Membrane Lipid Nanodomains - ACS Publications - American

Review Cite This: Chem. Rev. XXXX, XXX, XXX−XXX

pubs.acs.org/CR

Membrane Lipid Nanodomains Marek Cebecauer,* Mariana Amaro, Piotr Jurkiewicz, Maria João Sarmento, Radek Š achl, Lukasz Cwiklik, and Martin Hof*

Downloaded via UNIV OF SUNDERLAND on October 26, 2018 at 20:53:30 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences, Dolejškova 3, 18223 Prague 8, Czech Republic ABSTRACT: Lipid membranes can spontaneously organize their components into domains of different sizes and properties. The organization of membrane lipids into nanodomains might potentially play a role in vital functions of cells and organisms. Model membranes represent attractive systems to study lipid nanodomains, which cannot be directly addressed in living cells with the currently available methods. This review summarizes the knowledge on lipid nanodomains in model membranes and exposes how their specific character contrasts with large-scale phase separation. The overview on lipid nanodomains in membranes composed of diverse lipids (e.g., zwitterionic and anionic glycerophospholipids, ceramides, glycosphingolipids) and cholesterol aims to evidence the impact of chemical, electrostatic, and geometric properties of lipids on nanodomain formation. Furthermore, the effects of curvature, asymmetry, and ions on membrane nanodomains are shown to be highly relevant aspects that may also modulate lipid nanodomains in cellular membranes. Potential mechanisms responsible for the formation and dynamics of nanodomains are discussed with support from available theories and computational studies. A brief description of current fluorescence techniques and analytical tools that enabled progress in lipid nanodomain studies is also included. Further directions are proposed to successfully extend this research to cells.

CONTENTS 1. Introduction 2. Lipid Nanodomains 3. Evidence of Lipid Nanodomains 3.1. Nanodomains in Model Membranes Composed of Neutral Glycerophospholipids, Sphingomyelins, and Cholesterol 3.1.1. Lipid Nanodomains Characterized by Förster Resonance Energy Transfer 3.1.2. Lipid Nanodomains Characterized by Fluorescence Correlation Spectroscopy 3.1.3. Direct Visualization and Tracking of Lipid Nanodomains: Interferometric Scattering Microscopy 3.1.4. Direct Visualization of Lipid Nanodomains: Atomic Force Microscopy 3.1.5. Lipid Nanodomains Characterized by Other Nonfluorescence Methods 3.2. Ceramide Domains 3.3. Glycosphingolipids and Nanodomains 3.3.1. Cerebrosides 3.3.2. Cerebroside Sulfates 3.3.3. Diglycosylceramides 3.3.4. Gangliosides 3.4. Nanodomains Enriched in Phosphoinositides 4. Protein-Induced/Modulated Lipid Domains 5. Ion-Induced/Modulated Lipid Domains 6. Membrane Asymmetry, Interleaflet Coupling, and Lipid Domains 7. Membrane Curvature and Lipid Domains © XXXX American Chemical Society

8. Mechanisms Controlling Lipid Nanodomains: Theory and Computational Studies 8.1. Theoretical Considerations 8.2. Nonequilibrium State 8.3. Support from Computational Studies 8.4. Theory and Models of Curvature-Modulated Lipid Nanodomains 9. Relevance of Nanodomain Model Systems for Cell Membranes 10. Conclusions Author Information Corresponding Authors ORCID Notes Biographies Acknowledgments References

A B D

E E G

H I I J K K L L L

V V W X Z AA AB AB AB AB AC AC AC AC

1. INTRODUCTION At some point in evolution the appearance of membranes was a crucial development toward life as we know it. Even before this turning point, prebiotic chemistry was likely to be accelerated within mesoporous compartments of rocks in geothermic fields where volume-confined complex chemical reactions could occur.1 Encapsulation of life-supporting reactions into vesicles formed of amphiphilic lipids enabled the formation of protocells and, later, the emergence of cellular

P R T T U

Received: May 21, 2018

A

DOI: 10.1021/acs.chemrev.8b00322 Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

debatable.17 We define “nanodomain” as any compartmentalization within a lipid membrane that has an estimated “diameter equivalent” within the range of 4−200 nm. This definition of lipid nanodomain is based on a diameter equivalent due to the limited knowledge of the exact geometry of the studied domains in many cases. The upper boundary is set by the resolution limit of standard optical microscopy (Abbe’s law18), which is around 200 nm. In other words, we consider lipid nanodomains to be membrane heterogeneities that cannot be resolved by standard fluorescence microscopy (Figure 1). Lateral diffusion of cell membrane components has

organisms.2 In the present days, lipid membranes do not solely encapsulate and protect the interior of cells but also provide subcompartmentalization and molecular organization of vital cellular processes. Structurally, lipids can form planar lipid bilayers, but other arrangements such as monolayers or hexagonal structures can be found in organisms, for example, in mammalian eyes and lungs or fusing vesicles, respectively. In this review, we will focus exclusively on lipid bilayers as this is the dominant structure of cellular membranes in both prokaryotic and eukaryotic cells. Similarly to the prebiotic reactions, it is favorable to organize and compartmentalize vital cellular processes for better control. Organelles function as primary organization units in eukaryotes. These are often surrounded by one or more lipid membranes that provide high selectivity and stability. However, some processes (e.g., translation, endocytosis, neurotransmission, ...) require rapid changes of molecular composition and physical state of membranes. Thus, it is more efficient for these processes to occur in more dynamic compartments. These functional territories can exist as threedimensional structures or as two-dimensional platforms on cell membranes.3 Macroscopic membrane domains, such as phagocytic cups or synaptic buttons, have been observed soon after the appearance of the first fluorescence microscopes.4,5 Modern advanced fluorescence microscopy and other imaging and spectroscopy techniques have demonstrated recently that the organization of cell membrane components appears nonrandom at the nanoscale.6−11 This new evidence suggests that both microscopic and nanoscopic membrane domains contribute to the function of living organisms.4,12,13 However, the details of nanoscopic membrane organization in cells remain elusive and strongly understudied. This is caused by the lack of techniques capable to visualize nanoscopic objects in cells and by the complexity of the cellular environment. Even though super-resolution techniques have improved the detail detectable by light microscopy, to some extent this has been achieved by sacrificing the temporal resolution required for live-cell imaging. Thus, an alternative approach is needed to investigate the mechanisms regulating the nanoscopic organization of lipid membranes. One option is to use model lipid membranes, such as multi- or unilamellar vesicles and supported planar bilayers, to study membrane organization in vitro. In this review, we summarize available experimental evidence of the existence of nanoscopic lipid domains in model membranes, emphasize their specific properties, and describe suggested mechanisms that can lead to their formation. Theoretical and in silico studies are also included and provide support for the mechanistic models. Finally, we discuss the relevance of these observations for cellular membranes.

Figure 1. Schematic representation of nanodomains existing in otherwise optically homogeneous membranes (and membrane patches). Membranes of giant unilamellar vesicles (GUVs) displaying a homogeneous distribution of fluorescent lipid probes, as determined by diffraction-limited microscopy (top left), can exhibit a random distribution of its components (bottom left) or nanohetorogeneities formed by specific molecules, i.e., nanodomains (bottom middle). Such nanoscopic membrane organization can be investigated only by using newly developed fluorescence and force microscopy techniques. Similarly, GUVs with large-scale phase separation (top right) can contain nanoscopic heterogeneities in either phase (bottom middle and right, respectively).

been measured to be in the range of 0.001−5 μm2/s;19,20 thus, the typical period any molecule might spend in these domains (microseconds up to seconds) is relevant for the time-scale of biochemical reactions (e.g., enzymatic).21 Moreover, intradomain contacts can be further stabilized by protein−protein interactions in cells, for example, thus adapting these entities for biological processes that may need longer periods for their completion. For the purpose of this review, we would also like to distinguish between lipid nanodomains and “lipid rafts”. Lipid rafts22 have been redefined during the Keystone Symposium on Lipid Rafts and Cell Function (March 23− 28, 2006 in Steamboat Springs, CO, USA) as “small (10−200 nm), heterogeneous, highly dynamic, sterol- and sphingolipidenriched domains that compartmentalise cellular processes”.23 In this review, we define a more general notion of nanodomains that does not select just a few lipids or their properties. Note that in the literature these two terms are sometimes used interchangeably.

2. LIPID NANODOMAINS Model lipid bilayers can be formed of a single phospholipid and be considered as homogeneous in terms of chemical composition. The addition of a second lipid or more lipids can lead to the formation of molecular clusters (usually not exceeding 4 nm in size) or domains with sizes ranging from nano- up to micrometers due to nonideal mixing of the different membrane components.14−16 This behavior of model membranes is reminiscent of cell membranes, which exhibit heterogeneities in this range. In this review, we focus on nanodomains and will not address the formation of molecular clusters, whose function in cell membranes is unknown and B


Chemical Reviews

Review

Figure 2. Four types of lipid nanodomains described in section 3. Schematic drawings of the structure of lipid nanodomains formed by (A) glycerophospholipids, sphingomyelins, and cholesterol, (B) ceramides, (C) glycosphingolipids, and (D) phosphoinositides. See also Tables 1 and 2 for general and lipid-specific properties contributing to the formation of lipid nanodomains. Nanodomain character is symbolized by corresponding photographies of (E) oil droplets, (F) ice cubes, (G) branchlet-rafts, and (H) charged oil droplets in a bowl of water.

Membrane domains have different chemical composition and/or physical properties compared to their surrounding lipid environment.24 Microscopic lipid domains are in fact often of different physical phase state than the remaining membrane. Lamellar lipid bilayers can exist in three main different phases: (i) Lβ phase, also called solid phase (So), is characterized by freezing of the lipid hydrocarbon chains. All molecular motions are highly restricted, which blocks lateral mobility of membrane components. The hydrophobic part of the bilayer is densely packed, thick, and ordered. (ii) Liquid crystalline Lα phase, named later liquid disordered phase (Ld), is highly fluid with maximal translational and rotational freedom, which guarantees membrane plasticity and efficient transport of membrane lipids and proteins. (iii) Finally, phospholipids can form a liquid crystalline ordered phase (Lo) in the presence of cholesterol.17 It has higher order of the hydrocarbon chains and reduced diffusion compared to the Ld phase, being considered as having intermediate properties between the So and the Ld phases. Large-scale (microscopic) phase separation in model lipid membranes has been demonstrated many times (Figure 1; e.g., ref 25). Similar microscopic segregation of membrane components has been observed also in giant plasma membrane vesicles (GPMVs) derived from cultured cells.26,27

Phase separation in these works has been demonstrated using fluorescent probes with different partitioning between phases. Nonetheless, experiments with Laurdan, a probe sensitive to membrane environment, suggest that the physical properties of membrane domains strongly depend on their lipid composition and that their categorization as an Ld/Lo phase might not be straightforward.28,29 This basic knowledge of the physical properties of lipid bilayers should be considered when interpreting specific observations but cannot be directly applied for describing nanodomains consisting of only 100 (diameter ≈ 5 nm) or hundreds to thousands of lipids (diameter ≈ 50 nm). At this point we would like to note that in this article we use frequently the terms “membrane order” and “membrane ordering” (as replacement of the longer, but more precise, “lipid acyl chain conformational order”) to convey concepts such as “Lateral and rotational freedom of molecules is reduced in more ordered membranes in comparison to less ordered membranes”. We preferentially use these terms because the Lo/Ld terminology cannot be straightforwardly applied to nanodomains. C


Chemical Reviews

Review

Figure 3. Illustration of the diversity of glycerophospholipid molecules in cellular membranes. Chemical structure of a glycerophospholipid molecule with indicated headgroup, glycerol, and acyl chain components is shown on the left-hand side. Fatty acid acyl chains of lipids varying in chain length and degree of saturation are shown. Most common headgroup types in cellular membranes: phosphatidylcholine (PC), phosphatidylglycerol (PG), phoshatidylethanolamine (PE), and phosphatidic acid (PA) are shown in the middle-right panel. For comparison, cholesterol is on the right-hand side. (Top panels) Stereochemical models−3D visualizations of molecules with atoms represented as solid van der Waals spheres rendered using VMD software.33,34 (Bottom panels) Chemical structures.

3. EVIDENCE OF LIPID NANODOMAINS Even though the existence of nanodomains in simple phospholipid mixtures was predicted a long time ago (e.g., ref 30), experimental evidence of their existence has been enabled only recently by the ability of Förster resonance energy transfer (FRET) to detect nanodomains. The first hints for the existence of lipid-driven nanodomains have been found by the groups of Manuel Prieto31 and Gerald Feigenson32 in 2001. Domains smaller than 20 nm have been detected in mixtures of dimyristoylphosphatidylcholine (DMPC) with 15, 20, and 25 mol % cholesterol at 30 and 40 °C31 using the FRET acceptor Rhodamine-dimyristoylphosphatidylethanolamine (Rhod-DMPE), which prefers less ordered bilayers and the donor nitrobenzoxadiazole-DMPE (NBD-DMPE) that has increased affinity for more ordered membranes. Similarly, using the fluorescent dyes DiI-C20:0 and DiO-C18:2, Feigenson and colleagues have found nanoscopic domains in large unilamellar vesicles (LUVs) composed of dipalmitoylphosphatidylcholine/dilauroylphosphatidylcholine/cholesterol (DPPC/DLPC/cholesterol) at room temperature. The authors concluded that the observed nanodomains should be larger than 5 nm.32 These initial investigations could be considered as a breakthrough in membrane heterogeneity studies. Later, numerous studies in membranes of different lipid species and in membranes of higher complexity in composition have found

both common and distinctive properties of lipid nanodomains that we shall describe in more detail in subsequent sections. The sections have been split into four categories according to the major lipid players involved in nanodomain formation: (i) glycerophospholipids, sphingomyelins, and cholesterol, (ii) ceramides, (iii) glycosphingolipids, and (iv) phosphoinositides (Figure 2). In the first section, we summarize the accumulating evidence on the existence of nanodomains in model membranes composed of diverse glycerophospholipids (Figure 3) and sterols (and sphingomyelins), bringing to light the effects of acyl chains and the presence of cholesterol. In the following sections, we review experiments demonstrating the formation of nanodomains by other physiologically relevant lipids, whose specific character is given either by geometry (ceramides), charge of headgroup (phosphoinositides) or the presence of sugars in headgroups (glycosphingolipids). Note that these characteristics do not apply exclusively to the lipid species highlighted. More general, physiologically relevant factors which may contribute to the formation of lipid nanodomains are summarized in Table 1. The cases in which the availability of data detecting subresolution domains are rare; thus, we often discuss potential nano-organization of lipids by extrapolating results acquired at large-scale membrane separation. D


Chemical Reviews

Review

Table 1. Elementary Factorsa Contributing to the Formation of Lipid Nanodomains in Model Membranes

Table 2. Characteristic Properties Contributing to the Formation of Lipid Nanodomains by Specific Lipid Families

• multicomponent character (two or more lipids forming a membrane) • nonideal lipid mixing due to (1) diverse length and saturation of lipid acyl chains (e.g., hydrophobic mismatch) (2) conflicting geometry (3) affinity/immiscibility to/with cholesterol • self-aggregation of one component • lateral networking of selected components (e.g., via favorable hydrogen bonding) • electrostatic interactions (e.g., with ions or proteins; see section 5) • interleaflet coupling and pinning (see section 6) • membrane curvatureb (see sections 7 and 8.3)

glycerophospholipids • highly heterogeneous acyl chain composition • impact of headgroup shape and charge sphingomyelins • capacity to form a network via hydrogen bonding • increased content of longer acyl chains • increased affinity to cholesterol ceramides • small headgroup (geometry) • highly rigid molecules • self-aggregation • capacity to form a network via hydrogen bonding • increased content of longer acyl chains cerebrosides • self-aggregation • capacity to form a network via hydrogen bonding • increased content of longer acyl chains • low miscibility with phosphatidylcholines • low miscibility with cholesterol diglycoceramides • capacity to form a network via hydrogen bonding • increased content of longer acyl chains • low miscibility with phosphatidylcholines • good miscibility with cholesterol gangliosides • self-aggregation • capacity to form a network via hydrogen bonding • increased content of longer acyl chains • cone shape (bulky headgroup) • good miscibility with phosphatidylcholines • good miscibility with cholesterol phosphoinositides • highly charged headgroup • interaction with divalent cations • association with cholesterol • association with specific binding proteins • aggregation via unspecific binding of basic-rich protein domains

a

Physiologically relevant factors were selected. bSeveral factors named above may cause membrane bending.

