A computational model for mechanistic investigation of negative feedback in interleukin-17 receptor signaling (CCR3P.201)

Abstract

Abstract IL-17 is a pro-inflammatory cytokine that promotes autoimmunity and protects against certain pathogens. Here, we present a computational model of signaling downstream of the IL-17 receptor. To manage the complexity of the system we use a rule-based modeling approach, in which signaling proteins are modeled as structured objects and rules describe their biochemical interactions, allowing us to consider all possible complexes and phosphoforms generated from a set of basic molecules. This model encompasses major signaling components downstream of IL-17R, which activate NF-κB and in turn promote the production of pro-inflammatory cytokines. NF-κB also promotes production of A20, a deubiquitinating enzyme that inhibits NF-κB activation in multiple settings. We recently showed that A20 acts as a negative regulator of the IL-17 signaling pathway. By modeling A20 inhibition of TRAF6 and IKK, we are able to recapitulate experimentally-observed oscillations in NF-κB activity and A20 expression. Novel experimental data identifying the dynamics of signaling intermediates has further constrained model behavior and informed model development. Modeling the depletion of IκB, followed by its rebound above baseline levels, required the addition of a stochastic model of transcription factor binding, paired with a deterministic model of signaling events. We are currently using the hybrid model to identify which of several potential mechanisms A20 uses to attenuate IL-17 signaling.