross-links, were isolated from E. coli 23S rRNA or 50S ribosomal subunits by two-dimensional gel electrophoresis. The interactions thus found were used as a first basis for constructing a secondary structure model of the 23S rRNA. Sequence comparison with the 23S rDNA from Z. mays chloroplasts, as well as with the 16S (large subunit) rDNA from human and mouse mitochondria, enabled the experimental model to be improved and extrapolated to give complete secondary structures of all four species. The structures are organized in well-defined domains, with over 450 compensating base changes between the two 23S species. Some ribosomal structural "'switches" were found, one involving 5S rRNA.     

Probing the structure of 16 S ribosomal RNA from Bacillus brevis

A majority (approximately 89%) of the nucleotide sequence of Bacillus brevis 16 S rRNA has been determined by a combination of RNA sequencing methods. Several experimental approaches have been used to probe its structure, including (a) partial RNase digestion of 30 S ribosomal subunits, followed by two-dimensional native/denatured gel electrophoresis, in which base-paired fragments were directly identified; (b) identification of positions susceptible to cleavage by RNase A and RNase T1 in 30 S subunits; (c) sites of attack by cobra venom RNase on naked 16 S rRNA; and (d) nucleotides susceptible to attack by bisulfite in 16 S rRNA. These data are discussed with respect to a secondary structure model for B. brevis 16 S rRNA derived by comparative sequence analysis.     

Matching the crystallographic structure of ribosomal protein S7 to a three-dimensional model of the 16S ribosomal RNA.

Two recently published but independently derived structures, namely the X-ray crystallographic structure of ribosomal protein S7 and the "binding pocket" for this protein in a three-dimensional model of the 16S rRNA, have been correlated with one another. The known rRNA-protein interactions for S7 include a minimum binding site, a number of footprint sites, and two RNA-protein crosslink sites on the 16S rRNA, all of which form a compact group in the published 16S rRNA model (despite the fact that these interactions were not used as primary modeling constraints in building that model). The amino acids in protein S7 that are involved in the two crosslinks to 16S rRNA have also been determined in previous studies, and here we have used these sites to orient the crystallographic structure of S7 relative to its rRNA binding pocket. Some minor alterations were made to the rRNA model to improve the fit. In the resulting structure, the principal positively charged surface of the protein is in contact with the 16S rRNA, and all of the RNA-protein interaction data are satisfied. The quality of the fit gives added confidence as to the validity of the 16S rRNA model. Protein S7 is furthermore known to be crosslinked both to P site-bound tRNA and to mRNA at positions upstream of the P site codon; the matched S7-16S rRNA structure makes a prediction as to the location of this crosslink site within the protein molecule.     

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.     

Comparative analysis of the 5'-terminal nucleotide sequence and central domains of 16S rRNA of Yersinia pestis and Escherichia coli]

Partial nucleotide sequence of 16S rRNA (16-989 nn.) of plague agent (Yersinia pestis) was determined after sequencing of cloned cDNA fragments. The comparison of Y. pestis 16S rRNA sequence with that of E. coli shows a number of point sequence variation due to base changes. The base changes are found in 16S rRNA secondary structure regions that are localized on the surface of 30S ribosome subunit (hairpins 6 and 18) as well as in the regions that bind the proteins S8, S15, S16 and S20. These proteins of Y. pestis differ from the same proteins of E. coli by electrophoretic mobility, when analyzed by two-dimensional co-electrophoresis in polyacrylamide gel. The correlation between the structure of the four proteins and the structure of their binding sites in 16S rRNA are discussed.     

MiCA: A Web-Based Tool for the Analysis of Microbial Communities Based on Terminal-Restriction Fragment Length Polymorphisms of 16S and 18S rRNA Genes

A web-based resource, Microbial Community Analysis (MiCA), has been developed to facilitate studies on microbial community ecology that use analyses of terminal-restriction fragment length polymorphisms (T-RFLP) of 16S and 18S rRNA genes. MiCA provides an intuitive web interface to access two specialized programs and a specially formatted database of 16S ribosomal RNA sequences. The first program performs virtual polymerase chain reaction (PCR) amplification of rRNA genes and restriction of the amplicons using primer sequences and restriction enzymes chosen by the user. This program, in silico PCR and Restriction (ISPaR), uses a binary encoding of DNA sequences to rapidly scan large numbers of sequences in databases searching for primer annealing and restriction sites while permitting the user to specify the number of mismatches in primer sequences. ISPaR supports multiple digests with up to three enzymes. The number of base pairs between the 5′ and 3′ primers and the proximal restriction sites can be reported, printed, or exported in various formats. The second program, APLAUS, infers a plausible community structure(s) based on T-RFLP data supplied by a user. APLAUS estimates the relative abundances of populations and reports a listing of phylotypes that are consistent with the empirical data. MiCA is accessible at .     

