Integrative Proteomics and Genome-Scale Modeling Elucidate Metabolic Flux Shifts in Chlamydomonas reinhardtii CC-4414 Under Light Stress

The green microalga Chlamydomonas reinhardtii holds substantial promise as a photosynthetic cell factory for sustainable bioproduction, yet strain-specific metabolic responses to environmental stress remain underexplored. Here, we reconstructed a genome-scale metabolic network (GSMN) of the light-tolerant strain C. reinhardtii CC-4414 by combining de novo genome assembly with integrative proteomics. The resulting model, designated iYH2021, comprises 2021 genes, 2601 reactions, and 2089 metabolites, representing the first genome-scale metabolic reconstruction reported for this strain. To elucidate light-driven metabolic remodeling, we integrated quantitative proteomic data from high-light (HL) and low-light (LL) conditions into the model using the iMAT algorithm and performed metabolic flux analysis (MFA). Because these context-specific networks integrate only proteomically supported reactions, flux predictions were evaluated using an objective function that captures photon-handling capacity, providing a direct readout of how the photosynthetic apparatus reallocates flux under each condition. Our predictions revealed that HL conditions significantly enhanced fluxes through photosynthetic electron transport, the Calvin–Benson cycle, glycolysis, and the tricarboxylic acid (TCA) cycle, indicating a reallocation of carbon and energy metabolism toward light-driven pathways, while amino acid metabolism showed a mixed pattern of change. In contrast, LL conditions induced a marked upregulation of purine metabolism, suggesting accelerated nucleotide turnover as an adaptive response to light limitation. A complementary growth-rate analysis using the unconstrained network further indicated a higher maximum growth rate under HL than LL, consistent with the expected benefit of greater light availability. These condition-specific flux redistributions highlight the metabolic plasticity of C. reinhardtii CC-4414 and provide a systems-level understanding of how light intensity shapes carbon and energy allocation. The iYH2021 model thus offers a robust predictive platform for guiding metabolic engineering strategies aimed at optimizing the production of biofuels and high-value bioproducts in this industrially relevant microalga under stress conditions. These findings represent model-derived predictions and have not yet been experimentally validated. We emphasize that this predictive platform requires experimental validation before its use in guiding metabolic engineering decisions.