Mathematical modeling reveals quantitative properties of KEAP1-NRF2 signaling

Under oxidative and electrophilic stresses, cells launch an NRF2-mediated transcriptional antioxidant program. The activation of NRF2 depends on a redox sensor, KEAP1, which promotes the ubiquitination and degradation of NRF2. While a great deal has been learned about this duo, its quantitative signaling properties are largely unexplored. Here we examined these properties, including half-life, maximal activation, and response steepness (ultrasensitivity) of NRF2, through mathematical modeling. The models describe the binding of KEAP1 and NRF2 via ETGE and DLG motifs, NRF2 production, KEAP1-dependent and independent NRF2 degradation, and perturbations by different classes of NRF2 activators. Simulations revealed at the basal condition, NRF2 is sequestered by KEAP1 and the KEAP1-NRF2 complex is distributed comparably in an ETGE-bound (open) state and an ETGE and DLG dual-bound (closed) state. When two-step ETGE binding is considered, class I-V, electrophilic NRF2 activators shift the balance to a closed state incompetent to degrade NRF2, while the open and closed KEAP1-NRF2 complexes transition from operating in cycle mode to equilibrium mode. Ultrasensitive NRF2 activation (a steep rise of free NRF2) can occur when NRF2 nearly saturates KEAP1. The ultrasensitivity results from zero-order degradation through DLG binding and protein sequestration through ETGE binding. Optimal abundances of cytosolic and nuclear KEAP1 exist to maximize ultrasensitivity. These response characteristics do not require disruption of DLG binding as suggested by the hinge-latch hypothesis. In comparison, class VI NRF2 activators cause a shift to the open KEAP1-NRF2 complex and ultimately its complete dissociation, resulting in a fast release of NRF2 followed by stabilization. However, ultrasensitivity is lost due to decreasing free KEAP1 abundance. In summary, by simulating the dual role of KEAP1, i.e., sequestering and promoting degradation of NRF2, our modeling provides novel quantitative insights into NRF2 activation, which may help design novel NRF2 modulators and understand the oxidative actions of environmental stressors.