Modeling Pheromone Induced Plasmid and Drug Resistance Transfer

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DESCRIPTION (provided by applicant): Pheromone-induced conjugative pCF10 plasmid transfer in Enterococcus faecalis is implicated in the transmission of antibiotic resistance contributing to the pathogenesis of opportunistic enterococcal infections. The complexity and the unique features of the Enterococcus pheromone system reside in the two distinct cell types involved, in contrast to systems in which pheromone merely acts to synchronize a population of a single cell type. The host cell chromosome encodes the signaling molecule while the genetic elements responsible for sensing and responding to the signaling molecule are on a plasmid that is transferable by conjugation. Upon the transfer of the plasmid to the recipient, the recipient also receives the drug resistance gene on the plasmid and becomes a donor. Conjugation occurs when the recipient population reaches a threshold density and the signaling molecule, which is also produced by the donor cell as it is encoded on the chromosome, reaches a critical concentration. As conjugation proceeds, the recipient cell population gradually becomes "identical" to the donor cell. This application aims to develop a stochastic population balance model to elucidate the regulatory mechanism of the conjugation system encoded by the enterococcal plasmid pCF10. To that end we will first develop a deterministic mathematical model for pCF10-containing cell (donor cell) to assess the dynamics and the stability behavior of the cCF10 pheromone system. Subsequently a population balance model which considers donors of different plasmid copy numbers and recipient cells and incorporates stochasticity will be developed to better examine the behavior of this signaling system under clinically relevant conditions. Experimentation will be carried out to quantify the parameters and fine tune the model. The models will be used to guide experimental design for investigation of the dissimilation of antibiotic resistance. The work, by taking a systems approach and considering population distribution and stochasticity, will introduce a fresh perspective on this medically important problem and provide much insight into the mechanism of conjugational antibiotic resistance transfer.