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Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

Bottom Line: Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network.Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated.However, simulations suggest spatial segregation of phage particles does not play a significant role.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College, London, United Kingdom.

ABSTRACT
Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.

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Decision Making in lambda phage.A: The role of the  and  promoters involved in lambda phage decision. Dashed lines denote transcriptional events that require no activation while solid lines denote transcriptional events that require activation. B: Schematic of the core genetic network involved in lysis-lysogeny decision. CI gene promotes itself and represses the other genes. Cro represses everything, while CII promotes CI. C: Rate of lysogeny as observed from experimental observations in Zeng et al. [24] as a function of viral concentration for different MOI. D: Rate of lysogeny as a function of MOI for different volumes. Bacterial volumes and viral concentrations are expressed in arbitrary units using the normalized cell lengths following Zeng et al. [24].
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pone-0103636-g001: Decision Making in lambda phage.A: The role of the and promoters involved in lambda phage decision. Dashed lines denote transcriptional events that require no activation while solid lines denote transcriptional events that require activation. B: Schematic of the core genetic network involved in lysis-lysogeny decision. CI gene promotes itself and represses the other genes. Cro represses everything, while CII promotes CI. C: Rate of lysogeny as observed from experimental observations in Zeng et al. [24] as a function of viral concentration for different MOI. D: Rate of lysogeny as a function of MOI for different volumes. Bacterial volumes and viral concentrations are expressed in arbitrary units using the normalized cell lengths following Zeng et al. [24].

Mentions: To address the role of multiplicity of infection (MOI) in cellular decision making, we employ a relatively simple model of the lambda phage genetic circuit [12] (Fig. 1A and Methods). The gene regulatory network is composed of interlocked positive and negative feedback loops involving three early viral genes CI, Cro and CII (Figure 1B). There are multiple factors involved in the decision making process, but it is believed that the CII protein has an important role [8], [12]. High levels of CII promotes production of CI and lysogeny, whereas low levels of CII promotes production of Cro and bacterial lysis.


Stochastic cellular fate decision making by multiple infecting lambda phage.

Robb ML, Shahrezaei V - PLoS ONE (2014)

Decision Making in lambda phage.A: The role of the  and  promoters involved in lambda phage decision. Dashed lines denote transcriptional events that require no activation while solid lines denote transcriptional events that require activation. B: Schematic of the core genetic network involved in lysis-lysogeny decision. CI gene promotes itself and represses the other genes. Cro represses everything, while CII promotes CI. C: Rate of lysogeny as observed from experimental observations in Zeng et al. [24] as a function of viral concentration for different MOI. D: Rate of lysogeny as a function of MOI for different volumes. Bacterial volumes and viral concentrations are expressed in arbitrary units using the normalized cell lengths following Zeng et al. [24].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126663&req=5

pone-0103636-g001: Decision Making in lambda phage.A: The role of the and promoters involved in lambda phage decision. Dashed lines denote transcriptional events that require no activation while solid lines denote transcriptional events that require activation. B: Schematic of the core genetic network involved in lysis-lysogeny decision. CI gene promotes itself and represses the other genes. Cro represses everything, while CII promotes CI. C: Rate of lysogeny as observed from experimental observations in Zeng et al. [24] as a function of viral concentration for different MOI. D: Rate of lysogeny as a function of MOI for different volumes. Bacterial volumes and viral concentrations are expressed in arbitrary units using the normalized cell lengths following Zeng et al. [24].
Mentions: To address the role of multiplicity of infection (MOI) in cellular decision making, we employ a relatively simple model of the lambda phage genetic circuit [12] (Fig. 1A and Methods). The gene regulatory network is composed of interlocked positive and negative feedback loops involving three early viral genes CI, Cro and CII (Figure 1B). There are multiple factors involved in the decision making process, but it is believed that the CII protein has an important role [8], [12]. High levels of CII promotes production of CI and lysogeny, whereas low levels of CII promotes production of Cro and bacterial lysis.

Bottom Line: Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network.Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated.However, simulations suggest spatial segregation of phage particles does not play a significant role.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College, London, United Kingdom.

ABSTRACT
Bacteriophage lambda is a classic system for the study of cellular decision making. Both experiments and mathematical models have demonstrated the importance of viral concentration in the lysis-lysogeny decision outcome in lambda phage. However, a recent experimental study using single cell and single phage resolution reported that cells with the same viral concentrations but different numbers of infecting phage (multiplicity of infection) can have markedly different rates of lysogeny. Thus the decision depends on not only viral concentration, but also directly on the number of infecting phage. Here, we attempt to provide a mechanistic explanation of these results using a simple stochastic model of the lambda phage genetic network. Several potential factors including intrinsic gene expression noise, spatial dynamics and cell-cycle effects are investigated. We find that interplay between the level of intrinsic noise and viral protein decision threshold is a major factor that produces dependence on multiplicity of infection. However, simulations suggest spatial segregation of phage particles does not play a significant role. Cellular image processing is used to re-analyse the original time-lapse movies from the recent study and it is found that higher numbers of infecting phage reduce the cell elongation rate. This could also contribute to the observed phenomena as cellular growth rate can affect transcription rates. Our model further predicts that rate of lysogeny is dependent on bacterial growth rate, which can be experimentally tested. Our study provides new insight on the mechanisms of individual phage decision making. More generally, our results are relevant for the understanding of gene-dosage compensation in cellular systems.

Show MeSH
Related in: MedlinePlus