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Regulatory mechanisms link phenotypic plasticity to evolvability.

van Gestel J, Weissing FJ - Sci Rep (2016)

Bottom Line: Using individual-based simulations, we compare the RN and GRN approach and find a number of striking differences.Most importantly, the GRN model results in a much higher diversity of responsive strategies than the RN model.The regulatory mechanisms that control plasticity therefore critically link phenotypic plasticity to the adaptive potential of biological populations.

View Article: PubMed Central - PubMed

Affiliation: Groningen Institute for Evolutionary Life Sciences, University of Groningen, P.O. Box 11103, Groningen 9700 CC, The Netherlands.

ABSTRACT
Organisms have a remarkable capacity to respond to environmental change. They can either respond directly, by means of phenotypic plasticity, or they can slowly adapt through evolution. Yet, how phenotypic plasticity links to evolutionary adaptability is largely unknown. Current studies of plasticity tend to adopt a phenomenological reaction norm (RN) approach, which neglects the mechanisms underlying plasticity. Focusing on a concrete question - the optimal timing of bacterial sporulation - we here also consider a mechanistic approach, the evolution of a gene regulatory network (GRN) underlying plasticity. Using individual-based simulations, we compare the RN and GRN approach and find a number of striking differences. Most importantly, the GRN model results in a much higher diversity of responsive strategies than the RN model. We show that each of the evolved strategies is pre-adapted to a unique set of unseen environmental conditions. The regulatory mechanisms that control plasticity therefore critically link phenotypic plasticity to the adaptive potential of biological populations.

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The diversity in evolved reaction norms in the RN model.(a) Phenogram based on the distance between reaction norms of the most frequent genotypes in the 500 replicate simulations at the end of evolution. The distance between two reaction norms is given by the fraction of conditions at which they prescribe a different response. Colours indicate spore production of genotypes: low (red), intermediate (blue) and high (green). (b) The reaction norms and corresponding colonies that are associated with the tips of the branches in the phenogram (numbered 1–4) and the most productive genotype (F).
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f4: The diversity in evolved reaction norms in the RN model.(a) Phenogram based on the distance between reaction norms of the most frequent genotypes in the 500 replicate simulations at the end of evolution. The distance between two reaction norms is given by the fraction of conditions at which they prescribe a different response. Colours indicate spore production of genotypes: low (red), intermediate (blue) and high (green). (b) The reaction norms and corresponding colonies that are associated with the tips of the branches in the phenogram (numbered 1–4) and the most productive genotype (F).

Mentions: In order to characterize the diversity of reaction norms at generation 400, we compared the reaction norms of all 500 replicate simulations in a pairwise fashion. For each pairwise combination, we determined the fraction of conditions (N, S and E) for which cells with the associated reaction norms would take different decisions: one cell would sporulate, while the other cell would not. Using a hierarchical cluster analysis (see Material and Methods), the pairwise differences were converted into a phenogram, which illustrates the diversity of reaction norms present at the end of evolution (Fig. 4a). Each dot is associated with a single evolved genotype and its associated reaction norm (corresponding to one simulation) and the branch lengths correspond to the differences between the reaction norms. The colors of the dots correspond to the productivity of the given genotypes, which is defined by the number of spores present at the end of colony growth (an accurate proxy for a genotype’s fitness, see Supplementary Fig. S1).


Regulatory mechanisms link phenotypic plasticity to evolvability.

van Gestel J, Weissing FJ - Sci Rep (2016)

The diversity in evolved reaction norms in the RN model.(a) Phenogram based on the distance between reaction norms of the most frequent genotypes in the 500 replicate simulations at the end of evolution. The distance between two reaction norms is given by the fraction of conditions at which they prescribe a different response. Colours indicate spore production of genotypes: low (red), intermediate (blue) and high (green). (b) The reaction norms and corresponding colonies that are associated with the tips of the branches in the phenogram (numbered 1–4) and the most productive genotype (F).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834480&req=5

f4: The diversity in evolved reaction norms in the RN model.(a) Phenogram based on the distance between reaction norms of the most frequent genotypes in the 500 replicate simulations at the end of evolution. The distance between two reaction norms is given by the fraction of conditions at which they prescribe a different response. Colours indicate spore production of genotypes: low (red), intermediate (blue) and high (green). (b) The reaction norms and corresponding colonies that are associated with the tips of the branches in the phenogram (numbered 1–4) and the most productive genotype (F).
Mentions: In order to characterize the diversity of reaction norms at generation 400, we compared the reaction norms of all 500 replicate simulations in a pairwise fashion. For each pairwise combination, we determined the fraction of conditions (N, S and E) for which cells with the associated reaction norms would take different decisions: one cell would sporulate, while the other cell would not. Using a hierarchical cluster analysis (see Material and Methods), the pairwise differences were converted into a phenogram, which illustrates the diversity of reaction norms present at the end of evolution (Fig. 4a). Each dot is associated with a single evolved genotype and its associated reaction norm (corresponding to one simulation) and the branch lengths correspond to the differences between the reaction norms. The colors of the dots correspond to the productivity of the given genotypes, which is defined by the number of spores present at the end of colony growth (an accurate proxy for a genotype’s fitness, see Supplementary Fig. S1).

Bottom Line: Using individual-based simulations, we compare the RN and GRN approach and find a number of striking differences.Most importantly, the GRN model results in a much higher diversity of responsive strategies than the RN model.The regulatory mechanisms that control plasticity therefore critically link phenotypic plasticity to the adaptive potential of biological populations.

View Article: PubMed Central - PubMed

Affiliation: Groningen Institute for Evolutionary Life Sciences, University of Groningen, P.O. Box 11103, Groningen 9700 CC, The Netherlands.

ABSTRACT
Organisms have a remarkable capacity to respond to environmental change. They can either respond directly, by means of phenotypic plasticity, or they can slowly adapt through evolution. Yet, how phenotypic plasticity links to evolutionary adaptability is largely unknown. Current studies of plasticity tend to adopt a phenomenological reaction norm (RN) approach, which neglects the mechanisms underlying plasticity. Focusing on a concrete question - the optimal timing of bacterial sporulation - we here also consider a mechanistic approach, the evolution of a gene regulatory network (GRN) underlying plasticity. Using individual-based simulations, we compare the RN and GRN approach and find a number of striking differences. Most importantly, the GRN model results in a much higher diversity of responsive strategies than the RN model. We show that each of the evolved strategies is pre-adapted to a unique set of unseen environmental conditions. The regulatory mechanisms that control plasticity therefore critically link phenotypic plasticity to the adaptive potential of biological populations.

No MeSH data available.


Related in: MedlinePlus