title: Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions.
abstract: Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo, we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ∼66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA.|Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA.
authors.forename: Elsje
authors.initials: E
authors.lastname: Pienaar
authors.affiliation: Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA.
authors.forename: William M
authors.initials: WM
authors.lastname: Matern
authors.affiliation: Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Linderman
authors.affiliation: Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA joel.bader@jhu.edu kirschne@umich.edu.
authors.forename: Joel S
authors.initials: JS
authors.lastname: Bader
authors.affiliation: Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA joel.bader@jhu.edu kirschne@umich.edu.
authors.forename: Denise E
authors.initials: DE
authors.lastname: Kirschner
mesh_terms: D000222:Adaptation, Physiological; D000818:Animals; D002244:Carbon / Q000378:metabolism; D003198:Computer Simulation; D004195:Disease Models, Animal; D006099:Granuloma / Q000382:microbiology* / Q000473:pathology*; D050356:Lipid Metabolism; D053858:Metabolic Networks and Pathways / Q000235:genetics; D050296:Microbial Viability; D009169:Mycobacterium tuberculosis / Q000254:growth & development / Q000276:immunology* / Q000378:metabolism / Q000502:physiology*; D011323:Primates; D011817:Rabbits; D014376:Tuberculosis / Q000382:microbiology / Q000473:pathology*
chemical_list: D002244:Carbon