Identification of bacterial small non-coding RNAs: experimental approaches

Almost 140 bacterial small RNAs (sRNAs; sometimes referred to as non-coding RNAs) have been discovered in the past six years. The majority of these sRNAs were discovered in Escherichia coli, and a smaller subset was characterized in other bacteria, many of which were pathogenic. Many of these genes were identified as a result of systematic screens using computational prediction of sRNAs and experimental-based approaches, including microarray and shotgun cloning. A smaller number of sRNAs were discovered by direct labeling or by functional genetic screens. Many of the discovered genes, ranging in size from 50 to 500 nucleotides, are conserved and located in intergenic regions, in-between open reading frames. The expression of many of these genes is growth phase dependent or stress related. As each search employed specific parameters, this led to the identification of genes with distinct characteristics. Consequently, unique sRNAs such as those that are species-specific, sRNA genes that are transcribed under unique conditions or genes located on the antisense strand of protein-encoding genes, were probably missed.     

Complete DNA Sequence of the Mitochondrial Genome of the Black Chiton, Katharina Tunicata

The DNA sequence of the 15,532-base pair (bp) mitochondrial DNA (mtDNA) of the chiton Katharina tunicata has been determined. The 37 genes typical of metazoan mtDNA are present: 13 for protein subunits involved in oxidative phosphorylation, 2 for rRNAs and 22 for tRNAs. The gene arrangement resembles those of arthropods much more than that of another mollusc, the bivalve Mytilus edulis. Most genes abut directly or overlap, and abbreviated stop codons are inferred for four genes. Four junctions between adjacent pairs of protein genes lack intervening tRNA genes; however, at each of these junctions there is a sequence immediately adjacent to the start codon of the downstream gene that is capable of forming a stem-and-loop structure. Analysis of the tRNA gene sequences suggests that the D arm is unpaired in tRNA(ser(AGN)), which is typical of metazoan mtDNAs, and also in tRNA(ser(UCN)), a condition found previously only in nematode mtDNAs. There are two additional sequences in Katharina mtDNA that can be folded into structures resembling tRNAs; whether these are functional genes is unknown. All possible codons except the stop codons TAA and TAG are used in the protein-encoding genes, and Katharina mtDNA appears to use the same variation of the mitochondrial genetic code that is used in Drosophila and Mytilus. Translation initiates at the codons ATG, ATA and GTG. A + T richness appears to have affected codon usage patterns and, perhaps, the amino acid composition of the encoded proteins. A 142-bp non-coding region between tRNA(glu) and CO3 contains a 72-bp tract of alternating A and T.     

Complete mitochondrial genome of a troglobite millipede Antrokoreana gracilipes (Diplopoda, Juliformia, Julida), and juliformian phylogeny

The complete mitochondrial genome of a troglobite millipede Antrokoreana gracilipes (Verhoeff, 1938) (Dipolopoda, Juliformia, Julida) was sequenced and characterized. The genome (14,747 bp) contains 37 genes (2 ribosomal RNA genes, 22 transfer RNA genes and 13 protein-encoding genes) and two large non-coding regions (225 bp and 31 bp), as previously reported for two diplopods, Narceus annularus (order Spirobolida) and Thyropygus sp. (order Spirostreptida). The A + T content of the genome is 62.1% and four tRNAs (tRNA(Ser(AGN)), tRNA(Cys), tRNA(Ile) and tRNA(Met)) have unusual and unstable secondary structures. Whereas Narceus and Thyropygus have identical gene arrangements, the tRNA(Thr) and tRNA(Trp) of Antrokoreana differ from them in their orientations and/or positions. This suggests that the Spirobolida and Spirostreptida are more closely related to each other than to the Dipolopoda. Three scenarios are proposed to account for the unique gene arrangement of Antrokoreana. The data also imply that the Duplication and Nonrandom Loss (DNL) model is applicable to the order Julida. Bayesian inference (BI) and maximum likelihood (ML) analyses using amino acid sequences deduced from the 12 mitochondrial protein-encoding genes (excluding ATP8) support the view that the three juliformian members are monophyletic (BI 100%; ML 100%), that Thyropygus (Spirostreptida) and Narceus (Spirobolida) are clustered together (BI 100%; ML 83%), and that Antrokoreana (Julida) is a sister of the two. However, due to conflict with previous reports using cladistic approaches based on morphological characteristics, further studies are needed to confirm the close relationship between Spirostreptida and Spirobolida.     

