Characterization of cholesterol crystalline domains in model and biological membranes using X-ray diffraction.

Abstract

In this review, we discuss the use of small-angle X-ray diffraction approaches in studying the formation of cholesterol crystalline domains in membranes derived from model and biological sources. Numerous studies have shown that monomeric cholesterol can self-associate and form immiscible, membrane-restricted domains as a result of increased membrane concentration or systematic oxidation of membrane phospholipids. These domains are observed, in an X-ray diffraction pattern, as reflections describing a unit cell periodicity of 34 Å, which is consistent with cholesterol molecules arranged in a tail-to-tail, bilayer conformation. In vascular smooth muscle cells isolated from animal models of atherosclerosis, plasmalemmal cholesterol domain formation is associated with cellular dysfunction, including the disruption of calcium transport mechanisms. Cholesterol domains are also observed in macrophages and precede the deposition of extracellular cholesterol crystals in the atheroma. We have also shown that cholesterol domains can be produced in model membranes following exposure to oxidative stress and other disease-like conditions such as hyperglycemia. By contrast, cholesterol domains appear to be essential to the normal physiology of certain cell groups such as those of the human ocular lens. Cholesterol domains are a prominent structural feature of the lens fiber cell plasma membrane where they are believed to facilitate lens tissue transparency and interfere with various mechanisms of cataract formation. These contrasting biologic effects highlight the complex role that cholesterol domains play in cell plasma membrane structure-function relationships in both health and disease.KeywordsCholesterol DomainsFiber Cell Plasma MembraneFiber CellsCalcium Transport MechanismsTulenkoThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.