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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

Manipulation of TNF-mediated NF-κB signaling for improving granuloma function. Comparison of the dynamics of (A) bacteria growth, (B) activated fraction of macrophages, and (C) granuloma snapshots among three different treatment methods for enhancing NF-κB activities. In all treatments, we first simulate formation of a granuloma that is unable to control bacteria growth due to impaired NF-κB signaling at high rates of IKKK inactivation (ki = 3.16 × 10−2 s−1) for 100 days (all other parameter values are as listed in Tables A1, A3, and A5 in Appendix). Then, we change one or more of the NF-κB-associated parameters to restore NF-κB activities within the granuloma and resume simulation for another 100 days. Parameter changes in each treatment are as follows: treatment I: ki = 1 × 10−2 s−1, Treatment II: ki = 3.16 × 10−3 s−1, Treatment III: ki = 1 × 10−2 s−1, t1/2(TNF) = 3 h, t1/2(ACT) = 30 min, t1/2(TNF) = 1 h. Simulation results were averaged over 10 repetitions. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2.
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Figure 7: Manipulation of TNF-mediated NF-κB signaling for improving granuloma function. Comparison of the dynamics of (A) bacteria growth, (B) activated fraction of macrophages, and (C) granuloma snapshots among three different treatment methods for enhancing NF-κB activities. In all treatments, we first simulate formation of a granuloma that is unable to control bacteria growth due to impaired NF-κB signaling at high rates of IKKK inactivation (ki = 3.16 × 10−2 s−1) for 100 days (all other parameter values are as listed in Tables A1, A3, and A5 in Appendix). Then, we change one or more of the NF-κB-associated parameters to restore NF-κB activities within the granuloma and resume simulation for another 100 days. Parameter changes in each treatment are as follows: treatment I: ki = 1 × 10−2 s−1, Treatment II: ki = 3.16 × 10−3 s−1, Treatment III: ki = 1 × 10−2 s−1, t1/2(TNF) = 3 h, t1/2(ACT) = 30 min, t1/2(TNF) = 1 h. Simulation results were averaged over 10 repetitions. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2.

Mentions: Our analysis, as depicted in Figure 7, indicates that reducing ki (IKKK inactivation rate constant) from high values to intermediate (containment-level) values (Treatment I) enhances the ability of a granuloma to control bacteria. However, average bacteria levels for a 200-day granuloma after changing ki are generally higher than bacteria levels resulting from simulating a containment scenario. A further decrease in the value of ki (Treatment II) is more successful in killing bacteria. However, it leads to uncontrolled activation of macrophages and excessive inflammation in tissue. This suggests that targeting the process of IKKK inactivation alone is not sufficient for infection control at the granuloma scale. In another set of simulations (Treatment III), decreasing ki to intermediate values, together with manipulating stability of mRNA transcripts associated with NF-κB-mediated responses (based on results from Figure 5) leads to better outcomes. Increasing the half-life of TNF mRNA transcripts to 3 h, reducing the half-life of ACT mRNA transcripts to 30 min, and setting the IAP mRNA transcripts to 1 h improves the granuloma outcome, inducing efficient killing of bacteria without excessive inflammation. Overall, this suggests that manipulating the dynamics of NF-κB-mediated responses, particularly macrophage activation, TNF and IAP expression, can improve the function of a TB granuloma.


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

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

Manipulation of TNF-mediated NF-κB signaling for improving granuloma function. Comparison of the dynamics of (A) bacteria growth, (B) activated fraction of macrophages, and (C) granuloma snapshots among three different treatment methods for enhancing NF-κB activities. In all treatments, we first simulate formation of a granuloma that is unable to control bacteria growth due to impaired NF-κB signaling at high rates of IKKK inactivation (ki = 3.16 × 10−2 s−1) for 100 days (all other parameter values are as listed in Tables A1, A3, and A5 in Appendix). Then, we change one or more of the NF-κB-associated parameters to restore NF-κB activities within the granuloma and resume simulation for another 100 days. Parameter changes in each treatment are as follows: treatment I: ki = 1 × 10−2 s−1, Treatment II: ki = 3.16 × 10−3 s−1, Treatment III: ki = 1 × 10−2 s−1, t1/2(TNF) = 3 h, t1/2(ACT) = 30 min, t1/2(TNF) = 1 h. Simulation results were averaged over 10 repetitions. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2.
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Related In: Results  -  Collection

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Figure 7: Manipulation of TNF-mediated NF-κB signaling for improving granuloma function. Comparison of the dynamics of (A) bacteria growth, (B) activated fraction of macrophages, and (C) granuloma snapshots among three different treatment methods for enhancing NF-κB activities. In all treatments, we first simulate formation of a granuloma that is unable to control bacteria growth due to impaired NF-κB signaling at high rates of IKKK inactivation (ki = 3.16 × 10−2 s−1) for 100 days (all other parameter values are as listed in Tables A1, A3, and A5 in Appendix). Then, we change one or more of the NF-κB-associated parameters to restore NF-κB activities within the granuloma and resume simulation for another 100 days. Parameter changes in each treatment are as follows: treatment I: ki = 1 × 10−2 s−1, Treatment II: ki = 3.16 × 10−3 s−1, Treatment III: ki = 1 × 10−2 s−1, t1/2(TNF) = 3 h, t1/2(ACT) = 30 min, t1/2(TNF) = 1 h. Simulation results were averaged over 10 repetitions. The colors representing cells of different type and status in granuloma snapshots are the same as those shown and defined in Figure 2.
Mentions: Our analysis, as depicted in Figure 7, indicates that reducing ki (IKKK inactivation rate constant) from high values to intermediate (containment-level) values (Treatment I) enhances the ability of a granuloma to control bacteria. However, average bacteria levels for a 200-day granuloma after changing ki are generally higher than bacteria levels resulting from simulating a containment scenario. A further decrease in the value of ki (Treatment II) is more successful in killing bacteria. However, it leads to uncontrolled activation of macrophages and excessive inflammation in tissue. This suggests that targeting the process of IKKK inactivation alone is not sufficient for infection control at the granuloma scale. In another set of simulations (Treatment III), decreasing ki to intermediate values, together with manipulating stability of mRNA transcripts associated with NF-κB-mediated responses (based on results from Figure 5) leads to better outcomes. Increasing the half-life of TNF mRNA transcripts to 3 h, reducing the half-life of ACT mRNA transcripts to 30 min, and setting the IAP mRNA transcripts to 1 h improves the granuloma outcome, inducing efficient killing of bacteria without excessive inflammation. Overall, this suggests that manipulating the dynamics of NF-κB-mediated responses, particularly macrophage activation, TNF and IAP expression, can improve the function of a TB granuloma.

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