An Easy-To-Use Simulation Program Demonstrates Variations in Bacterial Cell Cycle Parameters Depending on Medium and Temperature
by Caroline Stokke, Ingvild Flåtten, Kirsten Skarstad
Many studies are performed on chromosome replication and segregation in Escherichia coli and other bacteria capable of complex replication with C phases spanning several generations. For such investigations an understanding of the replication patterns, including copy numbers of origins and replication forks, is crucial for correct interpretation of the results.
Flow cytometry is an important tool for generation of experimental DNA distributions of cell populations. Here, a Visual Basic based simulation program was written for the computation of theoretical DNA distributions for different choices of cell cycle parameters (C and D phase durations, doubling time etc). These cell cycle parameters can be iterated until the best fit between the experimental and theoretical DNA histograms is obtained. The Excel file containing the simulation software is attached as supporting information.
Cultures of Escherichia coli were grown at twelve different media and temperature conditions, with following measurements by flow cytometry and simulation of the DNA distributions. A good fit was found for each growth condition by use of our simulation program. The resulting cell cycle parameters displayed clear inter-media differences in replication patterns, but indicated a high degree of temperature independence for each medium. The exception was the poorest medium (acetate), where the cells grew with overlapping replication cycles at 42°C, but without at the lower temperatures.
We have developed an easy-to-use tool for determination of bacteria’s cell cycle parameters, and consequently the cells’ chromosome configurations. The procedure only requires DNA distribution measurements by flow cytometry. Use of this simulation program for E. coli cultures shows that even cells growing quite slowly can have overlapping replication cycles. It is therefore always important not only to assume cells’ replication patterns, but to actually determine the cell cycle parameters when changing growth conditions.
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