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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

Examples of virtual control experiments for the multi-scale computational model of granuloma formation in response to Mtb infection. (A–C) Granuloma snapshots for (A) a scenario of containment (200 days post-infection), (B) a TNFR1 knockout (TNFR1mac = TNFR1Tcell = 0) scenario resulting in uncontrolled growth of bacteria 200 days post-infection, and (C) a scenario of blocking TNFR1 internalization (kint1 = 0) resulting in excessive inflammation 5 weeks post-infection, respectively. All other model parameter values used for these experiments are listed in Tables A1, A3, and A5 in Appendix. Cell types and status are shown by different color squares, as indicated on the right side of the figure (Mr, resting macrophage; Mi, infected macrophage; Mci, chronically infected macrophage; Ma, activated macrophage; Be, extracellular bacteria; Tγ, pro-inflammatory IFN-γ producing T cell; Tc, cytotoxic T cell; Treg, regulatory T cell). Caseation and vascular sources are also indicated.
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Figure 2: Examples of virtual control experiments for the multi-scale computational model of granuloma formation in response to Mtb infection. (A–C) Granuloma snapshots for (A) a scenario of containment (200 days post-infection), (B) a TNFR1 knockout (TNFR1mac = TNFR1Tcell = 0) scenario resulting in uncontrolled growth of bacteria 200 days post-infection, and (C) a scenario of blocking TNFR1 internalization (kint1 = 0) resulting in excessive inflammation 5 weeks post-infection, respectively. All other model parameter values used for these experiments are listed in Tables A1, A3, and A5 in Appendix. Cell types and status are shown by different color squares, as indicated on the right side of the figure (Mr, resting macrophage; Mi, infected macrophage; Mci, chronically infected macrophage; Ma, activated macrophage; Be, extracellular bacteria; Tγ, pro-inflammatory IFN-γ producing T cell; Tc, cytotoxic T cell; Treg, regulatory T cell). Caseation and vascular sources are also indicated.

Mentions: Immunity to Mtb in humans and animal studies has been attributed to activities of a variety of factors, including specific immune cells (e.g., macrophages and T cells), cytokines (e.g., TNF and IFN-γ), chemokines (e.g., CCL2, CCL5, CXCL9/10/11), immune receptors (e.g., TNFR1), and signaling pathways (e.g., NF-κB). Our new multi-scale computational model [resulting from the incorporation of the single-cell level NF-κB signaling dynamics (Tay et al., 2010), as indicated in Figure 1, into our previous generation model (Fallahi-Sichani et al., 2011)] must retain its ability to reproduce experimental findings regarding the importance of these factors in control of infection. Our model is able to recapitulate different types of granuloma with different abilities to control infection and inflammation (Figure 2). Using a baseline set of values for model parameters (Tables A1, A3, and A5 in Appendix), our model captures a state of equilibrium between the host and Mtb termed bacterial containment (Figure 2A). This state represents control of infection for more than 200 days within a well-circumscribed granuloma containing stable bacteria numbers (<103 total bacteria). Simulated containment granulomas closely represent experimentally characterized solid granulomas (Algood et al., 2003; Turner et al., 2003; Ulrichs et al., 2004; Lin et al., 2006; Morel et al., 2006; Tsai et al., 2006; Davis and Ramakrishnan, 2008) that are predominantly composed of uninfected macrophages surrounding a core of bacteria and infected and activated macrophages with T cells localized at the periphery. Varying values of important model parameters lead to other possibilities, including clearance of bacteria, uncontrolled growth of bacteria, or excessive inflammation.


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

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

Examples of virtual control experiments for the multi-scale computational model of granuloma formation in response to Mtb infection. (A–C) Granuloma snapshots for (A) a scenario of containment (200 days post-infection), (B) a TNFR1 knockout (TNFR1mac = TNFR1Tcell = 0) scenario resulting in uncontrolled growth of bacteria 200 days post-infection, and (C) a scenario of blocking TNFR1 internalization (kint1 = 0) resulting in excessive inflammation 5 weeks post-infection, respectively. All other model parameter values used for these experiments are listed in Tables A1, A3, and A5 in Appendix. Cell types and status are shown by different color squares, as indicated on the right side of the figure (Mr, resting macrophage; Mi, infected macrophage; Mci, chronically infected macrophage; Ma, activated macrophage; Be, extracellular bacteria; Tγ, pro-inflammatory IFN-γ producing T cell; Tc, cytotoxic T cell; Treg, regulatory T cell). Caseation and vascular sources are also indicated.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368390&req=5

Figure 2: Examples of virtual control experiments for the multi-scale computational model of granuloma formation in response to Mtb infection. (A–C) Granuloma snapshots for (A) a scenario of containment (200 days post-infection), (B) a TNFR1 knockout (TNFR1mac = TNFR1Tcell = 0) scenario resulting in uncontrolled growth of bacteria 200 days post-infection, and (C) a scenario of blocking TNFR1 internalization (kint1 = 0) resulting in excessive inflammation 5 weeks post-infection, respectively. All other model parameter values used for these experiments are listed in Tables A1, A3, and A5 in Appendix. Cell types and status are shown by different color squares, as indicated on the right side of the figure (Mr, resting macrophage; Mi, infected macrophage; Mci, chronically infected macrophage; Ma, activated macrophage; Be, extracellular bacteria; Tγ, pro-inflammatory IFN-γ producing T cell; Tc, cytotoxic T cell; Treg, regulatory T cell). Caseation and vascular sources are also indicated.
Mentions: Immunity to Mtb in humans and animal studies has been attributed to activities of a variety of factors, including specific immune cells (e.g., macrophages and T cells), cytokines (e.g., TNF and IFN-γ), chemokines (e.g., CCL2, CCL5, CXCL9/10/11), immune receptors (e.g., TNFR1), and signaling pathways (e.g., NF-κB). Our new multi-scale computational model [resulting from the incorporation of the single-cell level NF-κB signaling dynamics (Tay et al., 2010), as indicated in Figure 1, into our previous generation model (Fallahi-Sichani et al., 2011)] must retain its ability to reproduce experimental findings regarding the importance of these factors in control of infection. Our model is able to recapitulate different types of granuloma with different abilities to control infection and inflammation (Figure 2). Using a baseline set of values for model parameters (Tables A1, A3, and A5 in Appendix), our model captures a state of equilibrium between the host and Mtb termed bacterial containment (Figure 2A). This state represents control of infection for more than 200 days within a well-circumscribed granuloma containing stable bacteria numbers (<103 total bacteria). Simulated containment granulomas closely represent experimentally characterized solid granulomas (Algood et al., 2003; Turner et al., 2003; Ulrichs et al., 2004; Lin et al., 2006; Morel et al., 2006; Tsai et al., 2006; Davis and Ramakrishnan, 2008) that are predominantly composed of uninfected macrophages surrounding a core of bacteria and infected and activated macrophages with T cells localized at the periphery. Varying values of important model parameters lead to other possibilities, including clearance of bacteria, uncontrolled growth of bacteria, or excessive inflammation.

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