Resource allocation driving tolerance and improved growth in the microalga Picochlorum renovo

In this study, we reconstructed the first GEM for P. renovo, iPicre1230. The model was initially built using a semi-automated pipeline leveraging high-quality templates from related microalgae and was subsequently refined through extensive manual curation, including meticulous annotation of lipid metabolism pathways. To validate the model's predictive power, we performed experimental growth assays under both light and dark conditions using 14 different organic carbon sources, achieving high agreement between model predictions and observed growth patterns. We further investigated P. renovo's response to zinc by determining the optimal zinc concentration that maximizes growth and chlorophyll biosynthesis. Transcriptomic profiling was conducted under both zinc-depleted and zinc-optimal conditions to elucidate differential gene expression. Additionally, we reanalyzed publicly available transcriptomics data (Dahlin et al., 2019; LaPanse et al., 2024) from salinity stress experiments to explore broader stress responses. These transcriptomic datasets were integrated into the GEM to generate condition-specific models, revealing key metabolic trade-offs and adaptive strategies. Collectively, this work represents a significant advance in our understanding of P. renovo's metabolism and provides a valuable resource for future metabolic engineering and cultivation optimization efforts.