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Article

Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ

by
Fernando Silva-Lance
1,†,
Isabel Montejano-Montelongo
1,†,
Eric Bautista
1,
Lars K. Nielsen
1,2,
Pär I. Johansson
2,3 and
Igor Marin de Mas
1,2,*
1
Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
2
CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
3
Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Int. J. Mol. Sci. 2024, 25(10), 5406; https://doi.org/10.3390/ijms25105406
Submission received: 1 March 2024 / Revised: 30 April 2024 / Accepted: 8 May 2024 / Published: 15 May 2024

Abstract

Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.
Keywords: metabolic network analysis; genome-scale metabolic models; exo-metabolomics integration; sampling algorithms; endothelial cell metabolism metabolic network analysis; genome-scale metabolic models; exo-metabolomics integration; sampling algorithms; endothelial cell metabolism

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MDPI and ACS Style

Silva-Lance, F.; Montejano-Montelongo, I.; Bautista, E.; Nielsen, L.K.; Johansson, P.I.; Marin de Mas, I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Int. J. Mol. Sci. 2024, 25, 5406. https://doi.org/10.3390/ijms25105406

AMA Style

Silva-Lance F, Montejano-Montelongo I, Bautista E, Nielsen LK, Johansson PI, Marin de Mas I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. International Journal of Molecular Sciences. 2024; 25(10):5406. https://doi.org/10.3390/ijms25105406

Chicago/Turabian Style

Silva-Lance, Fernando, Isabel Montejano-Montelongo, Eric Bautista, Lars K. Nielsen, Pär I. Johansson, and Igor Marin de Mas. 2024. "Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ" International Journal of Molecular Sciences 25, no. 10: 5406. https://doi.org/10.3390/ijms25105406

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