3.1. Nanodomains in Model Membranes Composed of Neutral Glycerophospholipids, Sphingomyelins, and Cholesterol

The most solid proof of the existence of lipid nanodomains comes from model membranes composed of neutral glycerophospholipids with different acyl chains and cholesterol (Figure 3). Even though sphingomyelins clearly differ from glycerophospholipids in their chemical structure (Figure 4),

choice for detection of nanodomains due to its nanometer scale sensitivity. This is based on two pillars: (i) FRET between a single donor and a single acceptor occurs at distances between 1 and 10 nm and (ii) certain fluorescently labeled lipid analogues have different affinities for bilayers of different order and/or different composition. Importantly, independent of the choice of the donor/acceptor lipid analogues, FRET has provided overwhelming evidence for heterogeneities with diameters below 100 nm in binary, ternary, or quaternary mixtures of various lipids (Table 3). This underlines the importance of lipid-based nanoheterogeneities in the organization of membranes. While in most of the studies in Table 3 the phase state of the nanodomains has not been experimentally determined, the authors infer that the observed nanodomains are in the Lo phase state with the sole exception being Š achl et al.38 In this later study, FRET and 31P static and magic angle spinning (MAS) nuclear magnetic resonance (NMR) experiments demonstrate that nanodomains in dioleylphosphatidylcholine (DOPC)/cholesterol/sphingomyelin bilayers are fluid and disordered with only subtle acyl chain ordering differences from the surrounding disordered phase.38 Independently of the fact that macroscopic phases

Figure 4. Chemical structures (left) and stereochemical models (right) of a ceramide, sphingomyelin, and glycerophospholipid distearoylphosphatidylcholine (DSPC) molecules. In 3D visualization atoms are represented as solid van der Waals spheres (rendered using VMD software33,34).

these studies have been included in this section. Sphingomyelins are frequently used to induce nanodomains in model membranes. This effect is believed to be mainly driven by specific composition of their acyl chains and affinity to cholesterol (Table 2).35−37 This section is divided into several logical units to highlight that specific information on the domains and their properties can be acquired by different techniques. 3.1.1. Lipid Nanodomains Characterized by Förster Resonance Energy Transfer. In Table 3 we summarize almost 20 years of FRET experiments supporting the existence of nanodomains in membranes composed of glycerophospholipids and/or sphingomyelins with physiological levels of cholesterol. It is evident that FRET has been the method of E


Chemical Reviews

Review

Table 3. Nanodomains in Model Membranes Composed of Neutral Glycerophospholipids, Sphingomyelin, and Cholesterol lipid mixtures

estimated diametera

assumed phase state of the domain

used donor/acceptor FRET pair

DMPC:Chol χChol ≈ 0.15−0.3

5−6 nm

Lo

DiI-C20:0/DiO-C18:2

POPC:PSM:Chol χChol ≈ [0.05−0.2] and χPSM 30 mN/m)171,184 (Table 4). Comparison between the features of GM1 ganglioside nanodomains in monolayers and in supported hybrid bilayers (with the same composition of the top monolayer) shows small morphological differences: the elongated structures found in monolayers give place to irregularly shaped domains at low surface pressures or round-shaped domains at high pressures (>35 nN/m) in the bilayer system81,187 (Table 4). However, one should keep in mind that a different fatty acid composition of used gangliosides has the potential to alter its self-aggregation behavior.169,188 Therefore, comparison between studies from different laboratories or with different batches of gangliosides of animal origin should be done carefully. Thus, where available, we present the source of gangliosides in Table 4. As seen in a glance from the comprehensive list in Table 2, there have been almost no studies reported on gangliosides other than GM1. To date, the one report of AFM studies on supported hybrid bilayers containing GM3 ganglioside shows the presence of nanodomains with characteristics that are

Figure 10. Schematic illustration highlighting the difference between random and homogeneous distributions of molecules in membranes (2-dimensional systems). Truly homogeneous distribution of molecules has never been found in model or cellular membranes.

The ganglioside nanodomains contain a significant amount of the other lipid molecules that constitute the examined bilayers. This early observation from thermotropic studies169,189 is supported by recent more direct190 or indirect investigations. Data obtained by AFM, 170,171,181,185,187 SAXD,191 and FRET54,55 have allowed determination of the surface area coverage by ganglioside nanodomains. These numbers strongly exceed the calculated areas for domains of pure ganglioside, thus indicating that nanodomains must include other lipid components. Such a nature of the ganglioside nanodomains has implications and consequence for their dynamic behavior. The nanodomains can have a variable degree of fluidity and dynamics depending not only on their ganglioside component but also on the type of membrane where they are embedded, i.e., what other lipids compose the domain and temperature/pressure of the system. As a consequence, their phase characteristics will vary depending on the conditions of the model system. This variation is already evident in the literature where, for example, (i) GM1 clusters present in monolayers with liquid condensed characteristics have been shown to behave as a liquid condensed phase,185 (ii) while in more disordered membranes, such as free-standing liquid disordered bilayers, the ganglioside domains display liquid disordered phase characteristics.54,55 Ganglioside nanodomains can be found in membranes of diverse ordering of acyl chains (liquid condensed and liquid expanded, liquid crystalline ordered, and liquid crystalline disordered phases, Table 4), and therefore, a simple common description of their ordering and dynamics cannot provided.54,55,192 Gangliosides are typically thought of as cone-shaped molecules (Figures 8 and 11) due to the properties of their headgroup (geometry and charge). This can affect the topology of the membranes where they are inserted as the local concentration of gangliosides in a nanodomain can lead to the creation of local curvature. In this respect, it has been found that low amounts of GD1a ( K+.259,261,262 Cations are known to cluster a variety of negatively charged lipid species including PS,263−268 phosphatidylglycerol (PG),261,269,270 cardiolipin,271 and PA,265 although probably the strongest interactions are those of calcium with PIs (see section 3.4). These acidic phospholipids are not major components of lipid membranes, but they have important physiological functions and have been shown to influence membrane structure.272 Divalent cations, like calcium, adsorb also to neutral bilayers formed from zwitterionic lipids, which indicates that Coulomb attraction might not be the only force that governs lipid−ion interactions.267 As with the other types of interactions leading to lipid demixing (Table 1), most of the experimental evidence comes from model systems and large-scale domains. As early as 1973, calcium-induced phase separation has been found in PS/ lecithin model membranes at a low content of lecithin (a mixture of glycerophospholipids of natural origin) using electron spin resonance spectroscopy (ESR).273 Broadening of the ESR spectra of a spin-labeled lecithin, caused by enhancement of intermolecular spin−spin exchange, has been interpreted as lateral separation of lecithin molecules. On the basis of the same interpretation of ESR measurements, Galla and Sackmann identified heterogeneities in synthetic dipalmitoylphosphatidic acid (DPPA)/DPPC lipid bilayers.274 Their calculations reveal the size of the heterogeneities to be ∼8 and ∼4.5 nm in the absence and presence of Ca2+, respectively. These findings have been later confirmed using freeze-etched electron microscopy on GUVs composed of dioleoylphosphatidic acid (DOPA)/DOPC (1:1) and DOPC/cardiolipin (1:1) mixtures at high Ca2+ concentrations (∼1 M). The data demonstrates the presence of circular domains with 10−100 nm diameter that exhibit curvature toward the interior of the GUVs.275 The authors note that the effect induced by calcium chloride is similar to that of the cationic polypeptide poly-Llysine and ascribe both to the electrostatic interaction between cations and divalent anionic headgroups of DOPA. While the above-mentioned domains in negatively charged lipid bilayers are compact and relatively easy to detect, the evidence for nanodomains of anionic lipids in neutral (or zwitterionic) lipid environment is scarce. Nanoscale PS clusters in PC bilayers have been shown in MC simulations paired with measurements of the concentration of calcium available for BAPTA probe binding.276 Unfortunately, the assumption that calcium ions bind exclusively to PS molecules in the PC/PS mixtures is currently unacceptable (see, e.g., ref 267). In a more recent study, Annexin A1 is found to reveal the presence of palmitoyloleoylphosphatidylserine (POPS)-enriched calcium-dependent nanodomains in POPC/POPS-supported lipid bilayers at low (5, 10, and 20 mol %) POPS content and

6. MEMBRANE ASYMMETRY, INTERLEAFLET COUPLING, AND LIPID DOMAINS The compositional asymmetry of the plasma membrane has been known for more than 40 years (for an overview on the distribution of individual lipid classes between the two leaflets see a recent review by Fujmoto and Parmyd282). Membrane asymmetry governs a vast manifold of biological membrane functions (see, e.g., refs 283−285). Nonetheless, it has been only during the past decade that the extent and the mechanisms of interleaflet communication have started to be systematically characterized. Intrinsically, the small size and highly dynamic nature of nanodomains has made it impossible to conduct comprehensive experimental studies on asymmetric nanodomains. Therefore, experimental studies identifying acylchain interdigitation, cholesterol flip-flop, electrostatic coupling, composition-curvature coupling, and line and interleaflet tensions as possible mechanisms leading to interleaflet communication have been mainly performed on asymmetric lipid bilayers exhibiting large-scale phase separation. The mentioned mechanisms have been comprehensively summarized in a recent review.286 It is evident that interleaflet coupling and communication is a dominating phenomenon in lipid T


Chemical Reviews

Review

domains. To date, this is the only experimental proof of interleaflet coupling in lipid nanodomains to the best of our knowledge. More studies are needed in larger sets of membranes, but this mainly depends on technological developments to enable such investigations and allows us to understand the general principles associated with asymmetric membranes and interleaflet coupling.

bilayers, but its molecular origin is yet to be fully understood. When increasing the complexity and going from model membranes to biological membranes the influence of (asymmetric) transmembrane proteins on lipid distribution must be considered as well.286 Moreover, there is now a plethora of studies showing that the organization of outer leaflet lipids is influenced by the actin cytoskeleton.10,287−290 This is certainly the most convincing demonstration that molecular pinning is an essential player when it comes to interleaflet communication in biological membranes (Figure 14).

7. MEMBRANE CURVATURE AND LIPID DOMAINS It has been noted already in previous sections that curvature can be induced by the aggregation of lipids and that curvature can impact lipid demixing (Table 1). In this section, we summarize current knowledge on curved membranes and its impact on lipid nanoscale organization and provide deeper insight into this important aspect of lipid membranes (see also section 8). Biological membranes are mostly curved. Not only are smalltransporting vesicles and intracellular structures such as mitochondria, Golgi, or endoplasmic reticulum built of curved lipid bilayers but also the plasma membrane itself exhibits a significant amount of protrusions such as microvilli and filipodia (Figure 15) or invaginations (e.g., caveolae). Over

Figure 14. Membrane asymmetry and pinning. (A) Many cellular membranes exhibit asymmetry, for example, by accumulating sphingolipids and glycerophospholipids with long or saturated acyl chains in the outer leaflet of the plasma membrane. Negatively charged lipids (e.g., PS, PI) are selectively in the inner leaflet. (B) Binding of proteins with positively charged surface affects the organization of negatively charged lipids in the inner leaflet. Due to the interleaflet coupling, lipids of the outer leaflet are also reorganized at the site corresponding to the PI/PS nanodomain. This process is called pinning.

Figure 15. Membrane curvature. (A) Schematic illustration of curved membranes in biological systems. Microvilli and other membrane protrusions exhibit high curvature at their tips that potentially can lead to local sorting of specific lipids. There are currently no data available on the distribution of specific lipids in such cell membrane structures. (B) Images of dendritic and white blood cells (top and bottom images, respectively) acquired using scanning electron microscopy by Kim et al. (Adapted with permission from ref 293. Copyright 2018 Springer Nature, licensed under Creative Commons Attribution 4.0) and Jung et al. (Adapted with permission from ref 294. Copyright 2016 National Academy of Sciences). Images demonstrate how the plasma membrane of mammalian cells is densely populated with microvilli, ruffles, and other structures with curved membranes.

In an attempt to extrapolate from these large-scale studies down to nanodomains, it would seem straightforward that in both model and biological membranes the opposing leaflets mutually influence each other’s physical state and possibly composition. However, to what extent the short lifetime of nanodomains provides enough time for opposing leaflets to communicate is still unclear. A recent SANS study on asymmetric POPC/DSPC/cholesterol bilayers shows that ∼13 nm diameter domains are in register (coupled) across the bilayer leaflets.91 Noteworthy, in this pioneering study the vesicles are of 60 nm diameter size, and the proportion of domain area is about 30%. This fact might lead to the speculation that membrane curvature as well as high domain density supports the interleaflet coupling in these nano-

the years, membrane curvature has been suggested to play a role in lipid sorting and domain formation, mainly in cells. In studies of biological membranes, curvature has been mainly “used” to characterize specific interactions of particular proteins/peptides with lipids. Although protein−lipid interactions have been mentioned in previous parts of this review (see sections 3.3 and 3.4), the general principles of proteininduced membrane curvature are not the main focus of this work and are thoroughly described elsewhere.291,292 From the lipid point of view, membrane curvature imposes constraints, U


Chemical Reviews

Review

8. MECHANISMS CONTROLLING LIPID NANODOMAINS: THEORY AND COMPUTATIONAL STUDIES In the previous sections we summarized the available experimental evidence demonstrating the existence of lipid nanodomains in membranes with diverse chemical composition as well as physical state and shape. We also mentioned several factors (e.g., proteins, ions, curvature) with potential to induce and modulate lipid nanodomains in cellular membranes. In this section, we discuss theoretical considerations suggested to describe formation and modulation of lipid nanodomains. Some of these theories found support in computational studies which are summarized in the second part of this section. Additionally, theoretical and computational studies of curvature impact on lipid nanodomains are presented in the third part. Due to limited availability of similar studies for other factors or their total absence, we mentioned those in the previous sections describing particular effects described therein. Contrary to the very existence of lipid nanodomains in model membranes, these mechanisms still await their direct experimental proof.

and thus, it is naturally expected to influence local membrane composition. Lipids differ in their geometry (Figure 11), and this leads to differences in the spontaneous curvature of their monolayers.295 Hence, lipid demixing occurs based on their preference for membranes of different curvature, leading to the formation of heterogeneities. Interestingly, the shape of some lipids can be modulated as exemplified by cardiolipin. The molecule adopts a wider conical shape upon binding of magnesium ions, which destabilizes the lipid bilayer.271 Curvature affects also local transbilayer coupling in asymmetric lipid bilayers,296 which in turn can influence nanodomain formation as discussed in the previous section. There is very little experimental evidence on nanoscopic organization in model curved membranes. However, membrane curvature or domain-induced membrane bending has been fairly described in large-scale lipid domains.297−300 In general, domain bulging in phase-separated vesicles results from the energetic necessity to reduce the length of the domain boundary. Baumgart and co-workers used fluorescence microscopy on DOPC/cholesterol/sphingomyelin GUVs to directly visualize domaininduced curvature. Making use of two dyes partitioning to more ordered (perylene) and less ordered (Rhod-DPPE) lipid phases, the authors provide proof for a clear relationship between domain lipid composition and membrane curvature at the micrometer scale. The first observation of curved solid-phase lipid nanodomains has been done using freeze fracture electron microscopy and was described in detail ∼40 years ago.301 Fluid domains, however, are more difficult to study. A few groups have characterized curvature-related fluid nanodomains using model membrane systems.63,301,302 The combination of fluorescence and force measurements on membrane tubes of controlled diameter that are pulled from GUVs (DOPC/ cholesterol/sphingomyelin) have allowed Sorre and co-workers63 to quantitatively show that a difference in lipid composition (as determined by partitioning of fluorescent probes) can be sustained between curved and noncurved coexisting membranes. This has been enabled by using a membrane at the proximity of a demixing point. In the absence of induced curvature, the system would otherwise appear to have a homogeneous lipid distribution. In an alternative approach, Cheney et al. used supported lipid bilayers assembled over a nanopatterned surface (i.e., carboxylate modified polystyrene nanoparticles deposited on flat glass surface) to obtain defined regions of membrane curvature.302,303 The authors report differences in the distribution of diverse fluorescent probes between the curved and the flat regions of the POPC membrane. Moreover, highly confined motion is observed in the curved regions, confirming that dynamics of single molecules can be affected by membrane curvature.302 It should be noted that membrane curvature is often studied in small unilamellar vesicles (SUVs) with diameters ranging from 20 to 100 nm. Any phase separation in such small vesicles is imperatively nanometric. The size of the domains might however increase in membranes with a larger available area (e.g., LUVs or GUVs). Therefore, the biological relevance of nanodomains observed in SUVs may be limited to naturally occurring vesicles of sizes below 100 nm (e.g., secretory and endocytic vesicles).

