The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Thematic Review Series: Proteomics. An integrated omics analysis of eicosanoid biology
Open accessAbstract
Eicosanoids have been implicated in a vast number of devastating inflammatory conditions, including arthritis, atherosclerosis, pain, and cancer. Currently, over a hundred different eicosanoids have been identified, with many having potent bioactive signaling capacity. These lipid metabolites are synthesized de novo by at least 50 unique enzymes, many of which have been cloned and characterized. Due to the extensive characterization of eicosanoid biosynthetic pathways, this field provides a unique framework for integrating genomics, proteomics, and metabolomics toward the investigation of disease pathology. To facilitate a concerted systems biology approach, this review outlines the proteins implicated in eicosanoid biosynthesis and signaling in human, mouse, and rat. Applications of the extensive genomic and lipidomic research to date illustrate the questions in eicosanoid signaling that could be uniquely addressed by a thorough analysis of the entire eicosanoid proteome.
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Cites
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Self-citationComprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast
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Cited by
Omega-3 Fatty Acids and Cardiovascular Disease
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Eicosanoid storm in infection and inflammation
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Self-citationLipidomics reveals a remarkable diversity of lipids in human plasma
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Self-citationEndocannabinoid Hydrolysis Generates Brain Prostaglandins That Promote Neuroinflammation
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Metadata
- Authors
- Buczynski, M. W., Dumlao, D. S., & Dennis, E. A.
- Venue
- Journal of Lipid Research
- Publisher
- Elsevier BV
- Published
- 2009-06
- Volume
- 50
- Issue
- 6
- Pages
- 1015-1038
- ISSN
- 0022-2275 (ISSN-L)
- OpenAlex
- W2058541568
- Cited by
- 530
- References
- 12
- FWCI
- 17.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.