Advertisement

Molecular Biology

, Volume 34, Issue 4, pp 461–467 | Cite as

Computer analysis of control signals in bacterial genomes. Attenuators of operons of aromatic amino acids metabolism

  • A. G. Vitreshchak
  • M. S. Gel'fand
Article

Abstract

Here we predict attenuators in operons of aromatic amino acids metabolism in the γ-subclass of proteobacteriaSalmonella typhi, Yersinia pestis, Vibrio cholerae, Haemophilus influenzae, Actinobacillus actinomycetemcomitans, Xanthomonas campestris, and chlamydiaChlamydia trachomatis. Alternative secondary structures of mRNA governing transcription pathway selection as well as the related control leader peptide were constructed. Comparison with homologousEscherichia coli operons was used for the prediction. This control mechanism takes place in operonstrp (tryptophan),phe (phenylalanine), andpheST coding for the large and small subunits of phenylalanine tRNA synthase in the considered γ-proteobacteria. Secondary structures of mRNA in the leader region oftnaAB operon possibly involved in ρ-dependent attenuation were predicted in certain enterobacteria.

Key words

computer analysis functional signals gene expression control attenuation of transcription aromatic amino acid metabolism tnaAB operon 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Landick, R., Yanofsky, C.,et al., Escherichia coli and Salmonella. Cellular and Molecular Biology, Neidhardt, F.C., Ed., Washington DC: ASM, 1996, vol. 1, ch. 81, pp. 1263–1286.Google Scholar
  2. 2.
    Gish, K. and Yanofsky, C.,J. Bacteriol., 1995, vol. 177, pp. 7245–7254.PubMedGoogle Scholar
  3. 3.
    Keller, E.B. and Calvo, J.,Proc. Natl. Acad. Sci. USA, 1979, vol. 76, pp. 6186–6190.PubMedCrossRefGoogle Scholar
  4. 4.
    Grunberg-Manago, M.,Escherichia coli and Salmonella. Cellular and Molecular Biology, Neidhardt, F.C., Ed., Washington DC: ASM, 1996, vol. 1, ch. 91, pp. 1432–1457.Google Scholar
  5. 5.
    Levitt, M.,Nature, 1969, vol. 224, pp. 759–763.PubMedCrossRefGoogle Scholar
  6. 6.
    Woeseet al., Nucleic Acids Res., 1980, vol. 8, pp. 2275–2293.PubMedCrossRefGoogle Scholar
  7. 7.
    Moazed, D., Stern, S., and Noller, H.,J. Mol. Biol., 1986, vol. 187, pp. 399–416.PubMedCrossRefGoogle Scholar
  8. 8.
    Vitreshchak, A. and Gelfand, M.S.,Biofizika, 1999, vol. 44, no. 4, pp. 601–610.PubMedGoogle Scholar
  9. 9.
    Waterman, M.S.,Matematicheskie metody dlya analiza posledovatel'nostei DNK (Mathematical Methods for DNA Sequence Analysis), Moscow: Mir, 1999, chapters 7, 8.Google Scholar
  10. 10.
    Mironov, A.A., Koonin, E.V., Roytberg, M.A., and Gelfand, M.S.,Nucleic Acids Res., 1999, vol. 27, pp. 2981–2989.PubMedCrossRefGoogle Scholar
  11. 11.
    Zuker, M. and Stienger, P.,Nucleic Acids Res., 1981, vol. 9, pp. 133–148.PubMedCrossRefGoogle Scholar
  12. 12.
    Stephens, R.S.et al., Science, 1998, vol. 282, pp. 754–759.PubMedCrossRefGoogle Scholar
  13. 13.
    Mahillon, J. and Chandler, M.,Microbiology and Molecular Biology Reviews, 1998, vol. 62, pp. 725–774.PubMedGoogle Scholar

Copyright information

© MAIK “Nauka/Interperiodica” 2000

Authors and Affiliations

  • A. G. Vitreshchak
    • 1
  • M. S. Gel'fand
    • 2
  1. 1.Institute of Problems of Data TransmissionRussian Academy of SciencesMoscowRussia
  2. 2.State Research Institute of Genetics and Selection of Industrial MicroorganismsMoscowRussia

Personalised recommendations