Characterizing the dynamics of CD4+ T cell priming within a lymph node.
Generating adaptive immunity postinfection or immunization requires physical interaction within a lymph node T zone between Ag-bearing dendritic cells (DCs) and rare cognate T cells. Many fundamental questions remain regarding the dynamics of DC-CD4+ T cell interactions leading to priming. For example, it is not known how the production of primed CD4+ T cells relates to the numbers of cognate T cells, Ag-bearing DCs, or peptide-MHCII level on the DC. To address these questions, we developed an agent-based model of a lymph node to examine the relationships among cognate T cell frequency, DC density, parameters characterizing DC-T cell interactions, and the output of primed T cells. We found that the output of primed CD4+ T cells is linearly related to cognate frequency, but nonlinearly related to the number of Ag-bearing DCs present during infection. This addresses the applicability of two photon microscopy studies to understanding actual infection dynamics, because these types of experiments increase the cognate frequency by orders of magnitude compared with physiologic levels. We found a trade-off between the quantity of peptide-major histocompatibility class II on the surface of individual DCs and number of Ag-bearing DCs present in the lymph node in contributing to the production of primed CD4+ T cells. Interestingly, peptide-major histocompatibility class II t(1/2) plays a minor, although still significant, role in determining CD4+ T cell priming, unlike the primary role that has been suggested for CD8+ T cell priming. Finally, we identify several pathogen-targeted mechanisms that, if altered in their efficiency, can significantly effect the generation of primed CD4+ T cells.
Department of Chemical Engineering, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Jennifer J
JJ
Linderman
Thomas
T
Riggs
Manjusha
M
Pande
Mark
M
Miller
Simeone
S
Marino
Denise E
DE
Kirschner
D056704:Adaptive Immunity; D000818:Animals; D000906:Antibodies / Q000378:metabolism; D000941:Antigens / Q000276:immunology; D001424:Bacterial Infections / Q000276:immunology / Q000473:pathology; D015496:CD4-Positive T-Lymphocytes / Q000166:cytology / Q000276:immunology* / Q000378:metabolism; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology; D002448:Cell Adhesion / Q000276:immunology; D002450:Cell Communication / Q000276:immunology; D002465:Cell Movement / Q000276:immunology; D003198:Computer Simulation; D003713:Dendritic Cells / Q000166:cytology / Q000276:immunology / Q000382:microbiology; D004729:Endothelium, Lymphatic / Q000098:blood supply / Q000166:cytology / Q000276:immunology; D008198:Lymph Nodes / Q000166:cytology / Q000276:immunology* / Q000378:metabolism / Q000382:microbiology; D008213:Lymphocyte Activation / Q000276:immunology*; D051379:Mice; D018448:Models, Immunological; D056004:Molecular Dynamics Simulation; D016192:Resting Phase, Cell Cycle / Q000276:immunology
D000906:Antibodies; D000941:Antigens