Abstract
If improvements are to be made in tuberculosis (TB) treatment, an increased understanding of disease in the lung is needed. Studies have shown that bacteria in a less metabolically active state, associated with the presence of lipid bodies, are less susceptible to antibiotics, and recent results have highlighted the disparity in concentration of different compounds into lesions. Treatment success therefore depends critically on the responses of the individual bacteria that constitute the infection.We propose a hybrid, individual-based approach that analyses spatio-temporal dynamics at the cellular level, linking the behaviour of individual bacteria and host cells with the macroscopic behaviour of the microenvironment. The individual elements (bacteria, macrophages and T cells) are modelled using cellular automaton (CA) rules, and the evolution of oxygen, drugs and chemokine dynamics are incorporated in order to study the effects of the microenvironment in the pathological lesion. We allow bacteria to switch states depending on oxygen concentration, which affects how they respond to treatment. This is the first multiscale model of its type to consider both oxygen-driven phenotypic switching of the Mycobacterium tuberculosis and antibiotic treatment. Using this model, we investigate the role of bacterial cell state and of initial bacterial location on treatment outcome. We demonstrate that when bacteria are located further away from blood vessels, less favourable outcomes are more likely, i.e. longer time before infection is contained/cleared, treatment failure or later relapse. We also show that in cases where bacteria remain at the end of simulations, the organisms tend to be slower-growing and are often located within granulomas, surrounded by caseous material.
Highlights
Tuberculosis (TB) has long been both preventable and curable, a person dies from tuberculosis approximately every eighteen seconds (WHO Global Health Report 2011)
In order to study the relative importance of bacterial cell state and initial spatial location of bacteria, we study two scenarios: one with a fixed, uniform blood vessel distribution and initial bacterial locations (see four examples in Fig. 7(a)), and another where the vessel distribution and the initial locations of the extracellular bacteria are determined randomly for each simulation (see examples in Figs. 9–11 (a))
In addition to focusing on bacterial cell state, we investigate changes in spatial location of the bacteria and their influences on disease outcome
Summary
Tuberculosis (TB) has long been both preventable and curable, a person dies from tuberculosis approximately every eighteen seconds (WHO Global Health Report 2011). Oxygen concentration was one of the first mechanisms demonstrated in an in vitro model to result in dormancy, and in vitro models have been developed to explore the antibiotic susceptibility and metabolism of organisms in this slower-growing state (Wayne and Sramek, 1994) It has been hypothesised by multiple authors that lesions containing slower-growing bacteria are responsible for relapse (Grosset, 1980; Prideaux et al, 2015). Our model is the first multiscale model to consider both oxygen dynamics and antibiotic treatment effects within a tuberculosis lesion, in order to investigate the role of bacterial cell state heterogeneity and bacterial position within the tuberculosis lesion on the outcome of disease. This is a unique focus for this type of model
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