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Effects of promoter leakage on dynamics of gene expression.

Huang L, Yuan Z, Liu P, Zhou T - BMC Syst Biol (2015)

Bottom Line: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes.We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics.Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

View Article: PubMed Central - PubMed

Affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, PR China. hlfang208@163.com.

ABSTRACT

Background: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear.

Results: In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts.

Conclusions: Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

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Related in: MedlinePlus

Effects of promoter leakage on gene expression noise. (A) The dependence of the noise intensity on the promoter leakage rate in the case that the gene product amount is fixed at a certain value, showing the noise intensity is always a monotonically decreasing function of the leakage rate, regardless of ways to keep the average expression level fixed (e.g., decreasing the transition rate from OFF to ON (γ1) (solid red line); increasing the transition rate from ON to OFF (γ0) (black dotted line); increasing the feedback strength (f) (green dashed line); decreasing the maximum transcription rate (λ1) (blue dash dot line). (B) Results in the case that the mean expression is not fixed, showing that increasing the leakage rate reduces the expression noise, where 4 colored lines correspond to 4 different sets of parameter values: λ1 = 40, γ0 = 0.1, γ1 = 0.1, f = 0.01(green); λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0(red); λ1 = 40, γ0 = 0.2, γ1 = 0.1, f = 0(black); λ1 = 30, γ0 = 0.1, γ1 = 0.1, f = 0(blue). This subfigure shows that the conclusion that promoter leakage always reduces noise is independent of model parameters. (C) The noise as a function of the mean for different values of the leakage rate, where the parameter values are the same as those used in Figure 2(B). The subfigure shows that the larger the promoter leakage rate is, the more is the number of gene product molecules, implying that promoter leakage always reduces expression noise. In Figure 2(B) and (C), lines represent theoretical results whereas circles represent stochastically simulating results.
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Fig2: Effects of promoter leakage on gene expression noise. (A) The dependence of the noise intensity on the promoter leakage rate in the case that the gene product amount is fixed at a certain value, showing the noise intensity is always a monotonically decreasing function of the leakage rate, regardless of ways to keep the average expression level fixed (e.g., decreasing the transition rate from OFF to ON (γ1) (solid red line); increasing the transition rate from ON to OFF (γ0) (black dotted line); increasing the feedback strength (f) (green dashed line); decreasing the maximum transcription rate (λ1) (blue dash dot line). (B) Results in the case that the mean expression is not fixed, showing that increasing the leakage rate reduces the expression noise, where 4 colored lines correspond to 4 different sets of parameter values: λ1 = 40, γ0 = 0.1, γ1 = 0.1, f = 0.01(green); λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0(red); λ1 = 40, γ0 = 0.2, γ1 = 0.1, f = 0(black); λ1 = 30, γ0 = 0.1, γ1 = 0.1, f = 0(blue). This subfigure shows that the conclusion that promoter leakage always reduces noise is independent of model parameters. (C) The noise as a function of the mean for different values of the leakage rate, where the parameter values are the same as those used in Figure 2(B). The subfigure shows that the larger the promoter leakage rate is, the more is the number of gene product molecules, implying that promoter leakage always reduces expression noise. In Figure 2(B) and (C), lines represent theoretical results whereas circles represent stochastically simulating results.

Mentions: In previous studies [49-52], the effect of promoter leakage on gene expression was frequently neglected. Here, we numerically show that the promoter leakage has unneglectable effects on gene expression and in particular on expression noise. More precisely, the promoter leakage always reduces the noise in gene product. The numerical results are shown in Figure 2.Figure 2


Effects of promoter leakage on dynamics of gene expression.

