A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.

Autor: Wessler T; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America., Joslyn LR; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America., Borish HJ; Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America., Gideon HP; Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America., Flynn JL; Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America., Kirschner DE; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America., Linderman JJ; Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2020 May 20; Vol. 16 (5), pp. e1007280. Date of Electronic Publication: 2020 May 20 (Print Publication: 2020).
DOI: 10.1371/journal.pcbi.1007280
Abstrakt: Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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