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Langmuir. 2015 Jun 30;31(25):7091-9. doi: 10.1021/acs.langmuir.5b01173. Epub 2015 Jun 15.

Formation of Giant Unilamellar Proteo-Liposomes by Osmotic Shock.

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†Laboratoire de Physique Statistique, Ecole Normale Supérieure de Paris, Université Pierre et Marie Curie, Université Paris Diderot, Centre National de la Recherche Scientifique, UMR 8550, 24 rue Lhomond, 75005 Paris, France.
‡Department of Cell Biology, Nanobiology Institute, School of Medicine, Yale University, 333 Cedar Street, New Haven, Connecticut 06520, United States.
§Laboratoire de Cristallographie et RMN Biologiques, Université Paris Descartes, CNRS, UMR 8015, Paris, France.


Giant unilamellar vesicles (GUVs), composed of a phospholipid bilayer, are often used as a model system for cell membranes. However, the study of proteo-membrane interactions in this system is limited as the incorporation of integral and lipid-anchored proteins into GUVs remains challenging. Here, we present a simple generic method to incorporate proteins into GUVs. The basic principle is to break proteo-liposomes with an osmotic shock. They subsequently reseal into larger vesicles which, if necessary, can endure the same to obtain even larger proteo-GUVs. This process does not require specific lipids or reagents, works under physiological conditions with high concentrations of protein, the proteins remains functional after incorporation. The resulting proteo-GUVs can be micromanipulated. Moreover, our protocol is valid for a wide range of protein substrates. We have successfully reconstituted three structurally different proteins, two trans-membrane proteins (TolC and the neuronal t-SNARE), and one lipid-anchored peripheral protein (GABARAP-Like 1 (GL1)). In each case, we verified that the protein remains active after incorporation and in its correctly folded state. We also measured their mobility by performing diffusion measurements via fluorescence recovery after photobleaching (FRAP) experiments on micromanipulated single GUVs. The diffusion coefficients are in agreement with previous data.

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