8.1. Theoretical Considerations

We have defined lipid nanodomains as membrane heterogeneities with size between 4 and 200 nm. For such small lipid domains, global thermodynamic quantities, such as line tension, are not well defined.24 In particular, the Gibbs phase rule is not necessarily obeyed, and hence, the concept of phase diagrams must be considered with care.304 Of note, this does not mean that thermodynamic principles do not hold for lipid nanodomains but rather that the macroscopic limits of thermodynamics cannot be straightforwardly applied to such small objects. This issue can be resolved by the application of nanothermodynamics,304−306 but no systematic approach of this type has been used to describe lateral lipid domains so far. Therefore, we discuss the theory behind potential mechanisms responsible for lipid nanodomain formation and stabilization within the limits of available literature, which uses mainly largescale approaches. Nanoheterogeneities in lipid membranes, by their very nature, pose a significant challenge for theoretical description. An obvious first choice is to describe such lateral heterogeneities in terms of standard thermodynamics and phase diagrams neglecting, as a first approximation, the problems that global thermodynamics may encounter in such small systems. The thermodynamic description of phase transitions (e.g., between translationally and rotationally ordered and disordered lipid phases, i.e., solid and liquid) occurring in one component lipid bilayers is historically successful. Similarly, liquid-ordered/liquid-disordered phase transition and phase coexistence in cholesterol-containing bilayers are well described by phase diagrams, at least at the macroscopic length scale.307 However, in the presence of two or more components a more complex phase behavior is expected. The multifarious character of a system is then especially prominent in membranes with nanoscopic heterogeneities. On the other hand, even a pure thermodynamic phase under equilibrium does not represent a truly homogeneous distribution of molecules or their states (Figure 10). Consequently, so-called dynamic lateral heterogeneities of different density have been predicted even in singlecomponent lipid bilayers.308 V


Chemical Reviews

Review

one saturated and one polyunsaturated acyl chain; Figure 3), which are abundant in cellular membranes.37 If we assume that line tension is the sole driving force for lipid segregation into domains, it is theoretically predicted that lipid nanodomains of sizes in the range of 20−50 nm are metastable over time scales from minutes to days.318 However, domains smaller than 10 nm will be unstable under these conditions. Nonetheless, it has been demonstrated that line tension itself cannot fully reproduce lipid domain formation, aggregation, and domain size distributions observed in model membranes.299,310 Therefore, additional factors must contribute to domain formation and stability. For example, long-range repulsion between lipid nanodomains may contribute to their stabilization. As a step in this direction, Seul and Andelman introduced the concept of modulated phases.319 Here, minimization of line tension determines the perimeter of domains, and opposing longrange repulsion competes with line tension to break up domains into the two coexisting phases. Transformation of nanodomains to transient modulated phases has been observed in GUVs.320 The process resulted in the formation of largescale phase separation (as defined by global thermodynamics). Apart from repulsive forces, domains in modulated phases may also be kinetically entrapped.149,318 In another study focused on the importance of line tension, a threshold line tension value has been identified in model membranes. Below the threshold, nanodomains remain in quasi-equilibrium having their size distribution controlled by entropic entrapment, while above the threshold nanometer-sized domains coexist with larger ones.321 Theoretical studies also propose that the interdomain repulsion, which hampers domain coalescence, may be exerted by electrostatic forces (dipolar or Coulombic),322,323 by forces related to the spontaneous curvature of the coexisting phases (see section 8.3),324 as well as by hydrodynamic drag forces related to the motion of the domain.310,325 The need for the phenomenological introduction of repulsive forces is independently supported by other theoretical works in which, using field theory, it is shown that in complex model membranes mimicking cells at least three factors (dipolar repulsion, line tension, and intrinsic separation tendencies) are needed to explain the existence of nanomenter-sized lipid domains.322 Note that very slow relaxation kinetics has been shown in membrane organization, and even hours may be needed to achieve an equilibrium, both at nanoscale and for large-scale phase separation.149,326

In canonical thermodynamics, lateral domains may arise close to a phase transition due to either nucleation or spinodal decomposition (Figure 16). Nucleation prevails typically for

Figure 16. Schematic illustration of the mechanisms suggested to be responsible for the formation of lipid nanodomains. Evolution of membrane heterogeneities by nucleation (top; drawing) or spinodal decomposition (bottom). Spinodal decomposition is illustrated for random initial data using the Cahn−Hilliard equation.311 Solution was found numerically using Matlab 7.11 script based on Eyre’s method.312 Both mechanisms can theoretically result in the formation of lipid domains.

systems far from critical points, for instance, when a significant change of temperature or system composition occurs rapidly. It leads to the formation of a relatively small number of nuclei that subsequently grow and can further merge to form a new phase. This is typically a slow process as formation of nuclei is related to high free energy barriers. In contrast, spinodal decomposition (Figure 16) occurs typically in systems very close to critical points, i.e., with mild changes in conditions. Such changes result in the formation of numerous small initial domains (often molecular clusters) in the entire volume of the system, which subsequently grow and form a new phase. There should be no significant energetic barrier for the formation of small aggregates during spinodal decomposition. The process is determined mainly by diffusion of membrane molecules. In model lipid systems based on GPMVs it has been experimentally shown that phases coexist close to critical points.309 This suggests that spinodal decomposition is responsible for domain formation. In living cells, however, both small and large changes with respect to phase diagrams are expected at the same time, and hence, nucleation and spinodal decomposition may occur simultaneously.310 Following the spinodal decomposition view it must be assumed that barrierless formation of nanodomains in lipid membranes occurs only at low values of line tension. Line tension quantifies the energy of the edge of a 2-dimensional aggregate.313 It can be also understood as the 2-dimensional analogue of surface tension. In the extreme case of zero line tension, finite size domains would be thermodynamically stable under equilibrium.314 Hypothetically, “linactant” molecules (2dimensional equivalent of surfactants) can incorporate at the domain boundary and diminish the line tension in model (and potentially cellular) membranes. Indeed, such linactant properties are exhibited by hybrid lipids315−317 (phospholipids having

8.2. Nonequilibrium State

The discussion beforehand assumes conditions of thermodynamic equilibrium. However, equilibrium can hardly be related to the state of living cells.20,24 Cell membranes undergo constant changes (local and global) related, for instance, to metabolic turnover of lipids, signal transduction, or membrane trafficking. Lipid turnover has been shown to be able to stabilize domains in theoretical studies.327−329 Other relevant dynamic stimuli potentially affecting the existence of lipid nanodomains in cellular membranes include rapid turnover of local curvature (e.g., microvilli),330 reorganization of proteins upon external stimuli, lateral and transversal diffusion, and many other phenomena.3,20,331 These nonequilibrium events are in general coupled with the lipid demixing and phase transition-related phenomena described earlier. However, due to the time needed for nanoscopic and large-scale lipid organization (see above), lipid nanodomains in cells cannot be W


Chemical Reviews

Review

size of the observed domains is limited by the total size of the simulated system. Typically, a launch of structures larger than one-half the size of a simulated system would encounter socalled finite size artifacts. The mentioned simulations are typically limited to 10 000−100 000 lipids in one leaflet, which corresponds to membrane patches with the size of up to 250 × 250 nm. To observe formation of larger nanodomains, from 100 to 200 nm, simulations of larger systems are needed. This issue may be solved only through using massive parallelization techniques. Accordingly, formation of multiple one-component nanodomains with broad size distribution, up to 160 nm, has been demonstrated in recent models of big patches of fluid two-component lipid membranes consisting of up to 3.2 million lipids.340 Protein-induced lipid nanodomains have been investigated also in MC simulations. An interesting example of such simulations including protein−lipid interactions is a work by Almeida et al., where annexin-mediated lipid domain formation is studied in various PC/PS mixtures in the presence of calcium cations.341 The protein is immobilized, and the formation of PS nanodomains induced by annexin is observed. The influence of membrane environment, namely, the cytoskeleton, has been also tackled in MC simulations. Twocomponent DMPC/DSPC membranes in the presence of cytoskeleton modeled as fluctuating barriers has been simulated by Ehring et al.342,343 These simulations predict that the observed formation of nanoheterogeneities during the solid−fluid transition results from subdiffusion behavior. Of note, these studies have the limitation of the lattice character of MC simulations and of the different sizes and diffusion of lipids and proteins in lateral membranes that cannot be fully accounted for with this method. The MC simulations mentioned above were realized for inlattice models omitting most of molecular details of lipid molecules. In contrast, the structural and energetic details of molecules can be included in the model of MD simulations. Frequently, a fully atomistic description of lipid molecules building the bilayer under study is considered. As this is computationally demanding, two popular approximation approaches emerged. First, a united-atom approximation has been used in which some of the hydrogen atoms are not explicitly included. Here, the lipid force field introduced by Berger et al. is the most prominent.344 Second, more approximated coarse-grain models have been introduced. The most popular is the MARTINI model with typically four heavy atoms being considered to form a single pseudoatoma bead.345,346 There is one important distinction between MD and MC simulations in the context of lipid nanodomains. A typical MC simulation aims only at the equilibrium properties of the studied system. On the contrary, the system dynamics is properly described in MD by the very nature of the method. This fact is important for the description of domain formation, restructuring, and any intra- and interdomain interactions, since all of these phenomena can be fully characterized only using MD methods. Risselada and Marrink used the MARTINI coarse-grain approach to simulate ternary mixtures of phospholipids and cholesterol forming ordered domains.347 Importantly, both planar and vesicle-like membranes have been considered. The systems consisting of approximately 2000 lipids have been simulated for up to 20 μs. Formation and coexistence of more ordered nanodomains in less ordered membranes have been shown to occur within a few microseconds. Moreover, small

satisfactorily described by conventional thermodynamics. Other factors, such as the association with the extracellular matrix and cytoskeleton, further add to the multifaceted properties of cellular membranes.20 These properties have been theoretically shown to have the potential to directly influence lipid nanodomain formation.332 Currently, there is no coherent analytical theoretical approach able to tackle the issue of lipid nanodomains under such complex conditions. Indeed, this is similar to a majority of complex phenomena occurring in condensed phases. Nevertheless, for a few decades such systems have been successfully treated by computational simulation techniques, mainly MC and MD. These methods can be viewed also as “in silico experiments”. They allow inclusion of environmental influence, can describe out-of-equilibrium phenomena, and are particularly well suited for the analysis of local nanoscale heterogeneities. The advances of simulation techniques in studying lipid nanodomains are described below. 8.3. Support from Computational Studies

Historically, MC simulations have been used before MD in biophysical studies of lipid membranes. The MC method, especially its lattice Ising model-like approach, is particularly well suited for investigation of lipid domain formation in condensed phases and has been widely used for this purpose since its inception. 333,334 We present here the most representative studies, in our opinion, that directly or indirectly investigate the existence of lipid nanodomains. Mouritsen et al.335 have been the first to predict the formation of metastable nanodomains in membranes composed of a single lipid (DPPC) close to solid−liquid-phase transition. In this work, the authors demonstrate and emphasize that in the transition regions, where formation of nanodomains is expected, analytical theories are typically insufficient and the application of MC or other simulation approaches is required. In their following work, Mouritsen et al. studied solid−liquid transitions in binary PC lipid mixtures of different acyl chain lengths and find local ordering and tendency for lipid selfclustering at the nanoscale.336 Both of these seminal works have predicted the existence of lipid nanodomains in singleand multicomponent membranes long before their (wet) experimental demonstration. The size, lipid phase, and temperature dependency of nanoscale domains have been further studied in simulations of DMPC/DSPC mixtures by Sugar et al.337 Hac et al. then combined MC and experimental studies to investigate the influence of nanocluster formation on the dynamics of DMPC/DSPC membranes.338 Spontaneous fluctuations and lipid domain formation (small fluid domains in the solid phase and small solid domains in the liquid phase) have been observed in this system. FCS experiments have been computationally simulated, and good agreement between FCS and earlier MC simulations has been found, hence providing strong support for the hypothesis suggesting the existence of dynamic lipid nanodomains in membranes with diverse lipid composition. To increase the complexity of studied systems, two- and three-component lipid membranes of PC, sphingomyelin, and cholesterol have been simulated and consequences of the observed nanodomain formation directly tested in FRET experiments.38,53,339 Again, formation of dynamic lipid nanodomains has been demonstrated in these more physiological systems. The described MC simulations have one important limitation regarding lipid nanodomain studies, namely, the X


Chemical Reviews

Review

relatively limited time scale indicate the early onset of nanoscopic lateral lipid organization. Of note, atomistic MD simulations are preferred for the determination of lifetimes of domains since coarse-grained models are known to suffer from nonrealistic kinetics.357 To investigate the coexistence of phase-separated domains in atomistic MD simulations, Sodt and coauthors358 pre-equilibrated pure-phase membrane patches of a few hundreds of lipids followed by the artificial introduction of liquid-ordered domains into a bulk liquiddisordered membrane. Stable ordered nanodomains have been observed on a few microsecond time scale. Javanainen and coworkers performed detailed fully atomistic MD simulations of several binary mixtures of DPPC and cholesterol to demonstrate that in certain regions of the phase diagram cholesterol determines the formation of fluid, cholesterol-rich nanodomains of irregular shape and ∼10 nm size (Figure 17).359 Interestingly, these domains had internal structure as

surface tension between the two leaflets of the bilayer has been hypothesized to cause interleaflet coupling of the domains. The study with lipid vesicles has demonstrated curvature-induced lipid sorting and formation of nanodomains.348 MARTINI coarse-grain MD simulations of extensive lipid bilayer patches, with over 120 000 lipids and on nearly millisecond time scale, were performed in 2005 by Stevens.349 Binary mixtures of phospholipids with varying tail length have been studied at the intermediate temperature of corresponding melting points. Formation and growth of thick solid-phase domains from initially random mixtures has been observed, as well as interleaflet coupling of domains. In another large-scale MARTINI MD simulation study of membrane patches with up to 5500 lipids (DPPC:DLPC:cholesterol; 35:35:30) on 10 μs time scale, it has been found that at least 1500 lipids are required to observe formation of stripe-shaped nanodomains, whereas structurally converged nanodomains are extrapolated to require at least 10 000 lipids.350 Lately, Ingólfsson and coworkers aimed at modeling the compositional complexity of an average mammalian plasma membrane as well as neuronal plasma membrane.351,352 The systems consist of ∼20 000 lipid molecules, 63 lipid species with all major headgroups and are simulated on 40 μs time-scale. Moreover, the membrane is designed asymmetrically to better mimic the plasma membrane. Heterogenous lateral organization of lipids in nanodomains of varying size has been found in this highly complex system. Various types of nanodomains are found to coexist: domains of polyunsaturated lipids, domains of GSL in the outer leaflet, and significant PIPs clustering in the inner leaflet. Cholesterol molecules have been found to also be prone to lateral organization in transient ordered domains. Potential effects of linactant molecules on the stabilization of lipid nanodomains has been studied also by the MARTINI approach. In this study, a model of lipid bilayers with preformed coexisting liquid order and disorder regions has been employed. The dynamic, fluctuating interdomain interface is observed to attract some of hybrid-chain saturated/ unsaturated lipids.353 Similarly, linactant molecules that act as domain promoters as well as detergents with domain diminishing effects have been identified in coarse-grain MD simulations of a liquid-state binary lipid membrane in the presence of nonlipid amphiphiles.354 A more detailed picture regarding lipid organization can be obtained via MD simulations employing atomistic force fields. Early MD studies are significantly limited in the number of lipids included in the considered membrane patches. Prevalently, there are only 128 lipids in two leaflets, which gives approximately a 6 × 6 nm lateral membrane size. Moreover, the simulated time scales are often below 100 ns. These two limitations have made it impossible to explicitly simulate nanodomain formation or properties. Nevertheless, atomistic MD studies provide numerous details regarding the lateral organization and molecular clustering of lipids under varying conditions. These include ionic effects, influence of lipid composition, interactions with peripheral proteins, etc.355 Here, we will cover only contributions where lipid nanodomains are considered explicitly. In 2007, Nemielä and coauthors analyzed ternary lipid membranes consisting of ∼1000 single-species lipids simulated on 100 ns time scales by atomistic MD simulations.356 In the simulated liquid-ordered phase, nanoscale lateral heterogeneities have been observed with sizes of a few nanometers and lifetimes estimated to be within the range of 10−100 ns. These results obtained on a

Figure 17. Snapshot from an atomistic MD simulation of the lipid bilayer DPPC:cholesterol exhibiting phase separation. Side view of membrane with indicated disordered and ordered nanodomains. Acyl chains and headgroups of DPPC are in blue and green, respectively. Cholesterol molecules are in light gray. Thinner membrane and disordering of acyl chains can be recognized in the Ld phase. On the other hand, a thicker membrane and ordered acyl chains dominate in the Lo phase. Cholesterol accumulates in the Lo phase. Adapted with permission from from ref 359. Copyright 2017 Springer Nature. Licensed under Creative Commons Attribution 4.0.

they typically contained a cholesterol-free packed phospholipid nanocluster core surrounded by a fluid cholesterol-rich region. In this work, modified sterol molecules are also used in combination with DPPC to demonstrate that the very molecular structure of the cholesterol moiety is crucial to promote nanodomain formation. This was possible only in the atomistic approach where all structural details of lipid molecules are explicitly included. Results from the MD simulations described above provide an excellent source for understanding the mechanistic properties of lipid nanodomains. However, these approaches are still undergoing intense development, and further simulations of diverse lipid membranes (e.g., including proteins) are still needed. Indeed, with recent access to increased computational power and improved algorithms, MD simulations, especially the atomistic variant, are becoming excellent tools to investigate issues related to the early stages of lipid nanodomain formation and their molecular structure, dynamics, and potential interactions with proteins. We strongly recommend using MD simulations as a complementary tool for experimental techniques that has the capacity to acquire atomistic insight into the studied processes. Y


Chemical Reviews

Review

curvature by external forces.362 As predicted by theory324 and evidenced in the experimental studies mentioned in section 7,63,302 lipid sorting due to curvature has been also computationally simulated. In MARTINI coarse-grained MD simulations, GM3 molecules have been observed to cluster in concave regions of the outer leaflet of a membrane (Figure 19).363 Simultaneously, the concave regions of the cytosolic

8.4. Theory and Models of Curvature-Modulated Lipid Nanodomains

In selected theoretical and computational studies mentioned above, the effect of curvature on lipid organization has been discussed or observed, but we focus on basic principles of nanodomain formation and dynamics. Here, we concentrate on theoretical works specifically dealing with membrane curvature and lipid organization. In many cases, the theory is supported by in silico simulations. In theoretical considerations membrane curvature has been recognized as a promising contribution to the stabilization of nanodomains via the reduction of their line tension. This effect diminishes the process of nanodomain aggregation and fusion into larger domains. In the extreme cases, this mechanism can lead to domain-induced membrane budding in order to minimize the length of the domain boundary.300,360 More often, depending on domain size and bilayer elastic properties, it results in the formation of “dimpled domains” (Figure 18).361 The curvature of dimpled domains provides a

