Proteome Regulation Patterns Determine Escherichia coli Wild-Type and Mutant Phenotypes

Area of Science:

• Microbial physiology and metabolism
• Computational biology and bioinformatics
• Systems biology

Background:

• Proteins are central to microbial phenotypes and resource allocation is key for growth and adaptation.
• Constraint-based metabolic modeling often lacks accurate representation of proteome constraints and their impact on cellular phenotypes.

Purpose of the Study:

• To develop and validate a protein allocation model (PAM) for Escherichia coli that integrates protein constraints into constraint-based metabolic modeling.
• To enhance the predictive accuracy of microbial phenotypes and metabolic responses to genetic and environmental perturbations.

Main Methods:

• Consolidated a coarse-grained protein allocation approach with enzymatic constraints on reaction fluxes.
• Developed a protein allocation model (PAM) for Escherichia coli.
• Validated the PAM against wild-type phenotypes, flux distributions, and responses to genetic perturbations and heterologous protein expression.

Main Results:

• The PAM accurately represents wild-type phenotypes and flux distributions in Escherichia coli.
• The model successfully predicts metabolic responses to genetic perturbations, attributing mutant phenotypes to protein distribution patterns.
• The PAM accurately reflects metabolic adjustments under augmented protein burden from heterologous gene expression.

Conclusions:

• The protein allocation model (PAM) advances the predictability of microbial phenotypes and metabolic flux distributions.
• Integrating protein allocation constraints into constraint-based models is crucial for accurate strain analysis and metabolic engineering.
• The PAM offers a computationally lightweight yet accurate approach for model-driven metabolic research and strain design.