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Premium member Presentation Transcript Dendritic Cells Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis : Dendritic Cells Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis Simeone Marino Department of Microbiology and Immunology University of Michigan Medical School Ann Arbor, Michigan USA MBI, June 21-25 2004 OUTLINE : OUTLINE Introduction Mycobacterium tuberculosis (Mtb) pathogenesis Role of Dendritic Cell Materials and methods 2-compartmental mathematical model (main biological assumptions) Experiments and Results Tuberculosis in humans : Tuberculosis in humans A key issue is to understand why individuals infected with M. tuberculosis experience different clinical outcomes INTRACELLULAR pathogen (facultative extracellular) GOALS : GOALS Reproduce typical disease progressions by means of a mathematical model Manipulate the system in order to ask questions about interactions and rates within the system Reproduce and compare both virtual deletion and depletion experiments with known experimental results Perform new experiments that may be difficult or presently impossible to perform in the wet lab (DC deletion and depletion, Th1 depletion,…) Address controversial issues in TB (Th1/Th2,…) Dynamics of the Immune Response : Dynamics of the Immune Response M.tuberculosis–Host interactionHuman peripheral monocyte-derived cells (Ms and DCs) infected with M.tb : M.tuberculosis–Host interactionHuman peripheral monocyte-derived cells (Ms and DCs) infected with M.tb DCs + M.tb Strong up-regulation of adhesion and costimulatory molecules (MHC II, ICAM-1, B7, CD40, CD83) Production of Th1-inducing cytokine (IL-12) Ms + M.tb Down-regulation of MHC II Production of proinflammatory and immunosuppressive CKs (TNF- and IL-10) Previous model: LUNG compartment model : Previous model: LUNG compartment model Cell population Macrophages (Resting, Infected, Activated) Lymphocytes (Th0 or Precursor Th, Th1, Th2) Bacteria (extra and intracellular) Cytokines IL-12, IFN- (Type I) IL-10 and IL-4 (Type II) Wigginton, J. E., and D. Kirschner. 2001. A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. J Immunol 166:1951 New 2-compartmental model: LUNG + LN : New 2-compartmental model: LUNG + LN Compartments Lung (L) and draining Lymph Node (LN) New variables Dendritic Cells IDC (immature, Lung) MDC (mature, LN) Lymphocytes (in the LN) ThP (Precursor Helper Ts) T (Naïve T cells) Simeone Marino and Denise E. Kirschner. The Human Immune Response to Mycobacterium tuberculosis in Lung and Lymph Node. J Theor Biol. 2004 Apr 21;227(4):463-86. Simeone Marino, Pawar S., Fuller CL, Reinhart TA, Flynn JL and Kirschner DE, Dendritic Cell Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis. J. Immunol. 2004 in press Slide 9: Comparison Values for kinetics and interactions : Values for kinetics and interactions Literature (human, mouse and NHP data): very few data on Lymph Node and DCs in human TB NHP experiments and mathematical estimation From a single controlled in vitro experiment (reductionism) to build the intact system in vivo (reconstructionism) Mathematical modeling: nonlinear differential equations system (17 equations, 77 parameters) Methodology Markers of M. tuberculosis disease progressions in humans : Markers of M. tuberculosis disease progressions in humans No good markers of TB progression in humans presently exist CFUs (in the whole lung) greater than 108 translates to death (in mouse). Whether an equivalent threshold exists for humans is unclear CFUs in tissues correlates with disease status in monkeys By inference, we consider the bacterial load as the most informative marker of disease progression BACTERIA : BACTERIA POSITIVE CONTROLS NHP EXPERIMENTS vs VIRTUAL MODEL SIMULATIONS : POSITIVE CONTROLS NHP EXPERIMENTS vs VIRTUAL MODEL SIMULATIONS CFUs per gram of tissue LYMPHOCYTES POPULATIONS : LYMPHOCYTES POPULATIONS LATENCY ACTIVE TB DELETION/DEPLETION EXPERIMENTS : DELETION/DEPLETION EXPERIMENTS DC DELETION : DC DELETION DC DEPLETION : DC DEPLETION PARTIAL DC DEPLETION : PARTIAL DC DEPLETION ‘VIRTUAL’ REACTIVATION : ‘VIRTUAL’ REACTIVATION Identifying key system interactions and kineticsStratified Monte Carlo method (Latin Hypercube Sampling - LHS)+generalized correlation coefficient (Partial Rank Corr. Coeff. - PRCC) : Identifying key system interactions and kineticsStratified Monte Carlo method (Latin Hypercube Sampling - LHS)+generalized correlation coefficient (Partial Rank Corr. Coeff. - PRCC) PRESENTATION/ACTIVATION/DIFFERENTIATION (8) - interaction between Mature DCs and Naïve T cells in the LN TRAFFICKING (3) - rate of Immature DC migration into the LN, % of ThP migrating out of the LN