Transcriptional Control

Rossi, Fábio; Kringstein, Andrew M.; Spicher, Albert; Guicherit, Oivin; Blau, Helen M.

doi:10.1016/s1097-2765(00)00070-8

Cited by 129 publications

(73 citation statements)

References 35 publications

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“…As exemplified by CHO-HYST 44 , CHO-HYST 42 , and CHO-HYST 7 , our synthetic gene network gradually changes from hysteretic to graded expression profiles the more the tTA͞E-KRAB levels are biased toward tTA. Binding competition between tetracycline-dependent transactivators and transrepressors for the same operator has been suggested to account for conversion of a graded rheostat to a bistable all-or-nothing switch (30,31). Akin to such conversion, the bistable hysteretic network behavior (CHO-HYST 44 ) moves toward a graded rheostat as tTA outcompetes E-KRAB for binding to the hybrid operator module (CHO-HYST 7 ).…”

Section: Discussionmentioning

confidence: 99%

Hysteresis in a synthetic mammalian gene network

Kramer

Fussenegger

2005

Proc. Natl. Acad. Sci. U.S.A.

224

166

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Bistable and hysteretic switches, enabling cells to adopt multiple internal expression states in response to a single external input signal, have a pivotal impact on biological systems, ranging from cell-fate decisions to cell-cycle control. We have designed a synthetic hysteretic mammalian transcription network. A positive feedback loop, consisting of a transgene and transactivator (TA) cotranscribed by TA's cognate promoter, is repressed by constitutive expression of a macrolide-dependent transcriptional silencer, whose activity is modulated by the macrolide antibiotic erythromycin. The antibiotic concentration, at which a quasi-discontinuous switch of transgene expression occurs, depends on the history of the synthetic transcription circuitry. If the network components are imbalanced, a graded rather than a quasi-discontinuous signal integration takes place. These findings are consistent with a mathematical model. Synthetic gene networks, which are able to emulate natural gene expression behavior, may foster progress in future gene therapy and tissue engineering initiatives.synthetic biology ͉ synthetic gene networks ͉ bistability ͉ erythromycin ͉ feedback-loop

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Section: Discussionmentioning

confidence: 99%

Hysteresis in a synthetic mammalian gene network

Kramer

Fussenegger

2005

Proc. Natl. Acad. Sci. U.S.A.

224

166

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“…, where h is the Hill coefficient, K is the threshold, A is the amplitude, and N is the input [this functional form fits the experimentally observed response of switches generated by using competition between transcription factors (21)]. The effect of such a multiplication is to shift the peak of the distribution, and the relative shift of the peak is one measure of the sensitivity of the system to the switch.…”

Section: Implications Of Long-tailed Protein Distributionsmentioning

confidence: 97%

Stochastic simulations of the origins and implications of long-tailed distributions in gene expression

Krishna

Banerjee²,

Ramakrishnan³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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Gene expression noise results in protein number distributions ranging from long-tailed to Gaussian. We show how long-tailed distributions arise from a stochastic model of the constituent chemical reactions and suggest that, in conjunction with cooperative switches, they lead to more sensitive selection of a subpopulation of cells with high protein number than is possible with Gaussian distributions. Single-cell-tracking experiments are presented to validate some of the assumptions of the stochastic simulations. We also examine the effect of DNA looping on the shape of protein distributions. We further show that when switches are incorporated in the regulation of a gene via a feedback loop, the distributions can become bimodal. This might explain the bimodal distribution of certain morphogens during early embryogenesis.fluctuations ͉ genetic switches ͉ single cell T he inevitable noise in gene expression, manifested at the subcellular level as distributions in protein numbers, has been observed experimentally in both prokaryotes and eukaryotes (1-6). Recent studies have investigated how organisms tolerate this noise and the kinds of regulatory strategies they use to control or minimize it (7,8). One example where it has been suggested that noise is exploited for the benefit of the organism is bacterial chemotaxis (9). Analyses of noise in gene expression have highlighted the analogy with quantum many-body systems (10), and the authors of refs. 11-15 have explored the contribution of intrinsic and extrinsic sources, as well as the relative contribution of transcription and translation, focusing on the standard deviation of protein fluctuations. If the protein distributions were Gaussian, the mean, , and standard deviation, , would provide a complete description of the noise characteristics. However, recent experiments have revealed that protein distributions are often nonGaussian and also time-dependent, showing a crossover from long-tailed to Gaussian (3). It is important, therefore, to understand the origins and implications of the long-tailed nature of the protein distributions.We implement a stochastic chemical model of gene expression and show that it leads to distributions that fit the experimental observations, presented in this paper and in ref. 3, of protein distributions at different stages of bacterial growth. The predictions of the simulations were experimentally tested by single-cell-tracking experiments. We suggest that long-tailed protein distributions filtered by appropriate switches can lead to selection at the subcellular level. For example, a switch with a sharp threshold can be used to select a subpopulation of cells with a large concentration of a particular protein from a population of cells with a long-tailed distribution of that protein. By contrast, we show that symmetric Gaussian distributions are not as sensitive to switches.When long-tailed distributions are combined with switches via a positive feedback loop, we find that the system becomes bistable and can result in bimodal protein di...

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“…It has been shown that the same promoter can be regulated in either a graded or binary manner. Artificial tet-dependent promoter responds gradually to altered activity of either tet-activator or tet-repressor (Rossi et al, 2000). However, when both modulators are coexpressed, the transactivation is regulated in a binary manner.…”

Section: Discussionmentioning

confidence: 99%

“…C7 and B8 are two different clones expressing EGFP under the control of p53. A similar approach has been used previously to monitor transcription in single cells (Rossi et al, 2000;Biggar and Crabtree, 2001). C7 and B8 cells were treated with different concentrations of cpt or MMC (Figure 3a).…”

Section: P53 Protein Accumulates Uniformly In Every Cell After Genotomentioning

confidence: 99%

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p53-dependent transcription can exhibit both on/off and graded response after genotoxic stress

et al. 2004

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The p53 protein is a central player in cellular response to DNA damage. Induction of p53 by DNA-damaging agents involves elevation of its steady-state level and activation of its potency as a transcription factor. In the cell population, these responses can occur either homogeneously (where every single cell responds simultaneously and similarly to its neighbor) or heterogeneously (where only some cells of a population respond and the number of these increases with increasing dose of inducer). We have studied here the p53 response to DNA-damaging agents (camptothecin, mitomycin C) in individual cells. We show that the level of p53 protein is increased in every single cell of the population homogeneously, while the p53-dependent transcription can be subject to an on/off-type response. Depending on the structure of the target promoter, p53-dependent transcription can be regulated according to the binary or graded model. The on/off-type transcriptional activation pattern of p53 defines two distinct subpopulations of cells after DNA damage.

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Transcriptional Control

Cited by 129 publications

References 35 publications

Hysteresis in a synthetic mammalian gene network

Hysteresis in a synthetic mammalian gene network

Stochastic simulations of the origins and implications of long-tailed distributions in gene expression

p53-dependent transcription can exhibit both on/off and graded response after genotoxic stress

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