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

Wessler, Timothy; Joslyn, Louis R.; Borish, H. Jacob; Gideon, Hannah P.; Flynn, JoAnne L.; Kirschner, Denise E.; Linderman, Jennifer J.

doi:10.1371/journal.pcbi.1007280

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

Journal Article · Wed May 20 00:00:00 EDT 2020 · PLoS Computational Biology (Online)

DOI:https://doi.org/10.1371/journal.pcbi.1007280· OSTI ID:1904045

^[1]; ^[2]; ^[3]; ^[3]; ^[3]; Kirschner, Denise E. ^[2]; Linderman, Jennifer J. ^[2]

Univ. of Michigan, Ann Arbor, MI (United States); OSTI
Univ. of Michigan, Ann Arbor, MI (United States)
Univ. of Pittsburgh, PA (United States). School of Medicine

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.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC)

OSTI ID:: 1904045

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 5 Vol. 16; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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

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