Figure 19. Snapshot from a MARTINI coarse-grained MD simulation of curved membrane composed of POPC (25 mol %; light gray), POPE (25 mol %; red), POPS (7.5 mol %; blue), sphingomyelin (7.5 mol %; green), cholesterol (25 mol %; cyan), PI(4,5)P2 (5 mol %; yellow), and GM3 monosialoganglioside (5 mol %; magenta). MARTINI coarse-grained MD simulation was used to determine membrane curvature, which cannot be realistically captured using an atomistic simulation. (A) Side view showing the distribution of individual lipids in different parts of the curved lipid bilayer. (B) Schematic illustration highlighting the accumulation of GM3 and PI(4,5)P2 in areas of negative curvature on opposing leaflets of the asymmetric membrane, i.e., GM3 on top leaflet, PI(4,5)P2 on bottom leaflet. Adapted with permission from ref 363. Copyright 2014 Public Library of Science. Licensed under Creative Commons Attribution 4.0.

leaflet displayed an enrichment in PIP2 lipids (Figure 19). In both cases, PE accumulated in the regions of negative curvature, probably to structurally support the shape of the membrane. More recently, Boyd et al. found that cardiolipin segregates into regions of high negative curvature in the MARTINI model of the inner mitochondrial membrane.364 This finding has been explained by the fact that cardiolipin, possessing 4 alkyl chains, has inverted conical geometry and prefers the inverted hexagonal phase (HII) or lamellar phases with negative curvature. Since cardiolipin interacts specifically with many mitochondrial proteins, its sorting to negatively curved regions of the mitochondrial membrane can affect protein organization. The observed effect is specific for cardiolipin and could not be reproduced for PE (a lipid with conical geometry also present in mitochondrial membranes). The stability of nano- and large-scale domains can be modulated by the presence of linactants or local curvature in membranes. MC simulations show that membrane curvature stabilizes patterned (modulated) phases.365 MARTINI coarsegrained MD studies elucidate differences between nanoscale domains and local fluctuations366 and suggest compositional

Figure 18. Membrane curvature and lipid nanodomains. (A) Schematic illustration of a lipid bilayer with “dimpled” nanodomains. Negatively curved annulus of nanodomains prevents their coalescence into larger domains. (B) Lipids of conical shape can form nonregistered (top) or registered (bottom) lipid nanodomains. Registered, or coupled, nanodomains have a lower propensity to coalesce into larger domains.

mechanism for repelling neighboring domains as the topological deformation of the surrounding membrane keeps the domains apart (Figure 18). Various lipid species have different propensity to accumulate in curved areas or even to induce curvature. A good example is phosphatidylethanolamine (PE), which prefers negatively curved membranes and is known to destabilize flat bilayers by providing internal stress and facilitates the induction of Z


Chemical Reviews

Review

specific PIs on diverse cellular membranes (organelles and vesicles) is tightly regulated by a panel of enzymes and the availability of substrates as uncovered by diffraction-limited fluorescence microscopy (e.g., ref 381). Phosphorylation at different positions on the inositol ring provides an elegant way of controlling the local concentration of a specific PI variant. Moreover, divalent cations together with cholesterol can potentially reorganize PIs204,205 and generate platforms for PI-interacting proteins involved in cellular signaling or other cellular processes (see section 3.4). This selected example illustrates the capacity of lipids to rapidly respond to stimuli and form functional domains, potentially also at the nanoscale. Other lipid species, such as other glycerophospholipids or sphingolipids, are also highly metabolically active and can therefore similarly reorganize locally for rapid modulation of membrane-associated processes. In addition to headgroup modification of sphingolipids, the less well-known mechanism of acyl-chain remodeling37 also alters phospholipid chemistry and can lead to prompt changes of membrane physical properties and its nanoscopic (re)organization. Many such mechanisms involving lipid metabolism can potentially alter organization of cellular membranes but have not been investigated in cells to date. Nonetheless, further detailed understanding of the elementary mechanisms regulating lipid nanodomain dynamics is required for a meaningful progress of cell studies. For this, model membranes still represent the preferred experimental system. These studies are still incomplete, and more comprehensive investigations of lipid nanodomains in model membranes as well as systems involving lipid remodelling are needed. A better description of mechanisms determining the existence and dynamics of lipid nanodomains with theory and computational studies is also needed for proper interpretation of data from living cells. With the spread of advanced fluorescence techniques, including super-resolution microscopy, some observations have been interpreted as evidence for the existence of membrane nanoheterogeneities in living cells,6,9,382−384 including lipid rafts.385−390 As discussed in recent reviews,20,391 these observations may have many alternative explanations. One important limitation of current studies is that the 3dimensional nature of cellular membranes is flattened into 2dimensional images or maps, thus disregarding the impact of membrane topology (i.e., curvature) on their molecular organization. As discussed in the section about curvature (section 7), various lipids have different propensity to accumulate in or out of curved membranes.291 In cells this is further complicated by the presence of proteins, which form approximately one-half of the membrane mass.392 This means that most of the cell membrane lipids are in contact with proteins that exhibit large geometric irregularities in their transmembrane or membrane-associated segments. Additionally, the cytoskeleton and extracellular matrix further deform cellular membranes by inducing and/or supporting membrane protrusions in both directions, i.e., cytosolic and luminal/ extracellular. Finally, cells and their membranes are under constant changes (nonequilibrium character), which certainly affect their organization. For example, endo/exocytic events leading to the formation of small vesicles (50−200 nm in diameter) can rapidly change local composition and consequently the properties of the membrane. Less wellcharacterized and understood are membrane contact sites through which different organelles are in direct contact, e.g., endoplasmic reticulum and plasma membrane. Rapid metab-

bilayer asymmetry as an important source of membrane curvature.367 Already in the 1980s, Leibler and Alderman proposed a continuum model in which coupling of spontaneous monolayer curvature, bending, and composition provide a mechanism for stabilizing modulated structures and microemulsions in mixed planar membranes as well as in amphiphilic films.324 This concept has been further extended to asymmetric membranes368 and generalized to vesicles. It shows that in asymmetric vesicles, contrary to flat membranes, membrane bending caused by particular lipid components can induce domain formation at relevant degrees of coupling between the two monolayers (Figure 18).369 In case the two leaflets differ locally in their composition, local curvature is formed spontaneously and lipids are separated based on their propensity toward curved membranes. This can potentially lead to the formation of domains with sizes defined by the curved membrane regions. Biological membranes consist of many lipid species that differ in their spontaneous curvatures, and local changes in the composition of the two monolayers can be coupled to the local membrane curvature.370,371 Depending on the coupling strength, the appearance of curved domains with a size of about 100 nm has been predicted.371 Such domains have been also found in MC simulations.372 These curved domains are seemingly stable, exhibiting resistance to increased temperatures and not depending on membranes having to be close to phase transition. Recent computational work further explains the stabilization effect of curvature on membrane nanodomains.373 Relatively ordered nanodomains of ∼10 nm in diameter existing in both leaflets of a disordered bilayer of a two-component lipid system (artificial lipids P and C inspired by glycerophospholipids and cholesterol) are shown to have positive spontaneous curvature and oppose each other. This prevents large-scale bending of the membrane (Figure 18). Elastic interactions between the two leaflets of a nanodomain reduce the line tension between the phases, thus suppressing the growth of domains. In this work,373 the authors combine the Leibler−Andelman model324 with the model of Pincus and co-workers.374 The latter shows that finite spontaneous curvature of lipid molecules has a dramatic effect on the interactions of lipids with membrane inclusions such as transmembrane proteins. The contribution of elastic free energy to the stabilization of lipid nanodomains in curved membranes has been described by the combination of these two theoretical models.373 Line tension and its interplay with elastic energy has been proposed to also explain domain-induced membrane budding360,375 and nanodomain formation due to spatial lipid sorting in curved lipid bilayers.348,376 This again supports the importance of the interplay of line tension with additional factors in lipid domain formation as discussed above. A more detailed overview of the theoretical studies aiming at the relation between membrane lateral organization and their curvature can be found in recent reviews.286,299

9. RELEVANCE OF NANODOMAIN MODEL SYSTEMS FOR CELL MEMBRANES The nanoscopic organization of membrane lipids (and proteins) has been predicted to exist in cellular membranes377−380 and to have potentially critical roles for vital functions of the cells and organisms. A good example is the spatiotemporal organization of PIs. Even though the nanoscopic organization of these particular lipids in cells still awaits detailed characterization, the spatiotemporal organization of AA


Chemical Reviews

Review

olism and lipid transfer taking place in these sites393 is expected to constantly modulate the local membrane. The fact that living cells are not in equilibrium further complicates the interpretation of data on their lipid nanodomains, which are expected to be highly dynamic and fluid as the studies in model membranes indicate (Figure 6). It is important to emphasize here that all of the abovementioned studies, and many others, strongly support the view that cellular membranes exhibit heterogeneous organization of molecules at different length and time scales. It remains to be further evaluated how and to what extent lipids can be responsible for these heterogeneities and also whether the diversity of lipid species in cellular membranes is a consequence of physical and evolutionary forces selecting for molecules capable of forming robust and flexible lipid bilayers that adapt to the quick dynamics of living systems. Small molecules (e.g., methyl-β-cyclodextrin or latrunculin) have been employed to modify one or more cellular processes or change the chemical composition of cellular membranes. These have proved excellent tools for initial studies focused on understanding the very basics of cell structure and of molecules involved in a variety of physiological processes. However, these molecules are usually associated with extensive side effects and lack spatiotemporal selectivity. It is thus important to develop novel, probably genetic or opto-genetic approaches with improved biochemical and spatiotemporal selectivity to simplify interpretation of observed results in complex systems such as membranes of living cells.

modulate the size and character of nanodomains. Accepting that spinodal decomposition leads to nanodomains in model membranes, the size of the nanodomains is defined by the correlation length of the diffusing membrane molecules. The ability to resolve such correlation between a pair of lipid molecules depends on the sensitivity and time window of each individual method. These considerations rationalize the fact that different methods provide different values for the size of the domains and their relative area coverage. Binary mixtures represent the most minimalistic bilayer system for which the existence of nanodomains has been experimentally proven. Alongside the main glycerophospholipid, the second component can be another glycerophospholipid (including anionic glycerophospholipids), cholesterol, or a variety of different types of sphingolipids. The amount of the second component for forming detectable nanodomains varies substantially for the different components. For PIs, GSL, or ceramides, it is evident that only one or a few mole percent of each lipid is sufficient for forming nanodomains. On the other hand, it is harder to make such a generalization for simple glycerophospholipids, sphingomyelins, and cholesterol due to the wide variety in their lipid acyl chain and headgroup structure. However, it is apparent that in binary mixtures of these simple lipids it is necessary to have significantly larger amounts of the domain-inducing lipid in order to create detectable nanodomains. Increasing the complexity of the bilayer composition not only widens the propensity to find nanodomains but also increases the variety of different domain types in terms of morphological and physical characteristics. Among the ternary mixtures, the so-called “canonical lipid raft mixtures” of cholesterol, sphingomyelins, and phosphatidylcholines are not only used to study large-scale phase separation but also proved to be useful for understanding nanoscopic membrane heterogeneities. In particular, we would like to highlight that recent studies on these and other ternary mixtures show that the differences between the physical characteristics of nanodomains and the surrounding lipid matrix become very small as the size of the domains decreases. Nowadays there is quite comprehensive knowledge on the nanoscale organization of model membranes that allows us to predict the features of membrane organization for a given lipid mixture. Moreover, the considerable progress in computational modeling of membranes has brought us to a point where in silico studies provide important help in interpreting experimental findings on a molecular and even atomistic level. Many aspects of nanoscale membrane organization still require indepth investigation. For example, the influence of factors such as ions (except on PIs), transmembrane or peripheral membrane proteins, membrane asymmetry, and curvature is still heavily understudied. In saying that and having the highly complex cellular membranes in mind, we are certainly still far from understanding the fundamentals of nanoscale organization of cellular membranes.

10. CONCLUSIONS We start this closing section by reinforcing that even a pure thermodynamic phase under equilibrium does not represent a truly homogeneous distribution of molecules or their states (Figure 10). “Dynamic lateral heterogeneities” of different density have been predicted to exist in single-component lipid bilayers for more than 25 years. Increasing the complexity of lipid membranes by mixing different lipid species and even adding proteins will dramatically increase the variety of interactions between the individual membrane molecules (based on hydrophobicity, columbic forces, van der Waals dispersion, hydrogen bonding, hydration forces, and steric elastic strain).394 As a result of the manifold of these interactions and their cooperative character, the formation of nanoscopic heterogeneities, i.e., nanodomains, has to be viewed as the rule rather than the exception when discussing the lateral organization of membranes.24 The theoretical understanding of such nanodomains has been hampered by the fact that classical thermodynamic principles cannot be straightforwardly applied. Additionally, for a long time it has been experimentally difficult to observe and characterize such small objects. However, this situation has certainly changed within the last 20 years. While the recent development of nanothermodynamics might lead to a better theoretical understanding in the future, the characterization of nanodomains is already well underway through the use of several complementary state-of-the-art experimental techniques, as documented in this review. Combining the knowledge gained by fluorescence energy transfer, fluorescence correlation spectroscopy, interferometric scattering microscopy, electronspin-resonance spectroscopy, nuclear magnetic resonance spectroscopy, atomic force microscopy, X-ray techniques, and neutron scattering creates a quite defined picture of how specific lipid molecules and other membrane components

AUTHOR INFORMATION Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Marek Cebecauer: 0000-0002-4606-1218 Mariana Amaro: 0000-0002-4868-227X Piotr Jurkiewicz: 0000-0002-7823-8962 AB


Chemical Reviews

Review

Maria João Sarmento: 0000-0003-1765-4724 Radek Š achl: 0000-0002-0441-3908 Lukasz Cwiklik: 0000-0002-2083-8738 Martin Hof: 0000-0003-2884-3037

at the Royal Institute of Technology in Stockholm (Sweden, with Prof. Jerker Widengren). His research focuses on protein−membrane interactions and development of new fluorescence techniques. Lukasz Cwiklik obtained his Ph.D. degree in Theoretical Chemistry at Jagiellonian University, Krakow (Poland). After two postdoctoral stays, in the Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences (with Prof. Pavel Jungwirth) and in the Fritz Haber Center for Molecular Dynamics, Hebrew University in Jerusalem (Israel, with Prof. Victoria Buch), in 2010 he started working at the J. Heyrovsky Institute of Physical Chemistry of the Czech Academy of Sciences. In 2012 he obtained his habilitation. He studies lipid−protein systems with emphasis on their behavior under physiologically relevant conditions and in pharmacological context. The investigated systems span from lipid membranes, lung surfactant, to tear film.

Notes

The authors declare no competing financial interest. Biographies Marek Cebecauer is a senior scientist at J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences in Prague, Czech Republic. After graduating in Biochemistry at Comenius University in Bratislava (Slovak Republic) he received his Ph.D. degree in Immunology at Charles University (Czech Republic, with Prof. Vaclav Horejsi) in 2000. He studied the organization of plasma membrane in glycosphingolipid-enriched microdomains. He further analyzed organization of T-cell receptors during his postdoctoral position at the Ludwig Cancer Research Institute in Lausanne (Switzerland) and more general T-cell membrane properties as a Research Fellow at the Imperial College in London (UK, with Prof. Tony Magee). He established a new field of membrane physical biology in his current place.

Martin Hof is presently Director of the J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences. After receiving his Ph.D. degree in 1990 at the University of Wuerzburg (Germany, with Prof. Friedemann Schneider), he did research at the University of Carolina at Chapel Hill (USA, with Prof. Nancy Thompson), at the University Patras (Greece, Prof. Panagiotis Lianos), and at the Czech Technical University in Prague (Czech Republic, with Prof.Vlastimil Fidler). In 1997, he joined the J. Heyrovský Institute and built a laboratory developing fluorescence techniques and applying them in biophysics and biology.

Mariana Amaro is presently Head of the Department of Biophysical Chemistry at the J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences. She obtained her Diploma in Physics and Chemistry at University of Minho (Portugal) and received her Ph.D. degree in Physics at the University of Strathclyde, Glasgow (UK, with Prof. David Birch). She joined the J. Heyrovský Institute in 2011. Her main research interests focus on the interactions of amyloid proteins with membranes and understanding their significance in biology and disease, the photophysical characterization of novel fluorescent dyes and developing their applications in protein and membrane science, and the investigation of the structurefunction relationship of enzymes.

ACKNOWLEDGMENTS We thank Matti Javanainen for discussing computational analytical methods and his comments on Figure 17. The authors acknowledge financial support from the Czech Science Foundation (17-03160S). REFERENCES

Piotr Jurkiewicz obtained his Master’s degree in Biomedical Engineering (2002) and Ph.D. degree in Physics (2007) from the Wroclaw University of Technology (Poland) under the supervision of Prof. Marek Langner. Currently, he works in J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Science. His research interests focus on the biophysics of biological membranes, in particular, their interactions with salt ions and the effects of lipid and sterol oxidation.

