Metabolic Interaction in Infection by Ricardo Silvestre & Egídio Torrado

Author:Ricardo Silvestre & Egídio Torrado
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

6.2.1.2 Mycobacterium Genus

Mycobacterium tuberculosis is a member of the genus Mycobacterium causing one of the most devastating diseases, tuberculosis. It is associated with more than a million deaths worldwide each year owing to the growing antibiotic resistance crisis (Gengenbacher and Kaufmann 2012). The infection process of M. tuberculosis begins with the inhalation of contaminated aerosols. Then, the bacteria encounter alveolar macrophages in the lower respiratory tract. Once recognized, the pathogens are engulfed by the macrophages, but vitality of the pathogens is still maintained. Upon the activation of innate immune system, various chemokines and cytokines are produced, and other immune cells (e.g., dendritic cells, neutrophils, and lymphocytes) as well as more macrophages are recruited to the sites of infection to support removal of the pathogens (Silva Miranda et al. 2012; Ehlers and Schaible 2013). This cascade of intracellular events induces the formation of a collection of the recruited immune cells known as granuloma (i.e., organized aggregates of multinucleated giant cells, epithelioid cells, T cells, infected and uninfected macrophages) to cope with the bacteria (Silva Miranda et al. 2012; Pienaar et al. 2016). However, M. tuberculosis can exploit even granuloma as a safety shelter against other immune cells (Silva Miranda et al. 2012). Therefore, a deep understanding of the molecular interactions between the pathogen and its host is crucial to shed light on its infection mechanism.

To provide more insight into the interactions between M. tuberculosis and its host (i.e., human alveolar macrophage), an integrated metabolic network model including 2071 genes and 4489 reactions was developed. Besides, gene expression data from distinct M. tuberculosis infectious states (latent, pulmonary, and meningeal) were mapped onto the integrated model, and the differential metabolism of human alveolar macrophages based on the infection types was highlighted (Bordbar et al. 2010). Elucidation of regulatory relationships among transcription factors (TFs), metabolic target genes, and environmental conditions can open new avenues for the identification of novel drug targets. In this context, Ma and colleagues (Ma et al. 2015) constituted an improved model for M. tuberculosis to examine the effects of 104 TF knockout and upregulation events on the bacterial growth by using the Probabilistic Regulation of Metabolism (PROM) framework (Chandrasekaran and Price 2010; Simeonidis et al. 2013). It was revealed that this expanded model has a stronger ability to predict the metabolic consequences of the TF perturbations in comparison with the original M. tuberculosis model. More recently, Pienaar et al. investigated M. tuberculosis metabolism within granulomas to identify new drug targets (Pienaar et al. 2016). They developed a novel multi-scale computational model (GranSim-CBM) combining an agent-based model of granuloma generation and a dynamic constraint-based model of the pathogen metabolism and growth. The combined model reflects comprehensive rules and biological background of the host immune system, hostile environment, and M. tuberculosis metabolism within the granuloma (e.g., M. tuberculosis adaptation to hypoxia, lipid inclusion formation, nutrient limitation, oxygen levels, and so on). The GranSim-CBM represents dynamic profiles of M. tuberculosis residing within the granuloma, covering the abilities of the bacteria for actively dividing and responding to the changes in the dynamic granuloma microenvironments (e.

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