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Published April 2, 2019 | Submitted
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Quantitative characterization of random partitioning in the evolution of plasmid-encoded traits

Abstract

Plasmids are found across bacteria, archaea, and eukaryotes and play an important role in evolution. Plasmids exist at different copy numbers, the number of copies of the plasmid per cell, ranging from a single plasmid per cell to hundreds of plasmids per cell. This feature of a copy number greater than one can lead to a population of plasmids within a single cell that are not identical clones of one another, but rather have individual mutations that make a given plasmid unique. During cell division, this population of plasmids is partitioned into the two daughter cells, resulting in a random distribution of different plasmid variants in each daughter. In this study, we use stochastic simulations to investigate how random plasmid partitioning compares to a perfect partitioning model. Our simulation results demonstrate that random plasmid partitioning accelerates mutant allele fixation when the allele is beneficial and the selection is in an additive or recessive regime where increasing the copy number of the beneficial allele results in additional benefit for the host. This effect does not depend on the size of the benefit conferred or the mutation rate, but is magnified by increasing plasmid copy number.

Additional Information

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license. bioRxiv preprint first posted online Mar. 31, 2019. This work was funded by Caltech SURF, NIH T32 to ADH, and NSF GRFP to ADH. Research supported by the Air Force Office of Scientific Research, grant number FA9550-14-1-0060. We thank Justin Bois for providing code for the core Gillespie stochastic simulation algorithm used. The authors would also like to thank Sam Clamons, John Marken, Andrey Shur, and Rory Williams for helpful discussions.

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Created:
August 19, 2023
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October 20, 2023