Dynamics of the Immune Response during human infection with M.tuberculosis Denise Kirschner, Ph.D. Dept. of Microbiology/Immunology Univ. of Michigan Medical School

Outline of Presentation Introduction to TB immunobiology Modeling the host-pathogen interaction Experimental Method- temporal model Results: dynamics of infection depletion/deletion experiments Spatio-temporal models granuloma formation

Mycobacterium tuberculosis 1/3 of the world infected 3 million+ die each year no clear understanding of distinction between different disease trajectories: Exposure No infection Infection Latent disease Reactivation Acute disease 70% 30% 95% 5% 5-10%

HUMAN GRANULOMA- snap shot

Cell mediated immunity in M. tuberculosis infection What elements of the host-mycobacterial dynamical system contribute to different disease outcomes once exposed? Hypothesis: components of the cell mediated immune response determine either latency or active disease (primary or reactivation) Wigginton and Kirschner J Immunology 166: , 2001

Cell- mediated Immunity: Activated M  s Humoral- mediated immunity

Complex interactions between cytokines and T cells : black=production, green=upregulation, red=downregulation

Experimental Approach Build a virtual model of human TB describing temporal changes in broncoalveolar lavage fluid (BAL) to predict mechanisms underlying different disease outcomes Use model to ask questions about the system

Methodology for TB Model Describe separate cellular and cytokine interactions Translate into mathematical expressions nonlinear ordinary differential equations Estimate rates of interactions from data (parameter estimation) Simulate model and validate with data Perform experiments

Variables tracked in our model: Macrophages: resting, activated, chronically infected T cells: Th0, Th1, Th2 Cytokines: IFN-  IL-4, IL-10, IL-12 Bacteria: both extracellular and intracellular Define 4 submodels

Parameter Estimation: inclusion of experimental data Estimated from literature giving weight to humans or human cells and to M. tuberculosis over other mycobacteria species Units are cells/ml or pg/ml of BAL Sensitivity and Uncertainty analyses can be performed to test these values or estimate values for unknown parameters

Example: estimating growth rate of M. tuberculosis in vitro estimates for doubling times of H37Rv lab strain within macrophages ranged from 28 hours to 96 hours In mouse lung tissue, H37Rv estimated to have a doubling time of 63.2 hours We can estimate the growth rates of intracellular vs. extracellular growth rates from these values (rate=ln2/doub. time )

Model Outcomes: Virtual infection within humans over 500 days No infection - resting macrophages are at their average value in lung (3x10 5 /ml) (negative control) Clearance - a small amount of bacteria are introduced and infection is cleared (PPD-) latent TB (a few macrophages harbor all - may miss them in biopsy) Active, primary TB

What determines these different outcomes? Detailed Uncertainty and Sensitivity Analyses on all parameters in the system

Varying T cell killing of infected macrophages Total T cells Total bacteria

Parameters leading to different disease outcomes Production of IL-4 Rates of macrophage activation and infection Rate t cells lyse infected macrophages Rate extracellular bacteria are killed by activated macrophages Production of IFN-  from NK and CD8 cells

Virtual Deletion and Depletion Experiments: Deletion: mimic knockout (disruption) experiments where the element is removed from the system at day 0. D Depletion: mimic depletion of an element by setting it to zero after latency is achieved.

Summary of Deletion Experiments : IFN-  : Active disease within 100 days IL-12: Active disease within 100 days IL-10: oscillations around latent state – thus it is needed to maintain stability of latent state

Depletion Experiments IFN-  : progress to active disease within 500 days IL-12: still able to maintain latency; much higher bacterial load IL-10:

IL-10 Depletion

Present Work- cellular level Include in the temporal BAL model: CD8+ T cells and TNF-  D. Sud) Develop a spatio-temporal model of infection ** Granuloma Formation and Function 3 approaches Role of Dendritic cells in priming of T cells  compartment model: lymph nodes + lung (Dr. S. Marino)

Present Work: intracellular level Temporal specificity by M. tuberculosis inhibiting antigen presentation in macrophages (S. Chang) The balance of activation, killing and iron homeostasis in determining M. tuberculosis survival within a macrophage (J. Christian Ray)

Spatio-temporal models of granuloma formation Metapopulation Model (Drs. S. Ganguli & D. Gammack) Agent based model (Drs. J. S-Juarez & S. Ganguli) PDE model (Dr. D. Gammack)

Metapopulation Modeling

Discrete Spatial Model of Granuloma Development Partition space: nxn lattice of compartments Model diffusion between compartments movement based on local differences (gradient) Probabilistic movement Model interactions within compartments Existing temporal model n 2 Systems of ODEs

Modeling diffusion Example: Chemokine C diffuses out from a source C

Modeling diffusion Example: Chemokine C diffuses out from a source Diffusion of macrophages M is biased towards higher concentrations of C C M

Model: series of ODE systems Generate ODEs for C, M, … within each compartment: terms for source, decay, diffusion, etc. Solve ODE system over short time interval Generate new diffusion patterns based on updated values; generate new ODEs Iterate…

Discrete spatial model: simulations

Agent Based Modeling

Model Agents DISCRETE ENTITIES Cells Macrophages in different states: Activated, Resting, Infected and Chronically infected Effector T cells CONTINUOUS ENTITIES Chemokine Extracellular mycobacteria

Model Framework: lattice with agents and continuous entities

Rules: an example Resting macrophage phagocytosis

Rules: an example Macrophage activation by T cells

Granuloma formation- solid Resting macrophages Infected macrophages Chronically infected m. Activated macrophage Bacteria T cells Necrosis 2x2 mm sq.

Granuloma formation-necrotic Resting macrophages Infected macrophages Chronically infected m. Activated macrophage Bacteria T cells Necrosis

Acknowledgments Kirschner Group past &present Jose S.-Juarez, PhD David Gammack, PhD Simeone Marino, PhD Suman Ganguli, PhD Ping Ye, PhD Seema Bajaria, MS Ian Joseph Christian Ray Stewart Chang Dhruv Sud Joe Waliga NIH and The Whitaker Foundation Collaborators: JoAnne Flynn (Pitt) John Chan (Albert Einstein)