Table 1_Multiscale physiologically-based model of age-dependent CD4+ T-lymphocyte homeostasis.docx

ObjectiveTo develop a mechanistic physiologically-based model describing CD4+ T-lymphocyte homeostasis across the human lifespan, incorporating maturation, differentiation, migration aspects and the impact of age on distinct cell subpopulations.
MethodsA stepwise modeling approach was implemented by integrating published quantitative data on CD4+ T-cell concentration in blood and various tissues for narrowly defined age ranges, together with experimental kinetic parameters. The homeostatic CD4+ T-lymphocyte kinetics model was represented as a system of ordinary differential equations for four thymocyte subpopulations and six CD4+ T-lymphocyte subpopulations, incorporating five physiological compartments: the thymus, blood, lymphoid tissue, the gastro-intestinal tract, and lung tissue. A series of empirical functions was sequentially tested to describe age-related changes in homeostasis. Reciprocal cellular feedback functions were assessed for incorporation in the model, as an alternative to age-dependent functions. An extensive set of model evaluations was performed, including model validation on total and memory CD4+ T-cell concentrations, simulations of homeostasis perturbations following thymectomy, and global sensitivity analysis, to determine the processes most influential in shaping CD4+ T-cell homeostasis.
ResultsAge-related shifts in proliferation of naïve and activated cells, differentiation of memory subsets, survival of recent thymic emigrants (RTE) and migration aspects of CD4+ T-cells – together with reduced thymic output – were identified as key determinants of immune homeostasis. Sensitivity analyses showed that thymocyte and naïve cell homeostasis drives early differentiation stages, whereas clonal expansion dominates memory and effector cell maintenance, with the influence of all processes declining with age. Although increased naïve T-cell proliferation and reduced RTE death may partially compensate for thymic loss, these mechanisms are insufficient to restore long-term CD4+ T-cell counts after thymectomy.
ConclusionBy unifying diverse clinical and experimental observations within a multiscale mechanistic quantitative framework, the proposed model offers a robust tool for predicting CD4+ T-cell dynamics and assessing the impact of physiological changes or interventions on immune homeostasis.