Novel Data-Driven Mechanistic Modeling of Untargeted Metabolome Data Reveals Feed Component Effects in CHO Cell
Meeri E-L Mäkinen1,2, Markella Zacharouli1,2, Sigrid Särnlund1,2,3
1Competence Centre for Advanced Bioproduction by Continuous Processing, AdBIOPRO, Stockholm, Sweden.
Biotechnology Journal
|July 4, 2025
Summary
This study introduces a new method combining metabolic modeling and metabolomics to boost enzyme production in CHO cells. It identified key nutrients and pathways for improved cell culture performance.
Area of Science:
- Biotechnology
- Metabolic Engineering
- Systems Biology
Background:
- Untargeted metabolomics offers broad insights but faces challenges like data complexity and scarcity.
- Mechanistic metabolic modeling requires detailed pathway information, often unavailable for complex biological systems.
Purpose of the Study:
- To develop and apply a novel approach integrating untargeted metabolomics with mechanistic metabolic modeling.
- To identify key feed medium components and metabolic pathways enhancing enzyme production in Chinese Hamster Ovary (CHO) cells.
Main Methods:
- Applied a combined approach of untargeted liquid chromatography-tandem mass spectrometry (LC/MS/MS) metabolomics and mechanistic modeling.
- Expanded a stoichiometric reaction network from 127 to 370 reactions using metabolomics data.
- Utilized elementary flux modes-based column generation for pathway identification and simulation.
Main Results:
- Analyzed 563 cellular and 386 supernatant metabolites to pinpoint key contributors to productivity.
- Identified 21 significant metabolites, including unexpected compounds like citraconate and 5-aminovaleric acid.
- Revealed underlying metabolic pathways responsible for the observed productivity improvements.
Conclusions:
- The integrated approach effectively leverages untargeted metabolomics for objective metabolic screening.
- Provides a mechanistic understanding of nutrient effects on cell culture performance and enzyme production.
- Paves the way for rational, data-driven optimization of bioprocesses.
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