Back

Lowering the switching cost related to the activation of burdensome gene circuits promotes cell population homogeneity and productivity

Henrion, L.; Vandenbroucke, V.; Alvarez, J. A. M.; Kopp, J.; Telek, S.; Zicler, A.; Delvigne, F.

2024-10-16 systems biology
10.1101/2024.10.14.618176 bioRxiv
Show 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.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Biotechnology and Bioengineering
49 papers in training set
Top 0.1%
18.5%
2
PLOS Computational Biology
1633 papers in training set
Top 3%
10.1%
3
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 0.1%
8.4%
4
PLOS ONE
4510 papers in training set
Top 24%
7.1%
5
Scientific Reports
3102 papers in training set
Top 18%
6.3%
50% of probability mass above
6
npj Systems Biology and Applications
99 papers in training set
Top 0.4%
4.3%
7
Physical Biology
43 papers in training set
Top 0.4%
3.9%
8
Metabolic Engineering
68 papers in training set
Top 0.2%
3.9%
9
ACS Synthetic Biology
256 papers in training set
Top 1%
2.4%
10
Nature Communications
4913 papers in training set
Top 47%
2.1%
11
mSystems
361 papers in training set
Top 4%
2.1%
12
Frontiers in Microbiology
375 papers in training set
Top 5%
1.9%
13
Computational and Structural Biotechnology Journal
216 papers in training set
Top 4%
1.9%
14
Microbial Cell Factories
22 papers in training set
Top 0.2%
1.9%
15
iScience
1063 papers in training set
Top 15%
1.7%
16
BMC Bioinformatics
383 papers in training set
Top 5%
1.2%
17
Journal of The Royal Society Interface
189 papers in training set
Top 3%
1.2%
18
Synthetic and Systems Biotechnology
10 papers in training set
Top 0.3%
1.2%
19
Frontiers in Molecular Biosciences
100 papers in training set
Top 4%
0.9%
20
SLAS Technology
11 papers in training set
Top 0.2%
0.9%
21
Frontiers in Plant Science
240 papers in training set
Top 5%
0.8%
22
Molecular Systems Biology
142 papers in training set
Top 2%
0.7%
23
Cell Reports Methods
141 papers in training set
Top 5%
0.7%
24
Algal Research
20 papers in training set
Top 0.4%
0.7%