title: Integrative model of the immune response to a pulmonary macrophage infection: what determines the infection duration?
abstract: The immune mechanisms which determine the infection duration induced by pathogens targeting pulmonary macrophages are poorly known. To explore the impact of such pathogens, it is indispensable to integrate the various immune mechanisms and to take into account the variability in pathogen virulence and host susceptibility. In this context, mathematical models complement experimentation and are powerful tools to represent and explore the complex mechanisms involved in the infection and immune dynamics. We developed an original mathematical model in which we detailed the interactions between the macrophages and the pathogen, the orientation of the adaptive response and the cytokine regulations. We applied our model to the Porcine Respiratory and Reproductive Syndrome virus (PRRSv), a major concern for the swine industry. We extracted value ranges for the model parameters from modelling and experimental studies on respiratory pathogens. We identified the most influential parameters through a sensitivity analysis. We defined a parameter set, the reference scenario, resulting in a realistic and representative immune response to PRRSv infection. We then defined scenarios corresponding to graduated levels of strain virulence and host susceptibility around the reference scenario. We observed that high levels of antiviral cytokines and a dominant cellular response were associated with either short, the usual assumption, or long infection durations, depending on the immune mechanisms involved. To identify these mechanisms, we need to combine the levels of antiviral cytokines, including IFNγ, and IL10. The latter is a good indicator of the infected macrophage level, both combined provide the adaptive response orientation. Available PRRSv vaccines lack efficiency. By integrating the main interactions between the complex immune mechanisms, this modelling framework could be used to help designing more efficient vaccination strategies.
authors.affiliation: UR341 MIA, INRA, Jouy-en-Josas, France; LUNAM Université, Oniris, INRA UMR 1300 BioEpAR, Nantes, France.
authors.forename: Natacha
authors.initials: N
authors.lastname: Go
authors.affiliation: UR341 MIA, INRA, Jouy-en-Josas, France.
authors.forename: Caroline
authors.initials: C
authors.lastname: Bidot
authors.affiliation: LUNAM Université, Oniris, INRA UMR 1300 BioEpAR, Nantes, France.
authors.forename: Catherine
authors.initials: C
authors.lastname: Belloc
authors.affiliation: UMR1355 ISA, INRA, Université Nice Sophia Antipolis, CNRS, Sophia Antipolis, France; BIOCORE, Inria, Sophia Antipolis, France.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Touzeau
mesh_terms: D000465:Algorithms; D000818:Animals; D004198:Disease Susceptibility / Q000276:immunology; D058507:Host Specificity; D054884:Host-Pathogen Interactions; D006801:Humans; D007111:Immunity, Cellular; D016676:Macrophages, Alveolar / Q000276:immunology*; D008954:Models, Biological; D012680:Sensitivity and Specificity; D014611:Vaccination; D014774:Virulence; D014777:Virus Diseases / Q000276:immunology* / Q000517:prevention & control