Mathematical and Computational Analyses of Immunological Signaling Networks Affecting Mycobacterium tuberculosis Infection.

Mathematical Model;Mycobacterium Tuberculosis;Immunological Signaling;Evolutionary Design Principles;Biochemical Network;Microbiology and Immunology;Science;Microbiology & Immunology

Ray, Joe Christian JohnstoneSwanson, Joel A. ;

University of Michigan

关键词: Mathematical Model;    Mycobacterium Tuberculosis;    Immunological Signaling;    Evolutionary Design Principles;    Biochemical Network;    Microbiology and Immunology;    Science;    Microbiology & Immunology;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58442/jjray_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Immunological signaling pathways between and within cells are central determinants of the success of immune responses. One major characteristic of immune signaling is a balance that is struck between pro-inflammatory responses to pathogens and anti-inflammatory regulation that stabilizes and modulates immunity. Mycobacterium tuberculosis is a successful human pathogen that preferentially survives within host macrophages, the very immune cells that act to eliminate it. Exploitation of the balance between pro- and anti-inflammatory mechanisms may be a strategy for M. tuberculosis survival within macrophages. This work first explores the evolved design principles of intracellular macrophage activation pathways relevant to countering M. tuberculosis infection. I used a mathematical model of the macrophage intracellular signaling network to predict that multiple synergistic activation signals are balanced by negative (anti-inflammatory) feedback from a single output, the killing effector nitric oxide. Without the presence of two activation signals, the feedback is antagonistic toward high levels of activation. I next implemented a representation of a growing intracellular population of M. tuberculosis in the macrophage signaling model. This shows that negative feedback of nitric oxide to activation signaling may not optimally kill bacteria compared to a possible positive feedback design. However, the model predicts that negative feedback imparts a kinetic advantage to elevating nitric oxide levels. The kinetics of nitric oxide induction offset the disadvantage of negative feedback if the timing of activating cytokine delivery occurs near the time of macrophage infection. On a different biological scale, I explored the roles of activation signals in M. tuberculosis infection with a computational agent-based model of granuloma formation. Model results suggest that multiple effects of the pleiotropic cytokine tumor necrosis factor-a (TNF) are an essential feature of TNF function: loss of single TNF activities did not result in granuloma structures comparable to deletion of all TNF activity. Perturbation of multiple TNF activities simultaneously showed synergistic and competitive effects of individual TNF activities in granuloma formation. Finally, I explored possible ways to integrate a single-cell stochastic model of macrophage gene regulation into an agent-based model to simulate the roles of intracellular signaling in the context of the granuloma environment.