Dynamics of the Immune Response during human infection with M.tuberculosis

Denise Kirschner, Ph.D.Dept. of Microbiology/ImmunologyUniv. 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 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%

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 166:1951-1976,

2001

Cell-mediatedImmunity:ActivatedMs

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 dataPerform experiments

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

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

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 (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

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-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

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 ofgranuloma 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 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

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 ENTITIESCells

Macrophages in different states: Activated, Resting, Infected and Chronically infected

Effector T cells

CONTINUOUS ENTITIESChemokine Extracellular mycobacteria

Model Framework: lattice with agents and continuous entities

Rules: an exampleResting macrophage phagocytosis

Rules: an exampleMacrophage 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, 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)