Understanding the causes and implications of endothelial metabolic variation in cardiovascular disease through genome-scale metabolic modeling
Open accessAbstract
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.
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Genome-scale metabolic models can also be applied to investigate genetic variation in genotype-phenotype interactions and as such can be used to identify metabolic biomarkers and discover treatments based on genetic markers.
“Genome-scale metabolic models can also be applied to investigate genetic variation in genotype-phenotype interactions and as such can be used to identify metabolic biomarkers and discover treatments based on genetic markers (311).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [311]
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References 140 · Cited by 12
Research works printed but not linked: 1
- [81]Tabas I Garcia-Cardena G Owens GK The cell biology of disease: recent insights into the cellular biology of atherosclerosis. J Cell Biol (2015) 209(1): 13– 22. 10.1083/jcb.201412052 25869663 PMC4395483 · DOI · Paper page
- Authors
- McGarrity, S., Halldórsson, H., Palsson, S., Johansson, P. I., & Rolfsson, Ó.
- Affiliations
- University of Iceland (IS)
- University of Copenhagen (DK)
- Corresponding author
- Rolfsson, Óttar
- Venue
- Frontiers in Cardiovascular Medicine
- Publisher
- Frontiers Media SA
- Published
- 2016-04-18
- Volume
- 3
- Pages
- 10-10
- ISSN
- 2297-055X (ISSN-L)
- PMC
- PMC4834436
- OpenAlex
- W2340957367
- Cited by
- 12
- References
- 140
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 2 · 2018: 1 · 2017: 2 · 2016: 1
- FWCI
- 0.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Cardiovascular Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Icelandic Centre for Research (130591-051)
- European Research Council (641093)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- endothelial cell metabolism, cardiovascular disease, genome-scale metabolic models, endothelial dysfunction, metabolic biomarkers, genotype-phenotype correlation, drug resistance mechanisms, personalized medicine, genetic variation, systems biology, metabolic pathway discovery
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