The role of the T-helper/T-suppressor ratio in the adaptive immune response: a dynamical model

Abstract

Recent experimental studies have suggested the ratio between T-helper and T-suppressor lymphocytes as an index of immunosuppression in HIV, cancer, immunosenescence and inflammatory and auto-immune diseases. However, a quantitative understanding of the impact of this ratio on the immune response has lagged behind data and its validity as a tool for prognostic monitoring or therapeutic target remains an open question. In this work, we use statistical physics and dynamical systems approaches to analyze the time-dependent response to an antigen, of a simplified model of the adaptive immune system, which comprises B, T-helper and T-suppressor lymphocytes. The model is remarkably robust against changes in the noise level and kinetic parameters, but it is very sensitive to changes in the ratio between T-helper and T-suppressor lymphocytes, exhibiting, in particular, a transition from a responsive to an immuno-suppressed phase, as the ratio is lowered below a critical value, which is in line with experiments. This result supports the validity of the T-helper/T-suppressor ratio as an index of immunosuppression and may provide a useful theoretical benchmark to interpret and compare experiments.

Modelling the interplay between the CD4$$^{+}$$/CD8$$^{+}$$ T-cell ratio and the expression of MHC-I in tumours

“…The precise nature of the activation of T-cells is debated, but there is evidence to suggest that sufficient binding time is required for activation (Allard et al 2017;Tian et al 2007;Robert et al 2012;Tkach and Altan-Bonnet 2013;Aleksic et al 2010). Due to noise, inherent in biological systems, T-cells are likely to bind and unbind in a stochastic manner, therefore we treat T-cell activation as a stochastic process, following previous studies of T-cell activation in the literature (Wedagedera and Burroughs 2006;Lipniacki et al 2008;Agliari et al 2013;Annibale et al 2018).…”

“…Although our model is also a system of ODEs which describe the change in concentration of cells, our model differs from previous efforts by including the stochastic dynamics of T-cell activation. The latter is used in the derivation of our model, but our analysis is based on non-equilibrium statistical mechanics, as similarly implemented by Annibale et al (2018), allowing us to describe the macroscopic behaviour of the system deterministically. Statistical mechanics has a rich recent history in modelling the immune system (Perelson and Weisbuch 1997;Lucia and Maino 2002;Chakraborty and Košmrlj 2010;Mora et al 2010;Agliari et al 2013;Bartolucci et al 2016) and here we use it to address the unique modelling challenges that tumour immunology poses.…”

“…The latter is used in the derivation of our model, but our analysis is based on non-equilibrium statistical mechanics, as similarly implemented by Annibale et al. ( 2018 ), allowing us to describe the macroscopic behaviour of the system deterministically. Statistical mechanics has a rich recent history in modelling the immune system (Perelson and Weisbuch 1997 ; Lucia and Maino 2002 ; Chakraborty and Košmrlj 2010 ; Mora et al.…”