Growth‐stage‐dependent regulation of conjugation

Sysoeva, Tatyana A.; Kim, Youlim; Rodriguez, J.; Lopatkin, Allison J.; Li, You

doi:10.1002/aic.16848

Cited by 19 publications

(20 citation statements)

References 71 publications

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“…As high conjugation rates lead to a high number of plasmid-carriers and double-carriers even at low phage donor and recipient numbers, the ability to overcome the ‘phage death rate’ is largely independent of phage infection parameters, and hence valid for any particular prophage type. Furthermore, the ability of some plasmid types to conjugate at normal or even higher rates during stationary phase [37] could be an attempt at ‘catching up’ while most temperate (and many lytic) phages are unable to propagate.…”

Section: Discussionmentioning

confidence: 99%

Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates

Igler

Schwyter

Gehrig

et al. 2021

Preprint

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Antibiotic resistance spread via plasmids is a serious threat to successful treatment and makes understanding plasmid transfer in nature crucial to preventing the global rise of antibiotic resistance. However, plasmid dynamics have so far neglected one omnipresent factor: active prophages (phages that are integrated into the bacterial genome), whose activation can kill co-occurring bacteria. To investigate how prophages influence the spread of a multi-drug resistant plasmid, we combined experiments and mathematical modelling. Using E. coli, prophage lambda and the plasmid Rp4 we find that prophages clearly limit the spread of conjugative plasmids. This effect was even more pronounced when we studied this interaction in a phage-friendly environment that favoured phage adsorption. Our empirically parameterized model reproduces experimental dynamics in some environments well, but fails to predict them in others, suggesting more complex interactions between plasmid and prophage infection dynamics. Varying phage and plasmid infection parameters over an empirically realistic range suggests that plasmids can overcome the negative impact of prophages through high conjugation rates. Overall, we find that the presence of prophages introduces an additional death rate for plasmid carriers, the magnitude of which is determined in non-trivial ways by the environment, the phage and the plasmid.

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Section: Discussionmentioning

confidence: 99%

Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates

Igler

Schwyter

Gehrig

et al. 2021

Preprint

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“…This choice was informed by experimental results showing that cells enter stationary phase and conjugation ramps down to a negligible level as resources are depleted 13 . This pattern occurs for various plasmid incompatibility groups, but not all 29 . Simonsen et.…”

Section: Si Section 1 : Historical Theoretical Perspectivementioning

confidence: 86%

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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“…Plasmids typically carry fertility inhibition (FI) systems, which inhibit conjugation, either as an auto-regulatory mechanism (F plasmids), or to inhibit transfer of unrelated, co-resident plasmids [10,60] (Table S2). Activation is also influenced by diverse factors such as host cell physiology, the availability of recipients, or stress factors like antibiotics [61,62]. Such external activation signals can de-repress both co-infecting plasmids, increasing the chance of simultaneous transfer [63].…”

Section: Conjugation From Co-infected Cellsmentioning

confidence: 99%

“…via transformation, transduction or vessication) [1]. Lastly, exclusion is not active when recipients are in stationary phase [70,74], allowing infection by plasmids from metabolically active donors, or by plasmids that can conjugate in stationary phase [61].…”

Section: Conjugation From Co-infected Cellsmentioning

confidence: 99%

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

Igler

Huisman

Siedentop

et al. 2021

Preprint

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As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions, but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same host cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection--from systems to exclude infection by other plasmids to interactions in the regulation of plasmid copy number per cell--are well characterised at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics, and in turn, the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts eco-evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection.

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Growth‐stage‐dependent regulation of conjugation

Cited by 19 publications

References 71 publications

Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates

Conjugative plasmid transfer is limited by prophages but can be overcome by high conjugation rates

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

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

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