(1) Monnard, P.-A.; Walde, P. Current ideas about prebiological compartmentalization. Life 2015, 5, 1239−1263. (2) Deamer, D. The Role of Lipid Membranes in Life’s Origin. Life 2017, 7, 5. (3) Cebecauer, M.; Spitaler, M.; Serge, A.; Magee, A. I. Signalling complexes and clusters: functional advantages and methodological hurdles. J. Cell Sci. 2010, 123, 309−320. (4) Botelho, R. J.; Teruel, M.; Dierckman, R.; Anderson, R.; Wells, A.; York, J. D.; Meyer, T.; Grinstein, S. Localized biphasic changes in phosphatidylinositol-4,5-bisphosphate at sites of phagocytosis. J. Cell Biol. 2000, 151, 1353−1367. (5) Walton, J. N. The Fine Structure of the Nervous System. Neurons and Their Supporting Cells; Oxford University Press: New York, 1991. (6) Saka, S. K.; Honigmann, A.; Eggeling, C.; Hell, S. W.; Lang, T.; Rizzoli, S. O. Multi-protein assemblies underlie the mesoscale organization of the plasma membrane. Nat. Commun. 2014, 5, 4509. (7) Hirama, T.; Das, R.; Yang, Y.; Ferguson, C.; Won, A.; Yip, C. M.; Kay, J. G.; Grinstein, S.; Parton, R. G.; Fairn, G. D. Phosphatidylserine dictates the assembly and dynamics of caveolae in the plasma membrane. J. Biol. Chem. 2017, 292, 14292−14307. (8) Whited, A. M.; Park, P. S. H. Nanodomain organization of rhodopsin in native human and murine rod outer segment disc membranes. Biochim. Biophys. Acta, Biomembr. 2015, 1848, 26−34. (9) Honigmann, A.; Mueller, V.; Ta, H.; Schoenle, A.; Sezgin, E.; Hell, S. W.; Eggeling, C. Scanning STED-FCS reveals spatiotemporal heterogeneity of lipid interaction in the plasma membrane of living cells. Nat. Commun. 2014, 5, 5412. (10) Mueller, V.; Ringemann, C.; Honigmann, A.; Schwarzmann, G.; Medda, R.; Leutenegger, M.; Polyakova, S.; Belov, V. N.; Hell, S. W.; Eggeling, C. STED nanoscopy reveals molecular details of cholesterol-

Maria J. Sarmento graduated in Medical Biochemistry (2011) at the University of Lisbon. She earned her Ph.D. degree (2016) in Chemistry (focused on membrane biophysics) at the Instituto Superior Técnico in Lisbon (Portugal, with Dr. Fábio Fernandes and Prof. Manuel Prieto). She then was a postdoctoral fellow in the Department on Nanopysics at Istituto Italiano di Tecnologia in Genova (Italy, with Dr. Luca Lanzano and Prof.Alberto Diaspro). Currently, she is a postdoctoral fellow in the Department of Biophysical Chemistry at J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences. Her main research interests focus on membrane organization and its influence in the consequent lipid− protein interactions, especially during amyloid peptide oligomerisation processes. Radek Š achl is a scientist at J. Heyrovský Institute of Physical Chemistry of the Czech Academy of Sciences. He received his Ph.D. degree in Physical Chemistry at Umeå University (Sweden, with Prof. Lennart Johannson) in 2012. During his Ph.D. studies he developed a technique based on FRET for the quantitative determination of nanodomain sizes and concentration in lipid bilayers. He returned to the Czech Republic in 2014, after finishing his postdoctoral fellowship AC


Chemical Reviews

Review

and cytoskeleton-modulated lipid interactions in living cells. Biophys. J. 2011, 101, 1651−1660. (11) Wilson, B. S.; Pfeiffer, J. R.; Surviladze, Z.; Gaudet, E. a; Oliver, J. M. High resolution mapping of mast cell membranes reveals primary and secondary domains of FcϵRI and LAT. J. Cell Biol. 2001, 154, 645−658. (12) Dustin, M. L.; Depoil, D. New insights into the T cell synapse from single molecule techniques. Nat. Rev. Immunol. 2011, 11, 672− 684. (13) Triller, A.; Choquet, D. New concepts in synaptic biology derived from single-molecule imaging. Neuron 2008, 59, 359−374. (14) Heberle, F. A.; Feigenson, G. W. Phase separation in lipid membranes. Cold Spring Harbor Perspect. Biol. 2011, 3, a004630. (15) Chachaty, C.; Rainteau, D.; Tessier, C.; Quinn, P. J.; Wolf, C. Building up of the liquid-ordered phase formed by sphingomyelin and cholesterol. Biophys. J. 2005, 88, 4032−4044. (16) Ivankin, A.; Kuzmenko, I.; Gidalevitz, D. Cholesterolphospholipid interactions: new insights from surface x-ray scattering data. Phys. Rev. Lett. 2010, 104, 108101. (17) Mouritsen, O. G. The liquid-ordered state comes of age. Biochim. Biophys. Acta, Biomembr. 2010, 1798, 1286−1288. (18) Abbe, E. Beiträge zur theorie des mikroskops und der mikroskopischen wahrnehmung. Arch. fü r Mikroskopische Anat. 1873, 9, 413−418. (19) Tank, D. W. Enhanced molecular diffusibility in muscle membrane blebs: release of lateral constraints. J. Cell Biol. 1982, 92, 207−212. (20) Bernardino de la Serna, J.; Schütz, G. J.; Eggeling, C.; Cebecauer, M. There is no simple model of the plasma membrane organization. Front. Cell Dev. Biol. 2016, 4, 106. (21) Phillips, R.; Kondev, J.; Theriot, J. Physical Biology of the Cell; Garland Science: New York, 2009. (22) Simons, K.; Ikonen, E. Functional rafts in cell membranes. Nature 1997, 387, 569−572. (23) Pike, L. J. Rafts defined: a report on the Keystone symposium on lipid rafts and cell function. J. Lipid Res. 2006, 47, 1597−1598. (24) Mouritsen, O. G.; Bagatolli, L. A. Lipid domains in model membranes: a brief historical perspective. Essays Biochem. 2015, 57, 1−19. (25) Veatch, S. L.; Keller, S. L. Miscibility phase diagrams of giant vesicles containing sphingomyelin. Phys. Rev. Lett. 2005, 94, 148101. (26) Baumgart, T.; Hammond, A. T.; Sengupta, P.; Hess, S. T.; Holowka, D. A.; Baird, B. A.; Webb, W. W. Large-scale fluid/fluid phase separation of proteins and lipids in giant plasma membrane vesicles. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 3165−3170. (27) Levental, I.; Byfield, F. J.; Chowdhury, P.; Gai, F.; Baumgart, T.; Janmey, P. A. Cholesterol-dependent phase separation in cellderived giant plasma-membrane vesicles. Biochem. J. 2009, 424, 163− 167. (28) Sezgin, E.; Waithe, D.; Bernardino de la Serna, J.; Eggeling, C. Spectral imaging to measure heterogeneity in membrane lipid packing. ChemPhysChem 2015, 16, 1387−1394. (29) Sezgin, E.; Sadowski, T.; Simons, K. Measuring lipid packing of model and cellular membranes with environment sensitive probes. Langmuir 2014, 30, 8160−8166. (30) Nicolson, G. L. Topographic display of cell surface components and their role in transmembrane signaling. Immunological Approaches to Embryonic Development and Differentiation Part I. Current Topics in Developmental Biology; Elsevier, 1979; Vol. 13, pp 305−338. (31) Loura, L. M. S.; Fedorov, A.; Prieto, M. Fluid−fluid membrane microheterogeneity: a fluorescence resonance energy transfer study. Biophys. J. 2001, 80, 776−788. (32) Feigenson, G. W.; Buboltz, J. T. Ternary phase diagram of dipalmitoyl-PC/dilauroyl-PC/cholesterol: nanoscopic domain formation driven by cholesterol. Biophys. J. 2001, 80, 2775−2788. (33) Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14, 33−38.

(34) Stone, J. An efficient library for parallel ray tracing and animation. Intel Supercomputer Users Group Proceedings; 1995; pp 1− 5. (35) Nyström, J. H.; Lönnfors, M.; Nyholm, T. K. M. Transmembrane peptides influence the affinity of sterols for phospholipid bilayers. Biophys. J. 2010, 99, 526−533. (36) Nyholm, T. K. M. Lipid-protein interplay and lateral organization in biomembranes. Chem. Phys. Lipids 2015, 189, 48−55. (37) Harayama, T.; Riezman, H. Understanding the diversity of membrane lipid composition. Nat. Rev. Mol. Cell Biol. 2018, 19, 281− 296. (38) Koukalová, A.; Amaro, M.; Aydogan, G.; Gröbner, G.; Williamson, P. T. F.; Mikhalyov, I.; Hof, M.; Š achl, R. Lipid driven nanodomains in giant lipid vesicles are fluid and disordered. Sci. Rep. 2017, 7, 5460. (39) de Almeida, R. F. M.; Loura, L. M. S.; Fedorov, A.; Prieto, M. Lipid rafts have different sizes depending on membrane composition: a time-resolved fluorescence resonance energy transfer study. J. Mol. Biol. 2005, 346, 1109−1120. (40) Brown, A. C.; Towles, K. B.; Wrenn, S. P. Measuring raft size as a function of membrane composition in PC-based systems: part 1 − binary systems. Langmuir 2007, 23, 11180−11187. (41) Brown, A. C.; Towles, K. B.; Wrenn, S. P. Measuring raft size as a function of membrane composition in PC-based systems: part II − ternary systems. Langmuir 2007, 23, 11188−11196. (42) Heberle, F. A.; Wu, J.; Goh, S. L.; Petruzielo, R. S.; Feigenson, G. W. Comparison of three ternary lipid bilayer mixtures: FRET and ESR reveal nanodomains. Biophys. J. 2010, 99, 3309−3318. (43) Pathak, P.; London, E. Measurement of lipid nanodomain (raft) formation and size in sphingomyelin/POPC/cholesterol vesicles shows TX-100 and transmembrane helices increase domain size by coalescing preexisting nanodomains but do not induce domain formation. Biophys. J. 2011, 101, 2417−2425. (44) Petruzielo, R. S.; Heberle, F. A.; Drazba, P.; Katsaras, J.; Feigenson, G. W. Phase behavior and domain size in sphingomyelincontaining lipid bilayers. Biochim. Biophys. Acta, Biomembr. 2013, 1828, 1302−1313. (45) Konyakhina, T. M.; Feigenson, G. W. Phase diagram of a polyunsaturated lipid mixture: brain sphingomyelin/1-stearoyl-2docosahexaenoyl-sn-glycero-3-phosphocholine/cholesterol. Biochim. Biophys. Acta, Biomembr. 2016, 1858, 153−161. (46) Pathak, P.; London, E. The effect of membrane lipid composition on the formation of lipid ultrananodomains. Biophys. J. 2015, 109, 1630−1638. (47) Valeur, B. Molecular Fluorescence: Principles and Applications; Wiley-VCH Verlag GmbH: Weinheim, 2001. (48) Förster, T.; Energiewanderung, Z.; Von, F. Zwischenmolekulare energiewanderung und fluoreszenz. Ann. Phys. 1948, 437, 55−75. (49) Dale, R. E.; Eisinger, J.; Blumberg, W. E. The orientational freedom of molecular probes. The orientation factor in intramolecular energy transfer. Biophys. J. 1979, 26, 161−193. (50) Š achl, R.; Humpolíčková, J.; Š tefl, M.; Johansson, L. B.-Å.; Hof, M. Limitations of electronic energy transfer in the determination of lipid nanodomain sizes. Biophys. J. 2011, 101, L60−L62. (51) Baumann, J.; Fayer, M. D. Excitation transfer in disordered twodimensional and anisotropic three-dimensional systems: effects of spatial geometry on time-resolved observables. J. Chem. Phys. 1986, 85, 4087−4107. (52) Loura, L. M. S.; Prieto, M. Resonance energy transfer in heterogeneous planar and bilayer systems: theory and simulation. J. Phys. Chem. B 2000, 104, 6911−6919. (53) Š tefl, M.; Š achl, R.; Humpolíčková, J.; Cebecauer, M.; Macháň, R.; Kolárǒ vá, M.; Johansson, L. B.-Å.; Hof, M. Dynamics and size of cross-linking-induced lipid nanodomains in model membranes. Biophys. J. 2012, 102, 2104−2113. (54) Š achl, R.; Amaro, M.; Aydogan, G.; Koukalová, A.; Mikhalyov, I. I.; Boldyrev, I. A.; Humpolíčková, J.; Hof, M. On multivalent receptor activity of GM1 in cholesterol containing membranes. Biochim. Biophys. Acta, Mol. Cell Res. 2015, 1853, 850−857. AD


Chemical Reviews

Review

(55) Amaro, M.; Š achl, R.; Aydogan, G.; Mikhalyov, I. I.; Vácha, R.; Hof, M. GM1 ganglioside inhibits β-amyloid oligomerization induced by sphingomyelin. Angew. Chem., Int. Ed. 2016, 55, 9411−9415. (56) Krieger, J. W.; Singh, A. P.; Bag, N.; Garbe, C. S.; Saunders, T. E.; Langowski, J.; Wohland, T. Imaging fluorescence (cross-) correlation spectroscopy in live cells and organisms. Nat. Protoc. 2015, 10, 1948−1974. (57) Bag, N.; Ng, X. W.; Sankaran, J.; Wohland, T. Spatiotemporal mapping of diffusion dynamics and organization in plasma membranes. Methods Appl. Fluoresc. 2016, 4, 034003. (58) Macháň, R.; Hof, M. Recent developments in fluorescence correlation spectroscopy for diffusion measurements in planar lipid membranes. Int. J. Mol. Sci. 2010, 11, 427−457. (59) Š achl, R.; Bergstrand, J.; Widengren, J.; Hof, M. Fluorescence correlation spectroscopy diffusion laws in the presence of moving nanodomains. J. Phys. D: Appl. Phys. 2016, 49, 114002. (60) Wawrezinieck, L.; Rigneault, H.; Marguet, D.; Lenne, P.-F. Fluorescence correlation spectroscopy diffusion laws to probe the submicron cell membrane organization. Biophys. J. 2005, 89, 4029− 4042. (61) Ng, X. W.; Bag, N.; Wohland, T. Characterization of lipid and cell membrane organization by the fluorescence correlation spectroscopy diffusion law. Chimia 2015, 69, 112−119. (62) Macháň, R.; Hof, M. Lipid diffusion in planar membranes investigated by fluorescence correlation spectroscopy. Biochim. Biophys. Acta, Biomembr. 2010, 1798, 1377−1391. (63) Sorre, B.; Callan-Jones, A.; Manneville, J.-B.; Nassoy, P.; Joanny, J.-F.; Prost, J.; Goud, B.; Bassereau, P. Curvature-driven lipid sorting needs proximity to a demixing point and is aided by proteins. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 5622−5626. (64) Ewers, H.; Römer, W.; Smith, A. E.; Bacia, K.; Dmitrieff, S.; Chai, W.; Mancini, R.; Kartenbeck, J.; Chambon, V.; Berland, L.; et al. GM1 structure determines SV40-induced membrane invagination and infection. Nat. Cell Biol. 2010, 12, 11−18. (65) Schneider, F.; Waithe, D.; Clausen, M. P.; Galiani, S.; Koller, T.; Ozhan, G.; Eggeling, C.; Sezgin, E. Diffusion of lipids and GPIanchored proteins in actin-free plasma membrane vesicles measured by STED-FCS. Mol. Biol. Cell 2017, 28, 1507−1518. (66) Sarangi, N. K.; Ayappa, K. G.; Basu, J. K. Complex dynamics at the nanoscale in simple biomembranes. Sci. Rep. 2017, 7, 11173. (67) Jiang, Y.; Pryse, K. M.; Melnykov, A.; Genin, G. M.; Elson, E. L. Investigation of nanoscopic phase separations in lipid membranes using inverse FCS. Biophys. J. 2017, 112, 2367−2376. (68) Lindfors, K.; Kalkbrenner, T.; Stoller, P.; Sandoghdar, V. Detection and spectroscopy of gold nanoparticles using supercontinuum white light confocal microscopy. Phys. Rev. Lett. 2004, 93, No. 037401, DOI: 10.1103/PhysRevLett.93.037401. (69) Ortega-Arroyo, J.; Kukura, P. Interferometric scattering microscopy (iSCAT): new frontiers in ultrafast and ultrasensitive optical microscopy. Phys. Chem. Chem. Phys. 2012, 14, 15625−15636. (70) Ortega Arroyo, J.; Andrecka, J.; Spillane, K. M.; Billington, N.; Takagi, Y.; Sellers, J. R.; Kukura, P. Label-free, all-optical detection, imaging, and tracking of a single protein. Nano Lett. 2014, 14, 2065− 2070. (71) Kukura, P.; Ewers, H.; Müller, C.; Renn, A.; Helenius, A.; Sandoghdar, V. High-speed nanoscopic tracking of the position and orientation of a single virus. Nat. Methods 2009, 6, 923−927. (72) de Wit, G.; Danial, J. S. H.; Kukura, P.; Wallace, M. I. Dynamic label-free imaging of lipid nanodomains. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 12299−12303. (73) Connell, S. D.; Smith, D. A. The atomic force microscope as a tool for studying phase separation in lipid membranes (review). Mol. Membr. Biol. 2006, 23, 17−28. (74) Shan, Y.; Wang, H. The structure and function of cell membranes examined by atomic force microscopy and singlemolecule force spectroscopy. Chem. Soc. Rev. 2015, 44, 3617−3638. (75) Gumí-Audenis, B.; Costa, L.; Carlá, F.; Comin, F.; Sanz, F.; Giannotti, M. I. Structure and nanomechanics of model membranes

