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

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.