Mutation, Selection and Genetic Interactions in Bacteria


Mutation is the ultimate source of genetic variation. The rate at which new mutations typically occur, their effects on fitness and the strength and type of genetic interactions between different mutations are key for understanding the evolution of any population. Estimates of these parameters in organisms such as bacteria will have a profound impact on our understanding of their biology, diversity, rate of speciation and in our health. Experimental evolution with bacteria presents us with the opportunity to directly measure these parameters and to test theoretical predictions about the genetic basis of adaptive evolution. Evidence has been increasing to support the view that bacterial adaptation can be extraordinary fast, that competition between different adaptive mutations may be pervasive in bacterial populations and that epistasis is very common and possibly biased towards antagonism in bacteria.

Key Concepts:

  • The distribution of the effects of beneficial mutations shows the relative abundance of large versus small effect mutations contributing to adaptation.

  • Clonal interference leads to the loss of beneficial mutations with small effects.

  • Interaction between different alleles implies that the fitness of a genotype is dependent on the genetic background where it arises, this is termed epistasis.

  • Adaptation by compensatory evolution is particularly important in bacteria.

Keywords: mutation; selection; adaptation; epistasis; experimental evolution; mutation rate; fitness effect of mutations; antibiotic resistance; compensatory evolution

Figure 1.

Influence of clonal interference (CI) in the dynamics of fixation of beneficial mutations. Solid green line: fixation of a beneficial mutation with effect sa=0.03, in a population where no other beneficial mutations emerge. Dashed lines show the dynamics in a population under intense clonal interference: a mutation of effect sa=0.015 (dashed red line) rises in frequency, but then is replaced by another mutation with stronger effect (s=0.03, dashed blue line).

Figure 2.

Observed distributions of arising beneficial mutations in P. fluorescens in different environments (grey bars): (a) rich medium, (b) minimal medium with glucose, (c) with mannitol and (d) with sorbitol. Dots represent the exponential law predicted theoretically (Kassen and Bataillon, ). Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics, copyright (2006).

Figure 3.

Distribution of beneficial mutations that reach high frequencies in E. coli populations adapting to rich medium. Light grey: observed distribution in populations evolving under small effective sizes, mean effect E(sa)=0.013; Dark grey: observed distribution in populations evolving under large effective sizes where clonal interference is much more intense: E(sa)=0.023. The distributions are significantly different with the populations of large Ne showing mutations with much larger effect.

Figure 4.

Level of epistasis (ɛ) in E. coli between pairs of mutations conferring antibiotic resistance. Combinations exhibiting positive epistasis (bars above the zero line) are much more frequent those exhibiting negative epistasis (bars below the zero line).



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Further Reading

Babu M, Musso G, Díaz‐Mejía JJ et al. (2009) Systems‐level approaches for identifying and analyzing genetic interaction networks in Escherichia coli and extensions to other prokaryotes. Molecular BioSystems 5: 1439–1455.

Bell G (2009) The oligogenic view of adaptation. Cold Spring Harbor Symposia on Quantitative Biology doi: 10.1101/sqb.2009.74.003.

Bell G (2010) Experimental genomics of fitness in yeast. Proceedings of the Royal Society of London. Series B, Biological Sciences (in press).

Orr HA (2005) Theories of adaptation: what they do and don't say. Genetica 123(1–2): 3–13.

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Gordo, Isabel, and Sousa, Ana(May 2010) Mutation, Selection and Genetic Interactions in Bacteria. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0022175]