Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive...

Area of Science:

• Systems Biology
• Metabolic Engineering
• Genomics

Background:

• Identifying metabolic reprogramming from gene expression data during adaptive laboratory evolution (ALE) is challenging.
• Understanding cellular adaptation mechanisms requires robust analytical methods.
• Previous approaches lacked integration with metabolic models for comprehensive analysis.

Purpose of the Study:

• To develop a novel method, Metabolic Reprogramming Identifier (MRI), for detecting latent metabolic reprogramming.
• To identify key genes driving metabolic adaptation in response to perturbations.
• To evaluate the compatibility of gene expression patterns with maximal resource utilization.

Main Methods:

• Integrated gene expression data with a genome-scale metabolic model.
• Formulated a Mixed-Integer Linear Programming (MILP) problem to identify key reprogramming genes.
• Defined an adaptation score based on gene expression resource utilization.
• Applied the MRI method to Escherichia coli adaptive evolution experiments.

Main Results:

• Identified key genes involved in metabolic reprogramming by maximizing an adaptation score.
• Selected genes with complete expression usage and significant differences between wild-type and evolved strains.
• Discovered that cyoC and cydB genes are vital for E. coli reprogramming when switching from glucose to lactate.
• Predicted no significant reprogramming during E. coli evolution on glycerol.

Conclusions:

• The developed MRI method effectively identifies metabolic reprogramming and key genes.
• Inner membrane components (cyoC, cydB) play a crucial role in E. coli adaptation to new carbon sources.
• The method provides insights into cellular adaptation strategies and metabolic utilization patterns.