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Control and signal processing by transcriptional interference.

Buetti-Dinh A, Ungricht R, Kelemen JZ, Shetty C, Ratna P, Becskei A - Mol. Syst. Biol. (2009)

Bottom Line: When gene expression is induced weakly, the antagonistic activator can have a positive effect and can even trigger paradoxical activation.Indeed, a synthetic circuit generates a bell-shaped response, so that the induction of expression is limited to a narrow range of the input signal.The identification of conserved regulatory principles of interference will help to predict the transcriptional response of genes in their genomic context.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Biology, University of Zurich, Zurich, Switzerland.

ABSTRACT
A transcriptional activator can suppress gene expression by interfering with transcription initiated by another activator. Transcriptional interference has been increasingly recognized as a regulatory mechanism of gene expression. The signals received by the two antagonistically acting activators are combined by the polymerase trafficking along the DNA. We have designed a dual-control genetic system in yeast to explore this antagonism systematically. Antagonism by an upstream activator bears the hallmarks of competitive inhibition, whereas a downstream activator inhibits gene expression non-competitively. When gene expression is induced weakly, the antagonistic activator can have a positive effect and can even trigger paradoxical activation. Equilibrium and non-equilibrium models of transcription shed light on the mechanism by which interference converts signals, and reveals that self-antagonism of activators imitates the behavior of feed-forward loops. Indeed, a synthetic circuit generates a bell-shaped response, so that the induction of expression is limited to a narrow range of the input signal. The identification of conserved regulatory principles of interference will help to predict the transcriptional response of genes in their genomic context.

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Equilibrium (A–C) and non-equilibrium competition models (D–H) of upstream interference. (A) Scheme of the equilibrium competition model. The downstream promoter is occupied either by the interfering polymerase or by the activator, AUAS. (B, C) Equation (1) (Box 1) was fit to the data. KDA=0.37 and f(R)=2.9, 5.9, 14.1* and 41.4* for PtetO2-GAL1TATA (data re-plotted from Figure 1B) (B); KDA=0.024 and f(R)=2.6, 5.9, 18.5 and 42.7* for PtetO7-GAL1TATA (YABH34.5) (C). The asterisked f(R) values were obtained by fitting equation (1) to data points that had a normalized expression higher than 0.4 (see Materials and methods section). (D) In the non-equilibrium competition model, the interfering polymerase traverses the UAS and the TATA box in the downstream promoter, after which they bind the activator, AUAS, and the TBP with a higher affinity. (E) Gene expression as a function of AUAS was calculated from the non-equilibrium model with the parameter values fitted for PtetO2-GAL1TATA (Figure 1B). The concentration of the activator [P] driving the intergenic transcription is color coded. (F) Curves were re-calculated from (E). (G) Fold inhibition at P=100 was calculated for promoters with one (O1) and two (O2) operators as in (F), except for the parameters specified in the figure legend. The red dashed line stands for one operator with reduced affinity. (H) Fold inhibition was measured at 200 nM estradiol as the doxycycline concentration was varied. The curves were fit with the parameter values obtained for the corresponding constructs in (Figure 1B). To link the AUAS concentration to the doxycycline concentration, Atot=10.3 nM and Kind=2.6 were fit for promoters with CYC1TATA, measured on the same day. For PtetO1-CYC1TATA (YABH40.6), kON=0.0072 nM−1 min−1 and kOFF=0.13 min−1 were fitted to account for its lower binding constant in comparison with PtetO2-CYC1TATA. Source data is available for this figure at www.nature.com/msb
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f2: Equilibrium (A–C) and non-equilibrium competition models (D–H) of upstream interference. (A) Scheme of the equilibrium competition model. The downstream promoter is occupied either by the interfering polymerase or by the activator, AUAS. (B, C) Equation (1) (Box 1) was fit to the data. KDA=0.37 and f(R)=2.9, 5.9, 14.1* and 41.4* for PtetO2-GAL1TATA (data re-plotted from Figure 1B) (B); KDA=0.024 and f(R)=2.6, 5.9, 18.5 and 42.7* for PtetO7-GAL1TATA (YABH34.5) (C). The asterisked f(R) values were obtained by fitting equation (1) to data points that had a normalized expression higher than 0.4 (see Materials and methods section). (D) In the non-equilibrium competition model, the interfering polymerase traverses the UAS and the TATA box in the downstream promoter, after which they bind the activator, AUAS, and the TBP with a higher affinity. (E) Gene expression as a function of AUAS was calculated from the non-equilibrium model with the parameter values fitted for PtetO2-GAL1TATA (Figure 1B). The concentration of the activator [P] driving the intergenic transcription is color coded. (F) Curves were re-calculated from (E). (G) Fold inhibition at P=100 was calculated for promoters with one (O1) and two (O2) operators as in (F), except for the parameters specified in the figure legend. The red dashed line stands for one operator with reduced affinity. (H) Fold inhibition was measured at 200 nM estradiol as the doxycycline concentration was varied. The curves were fit with the parameter values obtained for the corresponding constructs in (Figure 1B). To link the AUAS concentration to the doxycycline concentration, Atot=10.3 nM and Kind=2.6 were fit for promoters with CYC1TATA, measured on the same day. For PtetO1-CYC1TATA (YABH40.6), kON=0.0072 nM−1 min−1 and kOFF=0.13 min−1 were fitted to account for its lower binding constant in comparison with PtetO2-CYC1TATA. Source data is available for this figure at www.nature.com/msb