by atomic force microscopy and spectroscopy: Insights into the role of cholesterol and sphingolipids. Membranes 2016, 6, 58. (76) Giocondi, M. C.; Vié, V.; Lesniewska, E.; Milhiet, P. E.; ZinkeAllmang, M.; Le Grimellec, C. Phase topology and growth of single domains in lipid bilayers. Langmuir 2001, 17, 1653−1659. (77) Tokumasu, F.; Jin, A. J.; Feigenson, G. W.; Dvorak, J. A. Nanoscopic lipid domain dynamics revealed by atomic force microscopy. Biophys. J. 2003, 84, 2609−2618. (78) Choucair, A.; Chakrapani, M.; Chakravarthy, B.; Katsaras, J.; Johnston, L. J. Preferential accumulation of Aβ(1−42) on gel phase domains of lipid bilayers: an AFM and fluorescence study. Biochim. Biophys. Acta, Biomembr. 2007, 1768, 146−154. (79) Ho, C. S.; Khadka, N. K.; Pan, J. Sub-ten-nanometer heterogeneity of solid supported lipid membranes determined by solution atomic force microscopy. Biochim. Biophys. Acta, Biomembr. 2016, 1858, 181−188. (80) Johnston, L. J. Nanoscale imaging of domains in supported lipid membranes. Langmuir 2007, 23, 5886−5895. (81) Yuan, C.; Furlong, J.; Burgos, P.; Johnston, L. J. The size of lipid rafts: an atomic force microscopy study of ganglioside GM1 domains in sphingomyelin/DOPC/cholesterol membranes. Biophys. J. 2002, 82, 2526−2535. (82) Veatch, S. L.; Polozov, I. V.; Gawrisch, K.; Keller, S. L. Liquid domains in vesicles investigated by NMR and fluorescence microscopy. Biophys. J. 2004, 86, 2910−2922. (83) Veatch, S. L.; Soubias, O.; Keller, S. L.; Gawrisch, K. Critical fluctuations in domain-forming lipid mixtures. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 17650−17655. (84) Bunge, A.; Müller, P.; Stöckl, M.; Herrmann, A.; Huster, D. Characterization of the ternary mixture of sphingomyelin, POPC, and cholesterol: support for an inhomogeneous lipid distribution at high temperatures. Biophys. J. 2008, 94, 2680−2690. (85) Yasuda, T.; Matsumori, N.; Tsuchikawa, H.; Lönnfors, M.; Nyholm, T. K. M.; Slotte, J. P.; Murata, M. Formation of gel-like nanodomains in cholesterol-containing sphingomyelin or phosphatidylcholine binary membrane as examined by fluorescence lifetimes and 2H NMR spectra. Langmuir 2015, 31, 13783−13792. (86) Kinnun, J. J.; Bittman, R.; Shaikh, S. R.; Wassall, S. R. DHA modifies the size and composition of raftlike domains: a solid-state 2H NMR study. Biophys. J. 2018, 114, 380−391. (87) Pencer, J.; Mills, T.; Anghel, V.; Krueger, S.; Epand, R. M.; Katsaras, J. Detection of submicron-sized raft-like domains in membranes by small-angle neutron scattering. Eur. Phys. J. E: Soft Matter Biol. Phys. 2005, 18, 447−458. (88) Masui, T.; Urakami, N.; Imai, M. Nano-meter-sized domain formation in lipid membranes observed by small angle neutron scattering. Eur. Phys. J. E: Soft Matter Biol. Phys. 2008, 27, 379−389. (89) Krivanek, R.; Jeworrek, C.; Czeslik, C.; Winter, R. Composition fluctuations in phospholipid-sterol vesicles - A small-angle neutron scattering study. Z. Phys. Chem. 2008, 222, 1679−1692. (90) Belička, M.; Weitzer, A.; Pabst, G. High-resolution structure of coexisting nanoscopic and microscopic lipid domains. Soft Matter 2017, 13, 1823−1833. (91) Nickels, J. D.; Cheng, X.; Mostofian, B.; Stanley, C.; Lindner, B.; Heberle, F. A.; Perticaroli, S.; Feygenson, M.; Egami, T.; Standaert, R. F.; et al. Mechanical properties of nanoscopic lipid domains. J. Am. Chem. Soc. 2015, 137, 15772−15780. (92) Mills, T. T.; Tristram-Nagle, S.; Heberle, F. A.; Morales, N. F.; Zhao, J.; Wu, J.; Toombes, G. E. S.; Nagle, J. F.; Feigenson, G. W. Liquid-liquid domains in bilayers detected by wide angle x-ray scattering. Biophys. J. 2008, 95, 682−690. (93) Heberle, F. A.; Petruzielo, R. S.; Pan, J.; Drazba, P.; Kučerka, N.; Standaert, R. F.; Feigenson, G. W.; Katsaras, J. Bilayer thickness mismatch controls domain size in model membranes. J. Am. Chem. Soc. 2013, 135, 6853−6859. (94) Heberle, F. A.; Doktorova, M.; Goh, S. L.; Standaert, R. F.; Katsaras, J.; Feigenson, G. W. Hybrid and nonhybrid lipids exert common effects on membrane raft size and morphology. J. Am. Chem. Soc. 2013, 135, 14932−14935. AE


Chemical Reviews

Review

(95) Carrer, D. C.; Maggio, B. Phase behavior and molecular interactions in mixtures of ceramide with dipalmitoylphosphatidylcholine. J. Lipid Res. 1999, 40, 1978−1989. (96) Silva, L.; de Almeida, R. F. M.; Fedorov, A.; Matos, A. P. a; Prieto, M. Ceramide-platform formation and -induced biophysical changes in a fluid phospholipid membrane. Mol. Membr. Biol. 2006, 23, 137−148. (97) Hannun, Y. A. Functions of ceramide in coordinating cellular responses to stress. Science 1996, 274, 1855−1859. (98) Goñi, F. M.; Alonso, A. Biophysics of sphingolipids I. Membrane properties of sphingosine, ceramides and other simple sphingolipids. Biochim. Biophys. Acta, Biomembr. 2006, 1758, 1902− 1921. (99) Goñi, F. M.; Alonso, A. Effects of ceramide and other simple sphingolipids on membrane lateral structure. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 169−177. (100) Castro, B. M.; Prieto, M.; Silva, L. C. Ceramide: A simple sphingolipid with unique biophysical properties. Prog. Lipid Res. 2014, 54, 53−67. (101) Holopainen, J. M.; Lehtonen, J. Y.; Kinnunen, P. K. J. Lipid microdomains in dimyristoylphosphatidylcholine−ceramide liposomes. Chem. Phys. Lipids 1997, 88, 1−13. (102) Holopainen, J. M.; Subramanian, M.; Kinnunen, P. K. J. Sphingomyelinase induces lipid microdomain formation in a fluid phosphatidylcholine/sphingomyelin membrane. Biochemistry 1998, 37, 17562−17570. (103) Montes, L. R.; Ruiz-Argüello, M. B.; Goñi, F. M.; Alonso, A. Membrane restructuring via ceramide results in enhanced solute efflux. J. Biol. Chem. 2002, 277, 11788−11794. (104) Veiga, M. P.; Arrondo, J. L. R.; Goñi, F. M.; Alonso, A. Ceramides in phospholipid membranes: Effects on bilayer stability and transition to nonlamellar phases. Biophys. J. 1999, 76, 342−350. (105) Sot, J.; Aranda, F. J.; Collado, M. I.; Goñi, F. M.; Alonso, A. Different effects of long- and short-chain ceramides on the gel-fluid and lamellar-hexagonal transitions of phospholipids: a calorimetric, NMR, and x-ray diffraction study. Biophys. J. 2005, 88, 3368−3380. (106) Chiantia, S.; Kahya, N.; Ries, J.; Schwille, P. Effects of ceramide on liquid-ordered domains investigated by simultaneous AFM and FCS. Biophys. J. 2006, 90, 4500−4508. (107) Silva, L. C.; de Almeida, R. F. M.; Castro, B. M.; Fedorov, A.; Prieto, M. Ceramide-domain formation and collapse in lipid rafts: membrane reorganization by an apoptotic lipid. Biophys. J. 2007, 92, 502−516. (108) Castro, B. M.; De Almeida, R. F. M.; Silva, L. C.; Fedorov, A.; Prieto, M. Formation of ceramide/sphingomyelin gel domains in the presence of an unsaturated phospholipid: a quantitative multiprobe approach. Biophys. J. 2007, 93, 1639−1650. (109) Pinto, S. N.; Silva, L. C.; de Almeida, R. F. M.; Prieto, M. Membrane domain formation, interdigitation, and morphological alterations induced by the very long chain asymmetric C24:1 ceramide. Biophys. J. 2008, 95, 2867−2879. (110) Carrer, D. C.; Kummer, E.; Chwastek, G.; Chiantia, S.; Schwille, P. Asymmetry determines the effects of natural ceramides on model membranes. Soft Matter 2009, 5, 3279−3286. (111) Pinto, S. N.; Silva, L. C.; Futerman, A. H.; Prieto, M. Effect of ceramide structure on membrane biophysical properties: The role of acyl chain length and unsaturation. Biochim. Biophys. Acta, Biomembr. 2011, 1808, 2753−2760. (112) Sot, J.; Bagatolli, L. A.; Goñi, F. M.; Alonso, A. Detergentresistant, ceramide-enriched domains in sphingomyelin/ceramide bilayers. Biophys. J. 2006, 90, 903−914. (113) Holopainen, J. M.; Lemmich, J.; Richter, F.; Mouritsen, O. G.; Rapp, G.; Kinnunen, P. K. J. Dimyristoylphosphatidylcholine/C16:0ceramide binary liposomes studied by differential scanning calorimetry and wide- and small-angle X-ray scattering. Biophys. J. 2000, 78, 2459−2469. (114) Nybond, S.; Björkqvist, Y. J. E.; Ramstedt, B.; Slotte, J. P. Acyl chain length affects ceramide action on sterol/sphingomyelin-rich domains. Biochim. Biophys. Acta, Biomembr. 2005, 1718, 61−66.

(115) Fidorra, M.; Duelund, L.; Leidy, C.; Simonsen, A. C.; Bagatolli, L. A. Absence of fluid-ordered/fluid-disordered phase coexistence in ceramide/POPC mixtures containing cholesterol. Biophys. J. 2006, 90, 4437−4451. (116) Ira; Johnston, L. J. Ceramide promotes restructuring of model raft membranes. Langmuir 2006, 22, 11284−11289. (117) Johnston, I.; Johnston, L. J. Sphingomyelinase generation of ceramide promotes clustering of nanoscale domains in supported bilayer membranes. Biochim. Biophys. Acta, Biomembr. 2008, 1778, 185−197. (118) Härtel, S.; Fanani, M. L.; Maggio, B. Shape transitions and lattice structuring of ceramide-enriched domains generated by sphingomyelinase in lipid monolayers. Biophys. J. 2005, 88, 287−304. (119) Fahy, E.; Subramaniam, S.; Brown, H. A.; Glass, C. K.; Merrill, A. H.; Murphy, R. C.; Raetz, C. R. H.; Russell, D. W.; Seyama, Y.; Shaw, W.; et al. A comprehensive classification system for lipids. J. Lipid Res. 2005, 46, 839−862. (120) van Meer, G.; Voelker, D. R.; Feigenson, G. W. Membrane lipids: where they are and how they behave. Nat. Rev. Mol. Cell Biol. 2008, 9, 112−124. (121) Wurtman, R. J.; Cansev, M.; Ulus, I. H. Chemistry, tissue and cellular distribution, and developmental profiles of neural sphingolipids. In Handbook of Neurochemistry and Molecular NeurobiologyNeural Lipids; Lajtha, A., Tettamanti, G., Goracci, G., Eds.; Springer US: Boston, MA, 2010; pp 443−502. (122) Tettamanti, G. Ganglioside/glycosphingolipid turnover: new concepts. Glycoconjugate J. 2003, 20, 301−317. (123) Wurtman, R. J.; Cansev, M.; Ulus, I. H. Handbook of Neurochemistry and Molecular Neurobiology; Lajtha, A., Tettamanti, G., Goracci, G., Eds.; Springer US: Boston, MA, 2010. (124) Saher, G.; Stumpf, S. K. Cholesterol in myelin biogenesis and hypomyelinating disorders. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2015, 1851, 1083−1094. (125) Aggarwal, S.; Yurlova, L.; Simons, M. Central nervous system myelin: structure, synthesis and assembly. Trends Cell Biol. 2011, 21, 585−593. (126) Ramstedt, B.; Slotte, J. P. Sphingolipids and the formation of sterol-enriched ordered membrane domains. Biochim. Biophys. Acta, Biomembr. 2006, 1758, 1945−1956. (127) Sonnino, S.; Prinetti, A.; Mauri, L.; Chigorno, V.; Tettamanti, G. Dynamic and structural properties of sphingolipids as driving forces for the formation of membrane domains. Chem. Rev. 2006, 106, 2111−2125. (128) Westerlund, B.; Slotte, J. P. How the molecular features of glycosphingolipids affect domain formation in fluid membranes. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 194−201. (129) Carreira, A. C.; Ventura, A. E.; Varela, A. R. P.; Silva, L. C. Tackling the biophysical properties of sphingolipids to decipher their biological roles. Biol. Chem. 2015, 396, 597−609. (130) Ruocco, M. J.; Shipley, G. G. Interaction of cholesterol with galactocerebroside and galactocerebroside-phosphatidylcholine bilayer membranes. Biophys. J. 1984, 46, 695−707. (131) Haas, N. S.; Graham Shipley, G. Structure and properties of N-palmitoleoylgalactosylsphingosine (cerebroside). Biochim. Biophys. Acta, Biomembr. 1995, 1240, 133−141. (132) Saxena, K.; Duclos, R. I.; Zimmermann, P.; Schmidt, R. R.; Shipley, G. G. Structure and properties of totally synthetic galactoand gluco-cerebrosides. J. Lipid Res. 1999, 40, 839−849. (133) Ohler, B.; Revenko, I.; Husted, C. Atomic force microscopy of nonhydroxy galactocerebroside nanotubes and their self-assembly at the air-water interface, with applications to myelin. J. Struct. Biol. 2001, 133, 1−9. (134) Maunula, S.; Björkqvist, Y. J. E.; Slotte, J. P.; Ramstedt, B. Differences in the domain forming properties of N-palmitoylated neutral glycosphingolipids in bilayer membranes. Biochim. Biophys. Acta, Biomembr. 2007, 1768, 336−345. (135) Bou Khalil, M.; Carrier, D.; Wong, P. T. T.; Tanphaichitr, N. Polymorphic phases of galactocerebrosides: spectroscopic evidence of AF


