Synergy between individual TNF-dependent functions determines granuloma performance for controlling Mycobacterium tuberculosis infection.
Mycobacterium tuberculosis is one of the world's most deadly human pathogens; an integrated understanding of how it successfully survives in its host is crucial to developing new treatment strategies. One notable characteristic of infection with M. tuberculosis is the formation of granulomas, aggregates of immune cells whose structure and function may reflect success or failure of the host to contain infection. One central regulator of host responses to infection, including granuloma formation, is the pleiotropic cytokine TNF-alpha. Experimental work has characterized roles for TNF in macrophage activation; regulation of apoptosis; chemokine and cytokine production; and regulation of cellular recruitment via transendothelial migration. Separating the effects of these functions is presently difficult or impossible in vivo. To this end, we applied a computational model to understand specific roles of TNF in control of tuberculosis in a single granuloma. In the model, cells are represented as discrete entities on a spatial grid responding to environmental stimuli by following programmed rules determined from published experimental studies. Simulated granulomas emerge as a result of these rules. After confirming the importance of TNF in this model, we assessed the effects of individual TNF functions. The model predicts that multiple TNF activities contribute to control of infection within the granuloma, with macrophage activation as a key effector mechanism for controlling bacterial growth. Results suggest that bacterial numbers are a strong contributing factor to granuloma structure with TNF. Finally, TNF-dependent apoptosis may reduce inflammation at the cost of impairing mycobacterial clearance.
Ray
J Christian J
JC
Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Flynn
JoAnne L
JL
Kirschner
Denise E
DE
D000818:Animals; D051017:Apoptosis Regulatory Proteins / Q000172:deficiency / Q000502:physiology / Q000627:therapeutic use; D018925:Chemokines / Q000096:biosynthesis / Q000378:metabolism / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004195:Disease Models, Animal; D017353:Gene Deletion; D006099:Granuloma / Q000276:immunology* / Q000382:microbiology* / Q000473:pathology; D006801:Humans; D008262:Macrophage Activation / Q000276:immunology; D009169:Mycobacterium tuberculosis / Q000254:growth & development / Q000276:immunology*; D013269:Stochastic Processes; D013601:T-Lymphocytes / Q000276:immunology / Q000378:metabolism / Q000473:pathology; D014397:Tuberculosis, Pulmonary / Q000276:immunology* / Q000382:microbiology* / Q000473:pathology; D014409:Tumor Necrosis Factor-alpha / Q000172:deficiency / Q000502:physiology* / Q000627:therapeutic use
D051017:Apoptosis Regulatory Proteins; D018925:Chemokines; D014409:Tumor Necrosis Factor-alpha