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Detecting Lateral Genetic Transfer

A Phylogenetic Approach

  • Protocol
Bioinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 452))

Abstract

Nucleotide sequences of microbial genomes provide evidence that genes have been shared among organisms, a phenomenon known as lateral genetic transfer (LGT). Hypotheses about the importance of LGT in the evolution and diversification of microbes can be tested by analyzing the extensive quantities of sequence data now available. Some analysis methods identify genes with sequence features that differ from those of the surrounding genome, whereas other methods are based on inference and comparison of phylogenetic trees. A large-scale search for LGT in 144 genomes using phylogenetic methods has revealed that although parent-to-offspring (“vertical”) inheritance has been the dominant mode of gene transmission, LGT has nonetheless been frequent, especially among organisms that are closely related or share the same habitat. This chapter outlines how bioinformatic and phylogenetic analyses can be built into a workflow to identify LGT among microbial genomes.

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References

  1. Gurney-Dixon, S. (1919)The Transmutation of Bacteria. Cambridge University Press, Cambridge, UK.

    Google Scholar 

  2. Jones, D., Sneath, P. H. A. (1970) Genetic transfer and bacterial taxonomy.Bacteriological Rev 34, 40–81.

    Google Scholar 

  3. Medigue, C., Rouxel, T., Vigier, P., et al. (1991) Evidence for horizontal transfer inEscherichia coli speciation.J Mol Biol 222, 851–856.

    Article  PubMed  CAS  Google Scholar 

  4. Beiko, R. G., Harlow, T. J., Ragan, M. A. (2005) Highways of gene sharing in prokaryotes.Proc Natl Acad Sci U S A 102, 14332–14337.

    Article  PubMed  CAS  Google Scholar 

  5. Jain, R., Rivera, M. C., Lake, J. A. (1999) Horizontal gene transfer among genomes: the complexity hypothesis.Proc Natl Acad Sci U S A 96, 3801–3806.

    Article  PubMed  CAS  Google Scholar 

  6. Charlebois, R. L., Beiko, R. G., Ragan, M. A. (2004) Genome phylogenies, in (Hirt, R. P., Horne, D. S., eds.),Organelles, Genomes and Eukaryote Phylogeny: An Evolutionary Synthesis in the Age of Genomics. CRC Press, Boca Raton, FL.

    Google Scholar 

  7. Deckert, G., Warren, P. V., Gaasterland, T., et al. (1998) The complete genome of the hyperthermophilic bacteriumAquifex aeolicus.Nature 392, 353–358.

    Article  PubMed  Google Scholar 

  8. Nelson, K. E., Clayton, R. A., Gill, S. R., et al. (1999) Evidence for lateral gene transfer between Archaea and bacteria from genome sequence ofThermotoga maritima.Nature 399, 323–329.

    Article  PubMed  CAS  Google Scholar 

  9. Ragan, M. A. (2001) On surrogate methods for detecting lateral gene transfer.FEMS Microbiol Lett 201, 187–191.

    Article  PubMed  CAS  Google Scholar 

  10. Ragan, M. A., Harlow, T. J., Beiko, R. G. (2006) Do different surrogate methods detect lateral genetic transfer events of different relative ages?Trends Microbiol 14, 4–8.

    Article  PubMed  CAS  Google Scholar 

  11. Ho, S. Y., Jermiin, L. (2004) Tracing the decay of the historical signal in biological sequence data.Syst Biol 53, 623–637.

    Article  PubMed  Google Scholar 

  12. Jermiin, L., Ho, S. Y., Ababneh, F., et al. (2004) The biasing effect of compositional heterogeneity on phylogenetic estimates may be underestimated.Syst Biol 53, 638– 643.

    Article  PubMed  Google Scholar 

  13. Tatusov, R. L., Fedorova, N. D., Jackson, J. D., et al. (2003) The COG database: an updated version includes eukaryotes.BMC Bioinformatics 4, 41.

