(448a) Using An Agent-Based Model of Mycobacterium Tuberculosis Infection to Understand the Roles of Interleukin-10 During Disease Progression

Introduction

Mycobacterium tuberculosis (Mtb) is the causative agent of the infectious disease tuberculosis (TB). Over 2 billion people are infected with Mtb, causing 2-3 million deaths each year. Although the root cause of the disease has been known for over a century, we still lack a fundamental understanding of many of the processes occurring during infection. The granuloma is a structure that forms in the lungs as a result of the immune response to inhaled Mtb. Granulomas consist of Mtb and infected macrophages in the center, an inner cuff of mainly resting and activated macrophages, and an outer cuff of predominantly T and B lymphocytes. Formation of a granuloma relies on many coordinated immunological processes directed by the molecular mediators known as cytokines¹. Interleukin-10 (IL-10), an anti-inflammatory cytokine, is thought to be a critical regulator of many processes during granuloma formation and function, but understanding its importance has been difficult due to the complexity and heterogeneity of a granuloma and the myriad of cellular and signaling processes acting across multiple spatial and temporal scales. Experiments attempting to elucidate these phenomena are either difficult or currently infeasible, thus we have developed a complementary in silico approach to study the immune response to Mtband its physiological consequences.

Methods

We developed a computational model of Mtb infection, validated the model against experimental observations in non-human primates (NHPs), and subsequently used the model to determine how IL-10 contributes to granuolma pathology and progression. We built a hybrid agent-based model (ABM) of Mtb infection that describes immune processes over three different biologoical scales: tissue, cellular, and molecular². At the molecular scale we modeled single-cell level receptor-ligand trafficking events, diffusion, and degradation of IL-10 and tumor necrosis factor-α (TNF-α), a pro-inflammatory cytokine, using ordinary and partial differential equations and linked these models to a tissue and cellular scale ABM². The ABM includes both macrophages and T cells, each with multiple states. We model three bacterial sub-populations: intracellular Mtb, extracellular Mtb, and non-replicating Mtb. The ABM represents multiple immune mechanisms using a set of rules and interactions, such as chemokine-induced cellular recruitment, growth of Mtb, macrophage phagocytosis of Mtb, cellular apoptosis, and T cell killing. We validated our ABM against observations in the NHP model of Mtb infection for wild type, Tnf-α^-/-, and Ifn-γ^-/- scenarios². We utilize uncertainty and sensitivity analysis to identify model parameters that have significant effects on model output and to create cell-specific IL-10 deletions to ascertain the roles of IL-10 production from different immune cells.

Results

Our in silico model predicts that multiple groups of IL-10 parameters, representing processes relevant to cytokine synthesis, signaling, and spatial distribution, control concentrations of TNF-α and IL-10 in a granuloma environment and eventually determine granuloma outcome, at the tissue scale, over the long-term. We demonstrate that each group of parameters is balancing a trade-off between host-induced tissue damage and bactericidal processes through various IL-10 mechanisms. We determine, for the first time, that the balance of TNF-α and IL-10 concentrations is an essential mediator of Mtb infection control and prevention of host-induced tissue damage. Our results predict that granulomas having higher average concentrations of IL-10 than TNF-α promote containment of bacteria and prevention of host tissue damage instead of bacterial clearance with high levels of healthy tissue damage.

We create models of cell-specific IL-10 deletions, including infected macrophage Il10^-/-, activated macrophage Il10^-/-, and regulatory T cell Il10^-/-, which suggest different roles for IL-10 produced by infected macrophages and activated macrophages. We show that production of IL-10 by activated macrophages is used as a host-protective mechanism to regulate tissue damage and control macrophage activation, while production of IL-10 by infected macrophages is used as an immune evasion mechanism to promote persistent infection. We then explore possible immunomodulation intervention strategies, such as the addition of anti-IL-10R antibodies, in order to limit Mtb-induced immune evasion mechanisms.

Conclusions

Our modeling approach represents a critical step towards fully understanding the role of IL-10 and its effects on Mtb infection outcome in the long-term. In addition, the hybrid ABM platform we developed will allow us to rapidly explore new intervention strategies, such as immunomodulation, to modify the immune response to Mtb, narrowing the large design space for future non-human primate experiments.

Acknowledgements

We acknowledge funding from NIH (R01 EB012579 and R01 HL110811).

References

1.        Flynn, J. L. & Chan, J. Immunology of tuberculosis. Annual review of immunology 19, 93–129 (2001).

2.        Cilfone, N. A., Perry, C. R., Kirschner, D. E. & Linderman, J. J. Multi-Scale Modeling Predicts a Balance of Tumor Necrosis Factor-α and Interleukin-10 Controls the Granuloma Environment During Mycobacterium tuberculosis Infection. PloS one (2013) - Accepted.