A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes

Chang Gong; Jennifer J. Linderman; Denise Kirschner

doi:10.3934/mbe.2015.12.625

Article Contents

2015, Volume 12, Issue 3: 625-642. Doi: 10.3934/mbe.2015.12.625

This issue Previous Article Optimal information dissemination strategy to promote preventive behaviors in multilayer epidemic networks Next Article

A population model capturing dynamics of tuberculosis granulomas predicts host infection outcomes

1.
6775 Medical Science Building II, Ann Arbor, MI 48109-5620
2.
B28-G045W NCRC, Ann Arbor, MI 48109-5620
3.
6730 Medical Science Building II, Ann Arbor, MI 48109-5620

Received: December 31, 2014

Revised: December 31, 2014

Published: January 2015

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Abstract

Granulomas play a centric role in tuberculosis (TB) infection progression. Multiple granulomas usually develop within a single host. These granulomas are not synchronized in size or bacteria load, and will follow different trajectories over time. How the fate of individual granulomas influence overall infection outcome at host scale is not understood, although computational models have been developed to predict single granuloma behavior. Here we present a within-host population model that tracks granulomas in two key organs during Mycobacteria tuberculosis (Mtb) infection: lung and lymph nodes (LN). We capture various time courses of TB progression, including latency and reactivation. The model predicts that there is no steady state; rather it is a continuous process of progressing to active disease over differing time periods. This is consistent with recently posed ideas suggesting that latent TB exists as a spectrum of states and not a single state. The model also predicts a dual role for granuloma development in LNs during Mtb infection: in early phases of infection granulomas suppress infection by providing additional antigens to the site of immune priming; however, this induces a more rapid reactivation at later stages by disrupting immune responses. We identify mechanisms that strongly correlate with better host-level outcomes, including elimination of uncontained lung granulomas by inducing low levels of lung tissue damage and inhibition of bacteria dissemination within the lung.

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Mathematics Subject Classification: Primary: 97M60; Secondary: 92B05.

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