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

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.

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

“…Previous ODE models have studied how viral dynamics affect the course of infection (Handel and Antia, 2008; Lee et al, 2009; Miao et al, 2010; Mitchell et al, 2011; Saenz et al, 2010; Crauste et al, 2015; Price et al, 2015). The use of an ABM can complement spatially homogeneous differential equation models (Beltman et al, 2007; Textor et al, 2014; Vroomans et al, 2012; Zheng et al, 2008).…”

“…Several earlier theoretical models have been proposed to study specific aspects of influenza infections. Ordinary differential equation (ODE) models that are fit to empirical data (Handel and Antia, 2008; Lee et al, 2009; Miao et al, 2010; Saenz et al, 2010; Murillo et al, 2013; Crauste et al, 2015; Price et al, 2015) have elucidated viral population level dynamics but are unable to perceive localized spatial effects. Spatial models, on the other hand, have been developed to examine interactions between dendritic cells and T cells in the lymph node (Beauchemin et al, 2005; Beltman et al, 2007; Zheng et al, 2008; Mirsky et al, 2011; Celli et al, 2012; Vroomans et al, 2012; Textor et al, 2014), but have not been extended to consider local conditions in the lung.…”

Identification of Nascent Memory CD8 T Cells and Modeling of Their Ontogeny

“…We previously established a minimal mathematical model able to simulate the dynamics of total CD8 T cells during a primary response, considering a naive/effector/memory differentiation scheme (Crauste et al, 2015;Terry et al, 2012). Given that transcriptomics data and CD44/Mki67/Bcl2 phenotypes revealed two effector phases, we modified our initial model to take into account these two compartments ( Figure S3A).…”

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

“…While a few models do avoid target cell depletion (32, 33), they assume either immediate replenishment of target cells (32) or a slow rate of viral invasion into target cells resulting in a much delayed peak of virus titer at day 5 postinfection (rather than the observed peak at day 2) (33). Moreover, models with missing or unspecified major immune components, e.g., no innate immunity (24, 25, 36, 38), no antibodies (24, 25, 33, 41, 42), or unspecified adaptive immunity (40), also indicate the need for further model development. For an in-depth review of the current viral dynamics literature on influenza, we refer the reader to the excellent article by Dobrovolny et al (39).…”