Literature DB >> 25846273

Predicting pathogen-specific CD8 T cell immune responses from a modeling approach.

F Crauste1, E Terry2, I Le Mercier3, J Mafille4, S Djebali5, T Andrieu6, B Mercier7, G Kaneko8, C Arpin9, J Marvel10, O Gandrillon11.   

Abstract

The primary CD8 T cell immune response constitutes a major mechanism to fight an infection by intra-cellular pathogens. We aim at assessing whether pathogen-specific dynamical parameters of the CD8 T cell response can be identified, based on measurements of CD8 T cell counts, using a modeling approach. We generated experimental data consisting in CD8 T cell counts kinetics during the response to three different live intra-cellular pathogens: two viruses (influenza, vaccinia) injected intranasally, and one bacteria (Listeria monocytogenes) injected intravenously. All pathogens harbor the same antigen (NP68), but differ in their interaction with the host. In parallel, we developed a mathematical model describing the evolution of CD8 T cell counts and pathogen amount during an immune response. This model is characterized by 9 parameters and includes relevant feedback controls. The model outputs were compared with the three data series and an exhaustive estimation of the parameter values was performed. By focusing on the ability of the model to fit experimental data and to produce a CD8 T cell population mainly composed of memory cells at the end of the response, critical parameters were identified. We show that a small number of parameters (2-4) define the main features of the CD8 T cell immune response and are characteristic of a given pathogen. Among these parameters, two are related to the effector CD8 T cell mediated control of cell and pathogen death. The parameter associated with memory cell death is shown to play no relevant role during the main phases of the CD8 T cell response, yet it becomes essential when looking at the predictions of the model several months after the infection.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  CD8 T cells; Immune response; Nonlinear model; Parameter value estimation; Predictive model

Mesh:

Year:  2015        PMID: 25846273     DOI: 10.1016/j.jtbi.2015.03.033

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  6 in total

1.  A spatial model of the efficiency of T cell search in the influenza-infected lung.

Authors:  Drew Levin; Stephanie Forrest; Soumya Banerjee; Candice Clay; Judy Cannon; Melanie Moses; Frederick Koster
Journal:  J Theor Biol       Date:  2016-02-23       Impact factor: 2.691

2.  IL-2 sensitivity and exogenous IL-2 concentration gradient tune the productive contact duration of CD8(+) T cell-APC: a multiscale modeling study.

Authors:  Xuefeng Gao; Christophe Arpin; Jacqueline Marvel; Sotiris A Prokopiou; Olivier Gandrillon; Fabien Crauste
Journal:  BMC Syst Biol       Date:  2016-08-17

3.  On the Role of CD8+ T Cells in Determining Recovery Time from Influenza Virus Infection.

Authors:  Pengxing Cao; Zhongfang Wang; Ada W C Yan; Jodie McVernon; Jianqing Xu; Jane M Heffernan; Katherine Kedzierska; James M McCaw
Journal:  Front Immunol       Date:  2016-12-20       Impact factor: 7.561

Review 4.  Towards multiscale modeling of the CD8+ T cell response to viral infections.

Authors:  Subhasish Baral; Rubesh Raja; Pramita Sen; Narendra M Dixit
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2019-02-27

5.  Modeling and characterization of inter-individual variability in CD8 T cell responses in mice.

Authors:  Chloe Audebert; Daphné Laubreton; Christophe Arpin; Olivier Gandrillon; Jacqueline Marvel; Fabien Crauste
Journal:  In Silico Biol       Date:  2021

Review 6.  Mathematical Models for Immunology: Current State of the Art and Future Research Directions.

Authors:  Raluca Eftimie; Joseph J Gillard; Doreen A Cantrell
Journal:  Bull Math Biol       Date:  2016-10-06       Impact factor: 1.758

  6 in total

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