Crauste F, Terry E, Mercier I Le, Mafille J, Djebali S, Andrieu T, Mercier B, Kaneko G, Arpin C, Marvel J, Gandrillon O
Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France; Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France.
Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France; Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France; Université de Lyon, Université Lyon 1, CNRS UMR 5534, Centre de Génétique et de Physiologie Moléculaire et Cellulaire, F-69622 Villeurbanne-Cedex, France.
J Theor Biol. 2015 Jun 7;374:66-82. doi: 10.1016/j.jtbi.2015.03.033. Epub 2015 Apr 3.
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.
初始CD8 T细胞免疫反应是对抗细胞内病原体感染的主要机制。我们旨在评估是否可以基于CD8 T细胞计数的测量结果,采用建模方法来确定CD8 T细胞反应中病原体特异性的动力学参数。我们生成了实验数据,这些数据包含了对三种不同的活细胞内病原体做出反应时CD8 T细胞计数的动力学情况:两种通过鼻内注射的病毒(流感病毒、痘苗病毒),以及一种通过静脉注射的细菌(单核细胞增生李斯特菌)。所有病原体都携带相同的抗原(NP68),但它们与宿主的相互作用有所不同。同时,我们开发了一个数学模型,用于描述免疫反应过程中CD8 T细胞计数和病原体数量的变化。该模型由9个参数表征,并包含相关的反馈控制。将模型输出与三个数据系列进行比较,并对参数值进行了详尽的估计。通过关注模型拟合实验数据的能力以及在反应结束时产生主要由记忆细胞组成的CD8 T细胞群体的能力,确定了关键参数。我们表明,少数参数(2 - 4个)定义了CD8 T细胞免疫反应的主要特征,并且是特定病原体所特有的。在这些参数中,有两个与效应CD8 T细胞介导的细胞和病原体死亡控制有关。与记忆细胞死亡相关的参数在CD8 T细胞反应的主要阶段显示出无关紧要的作用,但在感染数月后查看模型预测时,它变得至关重要。