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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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Fitness landscape affects the relative fitness of high-noise and low-noise genotypes. In each panel, the green curve shows f(x), the fitness of the cell with the expression level of gene X equal to x. The blue and red curves show the frequency distributions of the expression levels (x) of the high-noise and low-noise genotypes, respectively. The blue and red dots are the mean fitness of the high-noise and low-noise genotypes, respectively. When f(x) is convex, the mean fitness of the high-noise genotype is greater than that of the low-noise genotype, no matter whether the optimal expression level is higher (A) or lower (B) than the mean expression levels of the two genotypes. When f(x) is concave, the fitness of the high-noise genotype is smaller than that of the low-noise genotype, no matter whether the optimal expression level is higher (C) or lower (D) than the mean expression levels of the two genotypes. When f(x) is linear, the fitness of the high-noise genotype equals that of the low-noise genotype, no matter whether the optimal expression level is higher (E) or lower (F) than the mean expression levels of the two genotypes.
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f2: Fitness landscape affects the relative fitness of high-noise and low-noise genotypes. In each panel, the green curve shows f(x), the fitness of the cell with the expression level of gene X equal to x. The blue and red curves show the frequency distributions of the expression levels (x) of the high-noise and low-noise genotypes, respectively. The blue and red dots are the mean fitness of the high-noise and low-noise genotypes, respectively. When f(x) is convex, the mean fitness of the high-noise genotype is greater than that of the low-noise genotype, no matter whether the optimal expression level is higher (A) or lower (B) than the mean expression levels of the two genotypes. When f(x) is concave, the fitness of the high-noise genotype is smaller than that of the low-noise genotype, no matter whether the optimal expression level is higher (C) or lower (D) than the mean expression levels of the two genotypes. When f(x) is linear, the fitness of the high-noise genotype equals that of the low-noise genotype, no matter whether the optimal expression level is higher (E) or lower (F) than the mean expression levels of the two genotypes.

Mentions: Let us consider two genotypes A and B. The only difference between them is that A has a higher level of expression noise than B for gene X. The mean expression level (m) of X is identical between the two genotypes. The distribution of the expression noise (e) for gene X is described by probability density functions gA(e) and gB(e) for the two genotypes, respectively. Genome-wide expression noise data showed that e generally follows a normal distribution (Bar-Even et al, 2006; Newman et al, 2006). Let us assume that a population, having A and B cells, experiences an environmental change such that the mean expression level of X becomes suboptimal. Let f(x)=f(m+e) be the fitness of the cell that has an expression level of X equal to x. So, the fitness of genotype A, or the mean fitness of A cells, equals . Similarly, the fitness of genotype B equals . It can be shown that (i) when f(x) is a convex function (i.e. the second derivative of f(x) is positive), FA>FB; (ii) when f(x) is a concave function, FA<FB; and (iii) when f(x) is linear, FA=FB (Figure 2; Supplementary Figure S1; Supplementary information 1). As f(x) may not be concave or convex for all possible values of x, what matters is whether f(x) is concave or convex for the range of x realized in the majority (e.g. 95% or 99%) of A and B cells. Note that in our model, the optimal expression level can be either higher or lower than m (Figure 2). Although the shape of f(x) is generally unknown, it is reasonable to assume that, at least, for many genes if not most genes, it is bell shaped with the optimal expression level in the center (Kacser and Burns, 1981; Hartl et al, 1985; Bedford and Hartl, 2009). In such cases, f(x) is concave when x is close to the optimal expression level, but convex when x is far from the optimal. Thus, big environmental changes tend to generate conditions under which high noise is beneficial. Note that although we compared mean fitness values of cells with two different genotypes, there is no involvement of group selection in our model. When f(x) is convex, in a population fixed with the wild type, a mutant with a higher level of noise is expected to increase its frequency in the population because its fitness is greater than that of the wild type.


Positive selection for elevated gene expression noise in yeast.

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

Fitness landscape affects the relative fitness of high-noise and low-noise genotypes. In each panel, the green curve shows f(x), the fitness of the cell with the expression level of gene X equal to x. The blue and red curves show the frequency distributions of the expression levels (x) of the high-noise and low-noise genotypes, respectively. The blue and red dots are the mean fitness of the high-noise and low-noise genotypes, respectively. When f(x) is convex, the mean fitness of the high-noise genotype is greater than that of the low-noise genotype, no matter whether the optimal expression level is higher (A) or lower (B) than the mean expression levels of the two genotypes. When f(x) is concave, the fitness of the high-noise genotype is smaller than that of the low-noise genotype, no matter whether the optimal expression level is higher (C) or lower (D) than the mean expression levels of the two genotypes. When f(x) is linear, the fitness of the high-noise genotype equals that of the low-noise genotype, no matter whether the optimal expression level is higher (E) or lower (F) than the mean expression levels of the two genotypes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Fitness landscape affects the relative fitness of high-noise and low-noise genotypes. In each panel, the green curve shows f(x), the fitness of the cell with the expression level of gene X equal to x. The blue and red curves show the frequency distributions of the expression levels (x) of the high-noise and low-noise genotypes, respectively. The blue and red dots are the mean fitness of the high-noise and low-noise genotypes, respectively. When f(x) is convex, the mean fitness of the high-noise genotype is greater than that of the low-noise genotype, no matter whether the optimal expression level is higher (A) or lower (B) than the mean expression levels of the two genotypes. When f(x) is concave, the fitness of the high-noise genotype is smaller than that of the low-noise genotype, no matter whether the optimal expression level is higher (C) or lower (D) than the mean expression levels of the two genotypes. When f(x) is linear, the fitness of the high-noise genotype equals that of the low-noise genotype, no matter whether the optimal expression level is higher (E) or lower (F) than the mean expression levels of the two genotypes.
Mentions: Let us consider two genotypes A and B. The only difference between them is that A has a higher level of expression noise than B for gene X. The mean expression level (m) of X is identical between the two genotypes. The distribution of the expression noise (e) for gene X is described by probability density functions gA(e) and gB(e) for the two genotypes, respectively. Genome-wide expression noise data showed that e generally follows a normal distribution (Bar-Even et al, 2006; Newman et al, 2006). Let us assume that a population, having A and B cells, experiences an environmental change such that the mean expression level of X becomes suboptimal. Let f(x)=f(m+e) be the fitness of the cell that has an expression level of X equal to x. So, the fitness of genotype A, or the mean fitness of A cells, equals . Similarly, the fitness of genotype B equals . It can be shown that (i) when f(x) is a convex function (i.e. the second derivative of f(x) is positive), FA>FB; (ii) when f(x) is a concave function, FA<FB; and (iii) when f(x) is linear, FA=FB (Figure 2; Supplementary Figure S1; Supplementary information 1). As f(x) may not be concave or convex for all possible values of x, what matters is whether f(x) is concave or convex for the range of x realized in the majority (e.g. 95% or 99%) of A and B cells. Note that in our model, the optimal expression level can be either higher or lower than m (Figure 2). Although the shape of f(x) is generally unknown, it is reasonable to assume that, at least, for many genes if not most genes, it is bell shaped with the optimal expression level in the center (Kacser and Burns, 1981; Hartl et al, 1985; Bedford and Hartl, 2009). In such cases, f(x) is concave when x is close to the optimal expression level, but convex when x is far from the optimal. Thus, big environmental changes tend to generate conditions under which high noise is beneficial. Note that although we compared mean fitness values of cells with two different genotypes, there is no involvement of group selection in our model. When f(x) is convex, in a population fixed with the wild type, a mutant with a higher level of noise is expected to increase its frequency in the population because its fitness is greater than that of the wild type.

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