INFECTIVITY (2); - infection rate of lung macrophages, bacterial growth rate UPTAKE and KILLING (4) - Immature DC and resident macrophage killing of M. tuberculosis Host-pathogen processes governed by these parameters are likely targets for further basic science study Open questions : Open questions Th1/Th2 controversy Th1/Th2 in TB? Other roles for CD4+ T cells (not only macrophage activation)? “meaningfullness” of measurements (parameter values) Definition of a granuloma Static or dynamic entity? DYNAMIC (high turnover) Granuloma reaction: “innate” mechanism of immune response (IR) to pathogens? Does the bug want/need/facilitate granuloma formation/reaction? Is a granuloma a “damage” for the host? (Arturo) Innate vs Adaptive response to TB Do early events bias TB infection progression? Does the IR compensate “failures” in innate immunity? Role of Dendritic Cell Treatment or Vaccine? Conclusions : Conclusions DCs have a key role (establish protective immunity and contain Mtb infection) via Ag presentation and trafficking Delays in either DC migration to the DLN or T-cell trafficking to the site of infection can alter the outcome of Mtb infection Th1/Th2 paradigme might not be the right way to discriminate between latency and active disease in TB Development of a new generation of treatments: fast DC turnover strong DC activation (max maturation/migration/presentation) Acknowledgments : Acknowledgments Kirschner Lab Past & Present Seema Bajaria, MS Stewart Chang David Gammack, PhD Suman Ganguli, PhD Jose L. Segovia, PhD Ping Ye, MS Ian Joseph Christian Ray Dhruv Sud Denise Kirschner Lab (University of Michigan Medical School) in collaboration with Dr. JoAnne L. Flynn (Department of Molecular Genetics and Biochemistry, University of Pittsburgh School of Medicine) Dr. Todd L. Reinhart Department of Infectious Diseases & Microbiology, University of Pittsburgh Graduate School of Public Health DC equations (IDC and MDC) : DC equations (IDC and MDC) Lymphocyte equations (Lymph Node) : Lymphocyte equations (Lymph Node) Macrophages (Lung) : Macrophages (Lung) Cytokines (Lung) : Cytokines (Lung) T helper cells (Lung) : T helper cells (Lung) Bacteria (Lung) : Bacteria (Lung) You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
immunology of tb aSGuest77592 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 624 Category: Education License: All Rights Reserved Like it (2) Dislike it (0) Added: December 04, 2010 This Presentation is Public Favorites: 0 Presentation Description ppt Comments Posting comment... Premium member Presentation Transcript Dendritic Cells Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis : Dendritic Cells Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis Simeone Marino Department of Microbiology and Immunology University of Michigan Medical School Ann Arbor, Michigan USA MBI, June 21-25 2004 OUTLINE : OUTLINE Introduction Mycobacterium tuberculosis (Mtb) pathogenesis Role of Dendritic Cell Materials and methods 2-compartmental mathematical model (main biological assumptions) Experiments and Results Tuberculosis in humans : Tuberculosis in humans A key issue is to understand why individuals infected with M. tuberculosis experience different clinical outcomes INTRACELLULAR pathogen (facultative extracellular) GOALS : GOALS Reproduce typical disease progressions by means of a mathematical model Manipulate the system in order to ask questions about interactions and rates within the system Reproduce and compare both virtual deletion and depletion experiments with known experimental results Perform new experiments that may be difficult or presently impossible to perform in the wet lab (DC deletion and depletion, Th1 depletion,…) Address controversial issues in TB (Th1/Th2,…) Dynamics of the Immune Response : Dynamics of the Immune Response M.tuberculosis–Host interactionHuman peripheral monocyte-derived cells (Ms and DCs) infected with M.tb : M.tuberculosis–Host interactionHuman peripheral monocyte-derived cells (Ms and DCs) infected with M.tb DCs + M.tb Strong up-regulation of adhesion and costimulatory molecules (MHC II, ICAM-1, B7, CD40, CD83) Production of Th1-inducing cytokine (IL-12) Ms + M.tb Down-regulation of MHC II Production of proinflammatory and immunosuppressive CKs (TNF- and IL-10) Previous model: LUNG compartment model : Previous model: LUNG compartment model Cell population Macrophages (Resting, Infected, Activated) Lymphocytes (Th0 or Precursor Th, Th1, Th2) Bacteria (extra and intracellular) Cytokines IL-12, IFN- (Type I) IL-10 and IL-4 (Type II) Wigginton, J. E., and D. Kirschner. 