Affordable Access

deepdyve-link
Publisher Website

Metabolic Labeling of Glycerophospholipids via Clickable Analogs Derivatized at the Lipid Headgroup.

Authors
  • Ancajas, Christelle F1
  • Ricks, Tanei J1
  • Best, Michael D2
  • 1 Department of Chemistry, University of Tennessee, 1420 Circle Drive, Knoxville, TN, 37996, USA.
  • 2 Department of Chemistry, University of Tennessee, 1420 Circle Drive, Knoxville, TN, 37996, USA. Electronic address: [email protected]
Type
Published Article
Journal
Chemistry and physics of lipids
Publication Date
Sep 05, 2020
Pages
104971–104971
Identifiers
DOI: 10.1016/j.chemphyslip.2020.104971
PMID: 32898510
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Metabolic labeling, in which substrate analogs containing diminutive tags can infiltrate biosynthetic pathways and generate labeled products in cells, has led to dramatic advancements in the means by which complex biomolecules can be detected and biological processes can be elucidated. Within this realm, metabolic labeling of lipid products, particularly in a manner that is headgroup-specific, brings about a number of technical challenges including the complexity of lipid metabolic pathways as well as the simplicity of biosynthetic precursors to headgroup functionality. As such, only a handful of strategies for metabolic labeling of lipids have thus far been reported. However, these approaches provide enticing examples of how strategic modifications to substrate structures, particularly by introducing clickable moieties, can enable the hijacking of lipid biosynthesis. Furthermore, early work in this field has led to an explosion in diverse applications by which these techniques have been exploited to answer key biological questions or detect and track various lipid-containing biological entities. In this article, we review these efforts and emphasize recent advancements in the development and application of lipid metabolic labeling strategies. Copyright © 2020. Published by Elsevier B.V.

Report this publication

Statistics

Seen <100 times