Dynamic Metabolic Modeling of Photosynthetic Eukaryotes Reveals Bases on Organelle-Specific Carbon and Nitrogen Partitioning during Nutrients Limitation

The goal of this work was to gain new insights into the photosynthetic eukaryotes Phaeodactylum tricornutum and Chlorella vulgaris using a systems biology approach. We integrated various omics data into a metabolic modeling framework, to systematically identify and quantify the partitioning of carbon and nitrogen among cellular metabolism, cross-talk between organelles, and productive photosynthetic electron flow.

Diverse conditions, i.e. growth during day and night, and compartmental cellular organization require phototrophs to shift their proteome demands and therefore adjust their metabolism and biomass composition during the course of growth. The complex interplay between energy and carbon metabolism and its dynamics in phototrophs is still not fully understood. Constraint-based modeling is a systems biology tool that takes advantage of experimental data, such as uptake rates and biomass composition, for successful prediction of growth phenotypes. Currently, lack of time-course biomass composition data has restricted prediction accuracy, by forcing the models to assume that the biomass remains constant. Here, we used experimentally determined metabolomics data to determine biomass composition constrains for a genome-scale metabolic models of the diatom P. tricornutum and the algae Chlorella vulgaris. We found that time course constraints can be the sole driving force that causes the metabolic network to exhibit certain behaviors such as time-specific secretion rates, cross talk of organelles by the activation of the mitochondria, as well as activation of specific metabolic pathways. A growth rate sensitivity analysis of time-course flux distributions enabled identifying the main metabolite affecting growth. Our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.