Mentions: Next, we measured the changes in the mean GFP expression as the occupancy of the activator-binding sites within a downstream promoter was varied. For this purpose, doxycycline was used to modulate the binding of the transcriptional activator, rtTA, to two tet operators within the downstream promoter. The doxycycline-induced binding of rtTA to the promoter led to GFP expression (Figure 1B). We observed that the suppression of GFP expression by intergenic transcription was gradually relieved as the rtTA binding strengthened, when the doxycycline concentration was increased from intermediate to high levels (Figures 1B and 2B). This indicates that intergenic transcription competes with the rtTA-driven transcription.


Control and signal processing by transcriptional interference.

Buetti-Dinh A, Ungricht R, Kelemen JZ, Shetty C, Ratna P, Becskei A - Mol. Syst. Biol. (2009)

Equilibrium (A–C) and non-equilibrium competition models (D–H) of upstream interference. (A) Scheme of the equilibrium competition model. The downstream promoter is occupied either by the interfering polymerase or by the activator, AUAS. (B, C) Equation (1) (Box 1) was fit to the data. KDA=0.37 and f(R)=2.9, 5.9, 14.1* and 41.4* for PtetO2-GAL1TATA (data re-plotted from Figure 1B) (B); KDA=0.024 and f(R)=2.6, 5.9, 18.5 and 42.7* for PtetO7-GAL1TATA (YABH34.5) (C). The asterisked f(R) values were obtained by fitting equation (1) to data points that had a normalized expression higher than 0.4 (see Materials and methods section). (D) In the non-equilibrium competition model, the interfering polymerase traverses the UAS and the TATA box in the downstream promoter, after which they bind the activator, AUAS, and the TBP with a higher affinity. (E) Gene expression as a function of AUAS was calculated from the non-equilibrium model with the parameter values fitted for PtetO2-GAL1TATA (Figure 1B). The concentration of the activator [P] driving the intergenic transcription is color coded. (F) Curves were re-calculated from (E). (G) Fold inhibition at P=100 was calculated for promoters with one (O1) and two (O2) operators as in (F), except for the parameters specified in the figure legend. The red dashed line stands for one operator with reduced affinity. (H) Fold inhibition was measured at 200 nM estradiol as the doxycycline concentration was varied. The curves were fit with the parameter values obtained for the corresponding constructs in (Figure 1B). To link the AUAS concentration to the doxycycline concentration, Atot=10.3 nM and Kind=2.6 were fit for promoters with CYC1TATA, measured on the same day. For PtetO1-CYC1TATA (YABH40.6), kON=0.0072 nM−1 min−1 and kOFF=0.13 min−1 were fitted to account for its lower binding constant in comparison with PtetO2-CYC1TATA. Source data is available for this figure at www.nature.com/msb
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Related In: Results  -  Collection

