Agent Based Model of in vitro Mycobacterium tuberculosis Granuloma Dynamics


Identification: Hoerter-Alexis


Description

Agent Based Model of in vitro Mycobacterium tuberculosis Granuloma Dynamics
Alexis Hoerter(1), Alexa Petrucciani(1), Israel Guerrero(2), Charles Renshaw(2), Maria Montoya(2), Eusondia Arnett(2), Larry S. Schlesinger(2) and Elsje Pienaar(1)
(1) Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN
(2) Texas Biomedical Research Institute, San Antonio, TX

The hallmark of Mycobacterium tuberculosis (Mtb) infection is the formation of granulomas – dense structures composed of macrophages and lymphocytes that encapsulate infected macrophages and extracellular bacteria. These granulomas are the main site of infection and driver of disease progression, but studying intra-granuloma dynamics of human Mtb infection is challenging. Animal models, non-human primates in particular, have provided detailed insights into the in vivo biology of granulomas.  However, animal models are costly, low-throughput and limited in temporal resolution, all of which limit their application to study early immune responses and drug efficacy. In contrast, in vitro granuloma models allow for more mechanistic and dynamic investigations into granuloma formation. One such in vitro model generates 3D granulomas through infection of human donor PBMCs with Mtb. We have developed an agent-based model that simulates these complex in vitro granulomas to help analyze, interpret and guide Mtb granuloma exploration. Macrophages, CD4+ T cells and Mtb are represented as agents in the computational model that incorporates relevant mechanisms to mimic Mtb infection. These mechanisms include bacterial growth, macrophage phagocytosis resulting in bacterial death or macrophage infection, macrophage and T cell activation, T cell proliferation, and cytokine/chemokine diffusion and degradation. To characterize the balance between host and pathogen, we quantify the influence of activation timing and bacterial load on infection outcomes. We have performed virtual knockouts of the mechanisms that determine macrophage activation, STAT1 and NF-κB, in addition to exploring different multiplicities of infection (MOI). Our results suggest that there is a delay in macrophage activation at low MOIs (1:10; Mtb:macrophages) compared to higher MOIs (1:1 and 1:2) due to a delay in macrophage activation. At a low chemoattractant concentration threshold to activate the NF-κB pathway, the limiting factor to activate macrophages is STAT1. At a higher chemoattractant concentration threshold to activate the NF-κB pathway, the NF-κB pathway becomes the limiting factor to activate macrophages. This delay in activation leads to less bacterial killing at early times points for low MOIs compared to higher MOIs since activated macrophages have a higher probability of killing Mtb. There are also spatial differences in our virtual experiment. More granulomas form at higher MOIs suggesting that some of the observed differences in activation patterns between MOIs could be due to cell motion and/or access to infected macrophages within granuloma structures. Our results suggest that the relative timing and location of STAT1 and NF-κB activation is important in determining the number of granulomas and the growth of bacteria. This integrative model will accelerate the development of technology that can capture the important complexities of TB granulomas while maintaining flexibility and throughput needed to analyze and develop TB infection treatment strategies.

Acknowledgements: This work was funded by a PhRMA Foundation Research Starter Grant to EP and LS.

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