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Positive selection for elevated gene expression noise in yeast.

Zhang Z, Qian W, Zhang J - Mol. Syst. Biol. (2009)

Bottom Line: Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors.Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations.Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.

ABSTRACT
It is well known that the expression noise is lessened by natural selection for genes that are important for cell growth or are sensitive to dosage. In theory, expression noise can also be elevated by natural selection when noisy gene expression is advantageous. Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors. We propose a model that explains why and under what conditions elevated expression noise may be beneficial and subject to positive selection. Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations. Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded. Together, our theoretical model and empirical results suggest that, for yeast genes such as plasma-membrane transporters, elevated expression noise is advantageous, is subject to positive selection, and is a facilitator of adaptive gene expression evolution.

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Computer simulation shows faster adaptive evolution of expression level for a noisier genotype than a quieter genotype. (A) The noisier genotype reaches the optimal expression level sooner than the quieter genotype during evolution. (B) A typical case of expression evolution of a noisy genotype. The blue curve to the right of the figure is the fitness function f(x). Each vertical line in the heat map represents the frequency distribution of x in the population in a given generation, with different colors representing different frequencies of cells with given x. (C) A typical case of expression evolution of a quiet genotype.
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f4: Computer simulation shows faster adaptive evolution of expression level for a noisier genotype than a quieter genotype. (A) The noisier genotype reaches the optimal expression level sooner than the quieter genotype during evolution. (B) A typical case of expression evolution of a noisy genotype. The blue curve to the right of the figure is the fitness function f(x). Each vertical line in the heat map represents the frequency distribution of x in the population in a given generation, with different colors representing different frequencies of cells with given x. (C) A typical case of expression evolution of a quiet genotype.

Mentions: As the same advantageous mutation can enhance the fitness of the noisier genotype more than the quieter genotype, we predict faster adaptive evolutionary changes in mean expression level for noisier genes than for quieter genes. To see to what extent the noise level impacts the rate of adaptation, we conducted a computer simulation. Let us consider a population of yeast cells all with genotype A and another population all with genotype B. The two genotypes have the same mean expression level that is suboptimal. Genotype A has a higher expression noise level than genotype B. The two populations have the same population size, mutation rate, and mutation spectrum. Mutations are randomly generated with a size that follows a normal distribution. Here, mutation size refers to the difference between the mean expression level of the mutant and that of the wild type. We assume that the level of expression noise does not change. As shown in Figure 4A, under the parameters detailed in Methods section, genotype A adapts its expression level to the optimal level significantly faster than genotype B (P<10−48, t-test), and the difference in speed is on average 2.56-fold. Figure 4 also shows the adaptation process from one simulation replication, in which the noisier genotype (Figure 4B) adapts to the optimal expression level in about one-fifth the time required for the quieter genotype (Figure 4C). Thus, at least under some conditions, high expression noise leads to a substantially enhanced rate of adaptation of gene expression level because noise can facilitate positive selection for advantageous mutations. Note that although the number of generations required for adaptation seems very large in Figure 4, the actual time required can be much shorter if the mutations are larger or the mutation rate is higher. We found that our simulation result holds in a broad parameter space when we vary the mutation rate and the noise ratio of the high-noise and low-noise genotypes (Supplementary Figure S5).


Positive selection for elevated gene expression noise in yeast.

Zhang Z, Qian W, Zhang J - Mol. Syst. Biol. (2009)

Computer simulation shows faster adaptive evolution of expression level for a noisier genotype than a quieter genotype. (A) The noisier genotype reaches the optimal expression level sooner than the quieter genotype during evolution. (B) A typical case of expression evolution of a noisy genotype. The blue curve to the right of the figure is the fitness function f(x). Each vertical line in the heat map represents the frequency distribution of x in the population in a given generation, with different colors representing different frequencies of cells with given x. (C) A typical case of expression evolution of a quiet genotype.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2736655&req=5

f4: Computer simulation shows faster adaptive evolution of expression level for a noisier genotype than a quieter genotype. (A) The noisier genotype reaches the optimal expression level sooner than the quieter genotype during evolution. (B) A typical case of expression evolution of a noisy genotype. The blue curve to the right of the figure is the fitness function f(x). Each vertical line in the heat map represents the frequency distribution of x in the population in a given generation, with different colors representing different frequencies of cells with given x. (C) A typical case of expression evolution of a quiet genotype.
Mentions: As the same advantageous mutation can enhance the fitness of the noisier genotype more than the quieter genotype, we predict faster adaptive evolutionary changes in mean expression level for noisier genes than for quieter genes. To see to what extent the noise level impacts the rate of adaptation, we conducted a computer simulation. Let us consider a population of yeast cells all with genotype A and another population all with genotype B. The two genotypes have the same mean expression level that is suboptimal. Genotype A has a higher expression noise level than genotype B. The two populations have the same population size, mutation rate, and mutation spectrum. Mutations are randomly generated with a size that follows a normal distribution. Here, mutation size refers to the difference between the mean expression level of the mutant and that of the wild type. We assume that the level of expression noise does not change. As shown in Figure 4A, under the parameters detailed in Methods section, genotype A adapts its expression level to the optimal level significantly faster than genotype B (P<10−48, t-test), and the difference in speed is on average 2.56-fold. Figure 4 also shows the adaptation process from one simulation replication, in which the noisier genotype (Figure 4B) adapts to the optimal expression level in about one-fifth the time required for the quieter genotype (Figure 4C). Thus, at least under some conditions, high expression noise leads to a substantially enhanced rate of adaptation of gene expression level because noise can facilitate positive selection for advantageous mutations. Note that although the number of generations required for adaptation seems very large in Figure 4, the actual time required can be much shorter if the mutations are larger or the mutation rate is higher. We found that our simulation result holds in a broad parameter space when we vary the mutation rate and the noise ratio of the high-noise and low-noise genotypes (Supplementary Figure S5).

Bottom Line: Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors.Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations.Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA.

ABSTRACT
It is well known that the expression noise is lessened by natural selection for genes that are important for cell growth or are sensitive to dosage. In theory, expression noise can also be elevated by natural selection when noisy gene expression is advantageous. Here we analyze yeast genome-wide gene expression noise data and show that plasma-membrane transporters show significantly elevated expression noise after controlling all confounding factors. We propose a model that explains why and under what conditions elevated expression noise may be beneficial and subject to positive selection. Our model predicts and the simulation confirms that, under certain conditions, expression noise also increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations. Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression, even when all confounding factors are excluded. Together, our theoretical model and empirical results suggest that, for yeast genes such as plasma-membrane transporters, elevated expression noise is advantageous, is subject to positive selection, and is a facilitator of adaptive gene expression evolution.

Show MeSH
Related in: MedlinePlus