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. 2019 Nov 21;14(11):e0225166.
doi: 10.1371/journal.pone.0225166. eCollection 2019.

Drugs Modulating Stochastic Gene Expression Affect the Erythroid Differentiation Process

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Free PMC article

Drugs Modulating Stochastic Gene Expression Affect the Erythroid Differentiation Process

Anissa Guillemin et al. PLoS One. .
Free PMC article

Abstract

To better understand the mechanisms behind cells decision-making to differentiate, we assessed the influence of stochastic gene expression (SGE) modulation on the erythroid differentiation process. It has been suggested that stochastic gene expression has a role in cell fate decision-making which is revealed by single-cell analyses but studies dedicated to demonstrate the consistency of this link are still lacking. Recent observations showed that SGE significantly increased during differentiation and a few showed that an increase of the level of SGE is accompanied by an increase in the differentiation process. However, a consistent relation in both increasing and decreasing directions has never been shown in the same cellular system. Such demonstration would require to be able to experimentally manipulate simultaneously the level of SGE and cell differentiation in order to observe if cell behavior matches with the current theory. We identified three drugs that modulate SGE in primary erythroid progenitor cells. Both Artemisinin and Indomethacin decreased SGE and reduced the amount of differentiated cells. On the contrary, a third component called MB-3 simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Using single-cell analysis and modeling tools, we provide experimental evidence that, in a physiologically relevant cellular system, SGE is linked to differentiation.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Relative effect of entropy and average gene expression level under drug treatment during differentiation.
(A) Boxplots representing values of entropy per gene for each treatment relative to control values (red dotted line). Some outliers are not displayed for readability. We assessed the significance of the differences between untreated and treated conditions through a Wilcoxon test (tests with a p-value < 0.05 are represented by a star above each boxplot). (B) Correlation plots representing relative values of entropy per gene for each pair of drugs. We assessed the significance of the differences between values for each drug through a Pearson test (p-value < 0.05). When the correlation is significant, we displayed the linear regression line for all points (red dotted lines). (C) Correlation plots representing relative values of entropy as a function of relative values of cell mean expression per gene. We assessed the significance of the differences between values for each drug through a Pearson test (p-value < 0.05). When the correlation is significant, we displayed the linear regression line for all points (red dotted lines).
Fig 2
Fig 2. Drugs affect erythroid differentiation.
Control conditions were averaged (black line) for readability. Shown is the percentage of differentiated cells for all conditions. Error bars represent the standard-deviation between experiments (n = 3). We assessed the significance of the differences between each treated condition with their own control condition through a student test (p-value < 0.05).
Fig 3
Fig 3. Schematic diagram of the model.
Fig 4
Fig 4. Relative parameter values.
For each of the models selected by Akaike’s weights (S5 Fig), all the relative parameter values are represented by a dot for a treatment compared to the untreated condition (black dotted line). Among all the combinations of parameters that might vary under each treatment, 19 models were selected for the Indomethacin treatment using Akaike’s weights, 5 for the Artemisinin treatment and 3 for MB-3. The horizontal spacing between the values of each parameter was chosen randomly for readability.

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Grant support

This work was supported by funding from the French agency ANR (SinCity; ANR-17-CE12-0031) and La Ligue Contre le Cancer (Comite de Haute Savoie; LS 136994). There was no additional external funding received for this study. All these funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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