Estimating the Instability Parameters of Plasmid-Bearing Cells. I. Chemostat Culture

Ganusov, Vitaly V.; Brilkov, A. V.

doi:10.1006/jtbi.2002.3101

Cited by 25 publications

(22 citation statements)

References 40 publications

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“…The VS model is a major departure from other modeling approaches (Stewart and Levin 1977;Levin et al 1979;Levin 1980;Levin and Stewart 1980;Tavaré 1982, 1983;Freter et al 1983;Cooper et al 1987;Simonsen 1991;Proctor 1994;Tolker-Nielsen and Boe 1994;Boe 1996;Boe and Rasmussen 1996;Bergstrom et al 2000;Gasunov and Brilkov 2002;Wahl et al 2002;Tanaka et al 2003;Novozhilov et al 2005). The process noise included in the VS model represents the variability due to the environment, where the environment may be understood as the host itself and the host's growth environment (De Gelder et al 2007).…”

Section: Discussionmentioning

confidence: 99%

The Population Biology of Bacterial Plasmids: A Hidden Markov Model Approach

et al. 2007

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Horizontal plasmid transfer plays a key role in bacterial adaptation. In harsh environments, bacterial populations adapt by sampling genetic material from a horizontal gene pool through self-transmissible plasmids, and that allows persistence of these mobile genetic elements. In the absence of selection for plasmid-encoded traits it is not well understood if and how plasmids persist in bacterial communities. Here we present three models of the dynamics of plasmid persistence in the absence of selection. The models consider plasmid loss (segregation), plasmid cost, conjugative plasmid transfer, and observation error. Also, we present a stochastic model in which the relative fitness of the plasmid-free cells was modeled as a random variable affected by an environmental process using a hidden Markov model (HMM). Extensive simulations showed that the estimates from the proposed model are nearly unbiased. Likelihood-ratio tests showed that the dynamics of plasmid persistence are strongly dependent on the host type. Accounting for stochasticity was necessary to explain four of seven time-series data sets, thus confirming that plasmid persistence needs to be understood as a stochastic process. This work can be viewed as a conceptual starting point under which new plasmid persistence hypotheses can be tested.

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Section: Discussionmentioning

confidence: 99%

The Population Biology of Bacterial Plasmids: A Hidden Markov Model Approach

et al. 2007

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“…The specific growth rates of introduced plasmid‐free ( x F ) and plasmid‐bearing ( x P ) bacteria were calculated as ρ −1 e α X N C and ρ −1 (1− c )(1− s ) e (α X N C +β N XB ), respectively, where ρ is the cell carbon content, e is the growth efficiency, c is the cost of bearing the plasmid, which is assumed to affect growth efficiency negatively (see also in Table 2), and s is the loss rate of plasmids from hosts, i.e. the relative segregation rate of plasmids (Bhattacharya & Roy, 1995; Ganusov & Brilkov, 2002). We also assumed that segregation generates viable plasmid‐free cells ( x F ) at the same rate as segregation.…”

Section: Methodsmentioning

confidence: 99%

“…Initial value of x P 10 6 cells mL À1 0.1 y F (0) Initial value of y F 10 6 cells mL À1 z y P (0) Initial value of y P 10 6 cells mL À1 0.0 y NC (0) Initial value of y NC 10 6 cells mL À1 z HR Host range of plasmids -0.1 z assumed to affect growth efficiency negatively (see also eqns 1 and 2 in Table 2), and s is the loss rate of plasmids from hosts, i.e. the relative segregation rate of plasmids (Bhattacharya & Roy, 1995;Ganusov & Brilkov, 2002). We also assumed that segregation generates viable plasmid-free cells (x F ) at the same rate as segregation.…”

Section: Resource Consumption and Xenobiotic Degradationmentioning

confidence: 99%

Long-term dynamics of catabolic plasmids introduced to a microbial community in a polluted environment: a mathematical model

Miki¹,

Ueki²,

Kawabata³

et al. 2007

FEMS Microbiology Ecology

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The long-term dynamics of mobile plasmids in natural environments are unclear. This is the first study of the long-term dynamics of introduced plasmids with xenobiotic degradation abilities using a mathematical model that describes the horizontal gene transfer (HGT) of plasmids into indigenous bacteria via conjugation. We focussed on negative feedback between the spread of plasmids and their selective advantage, i.e. the severe competition between plasmid-bearing and plasmid-free bacteria resulting from a decrease in xenobiotic concentration caused by the gene expression of plasmids, favoring plasmid-free bacteria. Two types of HGT enhanced the persistence of plasmids and the degradation of the xenobiotic in different conditions: a relatively low rate of 'intergeneric HGT' from introduced to indigenous bacteria and a high rate of 'intraindigenous HGT' from indigenous to indigenous bacteria. In addition, when the indigenous resource supply rate was high and when the cost of bearing plasmids was low, both types of HGT made large contributions to xenobiotic degradation compared to the contribution of vertical transfer via plasmid replication within the introduced host population. Initial conditions were also important; a higher initial density of introduced plasmid-bearing bacteria led to a lower degradation rate over a long time scale.

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“…Consider three hypothetical plasmids: x, y, and Spread of conjugative plasmids in liquid culture is z. Plasmid x conjugates faster than plasmid y, but the often approximated through simple mass-action models trade-off causes slower growth of x-infected hosts. Plas-(Stewart and Levin 1977; Levin et al 1979; Bergmid z transfers only vertically, thereby causing the least strom et al Ganusov and Brilkov 2002;Pauls-host burden. The model predicts that x and y should son 2002).…”

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confidence: 99%

Phenotypic Plasticity in Bacterial Plasmids

Turner

2004

Genetics

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Plasmid pB15 was previously shown to evolve increased horizontal (infectious) transfer at the expense of reduced vertical (intergenerational) transfer and vice versa, a key trade-off assumed in theories of parasite virulence. Whereas the models predict that susceptible host abundance should determine which mode of transfer is selectively favored, host density failed to mediate the trade-off in pB15. One possibility is that the plasmid's transfer deviates from the assumption that horizontal spread (conjugation) occurs in direct proportion to cell density. I tested this hypothesis using Escherichia coli/pB15 associations in laboratory serial culture. Contrary to most models of plasmid transfer kinetics, my data show that pB15 invades static (nonshaking) bacterial cultures only at intermediate densities. The results can be explained by phenotypic plasticity in traits governing plasmid transfer. As cells become more numerous, the plasmid's conjugative transfer unexpectedly declines, while the trade-off between transmission routes causes vertical transfer to increase. Thus, at intermediate densities the plasmid's horizontal transfer can offset selection against plasmid-bearing cells, but at high densities pB15 conjugates so poorly that it cannot invade. I discuss adaptive vs. nonadaptive causes for the phenotypic plasticity, as well as potential mechanisms that may lead to complex transfer dynamics of plasmids in liquid environments.

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Estimating the Instability Parameters of Plasmid-Bearing Cells. I. Chemostat Culture

Cited by 25 publications

References 40 publications

The Population Biology of Bacterial Plasmids: A Hidden Markov Model Approach

The Population Biology of Bacterial Plasmids: A Hidden Markov Model Approach

Long-term dynamics of catabolic plasmids introduced to a microbial community in a polluted environment: a mathematical model

Phenotypic Plasticity in Bacterial Plasmids

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