Protein resource allocation across subcellular compartments revealed by large-scale absolute quantitative proteomics...

Subcellular compartmentalization within complex membrane-bound organelles is a hallmark of eukaryal life, enabling metabolic specialization through spatial segregation. Here, we compiled a dataset encompassing 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning, quantitative correlations among protein abundance, resource allocation, and cellular phenotypes were elucidated at a system level. We found that subcellular resource distribution is highly uneven and covaries with specific growth rate. Specifically, at high specific growth rates, the protein mass fraction (PMF) from certain compartments, (e.g., nucleus) is consistently reduced, coinciding with the increasing protein resource demand from ribosomes. Meanwhile, nutrient limitation may induce resource recycling by upregulating protein resources within specific compartments (e.g., vacuole) to sustain stress adaptation. Importantly, our integrative analysis indicates that PMF from a few subcellular compartments (e.g., nucleus and ribosomes) can accurately predict yeast physiological parameters (e.g., specific growth rate, oxygen uptake rate), and a limited protein set (≥ 23) could be used to capture the variation in resource allocation across seven major subcellular compartments. Furthermore, our comparative analysis between Crabtree-positive and Crabtree-negative species across conditions illustrates that subcellular resource allocation could be shaped by multiple factors and the two species differ markedly in their robustness to environmental perturbations. In summary, the high-quality quantitative proteomic dataset for yeast species offers an unprecedented opportunity for understanding the general principles underlying subcellular resource allocation, laying foundations for precision engineering of cell factories in synthetic biology.