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New insights into bacterial adaptation through in vivo and in silico experimental evolution

Key Points

  • Strategies for experimental evolution aim to reproduce Darwinian evolution in laboratory conditions in order to observe evolution in action. Bacteria are particularly well suited for such strategies, as they can provide frozen, revivable fossil records that allow direct comparisons of ancestors and evolved individuals over tens to tens of thousands of generations.

  • Various bacterial species can be used as ancestors to initiate replicate populations under different environmental conditions, providing information about the evolution of diverse phenotypes, including fitness, cell size, gene expression, metabolic traits, interactions with host cells, and altruistic and social behaviour. Evolved genetic changes can be rigorously identified and linked to phenotypic outcomes during evolution, providing new insights into the dynamics of cellular networks as a complement to traditional genetic studies.

  • Evolution experiments have also been designed with virtual 'organisms' that reproduce and mutate in silico. These experiments allow researchers to test the generic nature of evolutionary mechanisms identified in vivo and to increase the analytic and statistical power of in vivo experiments (using multiple repetitions, large parameter exploration and exhaustive records of evolutionary events).

  • Experimental evolution is associated with phenotypic innovations as well as frequent parallel (similar) phenotypic and genetic changes that occur in replicate populations evolving under similar conditions. Nevertheless, parallel evolution is not a general rule, and a high level of between-population allelic diversity is observed.

  • A high level of genetic diversity is generated within single populations, whatever the environment (homogeneous or heterogeneous). Stable polymorphisms, with several ecotypes coexisting, may eventually emerge, as well as cooperating groups of cells with altruistic and social behaviours that in turn select for the appearance of cheater cells.

  • Bacteria adapt through fine-tuning of their global regulatory networks rather than by local restructuring of more specific pathways. Adaptive mutations affecting global transcriptional regulators profoundly rewire regulatory networks through epistatic interactions and large pleiotropic effects.

  • Evolution has shaped bacterial cellular networks with tremendous plasticity that enables further adaptation to many perturbations. Bacterial and digital evolution experiments investigate the evolvability (the capacity for adaptive evolution) and robustness (the stability in the face of perturbations) of cellular networks.

Abstract

Microbiology research has recently undergone major developments that have led to great progress towards obtaining an integrated view of microbial cell function. Microbial genetics, high-throughput technologies and systems biology have all provided an improved understanding of the structure and function of bacterial genomes and cellular networks. However, integrated evolutionary perspectives are needed to relate the dynamics of adaptive changes to the phenotypic and genotypic landscapes of living organisms. Here, we review evolution experiments, carried out both in vivo with microorganisms and in silico with artificial organisms, that have provided insights into bacterial adaptation and emphasize the potential of bacterial regulatory networks to evolve.

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Figure 1: In vivo microbial and in silico evolution experiments.
Figure 2: Principle of in silico experimental evolution.
Figure 3: In vivo evolution of regulatory networks.
Figure 4: In silico evolution of regulatory networks.

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Acknowledgements

This research was funded by the Centre National de la Recherche Scientifique (CNRS; France), the Université Joseph Fourier (Grenoble, France), the Institut National des Sciences Appliquées (INSA) de Lyon (France), the Agence Nationale de la Recherche (ANR; France) programmes Blanc (ANR-08-BLAN-0283-01) and Génomique (ANR-08-GENM-023-001), the CNRS interdisciplinary programmes Projets Exploratoires/Premier Soutien (PEPS) and Projets Exploratoires Pluridisciplinaires Inter-Instituts (PEPII), and the Fondation Innovations en Infectiologie (FINOVI) foundation. The authors thank C. MacLean and the anonymous reviewers for their helpful comments.

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Correspondence to Dominique Schneider.

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Glossary

Variability

The potential or propensity of the phenotype to vary (whether or not it actually does in the present population or sample). This depends on the rates and patterns of mutation and recombination, and on the genotype–phenotype map.

Fitness

An integrated measure of the relative survival and reproductive rate of genotypes in a given environment.

Evolvability

The potential or propensity of the phenotype to vary in a possibly adaptive manner.

Digital organisms

Computational data structures that process resources, reproduce, mutate and therefore evolve. Such 'organisms' are used as tools to study Darwinian evolution.

Robustness

A measure of the invariance of a phenotype in the face of mutational or environmental perturbations. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein levels to behaviour at the organismal level.

Parallelism

The independent evolution of similar traits in replicate lineages that are propagated in similar environments.

Niche exclusion

The idea that a single niche can sustain only a single genotype.

Niche construction

Environmental changes that are generated by the evolving organisms themselves.

Hamilton's rule

The theory that altruism can be selected for when rb–c>0 (in which c is the fitness cost to the altruist, b is the fitness benefit to the beneficiary and r is the genetic relatedness of the two organisms).

Genotype–phenotype map

A representation of how the genetic architecture of an organism produces its phenotype through developmental interactions with the environment.

Indirect selection

Selection acting on a property of the mutational processes, genetic architecture or developmental system that is not adaptive by itself but facilitates adaptive phenotypic evolution.

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Hindré, T., Knibbe, C., Beslon, G. et al. New insights into bacterial adaptation through in vivo and in silico experimental evolution. Nat Rev Microbiol 10, 352–365 (2012). https://doi.org/10.1038/nrmicro2750

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