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Predicting metabolic preferences through transcriptomics: a data-driven approach to align metabolic signatures with
Lente J S Lerink1,2, Marleen J de Winter1, Jaap A Bakker3
1Department of Surgery & Transplant Centre, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands.
Biochemistry and Biophysics Reports
|November 5, 2025
Summary
Mammalian metabolic signatures in organs do not directly align with gene expression. Understanding these complex metabolic patterns is crucial for improving ex vivo model systems and their translatability.
Area of Science:
- Physiology
- Metabolomics
- Systems Biology
Background:
- Complex organisms possess sophisticated metabolic networks for energy demands.
- Ex vivo model systems lack integrated organ metabolic exchange, requiring external substrate management.
- Failure to meet ex vivo metabolic needs limits model translatability.
Purpose of the Study:
- To identify easily measurable surrogate markers of metabolism for ex vivo systems.
- To assess if organ-specific metabolite consumption/production patterns align with gene expression.
- To improve the support and assessment of tissue and cell metabolism ex vivo.
Main Methods:
- Investigated organ-specific metabolite consumption and production patterns (metabolic signatures).
- Utilized available arteriovenous flux data.
- Compared metabolic signatures with organ-specific metabolic gene expression patterns.
Main Results:
- Different tissues exhibit distinct metabolite consumption and production patterns.
- These metabolic signatures did not directly correlate with known metabolic gene expression.
- Findings highlight the intricate complexity of mammalian metabolism.
Conclusions:
- Current metabolic gene expression does not fully predict ex vivo metabolic signatures.
- Further research is needed to understand the complexity of mammalian metabolic networks.
- Improved understanding is essential for developing more translatable ex vivo models.
