Oxygen Modulates the Effectiveness of Granuloma Mediated Host Response to Mycobacterium tuberculosis: A Multiscale Computational Biology Approach

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.

Modelling the effects of environmental heterogeneity within the lung on the tuberculosis life-cycle

“…Computational models of the within-host dynamics of TB have been used extensively to improve our understanding of how disease progresses within the body, particularly at the scale of a single lesion [16,17,18] and with some models looking at the disease over the whole lung and the associated lymph nodes [34,35,40,41]. However, these models have not included the notions of heterogeneity of environmental conditions within the lung as shown in this paper, which are believed to be critical to the apical localisation of TB during the crucial post-primary stage [11].…”

“…In silico modelling of TB is still in its infancy, and most existing models of the disease have simulated the disease on the scale of a single lesion [16,17,18]. In [19], we presented the first in silico model of TB over the whole lung to incorporate the environmental heterogeneity present within the organ in order to understand how the differentials in factors such as blood perfusion and oxygen tension impact disease.…”

Modelling the effects of bacterial cell state and spatial location on tuberculosis treatment: Insights from a hybrid multiscale cellular automaton model

“…Individual-based models have already been shown to be useful in understanding tuberculosis disease progression (Segovia-Juarez et al, 2004;Marino et al, 2011;Cilfone et al, 2013;Pienaar et al, 2015Pienaar et al, , 2016Sershen et al, 2016). Here we have built a hybrid cellular automaton model that incorporates oxygen dynamics, which allows bacteria to change state, and includes antibiotic treatments.…”

“…In (Pienaar et al, 2016) the authors map metabolite and genescale perturbations , finding that slowly replicating phenotypes of M. tuberculosis preserve the bacterial population in vivo by continuously adapting to dynamic granuloma microenvironments. (Sershen et al, 2016) also combines a physiological model of oxygen dynamics, an agent-based model of cellular immune response and a systems-based model of M.tb metabolic dynamics. Their study suggests that the dynamics of granuloma organisation mediates oxygen availability and illustrates the immunological contribution of this structural host response to infection outcome.…”

Investigating the mechanism of hepatocellular carcinoma progression by constructing genetic and epigenetic networks using NGS data identification and big database mining method

“…The combined network matrix of GEN can be decomposed by singular value decomposition (SVD) as follows [88, 90–94] $H=U\times D\times V^T$…”