Multiscale Modeling of T Cells in Mycobacterium Tuberculosis Infection

Abstract

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the deadliest infectious diseases in the world and remains a significant global health burden. Central to the immune response against Mtb are T cells, a type of adaptive immune cell that can kill infected cells, secrete cytokines to activate other immune cells, and orchestrate the broader immune response. Over the past few decades, experimental studies have significantly furthered our understanding of T-cell biology and function during Mtb infection. However, these findings have yet to translate to a clinically effective TB vaccine. As a complementary approach to experimental studies, systems biology and computational modeling can provide context to T-cell function by describing T-cell interactions with other immune cells across multiple scales. In this thesis we utilize a systems biology approach to characterize T-cell behavior, function, and movement across multiple physiological and temporal scales during Mtb infection. In addition, we develop a whole-host model of the immune response to Mtb.

Following infection with Mtb, the immune response leads to the development of multiple lung granulomas – organized structures composed of immune cells that surround bacteria. Using a previously developed agent-based model of granuloma formation and function, we explore the role of T cells within the granuloma and predict that T-cell exhaustion, a type of T-cell dysfunction, is prevented from occurring by the physical structure of the granuloma. Next, we develop a novel whole lung model that tracks the formation of multiple granulomas. Using this model, we predict that a special type of T-cell, called a multi-functional CD8+ T cell, is key in preventing dissemination events - when bacteria escape one granuloma and seed the formation of a new one elsewhere in the lung. We also present a model of T-cell priming, proliferation, and differentiation within the lymph nodes and blood following TB vaccination and illustrate that non-human primates and humans respond similarly when receiving TB vaccination.

We mathematically link the whole lung model and lymph node and blood model to create a whole-host model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. Using this model, we predict that resident memory T cells are important mediators of protection against reinfection with Mtb and additionally predict the lifespan of these crucial cells in humans. Finally, we develop a protocol for calibrating mathematical and computational models to experimental datasets.

Overall, this dissertation builds on our knowledge of the various roles T cells play in responding to Mtb infection, presents a set of computational models for evaluating the T-cell response to either infection or vaccination, and identifies mechanisms that control different outcomes across multiple scales following Mtb infection, reinfection, or vaccination.