Abstract
Mycobacterium tuberculosis associated granuloma formation can be viewed as a structural immune response that can contain and halt the spread of the pathogen. In several mammalian hosts, including non-human primates, Mtb granulomas are often hypoxic, although this has not been observed in wild type murine infection models. While a presumed consequence, the structural contribution of the granuloma to oxygen limitation and the concomitant impact on Mtb metabolic viability and persistence remains to be fully explored. We develop a multiscale computational model to test to what extent in vivo Mtb granulomas become hypoxic, and investigate the effects of hypoxia on host immune response efficacy and mycobacterial persistence. Our study integrates a physiological model of oxygen dynamics in the extracellular space of alveolar tissue, an agent-based model of cellular immune response, and a systems biology-based model of Mtb metabolic dynamics. Our theoretical studies suggest that the dynamics of granuloma organization mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome. Furthermore, our integrated model demonstrates the link between structural immune response and mechanistic drivers influencing Mtbs adaptation to its changing microenvironment and the qualitative infection outcome scenarios of clearance, containment, dissemination, and a newly observed theoretical outcome of transient containment. We observed hypoxic regions in the containment granuloma similar in size to granulomas found in mammalian in vivo models of Mtb infection. In the case of the containment outcome, our model uniquely demonstrates that immune response mediated hypoxic conditions help foster the shift down of bacteria through two stages of adaptation similar to thein vitro non-replicating persistence (NRP) observed in the Wayne model of Mtb dormancy. The adaptation in part contributes to the ability of Mtb to remain dormant for years after initial infection.
Highlights
Tuberculosis (TB) disease, caused by the bacilli Mycobacterium tuberculosis (Mtb), remains a major global health concern, with an estimated 8.6 million infected globally and 1.2 million Mtb related deaths in 2012 (World Health Organization, 2013)
Using our agent based models (ABM)-PHYS model of TB disease, we explored the correlation between host immune response, physiological response with respect to oxygen, and outcome of infection
While the model architecture determined the general interaction between model components, we used uncertainty quantification methods to explore the parameter space and discover emergent system properties that correspond to bacterial clearance, containment/latency, or dissemination
Summary
Tuberculosis (TB) disease, caused by the bacilli Mycobacterium tuberculosis (Mtb), remains a major global health concern, with an estimated 8.6 million infected globally and 1.2 million Mtb related deaths in 2012 (World Health Organization, 2013). An initial innate immune response ensues followed by presentation of Mtb antigens by professional antigen presenting cells (e.g., macrophage and dendritic cells) to lymphocytes, leading to cellmediated immune response. Immune response to Mtb infection is characterized by sequential recruitment of leukocytes such as T, B, and NK cells as well as uninfected macrophages to the site of infection (Co, 2004). In the event that immune cells are unable to eliminate the infection (clearance scenario), these cells attempt to contain the spread of infection by aggregating in multiple layers around the infected host cell leading to the formation of granulomatous structures (containment scenario). That the host fails to clear or contain the pathogen, Mtb can spread, infecting other cells, tissues, and organs (dissemination scenario)
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