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

Huisman, Jana S.; Benz, Fabienne; Duxbury, Sarah J. N.; Visser, Allerdien; Hall, Alex R.; Fischer, E.A.J.; Bonhoeffer, Sebastian

doi:10.1101/2020.03.09.980862

Cited by 6 publications

(24 citation statements)

References 48 publications

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“…Given that 0 ( ) = Pr{ = 0}, we can use equation [11] to write the following timeincrement recursion for 0 ( ):…”

Section: Si Section : Alternative Laboratory Forms For Conjugation Esmentioning

confidence: 99%

“…al. 11 for a comprehensive overview. These conjugation ratios are descriptive statistics and have been shown to vary with initial densities ( 0 and 0 ) and incubation time () 10,11 .…”

Section: Introductionmentioning

confidence: 99%

“…al. 11 recently updated the SIM estimate, further extending its application by relaxing the assumption of identical growth and transfer rates for all strains.…”

Section: Introductionmentioning

confidence: 99%

“…Deterministic numerical simulations have been used to test the accuracy of the aforementioned methods (TDR, SIM, and ASM), revealing important limitations of each approach 11,16,17 . For instance, simulations have demonstrated that all the estimates can become inaccurate when the donor-recipient conjugation rate ( , hereafter "donor conjugation rate" for simplicity) differs from the transconjugant-recipient conjugation rate ( , hereafter "transconjugant conjugation rate" for simplicity).…”

Section: Introductionmentioning

confidence: 99%

“…11 for a comprehensive overview. These conjugation ratios are descriptive statistics and have been shown to vary with initial densities ( 0 and 0 ) and incubation time () 10,11 . While these statistics may be useful for within study comparisons of the combined processes of conjugation and transconjugant growth, they are not estimating the rate of conjugation events.…”

Section: Introductionmentioning

confidence: 99%

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

Kosterlitz

Elg

Top

et al. 2021

Preprint

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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.

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“…Given that 0 ( ) = Pr{ = 0}, we can use equation [11] to write the following timeincrement recursion for 0 ( ):…”

Section: Si Section : Alternative Laboratory Forms For Conjugation Esmentioning

confidence: 99%

“…al. 11 for a comprehensive overview. These conjugation ratios are descriptive statistics and have been shown to vary with initial densities ( 0 and 0 ) and incubation time () 10,11 .…”

Section: Introductionmentioning

confidence: 99%

“…al. 11 recently updated the SIM estimate, further extending its application by relaxing the assumption of identical growth and transfer rates for all strains.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Kosterlitz

Elg

Top

et al. 2021

Preprint

View full text Add to dashboard Cite

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Increased copy number couples the evolution of plasmid horizontal transmission and plasmid-encoded antibiotic resistance

Dimitriu

Matthews

Buckling

2021

Proc. Natl. Acad. Sci. U.S.A.

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Conjugative plasmids are mobile elements that spread horizontally between bacterial hosts and often confer adaptive phenotypes, including antimicrobial resistance (AMR). Theory suggests that opportunities for horizontal transmission favor plasmids with higher transfer rates, whereas selection for plasmid carriage favors less-mobile plasmids. However, little is known about the mechanisms leading to variation in transmission rates in natural plasmids or the resultant effects on their bacterial host. We investigated the evolution of AMR plasmids confronted with different immigration rates of susceptible hosts. Plasmid RP4 did not evolve in response to the manipulations, but plasmid R1 rapidly evolved up to 1,000-fold increased transfer rates in the presence of susceptible hosts. Most evolved plasmids also conferred on their hosts the ability to grow at high concentrations of antibiotics. This was because plasmids evolved greater copy numbers as a function of mutations in the copA gene controlling plasmid replication, causing both higher transfer rates and AMR. Reciprocally, plasmids with increased conjugation rates also evolved when selecting for high levels of AMR, despite the absence of susceptible hosts. Such correlated selection between plasmid transfer and AMR could increase the spread of AMR within populations and communities.

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Chicken gut microbiome members limit the spread of an antimicrobial resistance plasmid in Escherichia coli

et al. 2021

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Plasmid-mediated antimicrobial resistance is a major contributor to the spread of resistance genes within bacterial communities. Successful plasmid spread depends upon a balance between plasmid fitness effects on the host and rates of horizontal transmission. While these key parameters are readily quantified in vitro , the influence of interactions with other microbiome members is largely unknown. Here, we investigated the influence of three genera of lactic acid bacteria (LAB) derived from the chicken gastrointestinal microbiome on the spread of an epidemic narrow-range ESBL resistance plasmid, IncI1 carrying bla CTX-M-1 , in mixed cultures of isogenic Escherichia coli strains. Secreted products of LAB decreased E. coli growth rates in a genus-specific manner but did not affect plasmid transfer rates. Importantly, we quantified plasmid transfer rates by controlling for density-dependent mating opportunities. Parametrization of a mathematical model with our in vitro estimates illustrated that small fitness costs of plasmid carriage may tip the balance towards plasmid loss under growth conditions in the gastrointestinal tract. This work shows that microbial interactions can influence plasmid success and provides an experimental-theoretical framework for further study of plasmid transfer in a microbiome context.

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

Cited by 6 publications

References 48 publications

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

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

Increased copy number couples the evolution of plasmid horizontal transmission and plasmid-encoded antibiotic resistance

Chicken gut microbiome members limit the spread of an antimicrobial resistance plasmid in Escherichia coli

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