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Small Antisense Regulatory RNA Genes in Bacterial Genomes

  • Author(s): Mavromatis, K.
  • Kyrpides, N.C.
  • et al.
Abstract

Small RNAs (sRNA) can act as regulators of the cell functions, mainly in two ways(1,2). First, they bind to specific proteins and change their activity, an example case is 6S that binds to RNA polymerase and alters its activity. Second, sRNA molecules affect mRNA translation through base pairing interactions near the RBS. These interactions can alter mRNA structure and/or stability resulting either to inhibition or promotion of ribosome binding. Antisense sRNA are small RNA molecules that have a small region which is complementary to the target mRNA (3). Thus, they can inhibit translation by occluding the ribosome binding site, or activate translation by preventing the formation of inhibitory mRNA structures. The rest of the molecule folds creating secondary structure that is required for its function. The specificity of these molecules is based on the complementarity with the target mRNA (figure). Mutations can accumulate in these molecules provided that they do not affect this pairing and the overall structure of the molecule. As a result these sRNAs can become more diverse between distant phylogenetically species. The RNA-binding protein Hfq appears to play important role in the regulation of gene translation through the antisense RNA fashion (4). Hfq is a conserved, abundant protein that has been implicated in a number of RNA-mediated events. This interaction frequently results to the degradation of the mRNA. We developed a method based on the above mentioned observations for the identification of putative antisense RNAs in the currently public genomes. Our method identifies homologous intergenic regions that exhibit complementarity with homologous genes in different organisms. Further criteria for conservation of the RNA complementarity pattern (complement bases relative to the start of the gene), and predicted loops in the putative RNA gene are used to filter results. Enterobacterial organisms were used for the evaluation of the method and the results were compared to information known from the literature (5-8). Predictions made for Escherichia coli (K12) are currently experimentally studied. 1. Wassarman, K.M., Zhang, A. & Storz, G. Small RNAs in Escherichia coli. Trends Microbiol 7, 37-45 (1999). 2. Eddy, S.R. Non-coding RNA genes and the modern RNA world. Nat Rev Genet 2, 919-929 (2001). 3. Wassarman, K.M. Small RNAs in bacteria: diverse regulators of gene expression in response to environmental changes. Cell 109, 141-144 (2002). 4. Klein, R.J., Misulovin, Z. & Eddy, S.R. Noncoding RNA genes identified in AT-rich hyperthermophiles. Proc Natl Acad Sci U S A 99, 7542-7547 (2002). 5. Wassarman, K.M., Repoila, F., Rosenow, C., Storz, G. & Gottesman, S. Identification of novel small RNAs using comparative genomics and microarrays. Genes Dev 15, 1637-1651 (2001). 6. Rivas, E., Klein, R.J., Jones, T.A. & Eddy, S.R. Computational identification of noncoding RNAs in E. coli by comparative genomics. Curr Biol 11, 1369-1373 (2001). 7. Carter, R.J., Dubchak, I. & Holbrook, S.R. A computational approach to identify genes for functional RNAs in genomic sequences. Nucleic Acids Res 29, 3928-3938 (2001). 8. Zhang, A. et al. Global analysis of small RNA and mRNA targets of Hfq. Mol Microbiol 50, 1111-1124 (2003).

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