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Crowding-Induced Mixing Behavior of Lipid Bilayers: Examination of Mixing Energy, Phase, Packing Geometry, and Reversibility


In an effort to develop a general thermodynamic model from first-principles to describe the mixing behavior of lipid membranes, we examined lipid mixing induced by targeted binding of small (Green Fluorescent Protein (GFP)) and large (nanolipoprotein particles (NLPs)) structures to specific phases of phase-separated lipid bilayers. Phases were targeted by incorporation of phase-partitioning iminodiacetic acid (IDA)-functionalized lipids into ternary lipid mixtures consisting of DPPC, DOPC, and cholesterol. GFP and NLPs, containing histidine tags, bound the IDA portion of these lipids via a metal, Cu(2+), chelating mechanism. In giant unilamellar vesicles (GUVs), GFP and NLPs bound to the Lo domains of bilayers containing DPIDA, and bound to the Ld region of bilayers containing DOIDA. At sufficiently large concentrations of DPIDA or DOIDA, lipid mixing was induced by bound GFP and NLPs. The validity of the thermodynamic model was confirmed when it was found that the statistical mixing distribution as a function of crowding energy for smaller GFP and larger NLPs collapsed to the same trend line for each GUV composition. Moreover, results of this analysis show that the free energy of mixing for a ternary lipid bilayer consisting of DOPC, DPPC, and cholesterol varied from 7.9 × 10(-22) to 1.5 × 10(-20) J/lipid at the compositions observed, decreasing as the relative cholesterol concentration was increased. It was discovered that there appears to be a maximum packing density, and associated maximum crowding pressure, of the NLPs, suggestive of circular packing. A similarity in mixing induced by NLP1 and NLP3 despite large difference in projected areas was analytically consistent with monovalent (one histidine tag) versus divalent (two histidine tags) surface interactions, respectively. In addition to GUVs, binding and induced mixing behavior of NLPs was also observed on planar, supported lipid multibilayers. The mixing process was reversible, with Lo domains reappearing after addition of EDTA for NLP removal.

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