Chromosomal Proximity of Genes as an Indicator of Functional Linkage

Chapter
Part of the SpringerBriefs in Systems Biology book series (BRIEFSBIOSYS, volume 2)

Abstract

Mostly prokaryotic genes have a tendency to be organized as clusters across chromosomes. Chromosomal proximity of genes, irrespective of the relative gene orientation, has been shown to be an indicative of their co-regulation. Genes that participate in related biological processes are often observed to be co-regulated. Hence, chromosomal proximity of genes has been proposed as a parameter indicative of functional linkages between them. However, prokaryotic genomes have been subjected to random rearrangements during evolution but these rearrangements are conservative in nature which invariably maintain individual genes in very specific functional and regulatory contexts. Hence, it is possible to deduce these rearrangements of genes based on chromosomal proximity of orthologous genes in multiple reference genomes. This chapter introduces the concept of genomic re-arrangements and discusses chromosomal proximity based three protein–protein interaction prediction methods.

Keywords

Bacillus 

References

  1. 1.
    Koonin, E.V.: Evolution of genome architecture. Int. J. Biochem. Cell Biol. 41(2), 298–306 (2009)PubMedCrossRefGoogle Scholar
  2. 2.
    Salgado, H.: Operons in Escherichia coli: genomic analyses and predictions. Proc. Natl. Acad. Sci. USA 97(12), 6652–6657 (2000)PubMedCrossRefGoogle Scholar
  3. 3.
    Beckwith, J.: The operon as paradigm: normal science and the beginning of biological complexity. J. Mol. Biol. 409(1), 7–13 (2011)Google Scholar
  4. 4.
    Jacob, F., Monod, J.: Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961)PubMedCrossRefGoogle Scholar
  5. 5.
    Overbeek, R., et al.: The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA 96(6), 2896–2901 (1999)PubMedCrossRefGoogle Scholar
  6. 6.
    Keseler, I.M., et al.: EcoCyc: a comprehensive view of Escherichia coli biology. Nucleic Acids Res. 37(Database issue), D464–D470 (2009)Google Scholar
  7. 7.
    Mushegian, A.R., Koonin, E.V.: Gene order is not conserved in bacterial evolution. Trends Genet. 12(8), 289–290 (1996)PubMedCrossRefGoogle Scholar
  8. 8.
    Wolf, Y.I.: Genome alignment, evolution of prokaryotic genome organization, and prediction of gene function using genomic context. Genome Res. 11(3), 356–372 (2001)PubMedCrossRefGoogle Scholar
  9. 9.
    Itoh, T., et al.: Evolutionary instability of operon structures disclosed by sequence comparisons of complete microbial genomes. Mol. Biol. Evol. 16(3), 332–346 (1999)PubMedCrossRefGoogle Scholar
  10. 10.
    Dandekar, T., et al.: Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23(9), 324–328 (1998)PubMedCrossRefGoogle Scholar
  11. 11.
    Papp, B., Pal, C., Hurst, L.D.: Dosage sensitivity and the evolution of gene families in yeast. Nature 424(6945), 194–197 (2003)PubMedCrossRefGoogle Scholar
  12. 12.
    Lathe 3rd, W.C., Snel, B., Bork, P.: Gene context conservation of a higher order than operons. Trends Biochem. Sci. 25(10), 474–479 (2000)PubMedCrossRefGoogle Scholar
  13. 13.
    Rogozin, I.B., et al.: Connected gene neighborhoods in prokaryotic genomes. Nucleic Acids Res. 30(10), 2212–2223 (2002)PubMedCrossRefGoogle Scholar
  14. 14.
    Ullsperger, C., Cozzarelli, N.R.: Contrasting enzymatic activities of topoisomerase IV and DNA gyrase from Escherichia coli. J. Biol. Chem. 271(49), 31549–31555 (1996)PubMedCrossRefGoogle Scholar
  15. 15.
    Weiss, D.S.: Bacterial cell division and the septal ring. Mol. Microbiol. 54(3), 588–597 (2004)PubMedCrossRefGoogle Scholar
  16. 16.
    Koonin, E.V., Wolf, Y.I., Aravind, L.: Prediction of the archaeal exosome and its connections with the proteasome and the translation and transcription machineries by a comparative-genomic approach. Genome Res. 11(2), 240–252 (2001)PubMedCrossRefGoogle Scholar
