Computational Modeling of Granuloma Formation in Tuberculosis Yields Insights into both Infection and Treatment

Abstract

Tuberculosis (TB), a disease caused by Mycobacterium tuberculosis (Mtb), results in 1-2 million deaths/year. Disease control is hampered by our limited understanding of the relevant biology and by development of antibiotic resistance. Granulomas, organized collections of immune cells and bacteria that form in lungs, are central features of TB and serve as sites of host-pathogen interaction. Cytokines influence the behavior of immune cells, directing granuloma function and maintenance. Granuloma structure helps determine bacterial phenotypes, antibiotic distribution and efficacy. There is a critical need for an in silico platform to provide cost-effective means of understanding the immune response during Mtb infection and testing and optimizing new treatment strategies.We developed a multi-scale computational model of the immune response to Mtb. Molecular, cellular and tissue behavior over minutes to years can be computed, validated with in vitro and in vivo data, and used to understand and predict system behavior. The model incorporates tuneable resolution, allowing us to vary the aspect and level of detail for virtual experiments. We present two examples of model use. First, we study how concentrations of a pro-inflammatory cytokine, tumor necrosis factor-α, and an anti-inflammatory cytokine, interleukin-10, control granuloma formation and function. We find that a balance of concentrations defines a granuloma environment that may benefit both host and pathogen. Second, we explore the role of granuloma structure in antibiotic distribution and action. Antibiotic concentration gradients form within granulomas and could contribute to development of resistance. We compare dosing regimens of two first-line antibiotics, isoniazid and rifampicin, and demonstrate that intermittent high doses are less effective than daily low dose regimens. Our computational approach represents a critical step towards understanding the complex phenomena involved in Mtb infection and developing successful treatment strategies.