Journal of Lipid ResearchBiophysicsPeer-reviewed journal article
Lipid composition-driven sorting of ABHD5 between monolayer and bilayer surfaces
Shahnaz ParveeniD, Arvin NazariiD, James G. StebeltoniD, Kahlid Ali, Matthew Sanders, Amit KumariD, Katherine TruexiD, Yu‐ming M. HuangiD, James G. GrannemaniD, Christopher V. KellyiD
Abstract
Lipid droplets (LDs) are ubiquitous organelles that store neutral lipids and serve as central regulators of lipid homeostasis. Their structure includes a hydrophobic core of triacylglycerols and sterol esters surrounded by a phospholipid monolayer. This organization creates biophysical properties that guide selective protein recruitment. Among LD-associated proteins, /{beta}-hydrolase domain-containing protein 5 (ABHD5, also known as CGI-58) is a key regulator of lipolysis and broader lipid metabolism, yet the mechanisms guiding its distribution between endoplasmic reticulum (ER) bilayers and LD monolayers remain poorly understood. Because proper membrane association of ABHD5 is essential for activating PNPLA family lipases, identifying the determinants of its membrane selectivity is critical for understanding LD function in health and disease. In this study, we examined ABHD5 binding and sorting behavior using model membrane systems composed of giant unilamellar vesicles (GUVs) and droplet-embedded vesicles (DEVs) incorporating defined phospholipid and neutral lipid compositions. By integrating experimental assays with computational modeling, we quantified how ABHD5 partitions between bilayer membranes mimicking the ER and monolayer surfaces mimicking LDs. Systematic variation of membrane composition and physical properties allowed us to assess how packing defects and neutral lipid content shape ABHD5 localization. Our findings reveal the biophysical features that favor ABHD5 association with LD-like monolayers and provide new mechanistic insight into how cells target regulatory proteins to distinct membrane environments to control lipid metabolism.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- approach: experimental assay
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 46%
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