Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Psychoneuroimmunology wikipedia , lookup
Lymphopoiesis wikipedia , lookup
Cancer immunotherapy wikipedia , lookup
Adaptive immune system wikipedia , lookup
Innate immune system wikipedia , lookup
Molecular mimicry wikipedia , lookup
Immunosuppressive drug wikipedia , lookup
Modeling homeostatic T cells responses Benedict Seddon MRC National Institute for Medical Research London, U.K. T cell homeostasis : keeping the immune system the right size Activated/Effector T cells Naïve T cell pool Thymus Ag INPUT Resting memory Resource competition Death The homeostatic T cell response to lymphopenia Activated/Effector T cells Naïve T cell pool Thymus Ag INPUT Memory Lymphopenia • Infection, stress, HIV • Thymectomy/old age • Clinical intervention (chemo and radio therapy) Different signals for different subsets Naïve T cells Cytokines + TCR Memory T cells Cytokines CD4 (s)pMHC spMHC IL-7 dc dc Stromal cell CD8 IL-7 IL-15 IL-7 - survival IL-15- proliferation More division but more death also Measuring homeostatic T cell responses in vivo Lymphopenia Induced Proliferation (LIP) Dye label T cells Hosts with no T cells 14 d CFSE 1. Proliferation (CFSE) 2. Survival (cell recovery) Heterogeneous LIP response of polyclonal CD8 T cells Day 13 Day 6 Memory/effector 41% 91% Naive 59% CD44 9% CFSE Proliferative responses by different clones are heterogenous F5 - low avidity 10 10 10 5 10 4 10 3 10 5 4 3 0 0 0 OT1- high avidity 10 10 10 10 3 10 4 10 5 5 10 4 10 3 10 0 10 0 10 3 10 4 10 5 5 4 3 0 0 CD44 CD122 0 CFSE 10 3 10 4 10 5 CFSE 3 10 4 10 5 Antigen and homeostatic induced proliferation Foreign Homeostatic Self-pMHC TCR signal pMHC IL-7R IL-2R γc cytokine IL-2 growth factor proliferative IL-7 Trying to understand cell cycle control during LIP d3 d7 How can modeling help understand LIP ? Define cellular events underlying - Flu d14 the - flu response. Does LIP follow the same rules as antigen responses ? Are they governed by the same biological programme ? d3 Creating + flu successful models can + Flu identify key parameters that can help understand the biological CD44 processes Flu specific F5 TCR transgenic T cells CFSE Autopilot divisions model Stochastic divisions Generating time series data to model d7 d3 210 d0=91% 43 2 1 0 d0=31% d11 d14 > 6 6 543 2 1 0 > 6 6 543 2 1 0 d0=12% d0=7% Time series data for F5 TCR transgenic T cells 2.5 Mean divisions 2 1.5 1 0.5 0 0 2 4 6 8 10 Time (d) 12 14 Cell division + cell survival = expansion Observed expansion Predicted expansion from CFSE 10 10 Total Adjusted 8 Fold Expansion Fold Expansion 8 6 4 6 4 2 2 0 0 0 2 4 6 8 10 Time (d) 12 14 0 no/little cell death 5 10 Time (d) 15 20 Comparing the Autopilot and Single divisions models - mean divisions Autopilot Single divisions Comparing the Autopilot and Single divisions models - mean divisions Autopilot Single divisions Time dependent reduction in λ - µ λ(t) = λ0 exp(− µ t) Comparing the Autopilot and Single divisions models - CFSE profiles d3 d4 d6 d7 Frequency of cells in each division Observed Model prediction d11 d14 Parameters for model Autopilot Weibull distribution T (lagtime) = 0 ∆=2.88 d Single divisions Const λ : Reducing λ : T = 2.03d T = 2.44d λ= 0.21 λ0= 0.455 day-1 ∆ =6.65h ∆=6.19h µ=0.115 Testing the model • Parameter values are biological reasonable • Important predictions of the model ? – Single stochastic divisions – Lag time ~ 2 days to division onset – Slowing in division rate ‘Autopilot’ antigen induced proliferation TCR signal Naïve T cells IL-2 - γc cytokine - growth factor - proliferative Autocrine loop Homeostatic proliferation TCR signal Triggered by Self-pMHC IL-7 - γc cytokine - growth factor - proliferative Naïve T cells competition IL-7 Acknowledgements Present: Georgina Cornish Claire Pearson Manoj Saini Charles Sinclair Sim Tung LAB Past : Andrew Yates, Emory University, Atlanta, U.S. Robin Callard Institute for Child Health, UCL Rodolphe Thiébaut INSERM U875, Université Bordeaux Mark Coles Department of Biology and HYMS University of York, U.K. Anne Mathiot