Spatial lymphocyte dynamics in lymph nodes predicts the cytotoxic T Cell frequency needed for HIV infection control

The surveillance of host body tissues by immune cells is central for mediating their defense function. In vivo imaging technologies have been used to quantitatively characterize target cell scanning and migration of lymphocytes within lymph nodes (LNs). The translation of these quantitative insights into a predictive understanding of immune system functioning in response to various perturbations critically depends on computational tools linking the individual immune cell properties with the emergent behavior of the immune system. By choosing the Newtonian second law for the governing equations, we developed a broadly applicable mathematical model linking individual and coordinated T-cell behaviors. The spatial cell dynamics is described by a superposition of autonomous locomotion, intercellular interaction, and viscous damping processes. The model is calibrated using in vivo data on T-cell motility metrics in LNs such as the translational speeds, turning angle speeds, and meandering indices. The model is applied to predict the impact of T-cell motility on protection against HIV infection, i.e., to estimate the threshold frequency of HIV-specific cytotoxic T cells (CTLs) that is required to detect productively infected cells before the release of viral particles starts. With this, it provides guidance for HIV vaccine studies allowing for the migration of cells in fibrotic LNs. © 2019 Grebennikov, Bouchnita, Volpert, Bessonov, Meyerhans and Bocharov.

Grebennikov D. 1, 2, 3 , Bouchnita A.4 , Volpert V. 3, 5, 6 , Bessonov N.7 , Meyerhans A.8, 9 , Bocharov G. 2, 10
Frontiers Media S.A.
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  • 1 Moscow Institute of Physics and Technology, National Research University, Dolgoprudny, Russian Federation
  • 2 Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russian Federation
  • 3 Peoples' Friendship University of Russia (RUDN University), Moscow, Russian Federation
  • 4 Division of Scientific Computing, Department of Information Technology, Uppsala University, Uppsala, Sweden
  • 5 Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, France
  • 6 INRIA Team Dracula, INRIA Lyon la Doua, Villeurbanne, France
  • 7 Institute of Problems of Mechanical Engineering, Russian Academy of Sciences, Saint Petersburg, Russian Federation
  • 8 Infection Biology Laboratory, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
  • 9 Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
  • 10 Sechenov First Moscow State Medical University, Moscow, Russian Federation
Cell motility; Cytotoxic T cell scanning; Dissipative particle dynamics; HIV infection; Lymphoid tissue; Multicellular dynamics; Stochastic differential equation
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