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Modeling early events in Francisella tularensis pathogenesis.

Gillard JJ, Laws TR, Lythe G, Molina-París C - Front Cell Infect Microbiol (2014)

Bottom Line: The model is mechanistic and governed by a small number of experimentally verifiable parameters.The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics.Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

View Article: PubMed Central - PubMed

Affiliation: Defence Science and Technology Laboratory Porton Down, Salisbury, UK.

ABSTRACT
Computational models can provide valuable insights into the mechanisms of infection and be used as investigative tools to support development of medical treatments. We develop a stochastic, within-host, computational model of the infection process in the BALB/c mouse, following inhalational exposure to Francisella tularensis SCHU S4. The model is mechanistic and governed by a small number of experimentally verifiable parameters. Given an initial dose, the model generates bacterial load profiles corresponding to those produced experimentally, with a doubling time of approximately 5 h during the first 48 h of infection. Analytical approximations for the mean number of bacteria in phagosomes and cytosols for the first 24 h post-infection are derived and used to verify the stochastic model. In our description of the dynamics of macrophage infection, the number of bacteria released per rupturing macrophage is a geometrically-distributed random variable. When combined with doubling time, this provides a distribution for the time taken for infected macrophages to rupture and release their intracellular bacteria. The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics. Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

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The three states of activation of macrophages in the computational model. Initially, all macrophages are in the resting state. During the course of infection, some pass to a suppressed state, due to phagocytosis or the effect of TGF-β. Others are activated by the effect of pro-inflammatory signals, from DAMP or IFN-γ.
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Figure 6: The three states of activation of macrophages in the computational model. Initially, all macrophages are in the resting state. During the course of infection, some pass to a suppressed state, due to phagocytosis or the effect of TGF-β. Others are activated by the effect of pro-inflammatory signals, from DAMP or IFN-γ.

Mentions: In Figure 6 we illustrate the three states of activation of macrophages included in the computational model (Gordon, 2003). All macrophages are initially in the resting state, a = 0. A macrophage that phagocytoses a bacterium moves to the suppressed state, a = −1, when it is a source of anti-inflammatory signals (primarily TGF-β), that are responsible for inducing other macrophages to move to the same state. Activation, or change of macrophages to the activated state, is handled differently. Each time a macrophage ruptures and dies, inflammatory signals are released. These will include both Damage Associated Molecular Patterns and Pathogen Associated Molecular Patterns (DAMPs and PAMPs). For the purposes of our model it is assumed that these signals will affect one other macrophage in the same compartment where, if it is a resting macrophage, it becomes activated. Activated macrophages produce pro-inflammatory signals, such as interleukin IL-12, that cause lymphocytes to produce IFN-γ (Mosser, 2003; Mosser and Edwards, 2008). Activated macrophages compete for free bacteria on the same basis as resting and suppressed macrophages. Bacteria internalized into activated macrophages will either grow slower or be killed (Edwards et al., 2010). For the purposes of the model, we assume that such bacteria play no further role in the acute stage of the disease. Thus, the cytosolic bacterial load of activated macrophages is set to zero.


Modeling early events in Francisella tularensis pathogenesis.

Gillard JJ, Laws TR, Lythe G, Molina-París C - Front Cell Infect Microbiol (2014)

The three states of activation of macrophages in the computational model. Initially, all macrophages are in the resting state. During the course of infection, some pass to a suppressed state, due to phagocytosis or the effect of TGF-β. Others are activated by the effect of pro-inflammatory signals, from DAMP or IFN-γ.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263195&req=5

Figure 6: The three states of activation of macrophages in the computational model. Initially, all macrophages are in the resting state. During the course of infection, some pass to a suppressed state, due to phagocytosis or the effect of TGF-β. Others are activated by the effect of pro-inflammatory signals, from DAMP or IFN-γ.
Mentions: In Figure 6 we illustrate the three states of activation of macrophages included in the computational model (Gordon, 2003). All macrophages are initially in the resting state, a = 0. A macrophage that phagocytoses a bacterium moves to the suppressed state, a = −1, when it is a source of anti-inflammatory signals (primarily TGF-β), that are responsible for inducing other macrophages to move to the same state. Activation, or change of macrophages to the activated state, is handled differently. Each time a macrophage ruptures and dies, inflammatory signals are released. These will include both Damage Associated Molecular Patterns and Pathogen Associated Molecular Patterns (DAMPs and PAMPs). For the purposes of our model it is assumed that these signals will affect one other macrophage in the same compartment where, if it is a resting macrophage, it becomes activated. Activated macrophages produce pro-inflammatory signals, such as interleukin IL-12, that cause lymphocytes to produce IFN-γ (Mosser, 2003; Mosser and Edwards, 2008). Activated macrophages compete for free bacteria on the same basis as resting and suppressed macrophages. Bacteria internalized into activated macrophages will either grow slower or be killed (Edwards et al., 2010). For the purposes of the model, we assume that such bacteria play no further role in the acute stage of the disease. Thus, the cytosolic bacterial load of activated macrophages is set to zero.

Bottom Line: The model is mechanistic and governed by a small number of experimentally verifiable parameters.The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics.Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

View Article: PubMed Central - PubMed

Affiliation: Defence Science and Technology Laboratory Porton Down, Salisbury, UK.

ABSTRACT
Computational models can provide valuable insights into the mechanisms of infection and be used as investigative tools to support development of medical treatments. We develop a stochastic, within-host, computational model of the infection process in the BALB/c mouse, following inhalational exposure to Francisella tularensis SCHU S4. The model is mechanistic and governed by a small number of experimentally verifiable parameters. Given an initial dose, the model generates bacterial load profiles corresponding to those produced experimentally, with a doubling time of approximately 5 h during the first 48 h of infection. Analytical approximations for the mean number of bacteria in phagosomes and cytosols for the first 24 h post-infection are derived and used to verify the stochastic model. In our description of the dynamics of macrophage infection, the number of bacteria released per rupturing macrophage is a geometrically-distributed random variable. When combined with doubling time, this provides a distribution for the time taken for infected macrophages to rupture and release their intracellular bacteria. The mean and variance of these distributions are determined by model parameters with a precise biological interpretation, providing new mechanistic insights into the determinants of immune and bacterial kinetics. Insights into the dynamics of macrophage suppression and activation gained by the model can be used to explore the potential benefits of interventions that stimulate macrophage activation.

Show MeSH
Related in: MedlinePlus