title: Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation.
abstract: Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Fallahi-Sichani
authors.forename: Mohammed
authors.initials: M
authors.lastname: El-Kebir
authors.forename: Simeone
authors.initials: S
authors.lastname: Marino
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006099:Granuloma / Q000276:immunology* / Q000382:microbiology / Q000473:pathology; D006801:Humans; D018836:Inflammation Mediators / Q000737:chemistry / Q000378:metabolism / Q000502:physiology; D008024:Ligands; D018448:Models, Immunological; D056004:Molecular Dynamics Simulation; D009169:Mycobacterium tuberculosis / Q000254:growth & development* / Q000276:immunology* / Q000472:pathogenicity; D011237:Predictive Value of Tests; D047888:Receptors, Tumor Necrosis Factor, Type I / Q000737:chemistry / Q000378:metabolism / Q000502:physiology*; D015398:Signal Transduction / Q000276:immunology; D014397:Tuberculosis, Pulmonary / Q000276:immunology* / Q000382:microbiology / Q000473:pathology; D014409:Tumor Necrosis Factor-alpha / Q000737:chemistry / Q000378:metabolism / Q000502:physiology
chemical_list: D018836:Inflammation Mediators; D008024:Ligands; D047888:Receptors, Tumor Necrosis Factor, Type I; D014409:Tumor Necrosis Factor-alpha