Estimating the rate of plasmid transfer with an adapted Luria–Delbrück fluctuation analysis

Abstract

To increase our basic understanding of the ecology and evolution of conjugative plasmids, we need a reliable estimate of their rate of transfer between bacterial cells. Accurate estimates of plasmid transfer have remained elusive given biological and experimental complexity. Current methods to measure transfer rate can be confounded by many factors, such as differences in growth rates between plasmid-containing and plasmid-free cells. However, one of the most problematic factors involves situations where the transfer occurs between different strains or species and the rate that one type of cell donates the plasmid is not equal to the rate at which the other cell type donates. Asymmetry in these rates has the potential to bias transfer estimates, thereby limiting our capabilities for measuring transfer within diverse microbial communities. We develop a novel low-density method (“LDM”) for measuring transfer rate, inspired by the classic fluctuation analysis of Luria and Delbrück. Our new approach embraces the stochasticity of conjugation, which departs in important ways from the current deterministic population dynamic methods. In addition, the LDM overcomes obstacles of traditional methods by allowing different growth and transfer rates for each population within the assay. Using stochastic simulations, we show that the LDM has high accuracy and precision for estimation of transfer rates compared to other commonly used methods. Lastly, we implement the LDM to estimate transfer on an ancestral and evolved plasmid-host pair, in which plasmid-host co-evolution increased the persistence of an IncP-1β conjugative plasmid in its Escherichia coli host. Our method revealed the increased persistence can be at least partially explained by an increase in transfer rate after plasmid-host coevolution.

Estimating the Rate of Mutation to a Mutator Phenotype

“…In previous works dedicated to estimating the mutation rate from wild-type to mutant cells [45][46][47][48][49][50][51][52], methodologies with high accuracy have been developed. In this study, we relied on this literature, which is primarily based on the probability-generating function and its empirical counterpart [53].…”

Estimating plasmid conjugation rates: a new computational tool and a critical comparison of methods

“…Liquid [30] Donor conjugation rate [30] Table 1: Measures of conjugation proficiency reported in the literature. Here D, R, T stands for the population density of donors, recipients, and transconjugants at the time point of measurement, N is the total population density (N = D + R + T ), N 0 is the initial total population density, R 0 is the initial population density of recipients, ψ max is the maximum growth rate of the mating culture, and p 0 (t) is the probability that zero transconjugants are observed at time t.…”

“…Currently the package includes the SM, ASM, T/D, T/DR, T /( R + T ), , and . The package can be extended by other methods in the future, including recently proposed fluctuation test based methods [26].…”

“…The method further requires knowledge of the mating population growth rate and initial population density. Recently a new method was introduced which explicitly takes into account the stochasticity of conjugation [26]. This so-called ‘low-density method’ still assumes mass action kinetics of plasmid transfer, as well as deterministic and exponential growth of the donor and recipient populations.…”