"Recombination among plasmids boosts their fitness and the spread of antibiotic resistance genes"Acacia, EmmaAntibiotic resistance (AR) is one of the greatest threats for public health. AR genes widely circulate through genetic exchange between bacterial individuals, mainly through mobile genetic elements such as plasmids. Plasmids are genetic entities that exploit bacterial resources and have their own evolutionary strategy. The two main strategies often studied are their capacity for horizontal transfer and their capacity for vertical transmission, which may be facilitated by the fact that they carry accessory genes (e.g. the AR gene) that may be beneficial to their host. However, there is increasing evidence that plasmids can shuffle genes when they co-infect the same bacterial cell. Here, we hypothesize and test through modeling that genetic shuffling may be an optimal strategy that enables plasmids to cope with environmental stochasticity (exposure to antibiotic stresses) by changing their genetic composition. We also explore if such this strategy promotes the spread of AR genes within bacterial populations. By transposing host-pathogen models from epidemiology, we developed a dynamical model of plasmids transferring horizontally and vertically within a bacterial population. AR genes are carried by competing plasmids. When two related plasmids co-infect the same bacterial cell, they would exchange genes with a given recombination rate which represents their own strategy. We found that the optimal strategy depends on the degree of stress stochasticity. An intermediate recombination rate leads to a higher persistence probability of plasmids and confers them a selective advantage in many contexts of antibiotic stress regimes. Our work shows that the spread of AR genes is highly dependent on plasmid strategies. We think that the spread of AR genes is a matter of interaction between plasmids themselves and between plasmids and their bacterial host, alternating with conflict and cooperation. Mathematical modeling combined with genomic analysis may constitute a powerful tool to disentangle these complex evolutionary interactions. |
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