Horizontal gene transfer, mediated by conjugative plasmids, is a major driver of the global rise of antibiotic resistance. However, the relative contributions of factors that underlie the spread of plasmids and their roles in conjugation in vivo are unclear. To address this, we investigated the spread of clinical Extended Spectrum Beta-Lactamase (ESBL)-producing plasmids in the absence of antibiotics in vitro and in the mouse intestine. We hypothesised that plasmid properties would be the primary determinants of plasmid spread and that bacterial strain identity would also contribute. We found clinical Escherichia coli strains natively associated with ESBL-plasmids conjugated to three distinct E. coli strains and one Salmonella enterica serovar Typhimurium strain. Final transconjugant frequencies varied across plasmid, donor, and recipient combinations, with qualitative consistency when comparing transfer in vitro and in vivo in mice. In both environments, transconjugant frequencies for these natural strains and plasmids covaried with the presence/absence of transfer genes on ESBL-plasmids and were affected by plasmid incompatibility. By moving ESBL-plasmids out of their native hosts, we showed that donor and recipient strains also modulated transconjugant frequencies. This suggests that plasmid spread in the complex gut environment of animals and humans can be predicted based on in vitro testing and genetic data.

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Plasmids are important vectors for the spread of genes among diverse populations of bacteria.However, there is no standard method to determine the rate at which they spread horizontally via conjugation. Here, we compare commonly used methods on simulated data, and show that the conjugation rate estimates often depend strongly on the time of measurement, the initial population densities, or the initial ratio of donor to recipient populations. We derive a new 'end-point' measure to estimate conjugation rates, which extends the Simonsen method to include the effects of differences in population growth and conjugation rates from donors and transconjugants.We further derive analytical expressions for the parameter range in which these approximations remain valid. All tools to estimate conjugation rates are available in an R package and Shiny app.The result is a set of guidelines for easy, accurate, and comparable measurement of conjugation rates and tools to verify these rates.Plasmids are extra-chromosomal, self-replicating genetic elements that can spread between bac-1 teria via conjugation. They spread genes within and between bacterial species and are a primary 2 source of genetic innovation in the prokaryotic realm [1,2]. Genes disseminated by plasmids include 3 virulence factors, heavy metal and antibiotic resistance, metabolic genes, as well as genes involved 4 in cooperation and spite [2,3,4,5]. To understand how these traits shape the ecology and evolution 5 of bacteria [6], it is of fundamental importance to understand how plasmids spread. 6

Antibiotic resistance encoded on plasmids is a pressing global health problem. Predicting which plasmids spread/decline in the long term remains a huge challenge, even though some key parameters influencing plasmid stability have been identified, such as plasmid growth costs and horizontal transfer rates. Here, we show these parameters evolve in a strain-specific way among clinical plasmids/bacteria, and this occurs rapidly enough to alter the relative likelihoods of different bacterium-plasmid combinations spreading/declining. We used experiments with Escherichia coli and antibiotic-resistance plasmids isolated from patients, paired with a mathematical model, to show long-term plasmid stability (beyond antibiotic exposure) was better explained by evolutionary changes in plasmid-stability traits than by initial variation among bacterium-plasmid combinations. Evolutionary trajectories were specific to particular bacterium-plasmid combinations. Genome sequencing and genetic manipulation helped explain this, revealing epistatic (here, strain-dependent) effects of key genetic changes affecting horizontal plasmid transfer. Several genetic changes involved mobile elements and pathogenicity islands. Rapid strain-specific evolution can thus outweigh ancestral phenotypes as a predictor of plasmid stability. Accounting for strain-specific plasmid evolution in natural populations could improve our ability to anticipate and manage successful bacterium-plasmid combinations.

35Horizontal gene transfer, mediated by conjugative plasmids, is one of the main drivers of the 36 global spread of antibiotic resistance. However, the relative contributions of different factors 37 that underlie this plasmid spread are unclear, particularly for clinically relevant plasmids 38 harboring antibiotic resistance genes. Here, we analyze nosocomial outbreak-associated 39 plasmids that reflect the most relevant Extended Spectrum Beta-Lactamase (ESBL) mediated 40 drug resistance plasmids to i) quantify conjugative transfer dynamics, and ii) investigate why 41 some plasmid-strain associations are more successful than others, in terms of bacterial fitness 42 and plasmid spread. We show that, in the absence of antibiotics, clinical Escherichia coli 43 strains natively associated with ESBL-plasmids conjugate efficiently with three distinct E. coli 44 strains and one Salmonella enterica Serovar Typhimurium strain. In more than 40% of the in 45 vitro mating populations, ESBL-plasmids were transferred to recipients, reaching final 46 transconjugant frequencies of up to 1% within 23 hours. Variation of final transconjugant 47 Antibiotic resistance is a major obstacle to the treatment of bacterial infections in clinics. 58Plasmids encoding antibiotic resistance genes can spread between bacteria in a density-59 dependent manner and accelerate the rise of resistant bacterial strains. This is particularly 60 important for densely inhabited ecological niches such as the guts of humans and animals, 61 where many bacteria interact. Understanding the exact contribution plasmids make to the 62 global spread of antibiotic resistance remains an obstacle, because we lack quantitative 63 studies implementing large-scale experimental testing of conjugation rates between clinically 64 relevant bacterial strains. To counteract this knowledge gap, we studied clinical Escherichia 65 coli isolates from human patients that carry extended-spectrum beta-lactamase producing 66 plasmids. We found that these plasmids spread extensively through different bacterial 67 populations and that both bacterial-and plasmid-specific factors determined the extent of 68 plasmid spread. Our study combines detailed bioinformatic analyses, high-throughput in vitro 69 testing and validation in an animal model. It suggests a potential for laboratory testing to 70 understand and predict the spread of clinically relevant plasmids, including in the human gut 71 microbiota, and thereby generates insights into novel treatment strategies to manage 72 antibiotic resistance spread mediated by plasmids. 73 the clinical use of antibiotics [1]. Infections with bacteria resistant to antibiotics are 76 increasingly common and can result in death [2,3]. Importantly, resistance determinants are 77 often plasmid-encoded. Plasmids can be transferred horizontally between different bacterial 78 cells by conjugation, allowing rapid spread through diverse bacterial communities. This 79 includes transfer among and between clinically relevant bacterial pathogens and commensals...

Antibiotic resistance encoded on plasmids is a pressing global health problem. Predicting which plasmids spread in the long term remains very challenging, even though some key parameters influencing plasmid stability have been identified, such as plasmid growth costs and horizontal transfer rates. Here, we show these parameters evolve in a strain-specific way among clinical plasmids and bacteria, and this occurs rapidly enough to alter the relative likelihoods of different bacterium–plasmid combinations spreading. We used experiments with Escherichia coli and antibiotic-resistance plasmids isolated from patients, paired with a mathematical model, to track long-term plasmid stability (beyond antibiotic exposure). Explaining variable stability across six bacterium–plasmid combinations required accounting for evolutionary changes in plasmid stability traits, whereas initial variation of these parameters was a relatively poor predictor of long-term outcomes. Evolutionary trajectories were specific to particular bacterium–plasmid combinations, as evidenced by genome sequencing and genetic manipulation. This revealed epistatic (here, strain-dependent) effects of key genetic changes affecting horizontal plasmid transfer. Several genetic changes involved mobile elements and pathogenicity islands. Rapid strain-specific evolution can thus outweigh ancestral phenotypes as a predictor of plasmid stability. Accounting for strain-specific plasmid evolution in natural populations could improve our ability to anticipate and manage successful bacterium–plasmid combinations.