Mathematical Model of the Immunopathological Progression of Tuberculosis

Abstract

Tuberculosis is a worldwide persistent infectious disease. It is caused by bacteria from the Mycobacterium tuberculosis complex that mainly affects the lungs and can be fatal. Using an integrative systems biology approach, we study the immunopathological progression of this disease, analyzing the key interactions between the cells involved in the different phases of the infectious process. We integrated multiple in vivo and in vitro data from immunohistochemical, serological, molecular biology, and cell count assays into a mechanistic mathematical model. The ordinary differential equation (ODE) model captures the regulatory interplay between the phenotypic variation of the main cells involved in the disease progression and the inflammatory microenvironment. The model reproduces in vivo time course data of an experimental model of progressive pulmonary TB in mouse, accurately reflecting the functional adaptations of the host–pathogen interactions as the disease progresses through three phenotypically different phases. We used the model to assess the effect of genotypic variations (encoded as changes in parameters) on disease outcomes. For all genotypes, we found an all-or-nothing response, where the virtual mouse either completely clears the infection or suffers uncontrolled Tb growth. Results show that it is 84% probable that a mouse submitted to a progressive pulmonary TB assay will end up with an uncontrolled infection. The simulations also showed how the genotypic variations shape the transitions across phases, showing that 100% of the genotypes evaluated eventually progress to phase two of the disease, suggesting that adaptive immune response activation was unavoidable. All the genotypes of the network that avoided progressing to phase 3 cleared the infection. Later, by analyzing the three different phases separately, we saw that the anti-inflammatory genotype of phase 3 was the one with the highest probability of leading to uncontrolled bacterial growth, and the proinflammatory genotype associated with phase 2 had the highest probability of bacterial clearance. Forty-two percent of the genotypes evaluated showed a bistable response, with one stable steady state corresponding to infection clearance and the other one to bacteria reaching its carrying capacity. Our mechanistic model can be used to predict the outcomes of different experimental conditions through in silico assays.