Archaeology and evolution of transfer RNA genes in the Escherichia coli genome

Transfer RNA genes tend to be presented in multiple copies in the genomes of most organisms, from bacteria to eukaryotes. The evolution and genomic structure of tRNA genes has been a somewhat neglected area of molecular evolution. Escherichia coli, the first phylogenetic species for which more than two different strains have been sequenced, provides an invaluable framework to study the evolution of tRNA genes. In this work, a detailed analysis of the tRNA structure of the genomes of Escherichia coli strains K12, CFT073, and O157:H7, Shigella flexneri 2a 301, and Salmonella typhimurium LT2 was carried out. A phylogenetic analysis of these organisms was completed, and an archaeological map depicting the main events in the evolution of tRNA genes was drawn. It is shown that duplications, deletions, and horizontal gene transfers are the main factors driving tRNA evolution in these genomes. On average, 0.64 tRNA insertions/duplications occur every million years (Myr) per genome per lineage, while deletions occur at the slower rate of 0.30 per million years per genome per lineage. This work provides a first genomic glance at the problem of tRNA evolution as a repetitive process, and the relationship of this mechanism to genome evolution and codon usage is discussed.     

Nucleotide sequence and evolution of coding and noncoding regions of a quail mitochondrial genome

Summary Segments of the Japanese quail mito-chondrial genome encompassing many tRNA and protein genes, the small and part of the large rRNA genes, and the control region have been cloned and sequenced. Analysis of the relative position of these genes confirmed that the tRNA^Glu and ND6 genes in galliform mitochondrial DNA are located immediately adjacent to the control region of the molecule instead of between the cytochrome b and ND5 genes as in other vertebrates. Japanese quail and chicken display another distinctive characteristic, that is, they both lack an equivalent to the light-strand replication origin found between the tRNA^Cys and tRNA^Asn genes in all vertebrate mitochondrial genomes sequenced thus far. Comparison of the protein-encoding genes revealed that a great proportion of the substitutions are silent and involve mainly transitions. This bias toward transitions also occurs in the tRNA and rRNA genes but is not observed in the control region where transversions account for many of the substitutions. Sequence alignment indicated that the two avian control regions evolve mainly through base substitutions but are also characterized by the occurrence of a 57-bp deletion/addition event at their 5′ end. The overall sequence divergence between the two gallinaceous birds suggests that avian mitochondrial genomes evolve at a similar rate to other vertebrate mitochondrial DNAs.     

Regulation of synthesis of the branched-chain amino acids and cognate aminoacyl-transfer ribonucleic acid synthetases of Escherichia coli: a common regulatory element

Regulation of isoleucine, valine, and leucine biosynthesis and isoleucyl-, valyl-, and leucyl-transfer ribonucleic acid (tRNA) synthetase formation was examined in two mutant strains of Escherichia coli. One mutant was selected for growth resistance to the isoleucine analogue, ketomycin, and the other was selected for growth resistance to both trifluoroleucine and valine. Control of the synthesis of the branched-chain amino acids by repression was altered in both of these mutants. They also exhibited altered control of formation of isoleucyl-tRNA synthetase (EC 6.1.15, isoleucine:sRNA ligase, AMP), valyl-tRNA synthetase (EC 6.1.1.9, valine:sRNA ligase, AMP), and leucyl-tRNA synthetase (EC 6.1.1.4, leucine:sRNA ligase, AMP). These results suggest the existence of a common element for the control of these two classes of enzymes in Escherichia coli.     

