Computational modeling of cytokine signaling in microglia

Neuroinflammation due to glial activation has been linked to many CNS diseases. We developed a computational model of a microglial cytokine interaction network to study the regulatory mechanisms of microglia-mediated neuroinflammation. We established a literature-based cytokine network, including TNF alpha, TGF beta, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGF beta autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGF beta- and IL-10-mediated inhibition of TNF alpha was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNF alpha response profile to the initial TGF beta and IL-10 levels. The analysis showed two relatively shifted TNF alpha response profiles within separate domains of initial condition space. Further analysis revealed that TNF alpha exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGF beta and IL-10 on TNF alpha adaptation. Our analysis showed that TGF beta and IL-10 knockouts (TGF beta KO and IL-10 KO) exert divergent effects on adaptation. TFGb KO attenuated TNF alpha adaptation whereas IL-10 KO enhanced TNF alpha adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNF alpha adaptation in murine macrophages and found supporting evidence. These opposing effects could be explained by differential kinetics of negative feedback. Inhibition of IL-10 reduced early negative feedback that results in enhanced TNF alpha-mediated TGF beta expression. We propose that differential kinetics in parallel negative feedback loops constitute a novel mechanism underlying the complex and non-intuitive pro-versus anti-inflammatory effects of individual cytokine perturbations.