Using Gene Expression Noise to Understand Gene Regulation

Munsky, Brian; Neuert, Gregor; Oudenaarden, Alexander van

doi:10.1126/science.1216379

Cited by 730 publications

(764 citation statements)

References 35 publications

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“…As stated above, the levels of CLK per cell are similar in ClkWT and ClkSV40 flies; however, due to the strong post-transcriptional regulation provided by the Clk 3 0 UTR, the same levels of protein are achieved by much lower transcriptional activity of the Clk promoter in ClkSV40 flies. In agreement with the established inverse relationship between transcriptional levels and noise [52][53][54][55] , our model predicted that in ClkSV40 flies CLK-driven activity would be more sensitive to transcriptional noise and hence more variable. Clk is involved in the development of the circadian neurons as well as in the control of accurate circadian transcription.…”

Section: Resultssupporting

confidence: 84%

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

et al. 2015

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The transcription factor CLOCK (CLK) is essential for the development and maintenance of circadian rhythms in Drosophila. However, little is known about how CLK levels are controlled. Here we show that Clk mRNA is strongly regulated post-transcriptionally through its 3 0 UTR. Flies expressing Clk transgenes without normal 3 0 UTR exhibit variable CLK-driven transcription and circadian behaviour as well as ectopic expression of CLK-target genes in the brain. In these flies, the number of the key circadian neurons differs stochastically between individuals and within the two hemispheres of the same brain. Moreover, flies carrying Clk transgenes with deletions in the binding sites for the miRNA bantam have stochastic number of pacemaker neurons, suggesting that this miRNA mediates the deterministic expression of CLK. Overall our results demonstrate a key role of Clk post-transcriptional control in stabilizing circadian transcription, which is essential for proper development and maintenance of circadian rhythms in Drosophila.

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Section: Resultssupporting

confidence: 84%

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

et al. 2015

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“…Second, many cellular processes can interfere with gene expression through internal feedback loops whose effects are hard to predict. Third, the process of gene expression shows significant levels of noise (16)(17)(18). Given these limitations, novel experimental strategies are required to gain quantitative, real-time control of gene expression in vivo.…”

mentioning

confidence: 99%

Long-term model predictive control of gene expression at the population and single-cell levels

Uhlendorf

Miermont

Delaveau

et al. 2012

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

220

237

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Gene expression plays a central role in the orchestration of cellular processes. The use of inducible promoters to change the expression level of a gene from its physiological level has significantly contributed to the understanding of the functioning of regulatory networks. However, from a quantitative point of view, their use is limited to short-term, population-scale studies to average out cell-to-cell variability and gene expression noise and limit the nonpredictable effects of internal feedback loops that may antagonize the inducer action. Here, we show that, by implementing an external feedback loop, one can tightly control the expression of a gene over many cell generations with quantitative accuracy. To reach this goal, we developed a platform for real-time, closed-loop control of gene expression in yeast that integrates microscopy for monitoring gene expression at the cell level, microfluidics to manipulate the cells' environment, and original software for automated imaging, quantification, and model predictive control. By using an endogenous osmostress responsive promoter and playing with the osmolarity of the cells environment, we show that long-term control can, indeed, be achieved for both time-constant and time-varying target profiles at the population and even the single-cell levels. Importantly, we provide evidence that real-time control can dynamically limit the effects of gene expression stochasticity. We anticipate that our method will be useful to quantitatively probe the dynamic properties of cellular processes and drive complex, synthetically engineered networks.model based control | computational biology | high osmolarity glycerol pathway | quantitative systems biology U nderstanding the information processing abilities of biological systems is a central problem for systems and synthetic biology (1-6). The properties of a living system are often inferred from the observation of its response to static perturbations. Timevarying perturbations have the potential to be much more informative regarding the dynamics of cellular functions (7-12). Currently, it is not possible to precisely perturb protein levels in an analogous manner, even though this perturbation would be instrumental in our understanding of gene regulatory networks. Indeed, despite the development of novel regulatory systems, including various RNA-based solutions (13), transcriptional control by means of inducible promoters is still the preferred method for manipulating protein levels (14, 15). Unfortunately, inducible promoters have several generic limitations. First, there is a significant delay between gene expression activation and effective protein synthesis. Second, many cellular processes can interfere with gene expression through internal feedback loops whose effects are hard to predict. Third, the process of gene expression shows significant levels of noise (16)(17)(18). Given these limitations, novel experimental strategies are required to gain quantitative, real-time control of gene expression in vivo.Here, we see the...

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“…The mechanisms underlying gene expression heterogeneity in the pluripotent state are still not well understood, and evidence suggests that gene fluctuations could arise from stochastic noise in gene expression regulatory mechanisms 18, 19, 20, 21. These fluctuations in gene expression are possible due to a permissive chromatin configuration across the genome of PSCs 22, allowing stochastic expression of lineage‐specific and differentiation‐promoting genes even before cell‐fate commitment 3.…”

Section: Introductionmentioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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Using Gene Expression Noise to Understand Gene Regulation

Cited by 730 publications

References 35 publications

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

Long-term model predictive control of gene expression at the population and single-cell levels

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

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