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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.

No MeSH data available.


Related in: MedlinePlus

Colonies and the signal degradation rate in the RN and GRN model.For each of the twenty most productive genotypes (see Fig. 6b for their reaction norms), 10 replicate colonies are grown at five signal degradation rates (δ): 0.025, 0.05, 0.1, 0.2 and 0.4. Cells evolved at a signal degradation rate of 0.1. (a) Colonies of the most productive genotype in the GRN model at different signal degradation rate (blue = cells and red = spores). (b) Average spore production of twenty most productive genotypes over the different signal degradation rates. (c) Fraction of cells that failed to sporulate (i.e. having insufficient energy to sporulate). (d) Average nutrient concentration at which cells initiate sporulation. The lowest row of graphs show (e) the number of spores, (f) fraction of failed sporulation attempts and (g) average nutrient concentration at onset of sporulation for the twenty most productive genotypes of the RN model.
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f8: Colonies and the signal degradation rate in the RN and GRN model.For each of the twenty most productive genotypes (see Fig. 6b for their reaction norms), 10 replicate colonies are grown at five signal degradation rates (δ): 0.025, 0.05, 0.1, 0.2 and 0.4. Cells evolved at a signal degradation rate of 0.1. (a) Colonies of the most productive genotype in the GRN model at different signal degradation rate (blue = cells and red = spores). (b) Average spore production of twenty most productive genotypes over the different signal degradation rates. (c) Fraction of cells that failed to sporulate (i.e. having insufficient energy to sporulate). (d) Average nutrient concentration at which cells initiate sporulation. The lowest row of graphs show (e) the number of spores, (f) fraction of failed sporulation attempts and (g) average nutrient concentration at onset of sporulation for the twenty most productive genotypes of the RN model.

Mentions: Before looking at the overall picture, we first will focus on five environments that were generated by changing one parameter only: the signal degradation rate. In contrast to nutrients and energy, signal is not required for the sporulation process, but the local signal concentration can be used by cells to time the onset of sporulation. Thus, by varying the signal degradation rate, the environment that cells perceive is altered, but the selection pressures on the timing of sporulation remain the same (Supplementary Text S3). Figure 8a shows colonies of the most productive genotype from the GRN model at different signal degradation rates. In comparison to the signal degradation rate at which genotypes evolved (δ = 0.1), the spore production of the twenty most productive genotypes of the GRN model varies strongly with the new environment (Fig. 8b–d): whereas some genotypes robustly produce the same high number of spores under all signal degradation rates, others produce much fewer spores when encountering a new signal degradation rate. On average, genotypes tend to postpone sporulation at high signal degradation rates and advance sporulation at low signal degradation rates (e.g. Fig. 8a). High signal degradation rates result in low signal concentrations, which cells associate with high nutrient concentrations (see the negative correlation between the signal and nutrient concentration in the dividing zone of Fig. 7a). By the same token, low signal degradation rates result in high signal concentrations, which cells associate with low nutrient concentrations. Thus, by changing the signal degradation rate, cells get the ‘illusion’ that less or more nutrients are present in the environment and thereby falsely advance or postpone sporulation, which goes at the expense of spore production (Fig. 8b–d; see also Supplementary Text S4 and Supplementary Fig. S13). The most productive genotypes of the RN model are largely insensitive to the signal concentration (Supplementary Fig. S2 and Supplementary Text S1). Accordingly, these genotypes are hardly affected by a change in the signal degradation rate (Fig. 8e–g).


Regulatory mechanisms link phenotypic plasticity to evolvability.

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

Colonies and the signal degradation rate in the RN and GRN model.For each of the twenty most productive genotypes (see Fig. 6b for their reaction norms), 10 replicate colonies are grown at five signal degradation rates (δ): 0.025, 0.05, 0.1, 0.2 and 0.4. Cells evolved at a signal degradation rate of 0.1. (a) Colonies of the most productive genotype in the GRN model at different signal degradation rate (blue = cells and red = spores). (b) Average spore production of twenty most productive genotypes over the different signal degradation rates. (c) Fraction of cells that failed to sporulate (i.e. having insufficient energy to sporulate). (d) Average nutrient concentration at which cells initiate sporulation. The lowest row of graphs show (e) the number of spores, (f) fraction of failed sporulation attempts and (g) average nutrient concentration at onset of sporulation for the twenty most productive genotypes of the RN model.
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f8: Colonies and the signal degradation rate in the RN and GRN model.For each of the twenty most productive genotypes (see Fig. 6b for their reaction norms), 10 replicate colonies are grown at five signal degradation rates (δ): 0.025, 0.05, 0.1, 0.2 and 0.4. Cells evolved at a signal degradation rate of 0.1. (a) Colonies of the most productive genotype in the GRN model at different signal degradation rate (blue = cells and red = spores). (b) Average spore production of twenty most productive genotypes over the different signal degradation rates. (c) Fraction of cells that failed to sporulate (i.e. having insufficient energy to sporulate). (d) Average nutrient concentration at which cells initiate sporulation. The lowest row of graphs show (e) the number of spores, (f) fraction of failed sporulation attempts and (g) average nutrient concentration at onset of sporulation for the twenty most productive genotypes of the RN model.
Mentions: Before looking at the overall picture, we first will focus on five environments that were generated by changing one parameter only: the signal degradation rate. In contrast to nutrients and energy, signal is not required for the sporulation process, but the local signal concentration can be used by cells to time the onset of sporulation. Thus, by varying the signal degradation rate, the environment that cells perceive is altered, but the selection pressures on the timing of sporulation remain the same (Supplementary Text S3). Figure 8a shows colonies of the most productive genotype from the GRN model at different signal degradation rates. In comparison to the signal degradation rate at which genotypes evolved (δ = 0.1), the spore production of the twenty most productive genotypes of the GRN model varies strongly with the new environment (Fig. 8b–d): whereas some genotypes robustly produce the same high number of spores under all signal degradation rates, others produce much fewer spores when encountering a new signal degradation rate. On average, genotypes tend to postpone sporulation at high signal degradation rates and advance sporulation at low signal degradation rates (e.g. Fig. 8a). High signal degradation rates result in low signal concentrations, which cells associate with high nutrient concentrations (see the negative correlation between the signal and nutrient concentration in the dividing zone of Fig. 7a). By the same token, low signal degradation rates result in high signal concentrations, which cells associate with low nutrient concentrations. Thus, by changing the signal degradation rate, cells get the ‘illusion’ that less or more nutrients are present in the environment and thereby falsely advance or postpone sporulation, which goes at the expense of spore production (Fig. 8b–d; see also Supplementary Text S4 and Supplementary Fig. S13). The most productive genotypes of the RN model are largely insensitive to the signal concentration (Supplementary Fig. S2 and Supplementary Text S1). Accordingly, these genotypes are hardly affected by a change in the signal degradation rate (Fig. 8e–g).

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