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 -