2001. A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. J Immunol 166:1951 New 2-compartmental model: LUNG + LN : New 2-compartmental model: LUNG + LN Compartments Lung (L) and draining Lymph Node (LN) New variables Dendritic Cells IDC (immature, Lung) MDC (mature, LN) Lymphocytes (in the LN) ThP (Precursor Helper Ts) T (Naïve T cells) Simeone Marino and Denise E. Kirschner. The Human Immune Response to Mycobacterium tuberculosis in Lung and Lymph Node. J Theor Biol. 2004 Apr 21;227(4):463-86. Simeone Marino, Pawar S., Fuller CL, Reinhart TA, Flynn JL and Kirschner DE, Dendritic Cell Trafficking and Antigen Presentation in the Human Immune Response to Mycobacterium tuberculosis. J. Immunol. 2004 in press Slide 9: Comparison Values for kinetics and interactions : Values for kinetics and interactions Literature (human, mouse and NHP data): very few data on Lymph Node and DCs in human TB NHP experiments and mathematical estimation From a single controlled in vitro experiment (reductionism) to build the intact system in vivo (reconstructionism) Mathematical modeling: nonlinear differential equations system (17 equations, 77 parameters) Methodology Markers of M. tuberculosis disease progressions in humans : Markers of M. tuberculosis disease progressions in humans No good markers of TB progression in humans presently exist CFUs (in the whole lung) greater than 108 translates to death (in mouse). Whether an equivalent threshold exists for humans is unclear CFUs in tissues correlates with disease status in monkeys By inference, we consider the bacterial load as the most informative marker of disease progression BACTERIA : BACTERIA POSITIVE CONTROLS NHP EXPERIMENTS vs VIRTUAL MODEL SIMULATIONS : POSITIVE CONTROLS NHP EXPERIMENTS vs VIRTUAL MODEL SIMULATIONS CFUs per gram of tissue LYMPHOCYTES POPULATIONS : LYMPHOCYTES POPULATIONS LATENCY ACTIVE TB DELETION/DEPLETION EXPERIMENTS : DELETION/DEPLETION EXPERIMENTS DC DELETION : DC DELETION DC DEPLETION : DC DEPLETION PARTIAL DC DEPLETION : PARTIAL DC DEPLETION ‘VIRTUAL’ REACTIVATION : ‘VIRTUAL’ REACTIVATION Identifying key system interactions and kineticsStratified Monte Carlo method (Latin Hypercube Sampling - LHS)+generalized correlation coefficient (Partial Rank Corr. Coeff. - PRCC) : Identifying key system interactions and kineticsStratified Monte Carlo method (Latin Hypercube Sampling - LHS)+generalized correlation coefficient (Partial Rank Corr. Coeff. - PRCC) PRESENTATION/ACTIVATION/DIFFERENTIATION (8) - interaction between Mature DCs and Naïve T cells in the LN TRAFFICKING (3) - rate of Immature DC migration into the LN, % of ThP migrating out of the LN INFECTIVITY (2); - infection rate of lung macrophages, bacterial growth rate UPTAKE and KILLING (4) - Immature DC and resident macrophage killing of M. tuberculosis Host-pathogen processes governed by these parameters are likely targets for further basic science study Open questions : Open questions Th1/Th2 controversy Th1/Th2 in TB? Other roles for CD4+ T cells (not only macrophage activation)? “meaningfullness” of measurements (parameter values) Definition of a granuloma Static or dynamic entity? DYNAMIC (high turnover) Granuloma reaction: “innate” mechanism of immune response (IR) to pathogens? Does the bug want/need/facilitate granuloma formation/reaction? Is a granuloma a “damage” for the host? (Arturo) Innate vs Adaptive response to TB Do early events bias TB infection progression? Does the IR compensate “failures” in innate immunity? Role of Dendritic Cell Treatment or Vaccine? Conclusions : Conclusions DCs have a key role (establish protective immunity and contain Mtb infection) via Ag presentation and trafficking Delays in either DC migration to the DLN or T-cell trafficking to the site of infection can alter the outcome of Mtb infection Th1/Th2 paradigme might not be the right way to discriminate between latency and active disease in TB Development of a new generation of treatments: fast DC turnover strong DC activation (max maturation/migration/presentation) Acknowledgments : Acknowledgments Kirschner Lab Past & Present Seema Bajaria, MS Stewart Chang David Gammack, PhD Suman Ganguli, PhD Jose L. Segovia, PhD Ping Ye, MS Ian Joseph Christian Ray Dhruv Sud Denise Kirschner Lab (University of Michigan Medical School) in collaboration with Dr. JoAnne L. Flynn (Department of Molecular Genetics and Biochemistry, University of Pittsburgh School of Medicine) Dr. Todd L. Reinhart Department of Infectious Diseases & Microbiology, University of Pittsburgh Graduate School of Public Health DC equations (IDC and MDC) : DC equations (IDC and MDC) Lymphocyte equations (Lymph Node) : Lymphocyte equations (Lymph Node) Macrophages (Lung) : Macrophages (Lung) Cytokines (Lung) : Cytokines (Lung) T helper cells (Lung) : T helper cells (Lung) Bacteria (Lung) : Bacteria (Lung)