Computational modeling of cytokine signaling in microglia.
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ABSTRACT: 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?, TGF?, and IL-10, and fitted a mathematical model to published data from LPS-treated microglia. The addition of a previously unreported TGF? autoregulation loop to our model was required to account for experimental data. Global sensitivity analysis revealed that TGF?- and IL-10-mediated inhibition of TNF? was critical for regulating network behavior. We assessed the sensitivity of the LPS-induced TNF? response profile to the initial TGF? and IL-10 levels. The analysis showed two relatively shifted TNF? response profiles within separate domains of initial condition space. Further analysis revealed that TNF? exhibited adaptation to sustained LPS stimulation. We simulated the effects of functionally inhibiting TGF? and IL-10 on TNF? adaptation. Our analysis showed that TGF? and IL-10 knockouts (TGF? KO and IL-10 KO) exert divergent effects on adaptation. TFG? KO attenuated TNF? adaptation whereas IL-10 KO enhanced TNF? adaptation. We experimentally tested the hypothesis that IL-10 KO enhances TNF? 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?-mediated TGF? 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.
SUBMITTER: Anderson WD
PROVIDER: S-EPMC5520540 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
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