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

Background

A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1.

Results

The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases.

Conclusion

Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.     

Nucleotide sequence of the Aspergillus nidulans mitochondrial gene coding for the small ribosomal subunit RNA: homology to E. coli 16S rRNA.

The complete primary structure of the 1437 bp gene coding for mitochondrial 15S rRNA and its flanking regions was determined by Maxam-Gilbert sequencing of cloned HindIII fragment H3 of A. nidulans mtDNA. The gene product reveals significant homology (59%) to E. coli 16S rRNA, and the potential secondary structures of both rRNA molecules are very similar, except that the hairpin structures 7, 8 and 30 of the Brimacombe 16S rRNA model are deleted, and that two sequences of 8 and 31 nucleotides are inserted in the mitochondrial species.     

The nucleotide sequence of the gene coding for the 16S rRNA from the archaebacterium Halobacterium halobium

The complete 1473-bp sequence of the 16S rRNA gene from the archaebacterium Halobacterium halobium has been determined. Alignment with the sequences of the 16S rRNA gene from the archaebacteria Halobacterium volcanii and Halococcus morrhua reveals similar degrees of homology, about 88%. Differences in the primary structures of H. halobium and eubacterial (Escherichia coli) 16S rRNA or eukaryotic (Dictyostelium discoideum) 18S rRNA are much higher, corresponding to 63% and 56% homology, respectively. A comparison of the nucleotide sequence of the H. halobium 16S rRNA with those of its archaebacterial counterparts generally confirms a secondary structure model of the RNA contained in the small subunit of the archaebacterial ribosome.     

The accuracy of ribosomal RNA comparative structure models

The determination of the 16S and 23S rRNA secondary structure models was initiated shortly after the first complete 16S and 23S rRNA sequences were determined in the late 1970s. The structures that are common to all 16S rRNAs and all 23S rRNAs were determined using comparative methods from the analysis of thousands of rRNA sequences. Twenty-plus years later, the 16S and 23S rRNA comparative structure models have been evaluated against the recently determined high-resolution crystal structures of the 30S and 50S ribosomal subunits. Nearly all of the predicted covariation-based base pairs, including the regular base pairs and helices, and the irregular base pairs and tertiary interactions, were present in the 30S and 50S crystal structures.     

一种预测单链RNA分子二级结构的计算机算法

本文给出了一个利用已知能量数据构成具有最小自由能的单链RNA分子二级结构的计算机算法,并给出了此算法的可行性证明和应用实例。     

The complete nucleotide sequence of a 16S ribosomal RNA gene from a blue-green alga, Anacystis nidulans

The complete nucleotide sequence of a 16S ribosomal RNA gene from a blue-green alga, Anacystis nidulans, has been determined. Its coding region is estimated to be 1,487 base pairs long, which is nearly identical to those reported for chloroplast 16S rRNA genes and is about 4% shorter than that of the Escherichia coli gene. The 16S rRNA sequence of A. nidulans has 83% homology with that of tobacco chloroplast and 74% homology with that of E. coli. Possible stem and loop structures of A. nidulans 16S rRNA sequences resemble more closely those of chloroplast 16S rRNAs than those of E. coli 16S rRNA. These observations support the endosymbiotic theory of chloroplast origin.     

Two accurate sequence,structure, and phylogenetic template-based RNA alignment systems

Background

The analysis of RNA sequences, once a small niche field for a small collection of scientists whose primary emphasis was the structure and function of a few RNA molecules, has grown most significantly with the realizations that 1) RNA is implicated in many more functions within the cell, and 2) the analysis of ribosomal RNA sequences is revealing more about the microbial ecology within all biological and environmental systems. The accurate and rapid alignment of these RNA sequences is essential to decipher the maximum amount of information from this data.

Methods

Two computer systems that utilize the Gutell lab's RNA Comparative Analysis Database (rCAD) were developed to align sequences to an existing template alignment available at the Gutell lab's Comparative RNA Web (CRW) Site. Multiple dimensions of cross-indexed information are contained within the relational database - rCAD, including sequence alignments, the NCBI phylogenetic tree, and comparative secondary structure information for each aligned sequence. The first program, CRWAlign-1 creates a phylogenetic-based sequence profile for each column in the alignment. The second program, CRWAlign-2 creates a profile based on phylogenetic, secondary structure, and sequence information. Both programs utilize their profiles to align new sequences into the template alignment.