Huang L, Yuan Z, Liu P, Zhou T - BMC Syst Biol (2015)

Effects of promoter leakage on gene expression noise. (A) The dependence of the noise intensity on the promoter leakage rate in the case that the gene product amount is fixed at a certain value, showing the noise intensity is always a monotonically decreasing function of the leakage rate, regardless of ways to keep the average expression level fixed (e.g., decreasing the transition rate from OFF to ON (γ1) (solid red line); increasing the transition rate from ON to OFF (γ0) (black dotted line); increasing the feedback strength (f) (green dashed line); decreasing the maximum transcription rate (λ1) (blue dash dot line). (B) Results in the case that the mean expression is not fixed, showing that increasing the leakage rate reduces the expression noise, where 4 colored lines correspond to 4 different sets of parameter values: λ1 = 40, γ0 = 0.1, γ1 = 0.1, f = 0.01(green); λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0(red); λ1 = 40, γ0 = 0.2, γ1 = 0.1, f = 0(black); λ1 = 30, γ0 = 0.1, γ1 = 0.1, f = 0(blue). This subfigure shows that the conclusion that promoter leakage always reduces noise is independent of model parameters. (C) The noise as a function of the mean for different values of the leakage rate, where the parameter values are the same as those used in Figure 2(B). The subfigure shows that the larger the promoter leakage rate is, the more is the number of gene product molecules, implying that promoter leakage always reduces expression noise. In Figure 2(B) and (C), lines represent theoretical results whereas circles represent stochastically simulating results.
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Related In: Results  -  Collection

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Fig2: Effects of promoter leakage on gene expression noise. (A) The dependence of the noise intensity on the promoter leakage rate in the case that the gene product amount is fixed at a certain value, showing the noise intensity is always a monotonically decreasing function of the leakage rate, regardless of ways to keep the average expression level fixed (e.g., decreasing the transition rate from OFF to ON (γ1) (solid red line); increasing the transition rate from ON to OFF (γ0) (black dotted line); increasing the feedback strength (f) (green dashed line); decreasing the maximum transcription rate (λ1) (blue dash dot line). (B) Results in the case that the mean expression is not fixed, showing that increasing the leakage rate reduces the expression noise, where 4 colored lines correspond to 4 different sets of parameter values: λ1 = 40, γ0 = 0.1, γ1 = 0.1, f = 0.01(green); λ1 = 40, γ0 = 0.1, γ1 = 0.2, f = 0(red); λ1 = 40, γ0 = 0.2, γ1 = 0.1, f = 0(black); λ1 = 30, γ0 = 0.1, γ1 = 0.1, f = 0(blue). This subfigure shows that the conclusion that promoter leakage always reduces noise is independent of model parameters. (C) The noise as a function of the mean for different values of the leakage rate, where the parameter values are the same as those used in Figure 2(B). The subfigure shows that the larger the promoter leakage rate is, the more is the number of gene product molecules, implying that promoter leakage always reduces expression noise. In Figure 2(B) and (C), lines represent theoretical results whereas circles represent stochastically simulating results.
Mentions: In previous studies [49-52], the effect of promoter leakage on gene expression was frequently neglected. Here, we numerically show that the promoter leakage has unneglectable effects on gene expression and in particular on expression noise. More precisely, the promoter leakage always reduces the noise in gene product. The numerical results are shown in Figure 2.Figure 2

Bottom Line: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes.We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics.Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

View Article: PubMed Central - PubMed

Affiliation: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, 510275, PR China. hlfang208@163.com.

ABSTRACT

Background: Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects expression noise is unclear.

Results: In this work, we analyze a gene model with auto regulation, where the promoter is assumed to have one active state with highly efficient transcription and one inactive state with very lowly efficient transcription (termed as promoter leakage). We first derive the analytical distribution of gene product, and then analyze effects of promoter leakage on expression dynamics including bursting kinetics. Interestingly, we find that promoter leakage always reduces expression noise and that increasing the leakage rate tends to simplify phenotypes. In addition, higher leakage results in fewer bursts.

Conclusions: Our results reveal the essential role of promoter leakage in controlling expression dynamics and further phenotype. Specifically, promoter leakage is a universal mechanism of reducing expression noise, controlling phenotypes in different environments and making the gene produce generate fewer bursts.

Show MeSH
Related in: MedlinePlus