AbstractThe steroid hormone, Glucocorticoid (GC) is a well-known immunosuppressant that controls T cell-mediated adaptive immune response. In this work, we have developed a minimal kinetic network model of T-cell regulation connecting relevant experimental and clinical studies to quantitatively understand the long-term effects of GC on pro-inflammatory T-cell (Tpro) and anti-inflammatory T-cell (Tanti) dynamics. Due to the antagonistic relation between these two types of T-cells, their long-term steady-state population ratio helps us to characterize three classified immune-regulations: (i) weak ([Tpro]>[Tanti]); (ii) strong ([Tpro]<[Tanti]), and (iii) moderate ([Tpro] ∼ [Tanti]); holding the characteristic bistability). In addition to the differences in their long-term steady-state outcome, each immune-regulation shows distinct dynamical phases. In the pre-steady, a characteristic intermediate stationary phase is observed to develop only in the moderate regulation regime. In the medicinal field, the resting time in this stationary phase is distinguished as a clinical latent period. GC dose-dependent steady-state analysis shows an optimal level of GC to drive a phase-transition from the weak/auto-immune prone to the moderate regulation regime. Subsequently, the pre-steady state clinical latent period tends to diverge near that optimal GC level where [Tpro]: [Tanti] is highly balanced. The GC-optimized elongated stationary phase explains the rationale behind the requirement of long-term immune diagnostics, especially when long-term GC-based chemotherapeutics and other immunosuppressive drugs are administrated. Moreover, our study reveals GC sensitivity of clinical latent period which might serve as an early warning signal in the diagnosis of different immune phases and determining immune phase-wise steroid treatment.
Energy dissipation and fluctuations in a driven liquid