Structure and expression of two porcine genomic clones encoding class I MHC antigens

Two nonallelic porcine class I MHC (SLA) genes have been isolated and characterized. Both genes are expressed in mouse L cells, directing the synthesis of class I SLA molecules that carry common monomorphic determinants but are serologically distinct. The corresponding DNA sequences have been determined. The organization of both of these genes is similar to that of other class I genes: a leader exon, three exons encoding extracellular domains, a transmembrane exon, and three intracytoplasmic exons. The two genes are highly homologous in both exon and intron segments, with average homologies of 88% and 80%, respectively. Nucleotide changes in exon 2 are clustered, whereas those in the other exons are dispersed throughout. Comparison of the swine DNA sequences with class I genes from other species reveals a generally high conservation of exons 2, 3, 4, and 6 with lower homology in the remaining protein-encoding domains. Introns are markedly less well conserved, although moderate homology is found between swine and human class I MHC genes in both introns and 3' flanking regions. Taken together with comparisons of the deduced protein sequences, these data indicate an order of swine greater than human greater than rabbit greater than mouse in the relationship of class I genes.     

Ribosomal RNA genes of Trypanosoma brucei: mapping the regions specifying the six small ribosomal RNAs

There are six small ribosomal RNAs in trypanosome ribosomes. sRNA3 and sRNA5 of Trypanosoma brucei brucei have been partially sequenced. Sequence homologies indicate that sRNA3 is 5.8S RNA and sRNA5 is 5S RNA of T. b. brucei. The regions specifying these two, and the remaining four small RNAs, have been identified within clones of rRNA genes and in the genome. Five of the small RNAs, 1, 2, 3, 4 and 6, hybridise exclusively within the major rRNA gene repeat. A map of the regions specifying these small RNAs is presented. sRNA3 (5.8S RNA) hybridises to a region corresponding to the transcribed spacer of other eukaryotes. sRNA1 hybridises to a region between sequences specifying the two large subunit RNA molecules of 2.3 kb and 1.8 kb. Sequences specifying sRNAs 2 and 4 are present near the sequence specifying sRNA1, while sRNA6 appears to be specified 3' to the sequence specifying the 1.8-kb RNA sequence. In addition regions of secondary hybridisation for small RNAs 2, 3, 4 and 6 have also been identified. Though sRNA5 (5S RNA) hybridises within the major rRNA repeat, a separate 5S RNA gene repeat with unit size of 760 bp is also present. It is 10 to 20 times more abundant than the major rRNA gene repeat.     

Structural analysis of Arabidopsis thaliana chromosome 5. IX. Sequence features of the regions of 1,011,550 bp covered by seventeen P1 and TAC clones.

In this series of projects sequencing the entire genome of Arabidopsis thaliana chromosome 5, non-redundant P1 and TAC clones have been sequenced according to the fine physical map, and as of May 7, 1999, the sequences of 16.2 Mb representing approximately 60% of chromosome 5 have been accumulated and released at our web site. In parallel, structural features of the sequenced regions have been analyzed by applying a variety of computer programs, and to date we have predicted a total of 2380 potential protein-coding genes in the 10,154,580 bp regions, which are covered by 142 P1 and TAC clones. In this paper, we newly analyzed the structural features of the 1,011,550 bp regions covered by additional 17 P1 and TAC clones, and predicted 298 protein-coding genes. The average density of the genes identified was 1 gene per 3394 bp. Introns were observed in 67% of the genes, and the average number per gene and the average length of the introns were 3.2 and 159 bp, respectively. The gene density became higher than the value estimated in the previously analyzed regions (1 gene per 4,267 bp), as the data in this paper were compiled based on a new standard of gene assignment including the computer-predicted hypothetical genes. The regions also contained 8 tRNA genes when searched by similarity to reported tRNA genes and the tRNA scan-SE program. The sequence data and information on the potential genes are available on the database KAOS (Kazusa Arabidopsis data Opening Site) at http://www.kazusa.or.jp/arabi/.     

sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization

Background

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.

Results

Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.

Conclusions

sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.