Limits...
Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma.

Fallahi-Sichani M, Schaller MA, Kirschner DE, Kunkel SL, Linderman JJ - PLoS Comput. Biol. (2010)

Bottom Line: We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads.Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry.Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

View Article: PubMed Central - PubMed

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

ABSTRACT
Tuberculosis (TB) granulomas are organized collections of immune cells comprised of macrophages, lymphocytes and other cells that form in the lung as a result of immune response to Mycobacterium tuberculosis (Mtb) infection. Formation and maintenance of granulomas are essential for control of Mtb infection and are regulated in part by a pro-inflammatory cytokine, tumor necrosis factor-alpha (TNF). To characterize mechanisms that control TNF availability within a TB granuloma, we developed a multi-scale two compartment partial differential equation model that describes a granuloma as a collection of immune cells forming concentric layers and includes TNF/TNF receptor binding and trafficking processes. We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using our model, we then demonstrated that the organization of immune cells within a TB granuloma as well as TNF/TNF receptor binding and intracellular trafficking are two important factors that control TNF availability and may spatially coordinate TNF-induced immunological functions within a granuloma. Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry. To further elucidate the role of TNF in the process of granuloma development, our modeling and experimental findings on TNF-associated molecular scale aspects of the granuloma can be incorporated into larger scale models describing the immune response to TB infection. Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

Show MeSH

Related in: MedlinePlus

Model predictions for the effect of TNF-neutralizing drugs with various properties on the availability of TNF within a granuloma.(A) Class 1: the drug can only bind to sTNF with a binding ratio of 1∶1. (B) Class 2: the drug can bind to both mTNF and sTNF with a binding ratio of 1∶1. The star shows the location of a drug with TNF binding kinetics similar to etanercept. (C) Class 3: the drug can bind to both mTNF and sTNF with a binding ratio of 3∶1. The star shows the location of a drug with TNF binding kinetics similar to infliximab. (D) Model predictions for the effect of TNF/drug association rate constant on neutralization efficiency of drugs of different classes but identical affinities (Kd_Drug = koff_TNF/Drug/kon_TNF/Drug = 10−9 M). Model parameter values are the same as Figure 6. TNF neutralization-associated parameter values are as listed in Table 4.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2865521&req=5

pcbi-1000778-g007: Model predictions for the effect of TNF-neutralizing drugs with various properties on the availability of TNF within a granuloma.(A) Class 1: the drug can only bind to sTNF with a binding ratio of 1∶1. (B) Class 2: the drug can bind to both mTNF and sTNF with a binding ratio of 1∶1. The star shows the location of a drug with TNF binding kinetics similar to etanercept. (C) Class 3: the drug can bind to both mTNF and sTNF with a binding ratio of 3∶1. The star shows the location of a drug with TNF binding kinetics similar to infliximab. (D) Model predictions for the effect of TNF/drug association rate constant on neutralization efficiency of drugs of different classes but identical affinities (Kd_Drug = koff_TNF/Drug/kon_TNF/Drug = 10−9 M). Model parameter values are the same as Figure 6. TNF neutralization-associated parameter values are as listed in Table 4.

Mentions: We first consider a drug that binds to sTNF with a binding ratio of 1∶1, inhibiting it from binding to both TNFR1 and TNFR2. The effects of varying association and dissociation rate constants (kon_sTNF/drug and koff_sTNF/drug) for sTNF and drug are shown in Figure 7A. Model results show that depending on sTNF/drug association and dissociation rate constants, 0%–50% of total available sTNF in a granuloma can be neutralized. As expected, drugs with greater affinities for sTNF more efficiently neutralize TNF in the granuloma. Interestingly, increasing kon_sTNF/drug without changing drug affinity leads to an increase in the drug neutralization efficiency (Figure 7D, Class 1). This is because drugs compete with cell surface TNFRs for binding to sTNF and thus a drug with a greater kon_sTNF/drug can neutralize larger amounts of sTNF.


Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma.

Fallahi-Sichani M, Schaller MA, Kirschner DE, Kunkel SL, Linderman JJ - PLoS Comput. Biol. (2010)

Model predictions for the effect of TNF-neutralizing drugs with various properties on the availability of TNF within a granuloma.(A) Class 1: the drug can only bind to sTNF with a binding ratio of 1∶1. (B) Class 2: the drug can bind to both mTNF and sTNF with a binding ratio of 1∶1. The star shows the location of a drug with TNF binding kinetics similar to etanercept. (C) Class 3: the drug can bind to both mTNF and sTNF with a binding ratio of 3∶1. The star shows the location of a drug with TNF binding kinetics similar to infliximab. (D) Model predictions for the effect of TNF/drug association rate constant on neutralization efficiency of drugs of different classes but identical affinities (Kd_Drug = koff_TNF/Drug/kon_TNF/Drug = 10−9 M). Model parameter values are the same as Figure 6. TNF neutralization-associated parameter values are as listed in Table 4.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865521&req=5

pcbi-1000778-g007: Model predictions for the effect of TNF-neutralizing drugs with various properties on the availability of TNF within a granuloma.(A) Class 1: the drug can only bind to sTNF with a binding ratio of 1∶1. (B) Class 2: the drug can bind to both mTNF and sTNF with a binding ratio of 1∶1. The star shows the location of a drug with TNF binding kinetics similar to etanercept. (C) Class 3: the drug can bind to both mTNF and sTNF with a binding ratio of 3∶1. The star shows the location of a drug with TNF binding kinetics similar to infliximab. (D) Model predictions for the effect of TNF/drug association rate constant on neutralization efficiency of drugs of different classes but identical affinities (Kd_Drug = koff_TNF/Drug/kon_TNF/Drug = 10−9 M). Model parameter values are the same as Figure 6. TNF neutralization-associated parameter values are as listed in Table 4.
Mentions: We first consider a drug that binds to sTNF with a binding ratio of 1∶1, inhibiting it from binding to both TNFR1 and TNFR2. The effects of varying association and dissociation rate constants (kon_sTNF/drug and koff_sTNF/drug) for sTNF and drug are shown in Figure 7A. Model results show that depending on sTNF/drug association and dissociation rate constants, 0%–50% of total available sTNF in a granuloma can be neutralized. As expected, drugs with greater affinities for sTNF more efficiently neutralize TNF in the granuloma. Interestingly, increasing kon_sTNF/drug without changing drug affinity leads to an increase in the drug neutralization efficiency (Figure 7D, Class 1). This is because drugs compete with cell surface TNFRs for binding to sTNF and thus a drug with a greater kon_sTNF/drug can neutralize larger amounts of sTNF.

Bottom Line: We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads.Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry.Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

View Article: PubMed Central - PubMed

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

ABSTRACT
Tuberculosis (TB) granulomas are organized collections of immune cells comprised of macrophages, lymphocytes and other cells that form in the lung as a result of immune response to Mycobacterium tuberculosis (Mtb) infection. Formation and maintenance of granulomas are essential for control of Mtb infection and are regulated in part by a pro-inflammatory cytokine, tumor necrosis factor-alpha (TNF). To characterize mechanisms that control TNF availability within a TB granuloma, we developed a multi-scale two compartment partial differential equation model that describes a granuloma as a collection of immune cells forming concentric layers and includes TNF/TNF receptor binding and trafficking processes. We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using our model, we then demonstrated that the organization of immune cells within a TB granuloma as well as TNF/TNF receptor binding and intracellular trafficking are two important factors that control TNF availability and may spatially coordinate TNF-induced immunological functions within a granuloma. Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry. To further elucidate the role of TNF in the process of granuloma development, our modeling and experimental findings on TNF-associated molecular scale aspects of the granuloma can be incorporated into larger scale models describing the immune response to TB infection. Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

Show MeSH
Related in: MedlinePlus