Chemical Reviews

Review

lamellar crystalline structures. Biochim. Biophys. Acta, Biomembr. 2001, 1512, 158−170. (136) Stefaniu, C.; Ries, A.; Gutowski, O.; Ruett, U.; Seeberger, P. H.; Werz, D. B.; Brezesinski, G. Impact of structural differences in galactocerebrosides on the behavior of 2D monolayers. Langmuir 2016, 32, 2436−2444. (137) Xu, X.; Bittman, R.; Duportail, G.; Heissler, D.; Vilcheze, C.; London, E. Effect of the structure of natural sterols and sphingolipids on the formation of ordered sphingolipid/sterol domains (rafts). Comparison of cholesterol to plant, fungal, and disease-associated sterols and comparison of sphingomyelin, cerebrosides, and cer. J. Biol. Chem. 2001, 276, 33540−33546. (138) Johnston, D. S.; Champan, D. A calorimetric study of the thermotropic behaviour of mixtures of brain cerebrosides with other brain lipids. Biochim. Biophys. Acta, Biomembr. 1988, 939, 603−614. (139) Varela, A. R. P.; Gonçalves da Silva, A. M. P. S.; Fedorov, A.; Futerman, A. H.; Prieto, M.; Silva, L. C. Effect of glucosylceramide on the biophysical properties of fluid membranes. Biochim. Biophys. Acta, Biomembr. 2013, 1828, 1122−1130. (140) Lu, D.; Singh, D.; Morrow, M. R.; Grant, C. W. M. Effect of glycosphingolipid fatty acid chain length on behavior in unsaturated phosphatidylcholine bilayers: A deuterium NMR study. Biochemistry 1993, 32, 290−297. (141) Quinn, P. J. Long N-acyl fatty acids on sphingolipids are responsible for miscibility with phospholipids to form liquid-ordered phase. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 2267−2276. (142) Quinn, P. J. A synchrotron X-ray diffraction characterization of the structure of complexes formed between sphingomyelin and cerebroside. FEBS J. 2011, 278, 3518−3527. (143) Björkqvist, Y. J. E.; Brewer, J.; Bagatolli, L. A.; Slotte, J. P.; Westerlund, B. Thermotropic behavior and lateral distribution of very long chain sphingolipids. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 1310−1320. (144) Varela, A. R. P.; Couto, A. S.; Fedorov, A.; Futerman, A. H.; Prieto, M.; Silva, L. C. Glucosylceramide reorganizes cholesterolcontaining domains in a fluid phospholipid membrane. Biophys. J. 2016, 110, 612−622. (145) Lin, W. C.; Blanchette, C. D.; Longo, M. L. Fluid-phase chain unsaturation controlling domain microstructure and phase in ternary lipid bilayers containing GalCer and cholesterol. Biophys. J. 2007, 92, 2831−2841. (146) Rosetti, C. M.; Oliveira, R. G.; Maggio, B. The Folch−Lees proteolipid induces phase coexistence and transverse reorganization of lateral domains in myelin monolayers. Biochim. Biophys. Acta, Biomembr. 2005, 1668, 75−86. (147) Fenske, D. B.; Hamilton, K.; Jarrell, H. C.; Florio, E.; Barber, K. R.; Grant, C. W. M. Glycosphingolipids: deuterium NMR study of the influence of carbohydrate headgroup structure on ceramide acyl chain behavior in glycolipid-phospholipid bilayers. Biochemistry 1991, 30, 4503−4509. (148) Gumí-Audenis, B.; Sanz, F.; Giannotti, M. I. Impact of galactosylceramides on the nanomechanical properties of lipid bilayer models: an AFM-force spectroscopy study. Soft Matter 2015, 11, 5447−5454. (149) Blanchette, C. D.; Lin, W. C.; Ratto, T. V.; Longo, M. L. Galactosylceramide domain microstructure: impact of cholesterol and nucleation/growth conditions. Biophys. J. 2006, 90, 4466−4478. (150) Windschiegl, B.; Steinem, C. Influence of α-hydroxylation of glycolipids on domain formation in lipid monolayers. Langmuir 2006, 22, 7454−7457. (151) Singh, D.; Jarrell, H. C.; Florio, E.; Fenske, D. B.; Grant, C. W. M. Effects of fatty acid alpha-hydroxylation on glycosphingolipid properties in phosphatidylcholine bilayers. Biochim. Biophys. Acta, Biomembr. 1992, 1103, 268−274. (152) Björkqvist, Y. J. E.; Nybond, S.; Nyholm, T. K. M.; Slotte, J. P.; Ramstedt, B. N-palmitoyl-sulfatide participates in lateral domain formation in complex lipid bilayers. Biochim. Biophys. Acta, Biomembr. 2008, 1778, 954−962.

(153) Boggs, J. M.; Koshy, K. M.; Rangaraj, G. Effect of fatty acid chain length, fatty acid hydroxylation, and various cations on phase behavior of synthetic cerebroside sulfate. Chem. Phys. Lipids 1984, 36, 65−89. (154) Boggs, J. M.; Koshy, K. M.; Rangaraj, G. Influence of structural modifications on the phase behavior of semi-synthetic cerebroside sulfate. Biochim. Biophys. Acta, Biomembr. 1988, 938, 361−372. (155) Nabet, A.; Boggs, J. M.; Pézolet, M. Study by infrared spectroscopy of the interdigitation of C26:0 cerebroside sulfate into phosphatidylcholine bilayers. Biochemistry 1996, 35, 6674−6683. (156) Saxena, K.; Duclos, R. I.; Sripada, P. K.; Shipley, G. G. Unusual hydration properties of C16:0 sulfatide bilayer membranes. Biophys. J. 2000, 79, 385−393. (157) Boggs, J. M.; Mulholland, D.; Koshy, K. M. Mixtures of semisynthetic species of cerebroside sulfate with dipalmitoyl phosphatidylcholine. Thermotropic phase behavior and permeability. Biochem. Cell Biol. 1990, 68, 70−82. (158) Boggs, J. M.; Koshy, K. M.; Rangaraj, G. Thermotropic phase behavior of mixtures of long chain fatty acid species of cerebroside sulfate with different fatty acid chain length species of phospholipid. Biochemistry 1993, 32, 8908−8922. (159) Weerachatyanukul, W.; Probodh, I.; Kongmanas, K.; Tanphaichitr, N.; Johnston, L. J. Visualizing the localization of sulfoglycolipids in lipid raft domains in model membranes and sperm membrane extracts. Biochim. Biophys. Acta, Biomembr. 2007, 1768, 299−310. (160) Merrill, A. H. Sphingolipid and glycosphingolipid metabolic pathways in the era of sphingolipidomics. Chem. Rev. 2011, 111, 6387−6422. (161) Saxena, K.; Zimmermann, P.; Schmidt, R. R.; Shipley, G. G. Bilayer properties of totally synthetic C16:0-lactosyl-ceramide. Biophys. J. 2000, 78, 306−312. (162) Li, X. M.; Momsen, M. M.; Brockman, H. L.; Brown, R. E. Lactosylceramide: effect of acyl chain structure on phase behavior and molecular packing. Biophys. J. 2002, 83, 1535−1546. (163) Maggio, B.; Ariga, T.; Sturtevant, J. M.; Yu, R. K. Thermotropic behavior of glycosphingolipids in aqueous dispersions. Biochemistry 1985, 24, 1084−1092. (164) Slotte, J. P.; Oestman, A. L.; Kumar, E. R.; Bittman, R. Cholesterol interacts with lactosyl and maltosyl cerebrosides but not with glucosyl or galactosyl cerebrosides in mixed monolayers. Biochemistry 1993, 32, 7886−7892. (165) Yu, Z. W.; Calvert, T. L.; Leckband, D. Molecular forces between membranes displaying neutral glycosphingolipids: evidence for carbohydrate attraction. Biochemistry 1998, 37, 1540−1550. (166) Sharom, F. J.; Grant, C. W. M. A model for ganglioside behaviour in cell membranes. Biochim. Biophys. Acta, Biomembr. 1978, 507, 280−293. (167) Bertoli, E.; Masserini, M.; Sonnino, S.; Ghidoni, R.; Cestaro, B.; Tettamanti, G. Electron paramagnetic resonance studies on the fluidity and surface dynamics of egg phosphatidylcholine vesicles containing gangliosides. Biochim. Biophys. Acta, Biomembr. 1981, 647, 196−202. (168) Delmelle, M.; Dufrane, S. P.; Brasseur, R.; Ruysschaert, J. M. Clustering of gangliosides in phospholipid bilayers. FEBS Lett. 1980, 121, 11−14. (169) Masserini, M.; Palestini, P.; Freire, E. Influence of glycolipid oligosaccharide and long-chain base composition on the thermotropic properties of dipalmitoylphosphatidylcholine large unilamellar vesicles containing gangliosides. Biochemistry 1989, 28, 5029−5034. (170) Iijima, K.; Soga, N.; Matsubara, T.; Sato, T. Observations of the distribution of GM3 in membrane microdomains by atomic force microscopy. J. Colloid Interface Sci. 2009, 337, 369−374. (171) Frey, S. L.; Chi, E. Y.; Arratia, C.; Majewski, J.; Kjaer, K.; Lee, K. Y. C. Condensing and fluidizing effects of ganglioside GM1 on phospholipid films. Biophys. J. 2008, 94, 3047−3064. (172) Bordi, F.; De Luca, F.; Cametti, C.; Naglieri, A.; Misasi, R.; Sorice, M. Interactions of mono- and di-sialogangliosides with AG


Chemical Reviews

Review

phospholipids in mixed monolayers at air-water interface. Colloids Surf., B 1999, 13, 135−142. (173) Matsubara, T.; Nishihara, M.; Yasumori, H.; Nakai, M.; Yanagisawa, K.; Sato, T. Size and shape of amyloid fibrils induced by ganglioside nanoclusters: role of sialyl oligosaccharide in fibril formation. Langmuir 2017, 33, 13874−13881. (174) Ferraretto, A.; Pitto, M.; Palestini, P.; Masserini, M. E. Lipid domains in the membrane: thermotropic properties of sphingomyelin vesicles containing GM1 ganglioside and cholesterol. Biochemistry 1997, 36, 9232−9236. (175) Marushchak, D.; Gretskaya, N.; Mikhalyov, I. I.; Johansson, L. B.-Å. Self-aggregation - an intrinsic property of GM1 in lipid bilayers. Mol. Membr. Biol. 2007, 24, 102−112. (176) Maggio, B.; Ariga, T.; Calderón, R. O.; Yu, R. K. Ganglioside GD3 and GD3-lactone mediated regulation of the intermolecular organization in mixed monolayers with dipalmitoylphosphatidylcholine. Chem. Phys. Lipids 1997, 90, 1−10. (177) Diociaiuti, M.; Ruspantini, I.; Giordani, C.; Bordi, F.; Chistolini, P. Distribution of GD3 in DPPC monolayers: a thermodynamic and atomic force microscopy combined study. Biophys. J. 2004, 86, 321−328. (178) Vié, V.; Van Mau, N.; Lesniewska, E.; Goudonnet, J. P.; Heitz, F.; Le Grimellec, C. Distribution of ganglioside GM1 between twocomponent, two-phase phosphatidylcholine monolayers. Langmuir 1998, 14, 4574−4583. (179) Yuan, C.; Johnston, L. J. Distribution of ganglioside GM1 in L-α-dipalmitoylphosphatidylcholine/cholesterol monolayers: a model for lipid rafts. Biophys. J. 2000, 79, 2768−2781. (180) Burgos, P.; Yuan, C.; Viriot, M. L.; Johnston, L. J. Two-color near-field fluorescence microscopy studies of microdomains (“rafts”) in model membranes. Langmuir 2003, 19, 8002−8009. (181) Ohta, Y.; Yokoyama, S.; Sakai, H.; Abe, M. Membrane properties of mixed ganglioside GM1/phosphatidylcholine monolayers. Colloids Surf., B 2004, 33, 191−197. (182) Ohta, Y.; Yokoyama, S.; Sakai, H.; Abe, M. Membrane properties of binary and ternary systems of ganglioside GM1/ dipalmitoylphosphatidylcholine/dioleoylphosphatidylcholine. Colloids Surf., B 2004, 34, 147−153. (183) Yokoyama, S.; Ohta, Y.; Sakai, H.; Abe, M. Effect of membrane composition on surface states of ganglioside GM1/ dipalmitoylphosphatidylcholine/dioleoylphosphatidylcholine monolayers. Colloids Surf., B 2004, 34, 65−68. (184) Coban, O.; Burger, M.; Laliberte, M.; Ianoul, A.; Johnston, L. J. Ganglioside partitioning and aggregation in phase-separated monolayers characterized by bodipy GM1 monomer/dimer emission. Langmuir 2007, 23, 6704−6711. (185) Menke, M.; Künneke, S.; Janshoff, A. Lateral organization of GM1 in phase-separated monolayers visualized by scanning force microscopy. Eur. Biophys. J. 2002, 31, 317−322. (186) Shi, J.; Yang, T.; Kataoka, S.; Zhang, Y.; Diaz, A. J.; Cremer, P. S. GM1 clustering inhibits cholera toxin binding in supported phospholipid membranes. J. Am. Chem. Soc. 2007, 129, 5954−5961. (187) Yuan, C.; Johnston, L. J. Atomic force microscopy studies of ganglioside GM1 domains in phosphatidylcholine and phosphatidylcholine/cholesterol bilayers. Biophys. J. 2001, 81, 1059−1069. (188) Schütte, O. M.; Ries, A.; Orth, A.; Patalag, L. J.; Römer, W.; Steinem, C.; Werz, D. B. Influence of Gb3 glycosphingolipids differing in their fatty acid chain on the phase behaviour of solid supported membranes: chemical syntheses and impact of Shiga toxin binding. Chem. Sci. 2014, 5, 3104−3114. (189) Masserini, M. E.; Palestini, P.; Venerando, B.; Fiorilli, A.; Acquotti, D.; Tettamanti, G. Interactions of proteins with gangliosideenriched microdomains on the membrane: the lateral phase separation of molecular species of GD1a ganglioside, having homogeneous long-chain base composition, is recognized by Vibrio cholerae sialidase. Biochemistry 1988, 27, 7973−7978. (190) Lozano, M. M.; Hovis, J. S.; Moss, F. R.; Boxer, S. G. Dynamic reorganization and correlation among lipid raft components. J. Am. Chem. Soc. 2016, 138, 9996−10001.

(191) Matuoka, S.; Akiyama, M.; Yamada, H.; Tsuchihashi, K.; Gasa, S. Phase behavior in multilamellar vesicles of DPPC containing ganglioside GM3 with a C18:1 sphingoid base and a 24:0 acyl chain (GM3(18,24)) observed by X-ray diffraction. Chem. Phys. Lipids 2003, 123, 19−29. (192) De Luca, F.; Cametti, C.; Naglieri, A.; Bordi, F.; Misasi, R.; Sorice, M. Cluster organization of glycosphingolipid GD1a in lipid bilayer membranes: a dielectric and conductometric study. Langmuir 1999, 15, 2493−2499. (193) van Gorkom, L. C. M.; Cheetham, J. J.; Epand, R. M. Ganglioside GD1a generates domains of high curvature in phosphatidylethanolamine liposomes as determined by solid state 31P-NMR spectroscopy. Chem. Phys. Lipids 1995, 76, 103−108. (194) Maggio, B.; Cumar, F. A.; Caputto, R. Interactions of gangliosides with phospholipids and glycosphingolipids in mixed monolayers. Biochem. J. 1978, 175, 1113−1118. (195) Maggio, B. Favorable and unfavorable lateral interactions of ceramide, neutral glycosphingolipids and gangliosides in mixed monolayers. Chem. Phys. Lipids 2004, 132, 209−224. (196) Viaud, J.; Mansour, R.; Antkowiak, A.; Mujalli, A.; Valet, C.; Chicanne, G.; Xuereb, J.-M.; Terrisse, A.-D.; Séverin, S.; Gratacap, M.-P.; et al. Phosphoinositides: important lipids in the coordination of cell dynamics. Biochimie 2016, 125, 250−258. (197) Payrastre, B.; Missy, K.; Giuriato, S.; Bodin, S.; Plantavid, M.; Gratacap, M.-P. Phosphoinositides: key players in cell signalling, in time and space. Cell. Signalling 2001, 13, 377−387. (198) Lietha, D. Phosphoinositides-the seven species: conversion and cellular roles. Encyclopedia of Life Sciences; John Wiley & Sons, Ltd.: Chichester, UK, 2011. (199) Balla, T. Phosphoinositides: tiny lipids with giant impact on cell regulation. Physiol. Rev. 2013, 93, 1019−1137. (200) Picas, L.; Gaits-Iacovoni, F.; Goud, B. The emerging role of phosphoinositide clustering in intracellular trafficking and signal transduction. F1000Research 2016, 5, 422. (201) Yin, H. L.; Janmey, P. A. Phosphoinositide regulation of the actin cytoskeleton. Annu. Rev. Physiol. 2003, 65, 761−789. (202) Ferrell, J. E.; Huestis, W. H. Phosphoinositide metabolism and the morphology of human erythrocytes. J. Cell Biol. 1984, 98, 1992− 1998. (203) McLaughlin, S.; Wang, J.; Gambhir, A.; Murray, D. PIP2 and proteins: interactions, organization, and information flow. Annu. Rev. Biophys. Biomol. Struct. 2002, 31, 151−175. (204) Graber, Z. T.; Gericke, A.; Kooijman, E. E. Phosphatidylinositol-4,5-bisphosphate ionization in the presence of cholesterol, calcium or magnesium ions. Chem. Phys. Lipids 2014, 182, 62−72. (205) Jiang, Z.; Redfern, R. E.; Isler, Y.; Ross, A. H.; Gericke, A. Cholesterol stabilizes fluid phosphoinositide domains. Chem. Phys. Lipids 2014, 182, 52−61. (206) Sarmento, M. J.; Coutinho, A.; Fedorov, A.; Prieto, M.; Fernandes, F. Ca2+ induces PI(4,5)P2 clusters on lipid bilayers at physiological PI(4,5)P2 and Ca2+ concentrations. Biochim. Biophys. Acta, Biomembr. 2014, 1838, 822−830. (207) Ellenbroek, W. G.; Wang, Y.-H.; Christian, D. A.; Discher, D. E.; Janmey, P. A.; Liu, A. J. Divalent cation-dependent formation of electrostatic PIP2 clusters in lipid monolayers. Biophys. J. 2011, 101, 2178−2184. (208) Levental, I.; Christian, D. A.; Wang, Y.-H.; Madara, J. J.; Discher, D. E.; Janmey, P. A. Calcium-dependent lateral organization in phosphatidylinositol 4,5-bisphosphate (PIP2)- and cholesterolcontaining monolayers. Biochemistry 2009, 48, 8241−8248. (209) Carvalho, K.; Ramos, L.; Roy, C.; Picart, C. Giant unilamellar vesicles containing phosphatidylinositol(4,5)bisphosphate: characterization and functionality. Biophys. J. 2008, 95, 4348−4360. (210) Ji, C.; Zhang, Y.; Xu, P.; Xu, T.; Lou, X. Nanoscale landscape of phosphoinositides revealed by specific pleckstrin homology (PH) domains using single-molecule superresolution imaging in the plasma membrane. J. Biol. Chem. 2015, 290, 26978−26993. (211) Wang, Y.-H.; Collins, A.; Guo, L.; Smith-Dupont, K. B.; Gai, F.; Svitkina, T.; Janmey, P. A. Divalent cation-induced cluster AH


