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NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis.

Fallahi-Sichani M, Kirschner DE, Linderman JJ - Front Physiol (2012)

Bottom Line: The NF-κB signaling pathway is central to the body's response to many pathogens.We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung.We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, University of Michigan Ann Arbor, MI, USA.

ABSTRACT
The NF-κB signaling pathway is central to the body's response to many pathogens. Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this pathway have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung. NF-κB signaling is triggered via tumor necrosis factor-α (TNF) binding to receptors on macrophages; TNF has been shown to play a key role in infection dynamics in humans and multiple animal systems. Using our multi-scale model, we predict the impact of TNF-induced NF-κB-mediated responses on the outcome of infection at the level of a granuloma, an aggregate of immune cells and bacteria that forms in response to infection and is key to containment of infection and clinical latency. We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size. Because we incorporate intracellular signaling pathways explicitly, our analysis also elucidates NF-κB-associated signaling molecules and processes that may be new targets for infection control.

No MeSH data available.


Related in: MedlinePlus

NF-κB signaling dynamics control bacterial growth and inflammation level in tissue. (A) Granuloma snapshots for slow (ki = 3.2 × 10−3 s−1), intermediate (ki = 10−2 s−1), and rapid (ki = 3.2 × 10−2 s−1) rates of IKKK inactivation. Slow rates of IKKK inactivation lead to uncontrolled macrophage activation and excessive inflammation. An intermediate value of ki results in control of infection in a stable granuloma containing small numbers of bacteria. Rapid rates of IKKK inactivation lead to large numbers of bacteria and infected macrophages as well as widespread caseation. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2. (B–D) Simulation results showing the effects of four important parameters, as identified by sensitivity analysis, controlling NF-κB signaling dynamics on granuloma outcomes (total number of bacteria, tissue concentration of TNF, and macrophage activation). The parameters are: the average number of NF-κB molecules per cell (NF-κBtot), IKKK inactivation rate (ki), A20 and IκBα mRNA degradation rate (c3), and TNF mRNA degradation rate (c3rTNF). In each simulation, only one of these parameters is varied. The baseline (intermediate) values of these parameters lead to clearance or control of infection in stable granulomas with very low bacterial numbers, low levels of TNF, and low levels of macrophage activation. Perturbing the NF-κB signaling dynamics by varying values of these parameters impair the balance toward either uncontrolled growth of bacteria or excessive inflammation (high TNF concentrations and high levels of macrophage activation) in tissue. The baseline value of each parameter is as reported in Table A5 in Appendix and is as follows: NF-κBtot = 105, ki = 10−2 s−1, c3 = 7.5 × 10−4 s−1, c3rTNF = 3.8 × 10−4 s−1. The difference between the low value and high value presented in the figure is one order of magnitude.
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Figure 3: NF-κB signaling dynamics control bacterial growth and inflammation level in tissue. (A) Granuloma snapshots for slow (ki = 3.2 × 10−3 s−1), intermediate (ki = 10−2 s−1), and rapid (ki = 3.2 × 10−2 s−1) rates of IKKK inactivation. Slow rates of IKKK inactivation lead to uncontrolled macrophage activation and excessive inflammation. An intermediate value of ki results in control of infection in a stable granuloma containing small numbers of bacteria. Rapid rates of IKKK inactivation lead to large numbers of bacteria and infected macrophages as well as widespread caseation. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2. (B–D) Simulation results showing the effects of four important parameters, as identified by sensitivity analysis, controlling NF-κB signaling dynamics on granuloma outcomes (total number of bacteria, tissue concentration of TNF, and macrophage activation). The parameters are: the average number of NF-κB molecules per cell (NF-κBtot), IKKK inactivation rate (ki), A20 and IκBα mRNA degradation rate (c3), and TNF mRNA degradation rate (c3rTNF). In each simulation, only one of these parameters is varied. The baseline (intermediate) values of these parameters lead to clearance or control of infection in stable granulomas with very low bacterial numbers, low levels of TNF, and low levels of macrophage activation. Perturbing the NF-κB signaling dynamics by varying values of these parameters impair the balance toward either uncontrolled growth of bacteria or excessive inflammation (high TNF concentrations and high levels of macrophage activation) in tissue. The baseline value of each parameter is as reported in Table A5 in Appendix and is as follows: NF-κBtot = 105, ki = 10−2 s−1, c3 = 7.5 × 10−4 s−1, c3rTNF = 3.8 × 10−4 s−1. The difference between the low value and high value presented in the figure is one order of magnitude.

Mentions: The analysis above highlights various NF-κB signaling pathway-associated biochemical factors and intracellular interactions that show significant impacts on infection outcomes at all scales (molecular, cellular, and tissue). How do these responses influence granuloma formation? Does manipulation of these mechanisms alter infection outcome at the granuloma level? The effects of manipulation of four important NF-κB-associated factors as identified by sensitivity analysis - (i) average number of NF-κB molecules per cell, NF-κBtot, (ii) IKKK inactivation rate constant, ki, (iii) A20 and IκBα mRNA degradation rate constant, c3, and (iv) TNF mRNA degradation rate constant, c3rTNF - on granuloma formation, total number of bacteria, sTNF concentration, and macrophage activation after Mtb infection are shown in Figure 3.


NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis.