  17. 17.
    Makarova, K.S., et al.: Defense islands in bacterial and archaeal genomes and prediction of novel defense systems. J Bacteriol. 193(21), 6039–6056 (2011)Google Scholar
  18. 18.
    Makarova, K.S., et al.: Evolution and classification of the CRISPR-Cas systems. Nat. Rev. Microbiol. 9(6), 467–477 (2011)Google Scholar
  19. 19.
    Jensen, L.J., et al.: STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37(Database issue), D412–D416 (2009)Google Scholar
  20. 20.
    Rogozin, I.B., et al.: Purifying and directional selection in overlapping prokaryotic genes. Trends Genet. 18(5), 228–232 (2002)PubMedCrossRefGoogle Scholar
  21. 21.
    Rogozin, I.B., et al.: Congruent evolution of different classes of non-coding DNA in prokaryotic genomes. Nucleic Acids Res. 30(19), 4264–4271 (2002)PubMedCrossRefGoogle Scholar
  22. 22.
    Korbel, J.O., et al.: Analysis of genomic context: prediction of functional associations from conserved bidirectionally transcribed gene pairs. Nat. Biotechnol. 22(7), 911–917 (2004)PubMedCrossRefGoogle Scholar
  23. 23.
    Watanabe, H., et al.: Genome plasticity as a paradigm of eubacteria evolution. J. Mol. Evol. 44(Suppl 1), S57–S64 (1997)PubMedCrossRefGoogle Scholar
  24. 24.
    Brouwer, R.W., Kuipers, O.P., van Hijum, S.A.: The relative value of operon predictions. Brief Bioinform. 9(5), 367–375 (2008)PubMedCrossRefGoogle Scholar
  25. 25.
    Price, M.N., et al.: A novel method for accurate operon predictions in all sequenced prokaryotes. Nucleic Acids Res. 33(3), 880–892 (2005)PubMedCrossRefGoogle Scholar
  26. 26.
    Yellaboina, S., Goyal, K., Mande, S.C.: Inferring genome-wide functional linkages in E. coli by combining improved genome context methods: comparison with high-throughput experimental data. Genome Res. 17(4), 527–535 (2007)PubMedCrossRefGoogle Scholar
  27. 27.
    Janga, S.C., et al.: The distinctive signatures of promoter regions and operon junctions across prokaryotes. Nucleic Acids Res. 34(14), 3980–3987 (2006)PubMedCrossRefGoogle Scholar
  28. 28.
    Moreno-Hagelsieb, G., Collado-Vides, J.: A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18(Suppl 1), S329–S336 (2002)PubMedCrossRefGoogle Scholar
  29. 29.
    Ranjan, S., Gundu, R.K., Ranjan, A.: MycoperonDB: a database of computationally identified operons and transcriptional units in Mycobacteria. BMC Bioinform. 7(Suppl 5), S9 (2006)CrossRefGoogle Scholar
  30. 30.
    Bergman, N.H., et al.: Operon prediction for sequenced bacterial genomes without experimental information. Appl. Environ. Microbiol. 73(3), 846–854 (2007)PubMedCrossRefGoogle Scholar
  31. 31.
    Janga, S.C., Collado-Vides, J., Moreno-Hagelsieb, G.: Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons. Nucleic Acids Res. 33(8), 2521–2530 (2005)PubMedCrossRefGoogle Scholar
  32. 32.
    Tamames, J., et al.: Conserved clusters of functionally related genes in two bacterial genomes. J. Mol. Evol. 44(1), 66–73 (1997)PubMedCrossRefGoogle Scholar
  33. 33.
    Bowers, P.M., et al.: Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol. 5(5), R35 (2004)PubMedCrossRefGoogle Scholar
  34. 34.
    Ferrer, L., Dale, J.M., Karp, P.D.: A systematic study of genome context methods: calibration, normalization and combination. BMC Bioinform. 11, 493 (2010)Google Scholar
  35. 35.
    Bockhorst, J., et al.: Predicting bacterial transcription units using sequence and expression data. Bioinformatics 19(Suppl 1), i34–i43 (2003)PubMedCrossRefGoogle Scholar

Copyright information

© Vijaykumar Yogesh Muley 2013

Authors and Affiliations

  1. 1.Center of Excellence in EpigeneticsIndian Institute of Science Education and Research (IISER)PuneIndia
  2. 2.Biotechnology DivisionCSIR-Institute of Himalayan Bioresource Technology (IHBT)PalampurIndia

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