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Show All Figures
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f2: Equilibrium (A–C) and non-equilibrium competition models (D–H) of upstream interference. (A) Scheme of the equilibrium competition model. The downstream promoter is occupied either by the interfering polymerase or by the activator, AUAS. (B, C) Equation (1) (Box 1) was fit to the data. KDA=0.37 and f(R)=2.9, 5.9, 14.1* and 41.4* for PtetO2-GAL1TATA (data re-plotted from Figure 1B) (B); KDA=0.024 and f(R)=2.6, 5.9, 18.5 and 42.7* for PtetO7-GAL1TATA (YABH34.5) (C). The asterisked f(R) values were obtained by fitting equation (1) to data points that had a normalized expression higher than 0.4 (see Materials and methods section). (D) In the non-equilibrium competition model, the interfering polymerase traverses the UAS and the TATA box in the downstream promoter, after which they bind the activator, AUAS, and the TBP with a higher affinity. (E) Gene expression as a function of AUAS was calculated from the non-equilibrium model with the parameter values fitted for PtetO2-GAL1TATA (Figure 1B). The concentration of the activator [P] driving the intergenic transcription is color coded. (F) Curves were re-calculated from (E). (G) Fold inhibition at P=100 was calculated for promoters with one (O1) and two (O2) operators as in (F), except for the parameters specified in the figure legend. The red dashed line stands for one operator with reduced affinity. (H) Fold inhibition was measured at 200 nM estradiol as the doxycycline concentration was varied. The curves were fit with the parameter values obtained for the corresponding constructs in (Figure 1B). To link the AUAS concentration to the doxycycline concentration, Atot=10.3 nM and Kind=2.6 were fit for promoters with CYC1TATA, measured on the same day. For PtetO1-CYC1TATA (YABH40.6), kON=0.0072 nM−1 min−1 and kOFF=0.13 min−1 were fitted to account for its lower binding constant in comparison with PtetO2-CYC1TATA. Source data is available for this figure at www.nature.com/msb
Mentions: Next, we measured the changes in the mean GFP expression as the occupancy of the activator-binding sites within a downstream promoter was varied. For this purpose, doxycycline was used to modulate the binding of the transcriptional activator, rtTA, to two tet operators within the downstream promoter. The doxycycline-induced binding of rtTA to the promoter led to GFP expression (Figure 1B). We observed that the suppression of GFP expression by intergenic transcription was gradually relieved as the rtTA binding strengthened, when the doxycycline concentration was increased from intermediate to high levels (Figures 1B and 2B). This indicates that intergenic transcription competes with the rtTA-driven transcription.

Bottom Line: When gene expression is induced weakly, the antagonistic activator can have a positive effect and can even trigger paradoxical activation.Indeed, a synthetic circuit generates a bell-shaped response, so that the induction of expression is limited to a narrow range of the input signal.The identification of conserved regulatory principles of interference will help to predict the transcriptional response of genes in their genomic context.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Biology, University of Zurich, Zurich, Switzerland.

ABSTRACT
A transcriptional activator can suppress gene expression by interfering with transcription initiated by another activator. Transcriptional interference has been increasingly recognized as a regulatory mechanism of gene expression. The signals received by the two antagonistically acting activators are combined by the polymerase trafficking along the DNA. We have designed a dual-control genetic system in yeast to explore this antagonism systematically. Antagonism by an upstream activator bears the hallmarks of competitive inhibition, whereas a downstream activator inhibits gene expression non-competitively. When gene expression is induced weakly, the antagonistic activator can have a positive effect and can even trigger paradoxical activation. Equilibrium and non-equilibrium models of transcription shed light on the mechanism by which interference converts signals, and reveals that self-antagonism of activators imitates the behavior of feed-forward loops. Indeed, a synthetic circuit generates a bell-shaped response, so that the induction of expression is limited to a narrow range of the input signal. The identification of conserved regulatory principles of interference will help to predict the transcriptional response of genes in their genomic context.

Show MeSH