Mutation, Selection and Genetic Interactions in Bacteria

Isabel Gordo, Instituto Gulbenkian de Ciência, Oeiras, Portugal
Ana Sousa, Instituto Gulbenkian de Ciência, Oeiras, Portugal

Published online: May 2010

DOI: 10.1002/9780470015902.a0022175

Abstract

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).

close

References

Andersson DI (2006) The biological cost of mutational antibiotic resistance: any practical conclusions? Current Opinion in Microbiology 9: 461–465.

Atwood KC, Schneider LK and Ryan FJ (1951) Periodic Selection in Escherichia coli. Proceedings of the National Academy of Sciences of the USA 37: 146–155.

Azevedo RBR, Lohaus R, Srinivasan S, Dang KK and Burch CL (2006) Sexual reproduction selects for robustness and negative epistasis in artificial gene networks. Nature 440: 87–90.

Barrett RDH, MacLean RC and Bell G (2006) Mutations of intermediate effect are responsible for adaptation in evolving Pseudomonas fluorescens populations. Biology Letters 2: 236.

Butland G, Babu M, Díaz‐Mejía JJ et al. (2008) eSGA: E. coli synthetic genetic array analysis. Nature Methods 5(9): 789–795.

Costanzo M, Baryshnikova A, Bellay J et al. (2010) The genetic landscape of a cell. Science 327: 425–431.

Drake JW (1991) A constant rate of spontaneous mutation in DNA‐based microbes. Proceedings of the National Academy of Sciences of the USA 88: 7160–7164.

Elena S and Lenski R (2003) Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nature Reviews. Genetics 4: 457–469.

Elena SF and Lenski RE (1997) Test of synergistic interactions among deleterious mutations in bacteria. Nature 390: 395–398.

Gerrish P and Lenski R (1998) The fate of competing beneficial mutations in an asexual population. Genetica 103: 127–144.

Gerrish PJ, Colato A, Perelson AS and Sniegowski PD (2007) Complete genetic linkage can subvert natural selection. Proceedings of the National Academy of Sciences of the USA 104: 6266–6271.

He X, Qian W, Wang Z, Li Y and Zhang J (2010) Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks. Nature Genetics doi:10.1038/ng.524.

Hughes AL (2005) Evidence for abundant slightly deleterious polymorphisms in bacterial populations. Genetics 169: 533–538.

Imhof M and Schlotterer C (2001) Fitness effects of advantageous mutations in evolving Escherichia coli populations. Proceedings of the National Academy of Sciences of the USA 98: 1113–1117.

Jasnos L and Korona R (2007) Epistatic buffering of fitness loss in yeast double deletion strains. Nature Genetics 39: 550–554.

Kassen R and Bataillon T (2006) Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nature Genetics 38: 484.

Kibota TT and Lynch M (1996) Estimate of the genomic mutation rate deleterious to overall fitness in E. coli. Nature 381: 694–696.

Kondrashov AS (1988) Deleterious mutations and the evolution of sexual reproduction. Nature 336: 435–440.

Levin BR, Perrot V and Walker N (2000) Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics 154: 985–997.

MacLean RC and Buckling A (2009) The distribution of fitness effects of beneficial mutations in Pseudomonas aeruginosa. PLoS Genetics 5: e1000406.

Maisnier‐Patin S, Roth JR, Fredriksson A et al. (2005) Genomic buffering mitigates the effects of deleterious mutations in bacteria. Nature Genetics 37: 1376–1379.

Martin G, Elena SF and Lenormand T (2007) Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nature Genetics 39: 555–560.

Orr H (2005) The genetic theory of adaptation: a brief history. Nature Reviews. Genetics 6: 119–127.

Perfeito L, Fernandes L, Mota C and Gordo I (2007) Adaptive mutations in bacteria: high rate and small effects. Science 317: 813–815.

Phillips PC (2008) Epistasis – the essential role of gene interactions in the structure and evolution of genetic systems. Nature Reviews. Genetics 9: 855–867.

Provine W (1986) Sewall Wright and Evolutionary Biology. Chicago: The University of Chicago Press.

Roguev A, Bandyopadhyay S, Zofall M et al. (2008) Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322: 405–410.

Rozen D, de Visser J and Gerrish P (2002) Fitness effects of fixed beneficial mutations in microbial populations. Current Biology 12: 1040–1045.

Sanjuán R and Elena SF (2006) Epistasis correlates with genome complexity. Proceedings of the National Academy of Sciences of the USA 103(39): 14402–14405.

Segre D, DeLuna A, Church GM and Kishony R (2005) Modular epistasis in yeast metabolism. Nature Genetics 37: 77–83.

Sniegowski PD, Gerrish PJ and Lenski RE (1997) Evolution of high mutation rates in experimental populations of E. coli. Nature 387: 703–705.

Szathmary E (1993) Do deleterious mutations act synergistically? Metabolic control theory provides a partial answer. Genetics 133: 127–132.

Trindade S, Sousa A, Xavier KB et al. (2009) Positive epistasis drives the acquisition of multidrug resistance. PLoS Genetics 5: e1000578.

Trobner W and Piechocki R (1984) Selection against hypermutability in Escherichia coli during long term evolution. Molecular General Genetics 198: 177–178.

Ward H, Perron GG and Maclean RC (2009) The cost of multiple drug resistance in Pseudomonas aeruginosa. Journal of Evolutionary Biology 22: 997–1003.

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.

Related Sub-Topics:

Selection and Variation | Microbial Evolution and Taxonomy | Variation by Mutation and Recombination | Molecular Evolution
Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

Article Title: Mutation, Selection and Genetic Interactions in Bacteria
 
How to Cite close
Gordo, Isabel, and Sousa, Ana(May 2010) Mutation, Selection and Genetic Interactions in Bacteria. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0022175]