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Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

McGarrity S, Halldórsson H, Palsson S, Johansson PI, Rolfsson Ó - Front Cardiovasc Med (2016)

Bottom Line: The application of GEMs in personalized medicine is also highlighted.Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers.Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, University of Iceland , Reykjavik , Iceland.

ABSTRACT
High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

No MeSH data available.


Related in: MedlinePlus

Workflow of GEM construction and contribution to developing new strategies for the clinic. Biochemical data from cell culture and clinical studies are combined to form a comprehensive metabolic reconstruction, which is constrained to form a context-specific GEM and produce biologically well-founded predictions that will suggest future clinical interventions.
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Figure 2: Workflow of GEM construction and contribution to developing new strategies for the clinic. Biochemical data from cell culture and clinical studies are combined to form a comprehensive metabolic reconstruction, which is constrained to form a context-specific GEM and produce biologically well-founded predictions that will suggest future clinical interventions.

Mentions: Individualized hepatocellular carcinoma models have been used to predict patient outcomes based on the predicted production of acetate, identified as a key metabolic pathway for survival (114). Twenty-four individualized GEMs of erythrocytes were created based on genetic and metabolic data. These captured altered dynamics of erythrocyte metabolism and allowed the identification of individuals at risk to drug-induced anemia based upon their genomic sequence (115). These examples highlight a potential workflow, exemplified in Figure 2, to contribute to the personalization and stratification of medical treatments in the clinic. In the future, it is envisioned that an EC GEM could be used in a similar fashion by comparing GEMs CVD patients and healthy individuals to identify key metabolic changes to CVD for example those that increase production of atherosclerotic plaques.


Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

McGarrity S, Halldórsson H, Palsson S, Johansson PI, Rolfsson Ó - Front Cardiovasc Med (2016)

Workflow of GEM construction and contribution to developing new strategies for the clinic. Biochemical data from cell culture and clinical studies are combined to form a comprehensive metabolic reconstruction, which is constrained to form a context-specific GEM and produce biologically well-founded predictions that will suggest future clinical interventions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834436&req=5

Figure 2: Workflow of GEM construction and contribution to developing new strategies for the clinic. Biochemical data from cell culture and clinical studies are combined to form a comprehensive metabolic reconstruction, which is constrained to form a context-specific GEM and produce biologically well-founded predictions that will suggest future clinical interventions.
Mentions: Individualized hepatocellular carcinoma models have been used to predict patient outcomes based on the predicted production of acetate, identified as a key metabolic pathway for survival (114). Twenty-four individualized GEMs of erythrocytes were created based on genetic and metabolic data. These captured altered dynamics of erythrocyte metabolism and allowed the identification of individuals at risk to drug-induced anemia based upon their genomic sequence (115). These examples highlight a potential workflow, exemplified in Figure 2, to contribute to the personalization and stratification of medical treatments in the clinic. In the future, it is envisioned that an EC GEM could be used in a similar fashion by comparing GEMs CVD patients and healthy individuals to identify key metabolic changes to CVD for example those that increase production of atherosclerotic plaques.

Bottom Line: The application of GEMs in personalized medicine is also highlighted.Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers.Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

View Article: PubMed Central - PubMed

Affiliation: Center for Systems Biology, University of Iceland , Reykjavik , Iceland.

ABSTRACT
High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

No MeSH data available.


Related in: MedlinePlus