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Genome-Scale Assessment of Phenotypic Changes During Adaptive Evolution

  • Stephen S. Fong

Abstract

Adaptive evolution is a process that influences and alters all biological organisms over time. Changes involved in adaptive evolution begin with genetic mutations and can lead to large changes in phenotypic behavior. Thus, the relationship between genotype and phenotype is a central issue in studying adaptive evolution.

The whole-cell phenotype of an organism is the result of integrated functions at various levels of cellular organization. As methods have been developed and improved to study the components involved with the different levels of cellular organization in a high-throughput and genome-wide scale, it is becoming possible to establish the link between genotype and phenotype. In this chapter, different means of studying and establishing connections between genotype and phenotype in the context of adaptive evolution will be discussed.

Key Words

Adaptive evolution phenotype phenomics transcriptomics proteomics fluxomics metabolomics 

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References

  1. 1.
    Elena SF, Lenski RE. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 2003;4(6):457–469.PubMedCrossRefGoogle Scholar
  2. 2.
    Atwood KC, Schneider LK, Ryan FJ. Periodic selection in Escherichia coli. Proc Natl Acad Sci USA 1951;37(3):146–155.PubMedCrossRefGoogle Scholar
  3. 3.
    Lenski RE, Travisano M. Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc Natl Acad Sci USA 1994;91(15):6808–6814.PubMedCrossRefGoogle Scholar
  4. 4.
    Fong SS, Palsson BO. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat Genet 2004;36(10):1056–1058.PubMedCrossRefGoogle Scholar
  5. 5.
    Ibarra RU, Edwards JS, Palsson BO. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 2002;420(6912):186–189.PubMedCrossRefGoogle Scholar
  6. 6.
    Riehle MM, Bennett AF, Long AD. Genetic architecture of thermal adaptation in Escherichia coli. Proc Natl Acad Sci USA 2001;98(2):525–530.PubMedCrossRefGoogle Scholar
  7. 7.
    Velkov VV. New insights into the molecular mechanisms of evolution: Stress increases genetic diversity. Mol Biol 2002;36(2):209–215.CrossRefGoogle Scholar
  8. 8.
    Fleischmann RD, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 1995;269(5223):496–498, 507–512.PubMedCrossRefGoogle Scholar
  9. 9.
    Palsson BO. The challenges of in silico biology. Nat Biotechnol 2000;18 (November):1147–1150.PubMedCrossRefGoogle Scholar
  10. 10.
    Burger RM. Willensdorfer, Nowak M.A. Why are phenotypic mutation rates much higher than genotypic mutation rates? Genetics 2006;172(1):197–206.PubMedCrossRefGoogle Scholar
  11. 11.
    Schilling CH, Edwards JS, Palsson BO. Towards metabolic phenomics: Analysis of genomic data using flux balances. Biotechnol Progress 1999;15(3):288–295.CrossRefGoogle Scholar
  12. 12.
    Fong SS, Marciniak JY, Palsson BO. Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model. J Bacteriol 2003;185(21):6400–6408.PubMedCrossRefGoogle Scholar
  13. 13.
    Bochner BR, Gadzinski P, Panomitros E. Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res 2001;11(7):1246–1255.PubMedCrossRefGoogle Scholar
  14. 14.
    Kell DB, Brown M, Davey HM, et al. Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol 2005;3(7):557–565.PubMedCrossRefGoogle Scholar
  15. 15.
    Riehle MM, Bennett AF, Lenski RE, et al. Evolutionary changes in heat-inducible gene expression in lines of Escherichia coli adapted to high temperature. Physiol Genomics 2003;14(1):47–58.PubMedGoogle Scholar
  16. 16.
    Fong SS, Joyce AR, Palsson BO. Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states. Genome Res 2005;15(10):1365–1372.PubMedCrossRefGoogle Scholar
  17. 17.
    Cooper TF, Rozen DE, Lenski RE. Parallel changes in gene expression after 20,000 generations of evolution in Escherichia coli. Proc Natl Acad Sci USA 2003;100(3):1072–1077.PubMedCrossRefGoogle Scholar
  18. 18.
    Ferea TL, Botstein D, Brown PO, et al. Systematic changes in gene expression patterns following adaptive evolution in yeast. Proc Natl Acad Sci USA 1999;96(17):9721–9726.PubMedCrossRefGoogle Scholar
  19. 19.
    Sauer U. High-throughput phenomics: experimental methods for mapping fluxomes. Curr Opin Biotechnol 2004;15(1):58–63.PubMedCrossRefGoogle Scholar
  20. 20.
    Fong SS, Nanchen A, Palsson BO, et al. Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes. J Biol Chem 2005;281(12):8024–8033.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Stephen S. Fong
    • 1
  1. 1.Department of Chemical and Life Science EngineeringVirginia Commonwealth UniversityRichmondUSA

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