Contrasting effects of historical contingency on phenotypic and genomic trajectories during a two-step evolution experiment with bacteria.
BACKGROUND
The impact of historical contingency, i.e. the past evolutionary history of a population, on further adaptation is mostly unknown at both the phenotypic and genomic levels. We addressed this question using a two-step evolution experiment. First, replicate populations of Escherichia coli were propagated in four different environmental conditions for 1000 generations. Then, all replicate populations were transferred and propagated for further 1000 generations to a single new environment.


RESULTS
Using this two-step experimental evolution strategy, we investigated, at both the phenotypic and genomic levels, whether and how adaptation in the initial historical environments impacted evolutionary trajectories in a new environment. We showed that both the growth rate and fitness of the evolved populations obtained after the second step of evolution were contingent upon past evolutionary history. In contrast however, the genes that were modified during the second step of evolution were independent from the previous history of the populations.


CONCLUSIONS
Our work suggests that historical contingency affects phenotypic adaptation to a new environment. This was however not reflected at the genomic level implying complex relationships between environmental factors and the genotype-to-phenotype map.
Univ. Grenoble Alpes, Laboratoire Technologies de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications (TIMC-IMAG), F-38000, Grenoble, France.|Centre National de Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.
Jessica
J
Plucain
Univ. Grenoble Alpes, Laboratoire Technologies de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications (TIMC-IMAG), F-38000, Grenoble, France.|Centre National de Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.|Conservatoire national des arts et métiers, Paris, France.
Antonia
A
Suau
Direction des Sciences du Vivant, CEA, Institut de Génomique, Genoscope & CNRS-UMR8030, Évry, France.|Laboratoire d'Analyses Bioinformatiques en Génomique et Métabolisme, Évry, France.
Stéphane
S
Cruveiller
Direction des Sciences du Vivant, CEA, Institut de Génomique, Genoscope & CNRS-UMR8030, Évry, France.|Laboratoire d'Analyses Bioinformatiques en Génomique et Métabolisme, Évry, France.
Claudine
C
Médigue
Univ. Grenoble Alpes, Laboratoire Technologies de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications (TIMC-IMAG), F-38000, Grenoble, France.|Centre National de Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France.
Dominique
D
Schneider
Univ. Grenoble Alpes, Laboratoire Technologies de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications (TIMC-IMAG), F-38000, Grenoble, France. Mickael.Le.Gac@ifremer.fr.|Centre National de Recherche Scientifique (CNRS), TIMC-IMAG, F-38000, Grenoble, France. Mickael.Le.Gac@ifremer.fr.|Ifremer, DYNECO/Pelagos, 29280, Plouzané, France. Mickael.Le.Gac@ifremer.fr.
Mickaël
M
Le Gac
D000222:Adaptation, Physiological; D004777:Environment; D004926:Escherichia coli / Q000235:genetics*; D019143:Evolution, Molecular; D059647:Gene-Environment Interaction; D016680:Genome, Bacterial; D010641:Phenotype
Adaptation; Epistasis; Escherichia coli; Experimental evolution; Historical contingency