INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Area of Science:

• Biochemistry and Systems Biology
• Computational Biology and Bioinformatics
• Metabolomics and Transcriptomics

Background:

• Metabolism is regulated by enzyme expression and metabolite interactions.
• Separate analysis of metabolomics and transcriptomics data fails to capture hierarchical metabolic regulation.
• Integrating high-throughput data is crucial for understanding metabolic control.

Purpose of the Study:

• To develop and validate INTEGRATE, a computational pipeline for integrating metabolomics and transcriptomics data.
• To differentiate metabolic flux regulation at the gene expression versus metabolite interaction levels.
• To provide a framework for dissecting metabolic regulatory mechanisms.

Main Methods:

• Utilized constraint-based stoichiometric metabolic models as a scaffold.
• Computed differential reaction expression from transcriptomics data.
• Integrated metabolomics data to assess substrate availability's impact on metabolic flux.

Main Results:

• The INTEGRATE pipeline successfully integrates metabolomics and transcriptomics data.
• It discriminates between metabolic fluxes regulated by gene expression and/or metabolite interactions.
• Demonstrated pipeline utility in distinguishing regulatory mechanisms in breast cell lines.

Conclusions:

• Integrated analysis of metabolomics and transcriptomics data is essential for understanding metabolic regulation.
• The INTEGRATE pipeline offers a robust method for dissecting metabolic control layers.
• Identifying regulatory levels of metabolic reactions can inform personalized cancer therapies.