Results

The accuracies of the two CRWAlign programs were compared with the best template-based rRNA alignment programs and the best de-novo alignment programs. We have compared our programs with a total of eight alternative alignment methods on different sets of 16S rRNA alignments with sequence percent identities ranging from 50% to 100%. Both CRWAlign programs were superior to these other programs in accuracy and speed.

Conclusions

Both CRWAlign programs can be used to align the very extensive amount of RNA sequencing that is generated due to the rapid next-generation sequencing technology. This latter technology is augmenting the new paradigm that RNA is intimately implicated in a significant number of functions within the cell. In addition, the use of bacterial 16S rRNA sequencing in the identification of the microbiome in many different environmental systems creates a need for rapid and highly accurate alignment of bacterial 16S rRNA sequences.

     

Improved assessment of denitrifying, N2-fixing, and total-community bacteria by terminal restriction fragment length polymorphism analysis using multiple restriction enzymes

A database of terminal restriction fragments (tRFs) of the 16S rRNA gene was set up utilizing 13 restriction enzymes and 17,327 GenBank sequences. A computer program, termed TReFID, was developed to allow identification of any of these 17,327 sequences by means of polygons generated from the specific tRFs of each bacterium. The TReFID program complements and exceeds in its data content the Web-based phylogenetic assignment tool recently described by A. D. Kent, D. J. Smith, B. J. Benson, and E. W. Triplett (Appl. Environ. Microb. 69:6768-6766, 2003). The method to identify bacteria is different, as is the region of the 16S rRNA gene employed in the present program. For the present communication the software of the tRF profiles has also been extended to allow screening for genes coding for N2 fixation (nifH) and denitrification (nosZ) in any bacterium or environmental sample. A number of controls were performed to test the reliability of the TReFID program. Furthermore, the TReFID program has been shown to permit the analysis of the bacterial population structure of bacteria by means of their 16S rRNA, nifH, and nosZ gene content in an environmental habitat, as exemplified for a sample from a forest soil. The use of the TReFID program reveals that noncultured denitrifying and dinitrogen-fixing bacteria might play a more dominant role in soils than believed hitherto.     

The kink-turn: a new RNA secondary structure motif

Analysis of the Haloarcula marismortui large ribosomal subunit has revealed a common RNA structure that we call the kink-turn, or K-turn. The six K-turns in H.marismortui 23S rRNA superimpose with an r.m.s.d. of 1.7 A. There are two K-turns in the structure of Thermus thermophilus 16S rRNA, and the structures of U4 snRNA and L30e mRNA fragments form K-turns. The structure has a kink in the phosphodiester backbone that causes a sharp turn in the RNA helix. Its asymmetric internal loop is flanked by C-G base pairs on one side and sheared G-A base pairs on the other, with an A-minor interaction between these two helical stems. A derived consensus secondary structure for the K-turn includes 10 consensus nucleotides out of 15, and predicts its presence in the 5'-UTR of L10 mRNA, helix 78 in Escherichia coli 23S rRNA and human RNase MRP. Five K-turns in 23S rRNA interact with nine proteins. While the observed K-turns interact with proteins of unrelated structures in different ways, they interact with L7Ae and two homologous proteins in the same way.     

SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing

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

Genes encoding the 7S RNA and tRNA(Ser) are linked to one of the two rRNA operons in the genome of the extremely thermophilic archaebacterium Methanothermus fervidus

Analysis of gene structure in the extremely thermophilic archaebacterium, Methanothermus fervidus, has revealed the presence of a cluster of stable RNA-encoding genes arranged 5'-7S RNA-tRNA(Ser)-16S rRNA-tRNA(Ala)-23S rRNA-5S rRNA. The genome of M. fervidus contains two rRNA operons but only one operon has the closely linked 7S RNA-encoding gene. The sequences upstream from the two rRNA operons are identical for 206 bp but diverge at the 3' base of the tRNA(Ser) gene. The secondary structures predicted for the M. fervidus 7S, 16S rRNA, tRNA(Ala) and tRNA(Ser) have been compared with those of functionally homologous molecules from moderately thermophilic and mesophilic archaebacteria. A consensus secondary structure for archaebacterial 7S RNAs has been developed which incorporates bases and structural features also conserved in eukaryotic signal-recognition-particle RNAs and eubacterial 4.5S RNAs.