FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Area of Science:

• Biomedical Research
• Metabolomics
• Network Biology

Background:

• Metabolomics aids in understanding disease biomarkers but identifying core metabolites remains challenging.
• Reliable core metabolites are essential for accurate disease diagnosis and progression monitoring.

Purpose of the Study:

• To develop a novel framework (FNICM) for identifying core metabolites.
• To integrate detection-based metabolomics with computational network biology for function-guided and network-based metabolite identification.
• To validate the framework's utility in disease diagnosis and drug treatment.

Main Methods:

• Proposed the Function-guided and Network-based Identification of Core Metabolites (FNICM) framework.
• Integrated targeted metabolomics with network topology analysis.
• Utilized controllability analysis to identify and hierarchize core metabolites.

Main Results:

• Successfully identified 35 core metabolites associated with ulcerative colitis (UC).
• Validated the diagnostic potential of these metabolites using independent UC patient datasets.
• Demonstrated the framework's applicability across various diseases (IBD, CRC, ACS) and therapeutic contexts.

Conclusions:

• The FNICM methodology provides a robust framework for identifying reliable core metabolites.
• This approach offers a systemic and holistic perspective for disease diagnosis and drug development.
• FNICM enhances the discovery of biomarkers for conditions like ulcerative colitis and aids in evaluating therapeutic effects.