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

Hindré, Thomas; Knibbe, Carole; Beslon, Guillaume; Schneider, Dominique

doi:10.1038/nrmicro2750

Cited by 148 publications

(169 citation statements)

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“…Some studies have found that E. coli evolved at stressfully high temperature tend to restore their overall gene expression profiles toward the baseline levels corresponding to their ancestor when it is grown the optimal growth temperature of 37°C (49,50). This finding fits with the overall prominence of mutations in global regulatory processes in laboratory evolution experiments with microbes (51), including the LTEE (52).…”

Section: Discussionsupporting

confidence: 55%

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Deatherage

Kepner

Bennett

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

113

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Isolated populations derived from a common ancestor are expected to diverge genetically and phenotypically as they adapt to different local environments. To examine this process, 30 populations ofEscherichia coliwere evolved for 2,000 generations, with six in each of five different thermal regimes: constant 20 °C, 32 °C, 37 °C, 42 °C, and daily alternations between 32 °C and 42 °C. Here, we sequenced the genomes of one endpoint clone from each population to test whether the history of adaptation in different thermal regimes was evident at the genomic level. The evolved strains had accumulated ∼5.3 mutations, on average, and exhibited distinct signatures of adaptation to the different environments. On average, two strains that evolved under the same regime exhibited ∼17% overlap in which genes were mutated, whereas pairs that evolved under different conditions shared only ∼4%. For example, all six strains evolved at 32 °C had mutations innadR, whereas none of the other 24 strains did. However, a population evolved at 37 °C for an additional 18,000 generations eventually accumulated mutations in the signature genes strongly associated with adaptation to the other temperature regimes. Two mutations that arose in one temperature treatment tended to be beneficial when tested in the others, although less so than in the regime in which they evolved. These findings demonstrate that genomic signatures of adaptation can be highly specific, even with respect to subtle environmental differences, but that this imprint may become obscured over longer timescales as populations continue to change and adapt to the shared features of their environments.

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Section: Discussionsupporting

confidence: 55%

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Deatherage

Kepner

Bennett

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

113

101

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“…Note that mere changes in metabolite concentrations are not counted in this picture. RNA-chip studies have also revealed that the expression of many genes can be jointly altered by simple genetic changes, e.g., gene deletions (Wagner 2002) or single fixed mutations in experimental evolution (reviewed in Hindré et al 2012). …”

Section: Practical Illustrationsmentioning

confidence: 99%

Fisher’s Geometrical Model Emerges as a Property of Complex Integrated Phenotypic Networks

Martin

2014

Genetics

126

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Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.T HE distribution of the fitness effects (DFE) of random mutations is a central determinant of the evolutionary fate of a population, together with the rate of mutation. Obviously, it determines the rate of adaptation by de novo mutations, by setting the mutational input of fitness variance. Furthermore, by setting the distribution of fitness at mutation-selection balance, the DFE also determines the amount of standing variance in populations at equilibrium and their potential for future adaptation. The DFE is therefore central to evolutionary theory, for both adapting and equilibrium populations. There is, however, no widely accepted model that predicts the distribution of fitness effects of random mutations and how it is affected by various environmental or genetic contexts.Yet, predicting what happens under changed conditions is a minimum requirement for many applications of evolutionary theory. "Phenotype-fitness landscapes" provide a general tool for such inference: by defining changed conditions (genetic background or environment) as explicit alternative "positions" in the landscape, their effects can be handled quantitatively. "Mechanistic" landscapesOne such approach has seen considerable development in the past decade: models that explicitly describe the "direct" molecular effect of a mutation (on RNA secondary str...

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“…The relative simplicity of microbial systems, their capacity for rapid evolutionary change, and advances in technology that enable detailed genotypic traceability offer a unique opportunity to log moment-by-moment change in experimental populations, with the potential to contribute to understanding of how genotype shapes phenotype, how phenotype interplays with the environment, and how environment may shape genotype (Mortlock 1982;Lenski et al 1998;Rainey et al 2000;Beaumont et al 2009;Buckling et al 2009;Blount et al 2012;Hindré et al 2012). In the context of adaptive radiation, the bacterium Pseudomonas fluorescens SBW25 has served as a useful model (reviewed within MacLean 2005 andKassen 2009).…”

mentioning

confidence: 99%

Adaptive Divergence in Experimental Populations ofPseudomonas fluorescens. V. Insight into the Niche Specialist Fuzzy Spreader Compels Revision of the ModelPseudomonasRadiation

2013

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Pseudomonas fluorescens is a model for the study of adaptive radiation. When propagated in a spatially structured environment, the bacterium rapidly diversifies into a range of niche specialist genotypes. Here we present a genetic dissection and phenotypic characterization of the fuzzy spreader (FS) morphotype-a type that arises repeatedly during the course of the P. fluorescens radiation and appears to colonize the bottom of static broth microcosms. The causal mutation is located within gene fuzY (pflu0478)-the fourth gene of the five-gene fuzVWXYZ operon. fuzY encodes a b-glycosyltransferase that is predicted to modify lipopolysaccharide (LPS) O antigens. The effect of the mutation is to cause cell flocculation. Analysis of 92 independent FS genotypes showed each to have arisen as the result of a loss-of-function mutation in fuzY, although different mutations have subtly different phenotypic and fitness effects. Mutations within fuzY were previously shown to suppress the phenotype of mat-forming wrinkly spreader (WS) types. This prompted a reinvestigation of FS niche preference. Time-lapse photography showed that FS colonizes the meniscus of broth microcosms, forming cellular rafts that, being too flimsy to form a mat, collapse to the vial bottom and then repeatably reform only to collapse. This led to a reassessment of the ecology of the P. fluorescens radiation. Finally, we show that ecological interactions between the three dominant emergent types (smooth, WS, and FS), combined with the interdependence of FS and WS on fuzY, can, at least in part, underpin an evolutionary arms race with bacteriophage SBW25F2, to which mutation in fuzY confers resistance.A DAPTIVE radiation-the rapid emergence of phenotypic and ecological diversity within an expanding lineage-is among the most striking of evolutionary phenomena (Darwin 1859;Lack 1947;Dobzhansky 1951;Simpson 1953;Schluter 2000;Kassen 2009;Losos 2010). Fueled by competition and facilitated by ecological opportunity, successive radiations have shaped much of life's diversity. Of central importance are the phenotypic innovations that fashion the fit between organism and environment (Schluter 2000).Understanding the nature of these innovations and the pathways by which they emerge and rise to prominenceoften in parallel across estranged populations experiencing similar environments-is a central issue (Colosimo et al. 2005;Bantinaki et al. 2007;Conte et al. 2012). How mutational processes generate the variation presented to selection (McDonald et al. 2009;Braendle et al. 2010), how genetic architecture underpinning extant phenotypes determines the capacity of lineages to generate new and adaptive phenotypes (Poole et al. 2003;Wagner and Zhang 2011), and how ecological factors drive phenotypic divergence (Schluter 2009) are questions of seminal interest.The relative simplicity of microbial systems, their capacity for rapid evolutionary change, and advances in technology that enable detailed genotypic traceability offer a unique opportunity to log moment-by-...

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

Cited by 148 publications

References 150 publications

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures

Fisher’s Geometrical Model Emerges as a Property of Complex Integrated Phenotypic Networks

Adaptive Divergence in Experimental Populations ofPseudomonas fluorescens. V. Insight into the Niche Specialist Fuzzy Spreader Compels Revision of the ModelPseudomonasRadiation

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