Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ
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Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. / Silva-Lance, Fernando; Montejano-Montelongo, Isabel; Bautista, Eric; Nielsen, Lars K.; Johansson, Pär I.; Marin de Mas, Igor.
In: International Journal of Molecular Sciences, Vol. 25, No. 10, 5406, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ
AU - Silva-Lance, Fernando
AU - Montejano-Montelongo, Isabel
AU - Bautista, Eric
AU - Nielsen, Lars K.
AU - Johansson, Pär I.
AU - Marin de Mas, Igor
N1 - Publisher Copyright: © 2024 by the authors.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - endothelial cell metabolism
KW - exo-metabolomics integration
KW - genome-scale metabolic models
KW - metabolic network analysis
KW - sampling algorithms
U2 - 10.3390/ijms25105406
DO - 10.3390/ijms25105406
M3 - Journal article
C2 - 38791446
AN - SCOPUS:85194218512
VL - 25
JO - International Journal of Molecular Sciences (Online)
JF - International Journal of Molecular Sciences (Online)
SN - 1661-6596
IS - 10
M1 - 5406
ER -
ID: 393596875