    Article  PubMed  Google Scholar 

  14. Peterson, J. D., Umayam, L. A., Dickinson, T., et al. (2001) The comprehensive microbial resource.Nucleic Acids Res 29, 123–125.

    Article  PubMed  CAS  Google Scholar 

  15. Van Dongen, S. (2000) Graph clustering by flow simulation. Ph.D. Thesis: University of Utrecht, Utrecht.

    Google Scholar 

  16. Rigoutsos, I., Floratos, A. (1998) Combinatorial pattern discovery in biological sequences: the TEIRESIAS algorithm.Bio-informatics 14, 55–67.

    CAS  Google Scholar 

  17. Beiko, R. G., Chan, C. X., Ragan, M. A. (2005) A word-oriented approach to alignment validation.Bioinformatics 21, 2230– 2239.

    Article  PubMed  CAS  Google Scholar 

  18. Thompson, J. D., Higgins, D. G., Gibson, T. J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.Nucleic Acids Res 22, 4673– 4680.

    Article  PubMed  CAS  Google Scholar 

  19. Notredame, C., Higgins, D. G., Heringa, J. (2000) T-Coffee: A novel method for fast and accurate multiple sequence alignment.J Mol Biol 302, 205–217.

    Article  PubMed  CAS  Google Scholar 

  20. Lee, C., Grasso, C., Sharlow, M. F. (2002) Multiple sequence alignment using partial order graphs.Bioinformatics 18, 452–464.

    Article  PubMed  CAS  Google Scholar 

  21. Gotoh, O. (1996) Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement asassessed by reference to structural alignments.J Mol Biol 264, 823–838.

    Article  PubMed  CAS  Google Scholar 

  22. Katoh, K., Misawa, K., Kuma, K., et al. (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.Nucleic Acids Res 30, 3059–3066.

    Article  PubMed  CAS  Google Scholar 

  23. Huelsenbeck, J. P., Ronquist, F. (2001) MRBAYES: Bayesian inference of phyloge-netic trees.Bioinformatics 17, 754–755.

    Article  PubMed  CAS  Google Scholar 

  24. Castresana, J. (2000) Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis.Mol Biol Evol 17, 540–552.

    PubMed  CAS  Google Scholar 

  25. Creevey, C. J., McInerney, J. O. (2005) Clann: investigating phylogenetic information through supertree analyses.Bioinfor-matics 21, 390–392.

    Article  CAS  Google Scholar 

  26. Beiko, R. G., Hamilton, N. H. (2006) Phy-logenetic identification of lateral genetic transfer events.BMC Evol Biol 6, 15.

    Article  PubMed  Google Scholar 

  27. Altschul, S. F., Madden, T. L., Schaffer, A. A., et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res 25, 3389–3402.

    Article  PubMed  CAS  Google Scholar 

  28. Berman, H. M., Westbrook, J., Feng, Z., et al. (2000) The Protein Data Bank.Nucleic Acids Res 28, 235–242.

    Article  PubMed  CAS  Google Scholar 

  29. Harlow, T. J., Gogarten, J. P., Ragan, M. A. (2004) A hybrid clustering approach to recognition of protein families in 114 microbial genomes.BMC Bioinformatics 5, 45.

    Article  PubMed  Google Scholar 

  30. Sokal, R. R., Sneath, P. H. A. (1963)Principles of Numerical Taxonomy, W.H. Freeman & Co, London.

    Google Scholar 

  31. Maddison, D. R., Swofford, D. L., Mad-dison, W. P. (1997) NEXUS: an extensible file format for systematic information.Syst Biol 46, 590–621.

    Article  PubMed  CAS  Google Scholar 

  32. Beiko, R. G., Keith, J. M., Harlow, T. J., Ragan, M.A. (2006) Searching for convergence in Markov chain Monte Carlo.Syst Biol. 55, 553–565.

    Article  PubMed  Google Scholar 

  33. Baum, B. R. (1992) Combining trees as a way of combining data sets for phylogenetic inference, and the desirability of combining gene trees.Taxon 41, 3–10.

