Yeast optimizes metal utilization based on metabolic network and enzyme kinetics.

Area of Science:

• Metabolic Engineering
• Systems Biology
• Biochemistry

Background:

• Metal ions are essential enzyme cofactors, crucial for metabolic processes.
• Quantitative relationships between metal ions and cellular metabolism are not well understood.
• Understanding these interactions is key for optimizing cellular functions and biotechnological applications.

Purpose of the Study:

• To develop a genome-scale metabolic model (CofactorYeast) for Saccharomyces cerevisiae that incorporates proteome constraints and metal ion cofactors.
• To quantitatively estimate metal ion binding to enzymes under various conditions.
• To predict metabolic responses to metal ion availability and explore applications in cell factory engineering.

Main Methods:

• Reconstruction of a genome-scale metabolic model of Saccharomyces cerevisiae, incorporating proteome constraints and metal ion cofactors.
• Estimation of intracellular metal ion abundances bound to enzymes.
• Simulation of metabolic flux distributions under varying metal ion concentrations.

Main Results:

• The CofactorYeast model accurately estimates metal ion abundances in biomass.
• The model predicts distinct metabolic flux changes in response to altered metal ion levels.
• Simulations of iron deficiency in yeast reveal metabolic and gene expression adjustments consistent with optimization principles.

Conclusions:

• The developed model demonstrates the critical dependence of enzymes on metal ions for metabolic function.
• CofactorYeast successfully links metal ions to metabolism on a genome scale.
• The model shows potential for designing and optimizing cell factories for producing high-value compounds using heterologous enzymes.