Mathematical modeling of SUMOylation reveals tissue-specific and dynamic responses to abiotic stress

Abstract

SUMOylation is an essential and evolutionarily conserved post-translational modification that enables rapid cellular adaptation to environmental stress. Despite its importance, how the SUMOylation machinery controls its dynamics remains poorly understood. Here, we integrated a mathematical model of the plant SUMO cycle with quantitative imaging and biochemical data to decode the regulatory logic of SUMOylation dynamics. The model reveals a fundamental principle: steady-state SUMOylation levels are determined not by the full complement of SUMO-cycle components, but by the abundance of SUMO modifier proteins and E1 enzymes, together with SUMO protease abundance and activity. Strikingly, when we integrated fluorescence measurements from reporters for SUMO-cycle components in Arabidopsis roots after three hours of abiotic stress, the model predicted decreased SUMOylation levels across several tissues, contrary to prevailing evidence. Motivated by this unexpected prediction, we performed time-resolved imaging to better characterize the dynamics of key SUMO-cycle components after salt and osmotic stress. These data revealed highly dynamic component levels within the first three hours of stress that were not captured in the previous measurements. Incorporating this temporal data into the model uncovered a twin-peaked SUMOylation response to salt stress: an early surge underpinned by protease dynamics, followed by a decline and a second peak driven by elevated SUMO modifier and E2 levels. Western blot analysis revealed a corresponding twin-peaked pattern of SUMOylation. Together, our findings demonstrate that the plant SUMO cycle is a highly dynamic system that achieves tissue- and stress-specific control of SUMOylation through the coordinated interplay of multiple SUMO-cycle components.