    Article  Google Scholar 

  34. Ragan, M. A. (1992) Phylogenetic inference based on matrix representation of trees.Mol Phylogenet Evol 1, 53–58.

    Article  PubMed  CAS  Google Scholar 

  35. Geyer, C. J. (1992) Practical Markov chain Monte Carlo.Stat Sci 7, 473–483.

    Article  Google Scholar 

  36. Cowles, M. K., Carlin, B. P. (1996) Markov chain Monte Carlo convergence diagnostics: a comparative review.J Amer Statist Assoc 91, 883–904.

    Article  Google Scholar 

  37. Bininda-Emonds, O. R. P. (2004)Phylogenetic Supertrees: Combining Information to Yield the Tree of Life. Kluwer, Dordrecht.

    Google Scholar 

  38. Wilkinson, M., Cotton, J. A., Creevey, C., et al. (2005) The shape of supertrees to come: tree shape related properties of fourteen supertree methods.Syst Biol 54, 419–431.

    Article  PubMed  Google Scholar 

  39. Suzuki, Y., Glazko, G. V., Nei, M. (2002) Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics.Proc Natl Acad Sci U S A 99, 16138–16143.

    Article  PubMed  CAS  Google Scholar 

  40. Kass, R., Raftery, A. E. (1995) Bayes factors.J Amer Statist Assoc 90, 773–795.

    Article  Google Scholar 

  41. Linkkila, T. P., Gogarten, J. P. (1991) Tracing origins with molecular sequences: rooting the universal tree of life.Trends Biochem Sci 16, 287–288.

    Article  PubMed  CAS  Google Scholar 

  42. Addario-Berry, L., Chor, B., Hallett, M., et al. (2003) Ancestral maximum likelihood of evolutionary trees is hard.Algorithms Bioinformat Proc 2812, 202–215.

    Article  Google Scholar 

  43. MacLeod, D., Charlebois, R. L., Doolittle, F., et al. (2005) Deduction of probable events of lateral gene transfer through comparison of phylogenetic trees by recursive consolidation and rearrangement.BMC Evol Biol 5, 27.

    Article  PubMed  Google Scholar 

  44. Kuhner, M. K., Felsenstein, J. (1994) A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates.Mol Biol Evol 11, 459–468.

    PubMed  CAS  Google Scholar 

  45. Huelsenbeck, J. P. (1995) Performance of phylogenetic methods in simulation.Syst Biol 44, 17–48.

    Google Scholar 

  46. Waddell, P. J., Steel, M. A. (1997) General time-reversible distances with unequal rates across sites: mixing gamma and inverse Gaussian distributions with invariant sites.Mol Phylogenet Evol 8, 398–414.

    Article  PubMed  CAS  Google Scholar 

  47. Bruno, W. J., Halpern, A. L. (1999) Topological bias and inconsistency of maximum likelihood using wrong models.Mol Biol Evol 16, 564–566.

    PubMed  CAS  Google Scholar 

  48. Lawrence, J. G., Hendrickson, H. (2003) Lateral gene transfer: when will adolescence end?Mol Microbiol 50, 739–749.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

Cheong Xin Chan, Nicholas Hamilton, Tim Harlow, and Jonathan Keith provided vital assistance in developing and executing the phylogenetic pipeline described in this chapter. We acknowledge the Australian Research Council (CE0348221) and the Australian Partnership for Advanced Computing for support.

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© 2008 Humana Press, a part of Springer Science+Business Media, LLC

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Beiko, R.G., Ragan, M.A. (2008). Detecting Lateral Genetic Transfer. In: Keith, J.M. (eds) Bioinformatics. Methods in Molecular Biology™, vol 452. Humana Press. https://doi.org/10.1007/978-1-60327-159-2_21

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  • DOI: https://doi.org/10.1007/978-1-60327-159-2_21

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-707-5

  • Online ISBN: 978-1-60327-159-2

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