Transcriptome and Redox Proteome Reveal Temporal Scales of Carbon Metabolism Regulation in Model Cyanobacteria Under Light Disturbance

The success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we develop a state-of-the-art approach based on an energy-landscape concept to differentiate interactions involving redox activities and conformational changes of proteins and nucleic acids interactions in multi-layered protein-DNA regulatory networks under light disturbance. Our approach is a data-driven modeling workflow using a physics-informed machine learning algorithm to train a non-linear mathematical model for interpreting gene expression dynamics and to lead discovery for protein regulators using redox proteome analysis. We distinguish light-responsive elements within central carbon metabolism pathways from independent variables like circadian time using the publicly available transcriptome datasets of S. elongatus over diel cycles responding to light perturbations. Our approach provides interpretable de novo models for elucidating events of reactions in complex regulatory pathways in response to stressful disturbance from the environment. We discovered protein regulators in response to light disturbance in the proteome analysis involving shifts in protein abundance and cysteine redox states in S. elongatus under constant illumination and after two hours of darkness. Our results indicate that regulatory dynamics in reductant generation link photo-induced electron transport pathways and redox metabolic pathways with circadian rhythms through fast redox-induced conformational changes or slow expression regulations across networks, offering a mechanistic understanding of how molecular changes relate to specific phenotypic traits under the light or energy limitations over the diel cycle.