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Role of the chromatin dynamics on the stochasticity in gene expression in higher eukaryotic cells – STOCHAGENE

Chance at the heart ot the cell

Gene expression is an inherently random phenomenon that challenges the view of the cell as a small computer, subjected to the orders of a «genetic program.«

Understanding the causes and consequences of the probabilistic gene expression

It has now been demonstrated that gene expression is a probabilistic phenomenon. It is very important to study the causes and consequences, as it is a fundamental mechanism in the functioning of living, involved in major events such as cell differentiation during embryogenesis (eg skin cells, muscles, bones, brain, etc.) or cancer (that is characterized by an «anarchistic« gene expression). <br />Once we do understand the probability of gene expression we can then experimentally manipulate these phenomena, and to intervene in a therapeutic purpose. <br />We study the role of the structure called chromatin (made of DNA and proteins with which it interacts) and the so-called epigenetic changes. We show that these factors are capable of altering the expression of genes probabilities and can therefore play a regulatory role. Differentiation of cells depends on the activation of different sets of genes in different cell types. Our experiments are also intended to show that this phenomenon is generated by random gene expression (which allows cells to spontaneously express different genes) subjected to a selection and stabilization process dependent upon cellular metabolism and cell-cell interactions.

To highlight the probabilistic gene expression, one must analyze it in individual cells to obtain quantitative data on the levels of expression in many cells. These data demonstrate the random variations in gene expression from one cell to another and are integrated into mathematical or computer models that are then used to make predictions that can be tested in new experiences.
The techniques used so far are based upon the so-called «reporter« genes expressing fluorescent protein (the intensity of the fluorescence is proportional to the intensity of gene expression) .

We have cloned a reporter gene in different locations of a chicken blood progenitor cell and then we analyzed the probability of gene expression according to its position in the genome. These data were compared to a model in which gene expression depends on the dynamics of chromatin, specifically the frequency with which it is opens and closes
Experiments were also carried out experimentally by modifying the chromatin structure using chemical agents. Our analysis shows that gene expression strongly depends on the position of the genes in the genome and chromatin dynamics. This result indicates that the probability of gene expression is not due to chance fluctuations in the realization of a «genetic program« but it is a phenomenon that involves fundamental biophysics of chromatin leads us to reject the notion of a genetic program governing the cell.

The main application, the most important from a societal point of view, is to use the knowledge of the stochastic gene expression and cell differentiation in understanding cancer and developing new therapeutic strategies. This knowledge can also be used for biotechnology for a better control over the expression of cloned genes.

Viñuelas*, J., Kaneko*, G., Coulon, A., Vallin, E., Morin, V., Mejia-Pous, C., Kupiec, J.-J., Beslon, G. and Gandrillon, O. (2013). Quantifying the contribution of chromatin dynamics to stochastic gene expression reveals long, locus-dependent periods between transcriptional bursts. BMC Biology 11:15
equal contributors

Viñuelas, J., Kaneko, G., Coulon, A., Beslon , G. and Gandrillon, O. (2012). Toward experimental manipulation of stochasticity in gene expression. Progress in Biophysics & Molecular Biology, 110, pp 44-53.

Gandrillon, O., Kolesnik-Antoine, D., Kupiec, J.J. and Beslon, G. (2012). Chance at the heart of the cell. Progress in Biophysics & Molecular Biology 110, pp 1-4.

Kupiec, J.J. (2012) L’ontophylogenèse (Editions Quae, INRA)

The stochastic nature of gene expression at the cellular level is clearly established (for recent reviews, see [1]; and [2]). It should be noted that stochasticity does not by any means imply complete randomness; rather, constrained randomness, intermediate between rigid determinism and complete disorder is what is usually seen experimentally. Although it has been shown that stochasticity can play a very constructive role in physical phenomena, the biological role of stochasticity in gene expression (SGE) still has to be formally demonstrated, especially during a differentiation process in higher eukaryotic cells.
The situation is somewhat different in prokaryotes, especially in B. subtilis where a recent paper has recently demonstrated that SGE was clearly used by the micro-organism in order to augment its fitness in an uncertain environment [3]. This seminal work has demonstrated that in order to unravel the biological role of SGE, one must be able to manipulate its level experimentally. This in turn requires the understanding of the molecular mechanisms at stake. In short, we want to be able to manipulate SGE with the same ease with which one has known for years how to manipulate the mean expression level (through cDNA-based overexpression or shRNA-based inhibition).
This is therefore the goal of the present project to explore the role played by the chromatin context in the generation and control of SGE, by a joint modeling/experimental approach, that is rendered necessary by the fundamentally dynamic nature of noise generation and control.
For this we will:
1. Develop quantitative real time analyses of gene expression at the single cell level in order to evaluate the inter- and intra-cellular variability, using noise-reporter cell lines (both human and avian) which will allow to assess the role played by the chromatin context. For this we will rely upon site-directed recombination using the CRE/Lox recombination system [4].
2. Model the molecular causes of the stochasticity in gene expression, with a special focus on the spatial aspects underlying chromatin dynamics.
3. Confront models and measures to iterate the virtuous circle at the heart of a system’s biology approach. For this, each acquisition level will be complemented with a relevant modeling approach since it is our believe (and the results of years of transdisciplinary work for all of us) that the “virtuous circle” between modeling and experiments is not simply a single step in the project but is more a continuous process within the project.
The main success of the project would be to be able at the end of the project to correctly model some of the main molecular causes of gene expression stochasticity, to experimentally manipulate those causes and to demonstrates that we indeed can experimentally modulate the level of SGE in higher eukaryotic cells.

1. McCullagh, E., et al. Nat Chem Biol, 2009. 5: p. 699-704.
2. Eldar, A. and M.B. Elowitz. Nature, 2010. 467: p. 167-73.
3. Cagatay, T., et al. Cell, 2009. 139: p. 512-22.
4. Desprat, R. and E.E. Bouhassira. PLoS One, 2009. 4: p. e5956.

Project coordination


The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.



Help of the ANR 466,000 euros
Beginning and duration of the scientific project: August 2011 - 48 Months

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