Growth-Dependent Predation and Generalized Transduction of Antimicrobial Resistance by Bacteriophage

Leclerc, Quentin J.; Wildfire, Jacob; Gupta, Arya; Lindsay, Jodi A.; Knight, Gwenan M.

doi:10.1128/msystems.00135-22

Cited by 14 publications

(39 citation statements)

References 59 publications

(107 reference statements)

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“…This link between phage burst size and bacterial growth was identified as the most biologically plausible explanation for the dynamics we have previously observed between the bacteria and phage in our system [28]. This decrease happens as bacteria enter stationary phase, when the population approaches carrying capacity N max (as demonstrated previously [28,43,44]), but here the additional effect of antibiotics must be included. To capture this, we use the same effective scaling as the bacterial growth rate (Equation 4) with inclusion of the relative effect of antibiotics εE,θ and εT,θ in the phage burst size estimation.…”

Section: Bacterial Growth and Phage Predationsupporting

confidence: 78%

“…Figure 1: Mathematical model diagram. This model is an extension of our original model presented in [28], with the inclusion of antibiotic effects. Each bacteria strain (BE resistant to erythromycin, BT resistant to tetracycline, or BET resistant to both) can replicate (purple).…”

Section: Antibiotic Pharmacodynamicsmentioning

confidence: 99%

“…Phage predation is modelled as a frequency-dependent process. Previous work has suggested that this interaction is more biologically realistic than the more commonly used density-dependent interaction [28]. At any given time, the proportion λ of phage successfully infecting bacteria is calculated as…”

Section: Bacterial Growth and Phage Predationmentioning

confidence: 99%

“…Briefly, the parameters were estimated using the Markov chain Monte Carlo Metropolis-Hastings algorithm. We ran the algorithm with two chains until convergence was achieved (determined using the Gelman-Rubin diagnostic, with the multivariate potential scale reduction factor < 1.2 [45]), then for each parameter we generated 50,000 samples from the posterior distributions [28].…”

Section: Parameter Estimationmentioning

confidence: 99%

“…This allows us to generalise our conclusions as these parameters likely vary for different combinations of phage, antibiotics and bacteria. The ranges evaluated are shown in Table 1, and are either derived from our initial parameterisation of the model [28], or from other studies [49,50].…”

Section: Phage and Bacteria Parameters Variationmentioning

confidence: 99%

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Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: killers and drivers of resistance evolution

Leclerc

Lindsay

Knight

2022

Preprint

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Bacteriophage (phage) are bacterial predators which can also spread antimicrobial resistance (AMR) genes between bacteria by generalised transduction. Phage are often present alongside antibiotics in the environment, yet evidence of their joint killing effect on bacteria is conflicted, and the dynamics of transduction in such systems are unknown. Here, we combine in vitro data and mathematical modelling to identify conditions where phage and antibiotics act in synergy to remove bacteria or drive AMR evolution. We adapt a published model of phage-bacteria dynamics, including transduction, to add the pharmacodynamics of erythromycin and tetracycline, parameterised from new in vitro data. We simulate a system where two strains of Staphylococcus aureus are present at stationary phase, each carrying either an erythromycin or tetracycline resistance gene, and where multidrug-resistant bacteria can be generated by transduction only. We determine rates of bacterial clearance and multidrug-resistant bacteria appearance, when either or both antibiotics and phage are present at varying timings and concentrations. Although phage and antibiotics act in synergy to kill bacteria, by reducing bacterial growth antibiotics reduce phage production. A low concentration of phage introduced shortly after antibiotics fails to replicate and exert a strong killing pressure on bacteria, instead generating multidrug-resistant bacteria by transduction which are then selected for by the antibiotics. Multidrug-resistant bacteria numbers were highest when antibiotics and phage were introduced simultaneously. The interaction between phage and antibiotics leads to a trade-off between a slower clearing rate of bacteria (if antibiotics are added before phage), and a higher risk of multidrug-resistance evolution (if phage are added before antibiotics), exacerbated by low concentrations of phage or antibiotics. Our results form hypotheses to guide future experimental and clinical work on the impact of phage on AMR evolution, notably for studies of phage therapy which should investigate varying timings and concentrations of phage and antibiotics.

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