Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

Area of Science:

• Metabolomics
• Computational Biology
• Biochemistry

Background:

• Untargeted metabolomics using mass spectrometry and chromatography is powerful but faces challenges in identifying detected compounds.
• Accurate chemical identification is crucial for understanding cellular metabolism and biological processes.

Purpose of the Study:

• To present a novel computational workflow, extended metabolic model filtering (EMMF), for enhancing metabolite annotation in untargeted metabolomics.
• To improve the identification of known and potentially novel metabolites by expanding the candidate set beyond canonical metabolic models.

Main Methods:

• Developed an extended metabolic model (EMM) incorporating canonical and promiscuous enzyme-catalyzed metabolites.
• Applied the EMMF workflow to untargeted liquid chromatography-mass spectrometry (LC-MS) data from Chinese hamster ovary (CHO) cells and murine cecal microbiota.
• Experimentally validated predicted metabolites using mass spectrometry.

Main Results:

• EMMF generated candidate sets that matched, on average, 23.92% of measured masses, a >7-fold increase compared to reference metabolic models.
• The workflow identified numerous metabolites not cataloged in databases like PubChem.
• Successfully confirmed the presence of 4-hydroxyphenyllactate in CHO cells, a novel finding for this cell line.

Conclusions:

• EMMF offers a balanced approach to metabolite identification, increasing discovery potential while managing computational load.
• The workflow enhances the annotation of untargeted metabolomics data, revealing previously uncharacterized metabolic pathways.
• EMMF facilitates the discovery and validation of novel metabolites, advancing our understanding of biological systems.