Lipid and protein content profiling of isolated native autophagic vesicles

Schmitt, Daniel; Bozkurt, Suleyman; Henning-Domres, Pascale; Huesmann, Heike; Eimer, Stefan; Bindila, Laura; Behrends, Christian; Boyle, Emily; Wilfling, Florian; Tascher, Georg; Munch, Christian; Behl, Christian und Kern, Andreas (2022): Lipid and protein content profiling of isolated native autophagic vesicles. In: EMBO Reports, Bd. 23, Nr. 12

Abstract

Autophagy is responsible for clearance of an extensive portfolio of cargoes, which are sequestered into vesicles, called autophagosomes, and are delivered to lysosomes for degradation. The pathway is highly dynamic and responsive to several stress conditions. However, the phospholipid composition and protein contents of human autophagosomes under changing autophagy rates are elusive so far. Here, we introduce an antibody-based FACS-mediated approach for the isolation of native autophagic vesicles and ensured the quality of the preparations. Employing quantitative lipidomics, we analyze phospholipids present within human autophagic vesicles purified upon basal autophagy, starvation, and proteasome inhibition. Importantly, besides phosphoglycerides, we identify sphingomyelin within autophagic vesicles and show that the phospholipid composition is unaffected by the different conditions. Employing quantitative proteomics, we obtain cargo profiles of autophagic vesicles isolated upon the different treatment paradigms. Interestingly, starvation shows only subtle effects, while proteasome inhibition results in the enhanced presence of ubiquitin-proteasome pathway factors within autophagic vesicles. Thus, here we present a powerful method for the isolation of native autophagic vesicles, which enabled profound phospholipid and cargo analyses.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin > Adolf-Butenandt-Institut ; Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1469-221X
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198