title: Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma.
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.
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Fallahi-Sichani
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Schaller
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
authors.forename: Steven L
authors.initials: SL
authors.lastname: Kunkel
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
mesh_terms: D000465:Algorithms; D000818:Animals; D017209:Apoptosis / Q000502:physiology; D003198:Computer Simulation; D003713:Dendritic Cells / Q000276:immunology; D006099:Granuloma / Q000276:immunology / Q000378:metabolism* / Q000382:microbiology / Q000473:pathology; D008214:Lymphocytes / Q000276:immunology; D008264:Macrophages / Q000276:immunology; D051379:Mice; D008808:Mice, Inbred CBA; D008954:Models, Biological; D009169:Mycobacterium tuberculosis; D011485:Protein Binding; D018124:Receptors, Tumor Necrosis Factor / Q000378:metabolism; D014373:Tuberculin / Q000378:metabolism; D014376:Tuberculosis / Q000276:immunology / Q000378:metabolism* / Q000473:pathology; D014409:Tumor Necrosis Factor-alpha / Q000037:antagonists & inhibitors / Q000378:metabolism*
chemical_list: D018124:Receptors, Tumor Necrosis Factor; D014373:Tuberculin; D014409:Tumor Necrosis Factor-alpha
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