bioRxivSystems BiologyPreprint
Lowering the switching cost related to the activation of burdensome gene circuits promotes cell population homogeneity and productivity
Lucas HenrioniD, Vincent VandenbrouckeiD, Juan A. MartíneziD, Julian KoppiD, Samuel TelekiD, Andrew ZicleriD, Frank DelvigneiD
Abstract
The activation of gene circuits can impose a significant burden on cells, leading to heterogeneous expression and reduced productivity. In this work, we focused on the T7 production system in E. coli BL21, a prime example of a burdensome gene circuit, to investigate the main cause for this gene expression heterogeneity and methods to mitigate it. Based on continuous cultivation analyzed and control by automated flow cytometry, we quantified the trade-off between cellular growth and gene expression and tracked the cell-to-cell heterogeneity in gene expression (measured as entropy). We concluded that the growth reduction associated to the activation of the burdensome gene circuit, i.e., the switching cost, is at the origin of the population heterogeneity. The loss of growth rate imposed by the burdensome activation of the gene is compensated at the population level by the overgrowth of less induced cells that safeguard the population by generating entropy. We tried to homogenize the population by pulsing the inducer with increasing frequency but found that the population escapes control through promoter mutation, leading to a genotype exhibiting reduced gene expression, but also, reduced entropy. To engineer a more homogeneous population without sacrificing gene expression, we decreased the switching cost associated to the induction by lowering the quality of the main carbon source. This strategy successfully led to a more homogeneous and productive population. Our approach allows for a precise quantification of the trade-off between growth and gene expression in cell population cultivated under dynamic conditions and highlights the importance of the switching cost for designing efficient approaches of cell population control.
- organism: E. coli
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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