Fallahi-Sichani M, Kirschner DE, Linderman JJ - Front Physiol (2012)

NF-κB signaling dynamics control bacterial growth and inflammation level in tissue. (A) Granuloma snapshots for slow (ki = 3.2 × 10−3 s−1), intermediate (ki = 10−2 s−1), and rapid (ki = 3.2 × 10−2 s−1) rates of IKKK inactivation. Slow rates of IKKK inactivation lead to uncontrolled macrophage activation and excessive inflammation. An intermediate value of ki results in control of infection in a stable granuloma containing small numbers of bacteria. Rapid rates of IKKK inactivation lead to large numbers of bacteria and infected macrophages as well as widespread caseation. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2. (B–D) Simulation results showing the effects of four important parameters, as identified by sensitivity analysis, controlling NF-κB signaling dynamics on granuloma outcomes (total number of bacteria, tissue concentration of TNF, and macrophage activation). The parameters are: the average number of NF-κB molecules per cell (NF-κBtot), IKKK inactivation rate (ki), A20 and IκBα mRNA degradation rate (c3), and TNF mRNA degradation rate (c3rTNF). In each simulation, only one of these parameters is varied. The baseline (intermediate) values of these parameters lead to clearance or control of infection in stable granulomas with very low bacterial numbers, low levels of TNF, and low levels of macrophage activation. Perturbing the NF-κB signaling dynamics by varying values of these parameters impair the balance toward either uncontrolled growth of bacteria or excessive inflammation (high TNF concentrations and high levels of macrophage activation) in tissue. The baseline value of each parameter is as reported in Table A5 in Appendix and is as follows: NF-κBtot = 105, ki = 10−2 s−1, c3 = 7.5 × 10−4 s−1, c3rTNF = 3.8 × 10−4 s−1. The difference between the low value and high value presented in the figure is one order of magnitude.
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Related In: Results  -  Collection

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Figure 3: NF-κB signaling dynamics control bacterial growth and inflammation level in tissue. (A) Granuloma snapshots for slow (ki = 3.2 × 10−3 s−1), intermediate (ki = 10−2 s−1), and rapid (ki = 3.2 × 10−2 s−1) rates of IKKK inactivation. Slow rates of IKKK inactivation lead to uncontrolled macrophage activation and excessive inflammation. An intermediate value of ki results in control of infection in a stable granuloma containing small numbers of bacteria. Rapid rates of IKKK inactivation lead to large numbers of bacteria and infected macrophages as well as widespread caseation. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2. (B–D) Simulation results showing the effects of four important parameters, as identified by sensitivity analysis, controlling NF-κB signaling dynamics on granuloma outcomes (total number of bacteria, tissue concentration of TNF, and macrophage activation). The parameters are: the average number of NF-κB molecules per cell (NF-κBtot), IKKK inactivation rate (ki), A20 and IκBα mRNA degradation rate (c3), and TNF mRNA degradation rate (c3rTNF). In each simulation, only one of these parameters is varied. The baseline (intermediate) values of these parameters lead to clearance or control of infection in stable granulomas with very low bacterial numbers, low levels of TNF, and low levels of macrophage activation. Perturbing the NF-κB signaling dynamics by varying values of these parameters impair the balance toward either uncontrolled growth of bacteria or excessive inflammation (high TNF concentrations and high levels of macrophage activation) in tissue. The baseline value of each parameter is as reported in Table A5 in Appendix and is as follows: NF-κBtot = 105, ki = 10−2 s−1, c3 = 7.5 × 10−4 s−1, c3rTNF = 3.8 × 10−4 s−1. The difference between the low value and high value presented in the figure is one order of magnitude.
Mentions: The analysis above highlights various NF-κB signaling pathway-associated biochemical factors and intracellular interactions that show significant impacts on infection outcomes at all scales (molecular, cellular, and tissue). How do these responses influence granuloma formation? Does manipulation of these mechanisms alter infection outcome at the granuloma level? The effects of manipulation of four important NF-κB-associated factors as identified by sensitivity analysis - (i) average number of NF-κB molecules per cell, NF-κBtot, (ii) IKKK inactivation rate constant, ki, (iii) A20 and IκBα mRNA degradation rate constant, c3, and (iv) TNF mRNA degradation rate constant, c3rTNF - on granuloma formation, total number of bacteria, sTNF concentration, and macrophage activation after Mtb infection are shown in Figure 3.

Bottom Line: The NF-κB signaling pathway is central to the body's response to many pathogens.We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung.We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, University of Michigan Ann Arbor, MI, USA.

ABSTRACT
The NF-κB signaling pathway is central to the body's response to many pathogens. Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this pathway have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung. NF-κB signaling is triggered via tumor necrosis factor-α (TNF) binding to receptors on macrophages; TNF has been shown to play a key role in infection dynamics in humans and multiple animal systems. Using our multi-scale model, we predict the impact of TNF-induced NF-κB-mediated responses on the outcome of infection at the level of a granuloma, an aggregate of immune cells and bacteria that forms in response to infection and is key to containment of infection and clinical latency. We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size. Because we incorporate intracellular signaling pathways explicitly, our analysis also elucidates NF-κB-associated signaling molecules and processes that may be new targets for infection control.

No MeSH data available.


Related in: MedlinePlus