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Dynamics of the Immune Response during human infection with M.tuberculosis
Denise Kirschner, Ph.D.Dept. of Microbiology/ImmunologyUniv. of Michigan Medical School
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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
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Mycobacterium tuberculosis
1/3 of the world infected3 million+ die each yearno clear understanding of distinction
between different disease trajectories:
Exposure
No infection
Infection Latent disease
Reactivation
Acute disease
70%
30%
95%
5%
5-10%
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HUMAN GRANULOMA- snap shot
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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 166:1951-1976,
2001
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Cell-mediatedImmunity:ActivatedMs
Humoral-mediated immunity
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Complex interactions between cytokines and T cells: black=production, green=upregulation, red=downregulation
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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
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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 dataPerform experiments
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Variables tracked in our model:
Macrophages: resting, activated, chronically infected
T cells: Th0, Th1, Th2Cytokines: IFN-IL-4, IL-10, IL-12Bacteria: both extracellular and
intracellular Define 4 submodels
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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 BALSensitivity and Uncertainty analyses
can be performed to test these values or estimate values for unknown parameters
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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 )
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Model Outcomes: Virtual infection within humans over 500 days
No infection - resting macrophages are at their average value in lung (3x105/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
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What determines these different outcomes?
Detailed Uncertainty and Sensitivity Analyses on all parameters in the system
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Varying T cell killing of infected macrophages
Total T cells
Total bacteria
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Parameters leading to different disease outcomes
Production of IL-4Rates 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
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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.
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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
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Depletion Experiments
IFN-: progress to active disease within 500 days
IL-12: still able to maintain latency; much higher bacterial load
IL-10:
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IL-10 Depletion
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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)
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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)
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Spatio-temporal models ofgranuloma formation
Metapopulation Model (Drs. S. Ganguli & D. Gammack)
Agent based model (Drs. J. S-Juarez & S. Ganguli)
PDE model (Dr. D. Gammack)
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Metapopulation Modeling
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Discrete Spatial Modelof 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
n2 Systems of ODEs
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Modeling diffusion
Example:Chemokine C
diffuses out from a source
C
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Modeling diffusion
Example: Chemokine C
diffuses out from a source
Diffusion of macrophages M is biased towards higher concentrations of C
C
M
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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…
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Discrete spatial model:simulations
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Agent Based Modeling
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Model Agents
DISCRETE ENTITIESCells
Macrophages in different states: Activated, Resting, Infected and Chronically infected
Effector T cells
CONTINUOUS ENTITIESChemokine Extracellular mycobacteria
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Model Framework: lattice with agents and continuous entities
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Rules: an exampleResting macrophage phagocytosis
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Rules: an exampleMacrophage activation by T cells
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Granuloma formation- solid
Resting macrophages
Infected macrophages
Chronically infected m.
Activated macrophage
Bacteria
T cells
Necrosis
2x2 mm sq.
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Granuloma formation-necrotic
Resting macrophages
Infected macrophages
Chronically infected m.
Activated macrophage
Bacteria
T cells
Necrosis
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Acknowledgments Kirschner Group past &present
Jose S.-Juarez, PhD David Gammack, PhDSimeone Marino, PhDSuman Ganguli, PhDPing Ye, PhDSeema Bajaria, MSIan JosephChristian RayStewart ChangDhruv SudJoe Waliga NIH and The Whitaker Foundation
Collaborators: JoAnne Flynn (Pitt) John Chan (Albert Einstein)