Live-cell lipid biochemistry reveals a role of diacylglycerol side-chain composition for cellular lipid dynamics and protein affinities
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Every cell produces thousands of distinct lipid species, but insight into how lipid chemical diversity contributes to biological signaling is lacking, particularly because of a scarcity of methods for quantitatively studying lipid function in living cells. Using the example of diacylglycerols, prominent second messengers, we here investigate whether lipid chemical diversity can provide a basis for cellular signal specification. We generated photo-caged lipid probes, which allow acute manipulation of distinct diacylglycerol species in the plasma membrane. Combining uncaging experiments with mathematical modeling, we were able to determine binding constants for diacylglycerol-protein interactions, and kinetic parameters for diacylglycerol transbilayer movement and turnover in quantitative live-cell experiments. Strikingly, we find that affinities and kinetics vary by orders of magnitude due to diacylglycerol side-chain composition. These differences are sufficient to explain differential recruitment of diacylglycerol binding proteins and, thus, differing downstream phosphorylation patterns. Our approach represents a generally applicable method for elucidating the biological function of single lipid species on subcellular scales in quantitative live-cell experiments.
Original language | English |
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Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 117 |
Issue number | 14 |
Pages (from-to) | 7729-7738 |
Number of pages | 10 |
ISSN | 0027-8424 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Bibliographical note
Copyright © 2020 the Author(s). Published by PNAS.
- Adenosine Triphosphate/metabolism, Biosensing Techniques, Cell Membrane/metabolism, Cell Survival, Diglycerides/chemistry, Isoenzymes/metabolism, Kinetics, Light, Lipids/chemistry, Models, Biological, Protein Kinase C/metabolism, Proteins/metabolism, Signal Transduction
Research areas
ID: 334033263