Chemical Reviews

Review

formation by polyphosphoinositides in model membranes. J. Am. Chem. Soc. 2012, 134, 3387−3395. (212) Graber, Z. T.; Wang, W.; Singh, G.; Kuzmenko, I.; Vaknin, D.; Kooijman, E. E. Competitive cation binding to phosphatidylinositol4,5-bisphosphate domains revealed by X-ray fluorescence. RSC Adv. 2015, 5, 106536−106542. (213) Sarmento, M. J. M. J.; Coutinho, A.; Fedorov, A.; Prieto, M.; Fernandes, F. Membrane order is a key regulator of divalent cationinduced clustering of PI(3,5)P2 and PI(4,5)P2. Langmuir 2017, 33, 12463−12477. (214) Przybylo, M.; Sýkora, J.; Humpolíckova, J.; Benda, A.; Zan, A.; Hof, M. Lipid diffusion in giant unilamellar vesicles is more than 2 times faster than in supported phospholipid bilayers under identical conditions. Langmuir 2006, 22, 9096−9099. (215) Fernandes, F.; Loura, L. M. S.; Fedorov, A.; Prieto, M. Absence of clustering of phosphatidylinositol-(4,5)-bisphosphate in fluid phosphatidylcholine. J. Lipid Res. 2006, 47, 1521−1525. (216) Slochower, D. R.; Wang, Y.-H.; Radhakrishnan, R.; Janmey, P. A. Physical chemistry and membrane properties of two phosphatidylinositol bisphosphate isomers. Phys. Chem. Chem. Phys. 2015, 17, 12608−12615. (217) McLaughlin, S.; Murray, D. Plasma membrane phosphoinositide organization by protein electrostatics. Nature 2005, 438, 605− 611. (218) Lemmon, M. A. Membrane recognition by phospholipidbinding domains. Nat. Rev. Mol. Cell Biol. 2008, 9, 99−111. (219) Monteiro, M. E.; Sarmento, M. J.; Fernandes, F. Role of calcium in membrane interactions by PI(4,5)P 2 -binding proteins. Biochem. Soc. Trans. 2014, 42, 1441−1446. (220) Yamamoto, E.; Kalli, A. C.; Yasuoka, K.; Sansom, M. S. P. Interactions of pleckstrin homology domains with membranes: adding back the bilayer via high-throughput molecular dynamics. Structure 2016, 24, 1421−1431. (221) Buyan, A.; Kalli, A. C.; Sansom, M. S. P. Multiscale simulations suggest a mechanism for the association of the Dok7 PH domain with PIP-containing membranes. PLoS Comput. Biol. 2016, 12, e1005028. (222) McLaughlin, S. The electrostatic properties of membranes. Annu. Rev. Biophys. Biophys. Chem. 1989, 18, 113−136. (223) Wang, J.; Arbuzova, A.; Hangyás-Mihályné, G.; McLaughlin, S. The effector domain of myristoylated alanine-rich C kinase substrate binds strongly to phosphatidylinositol 4,5-bisphosphate. J. Biol. Chem. 2001, 276, 5012−5019. (224) Wang, J.; Gambhir, A.; Hangyás-Mihályné, G.; Murray, D.; Golebiewska, U.; McLaughlin, S. Lateral sequestration of phosphatidylinositol 4,5-bisphosphate by the basic effector domain of myristoylated alanine-rich C kinase substrate is due to nonspecific electrostatic interactions. J. Biol. Chem. 2002, 277, 34401−34412. (225) Rauch, M. E.; Ferguson, C. G.; Prestwich, G. D.; Cafiso, D. S. Myristoylated alanine-rich C kinase substrate (MARCKS) sequesters spin-labeled phosphatidylinositol 4,5-bisphosphate in lipid bilayers. J. Biol. Chem. 2002, 277, 14068−14076. (226) Gambhir, A.; Hangyás-Mihályné, G.; Zaitseva, I.; Cafiso, D. S.; Wang, J.; Murray, D.; Pentyala, S. N.; Smith, S. O.; McLaughlin, S. Electrostatic sequestration of PIP2 on phospholipid membranes by basic/aromatic regions of proteins. Biophys. J. 2004, 86, 2188−2207. (227) Wang, J.; Gambhir, A.; McLaughlin, S.; Murray, D. A computational model for the electrostatic sequestration of PI(4,5)P2 by membrane-adsorbed basic peptides. Biophys. J. 2004, 86, 1969− 1986. (228) Charlier, L.; Louet, M.; Chaloin, L.; Fuchs, P.; Martinez, J.; Muriaux, D.; Favard, C.; Floquet, N. Coarse-grained simulations of the HIV-1 matrix protein anchoring: Revisiting its assembly on membrane domains. Biophys. J. 2014, 106, 577−585. (229) Hedger, G.; Sansom, M. S. P.; Koldsø, H. The juxtamembrane regions of human receptor tyrosine kinases exhibit conserved interaction sites with anionic lipids. Sci. Rep. 2015, 5, 9198. (230) Abd Halim, K. B.; Koldsø, H.; Sansom, M. S. P. Interactions of the EGFR juxtamembrane domain with PIP2-containing lipid

bilayers: Insights from multiscale molecular dynamics simulations. Biochim. Biophys. Acta, Gen. Subj. 2015, 1850, 1017−1025. (231) Musse, A. A.; Gao, W.; Homchaudhuri, L.; Boggs, J. M.; Harauz, G. Myelin basic protein as a “PI(4,5)P2-modulin”: a new biological function for a major central nervous system protein. Biochemistry 2008, 47, 10372−10382. (232) Nomikos, M.; Mulgrew-Nesbitt, A.; Pallavi, P.; Mihalyne, G.; Zaitseva, I.; Swann, K.; Lai, F. A.; Murray, D.; McLaughlin, S. Binding of phosphoinositide-specific phospholipase C-ζ (PLC-ζ) to phospholipid membranes: Potential role of an unstructured cluster of basic residues. J. Biol. Chem. 2007, 282, 16644−16653. (233) Yandrapalli, N.; Lubart, Q.; Tanwar, H. S.; Picart, C.; Mak, J.; Muriaux, D.; Favard, C. Self assembly of HIV-1 Gag protein on lipid membranes generates PI(4,5)P2/Cholesterol nanoclusters. Sci. Rep. 2016, 6, 39332. (234) Nilsson, C. L. Lectins: analytical tools from nature. Lectins: Analytical Technologies; Elsevier, 2007; pp 1−13. (235) Chakrabandhu, K.; Huault, S.; Garmy, N.; Fantini, J.; Stebe, E.; Mailfert, S.; Marguet, D.; Hueber, A.-O. The extracellular glycosphingolipid-binding motif of Fas defines its internalization route, mode and outcome of signals upon activation by ligand. Cell Death Differ. 2008, 15, 1824−1837. (236) Campanero-Rhodes, M. A.; Smith, A.; Chai, W.; Sonnino, S.; Mauri, L.; Childs, R. A.; Zhang, Y.; Ewers, H.; Helenius, A.; Imberty, A.; et al. N-glycolyl GM1 ganglioside as a receptor for simian virus 40. J. Virol. 2007, 81, 12846−12858. (237) Bergan, J.; Dyve Lingelem, A. B.; Simm, R.; Skotland, T.; Sandvig, K. Shiga toxins. Toxicon 2012, 60, 1085−1107. (238) Johannes, L.; Parton, R. G.; Bassereau, P.; Mayor, S. Building endocytic pits without clathrin. Nat. Rev. Mol. Cell Biol. 2015, 16, 311−321. (239) Rydell, G. E.; Svensson, L.; Larson, G.; Johannes, L.; Römer, W. Human GII.4 norovirus VLP induces membrane invaginations on giant unilamellar vesicles containing secretor gene dependent α1,2fucosylated glycosphingolipids. Biochim. Biophys. Acta, Biomembr. 2013, 1828, 1840−1845. (240) Römer, W.; Berland, L.; Chambon, V.; Gaus, K.; Windschiegl, B.; Tenza, D.; Aly, M. R. E.; Fraisier, V.; Florent, J.-C.; Perrais, D.; et al. Shiga toxin induces tubular membrane invaginations for its uptake into cells. Nature 2007, 450, 670−675. (241) Windschiegl, B.; Orth, A.; Römer, W.; Berland, L.; Stechmann, B.; Bassereau, P.; Johannes, L.; Steinem, C. Lipid reorganization induced by Shiga toxin clustering on planar membranes. PLoS One 2009, 4, e6238. (242) Hammond, A. T.; Heberle, F. A.; Baumgart, T.; Holowka, D.; Baird, B.; Feigenson, G. W. Crosslinking a lipid raft component triggers liquid ordered-liquid disordered phase separation in model plasma membranes. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 6320− 6325. (243) Xiao, Q.; Ludwig, A.-K.; Romanò, C.; Buzzacchera, I.; Sherman, S. E.; Vetro, M.; Vértesy, S.; Kaltner, H.; Reed, E. H.; Möller, M.; et al. Exploring functional pairing between surface glycoconjugates and human galectins using programmable glycodendrimersomes. Proc. Natl. Acad. Sci. U. S. A. 2018, 115, E2509−E2518. (244) Safouane, M.; Berland, L.; Callan-Jones, A.; Sorre, B.; Römer, W.; Johannes, L.; Toombes, G. E. S.; Bassereau, P. Lipid cosorting mediated by Shiga toxin induced tubulation. Traffic 2010, 11, 1519− 1529. (245) Kabbani, A. M.; Kelly, C. V. Nanoscale membrane budding induced by CTxB and detected via polarized localization microscopy. Biophys. J. 2017, 113, 1795−1806. (246) Zhang, R. G.; Westbrook, M. L.; Nance, S.; Spangler, B. D.; Westbrook, E. M.; Scott, D. L.; Shipley, G. G. The three-dimensional crystal structure of cholera toxin. J. Mol. Biol. 1995, 251, 563−573. (247) Ling, H.; Boodhoo, A.; Hazes, B.; Cummings, M. D.; Armstrong, G. D.; Brunton, J. L.; Read, R. J. Structure of the Shigalike toxin I B-pentamer complexed with an analogue of its receptor Gb3. Biochemistry 1998, 37, 1777−1788. AI


Chemical Reviews

Review

(248) Neu, U.; Woellner, K.; Gauglitz, G.; Stehle, T. Structural basis of GM1 ganglioside recognition by simian virus 40. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 5219−5224. (249) Prasad, B. V. V.; Hardy, M. E.; Dokland, T.; Bella, J.; Rossmann, M. G.; Estes, M. K. X-ray crystallographic structure of the Norwalk virus capsid. Science 1999, 286, 287−290. (250) Marsh, D. Protein modulation of lipids, and vice-versa, in membranes. Biochim. Biophys. Acta, Biomembr. 2008, 1778, 1545− 1575. (251) Olsinova, M.; Jurkiewicz, P.; Sykora, J.; Sabo, J.; Hof, M.; Cwiklik, L.; Cebecauer, M. Roughness of a transmembrane peptide reduces lipid membrane dynamics. bioRxiv 2017, No. 093377. (252) Niemelä, P. S.; Miettinen, M. S.; Monticelli, L.; Hammaren, H.; Bjelkmar, P.; Murtola, T.; Lindahl, E.; Vattulainen, I. Membrane proteins diffuse as dynamic complexes with lipids. J. Am. Chem. Soc. 2010, 132, 7574−7575. (253) Ramadurai, S.; Holt, A.; Krasnikov, V.; van den Bogaart, G.; Killian, J. A.; Poolman, B. Lateral diffusion of membrane proteins. J. Am. Chem. Soc. 2009, 131, 12650−12656. (254) Frick, M.; Schmidt, K.; Nichols, B. J. Modulation of lateral diffusion in the plasma membrane by protein density. Curr. Biol. 2007, 17, 462−467. (255) Mouritsen, O. G.; Bloom, M. Mattress model of lipid-protein interactions in membranes. Biophys. J. 1984, 46, 141−153. (256) Anderson, R. G. W. A role for lipid shells in targeting proteins to caveolae, rafts, and other lipid domains. Science 2002, 296, 1821− 1825. (257) De Planque, M. R. R.; Greathouse, D. V.; Koeppe, R. E.; Schäfer, H.; Marsh, D.; Killian, J. A. Influence of lipid/peptide hydrophobic mismatch on the thickness of diacylphosphatidylcholine bilayers. A 2H NMR and ESR study using designed transmembrane α-helical peptides and gramicidin A. Biochemistry 1998, 37, 9333− 9345. (258) Nyholm, T. K. M.; Van Duyl, B.; Rijkers, D. T. S.; Liskamp, R. M. J.; Killian, J. A. Probing the lipid-protein interface using model transmembrane peptides with a covalently linked acyl chain. Biophys. J. 2011, 101, 1959−1967. (259) Garcia-Celma, J. J.; Hatahet, L.; Kunz, W.; Fendler, K. Specific anion and cation binding to lipid membranes investigated on a solid supported membrane. Langmuir 2007, 23, 10074−10080. (260) Jurkiewicz, P.; Cwiklik, L.; Vojtíšková, A.; Jungwirth, P.; Hof, M. Structure, dynamics, and hydration of POPC/POPS bilayers suspended in NaCl, KCl, and CsCl solutions. Biochim. Biophys. Acta, Biomembr. 2012, 1818, 609−616. (261) Yang, H.; Xu, Y.; Gao, Z.; Mao, Y.; Du, Y.; Jiang, H. Effects of Na+, K+, and Ca2+ on the structures of anionic lipid bilayers and biological implication. J. Phys. Chem. B 2010, 114, 16978−16988. (262) Roux, M.; Bloom, M. Calcium, magnesium, lithium, sodium, and potassium distributions in the headgroup region of binary membranes of phosphatidylcholine and phosphatidylserine as seen by deuterium NMR. Biochemistry 1990, 29, 7077−7089. (263) Ross, M.; Steinem, C.; Galla, H. J.; Janshoff, A. Visualization of chemical and physical properties of calcium-induced domains in DPPC/DPPS Langmuir-Blodgett layers. Langmuir 2001, 17, 2437− 2445. (264) Schultz, Z. D.; Pazos, I. M.; McNeil-Watson, F. K.; Lewis, E. N.; Levin, I. W. Magnesium-induced lipid bilayer microdomain reorganizations: implications for membrane fusion. J. Phys. Chem. B 2009, 113, 9932−9941. (265) Silvius, J. R. Calcium-induced lipid phase separations and interactions of phosphatidylcholine/anionic phospholipid vesicles. Fluorescence studies using carbazole-labeled and brominated phospholipids. Biochemistry 1990, 29, 2930−2938. (266) Boettcher, J. M.; Davis-Harrison, R. L.; Clay, M. C.; Nieuwkoop, A. J.; Ohkubo, Y. Z.; Tajkhorshid, E.; Morrissey, J. H.; Rienstra, C. M. Atomic view of calcium-induced clustering of phosphatidylserine in mixed lipid bilayers. Biochemistry 2011, 50, 2264−2273.

(267) Melcrová, A.; Pokorna, S.; Pullanchery, S.; Kohagen, M.; Jurkiewicz, P.; Hof, M.; Jungwirth, P.; Cremer, P. S.; Cwiklik, L. The complex nature of calcium cation interactions with phospholipid bilayers. Sci. Rep. 2016, 6, 38035. (268) Roux, M.; Bloom, M. Calcium binding by phosphatidylserine headgroups. Deuterium NMR study. Biophys. J. 1991, 60, 38−44. (269) Himeno, H.; Shimokawa, N.; Komura, S.; Andelman, D.; Hamada, T.; Takagi, M. Charge-induced phase separation in lipid membranes. Soft Matter 2014, 10, 7959−7967. (270) Tsai, H.-H. G.; Lai, W.-X.; Lin, H.-D.; Lee, J.-B.; Juang, W.-F.; Tseng, W.-H. Molecular dynamics simulation of cation−phospholipid clustering in phospholipid bilayers: Possible role in stalk formation during membrane fusion. Biochim. Biophys. Acta, Biomembr. 2012, 1818, 2742−2755. (271) Xie, Y.; Yang, L. Calcium and magnesium ions are membraneactive against stationary-phase Staphylococcus aureus with high specificity. Sci. Rep. 2016, 6, 20628. (272) Pöyry, S.; Vattulainen, I. Role of charged lipids in membrane structures  Insight given by simulations. Biochim. Biophys. Acta, Biomembr. 2016, 1858, 2322−2333. (273) Ohnishi, S.; Ito, T. Clustering of lecithin molecules in phosphatidylserine membranes induced by calcium ion binding to phosphatidylserine. Biochem. Biophys. Res. Commun. 1973, 51, 132− 138. (274) Galla, H.-J.; Sackmann, E. Chemically induced lipid phase separation in model membranes containing charged lipids: A spin label study. Biochim. Biophys. Acta, Biomembr. 1975, 401, 509−529. (275) Hartmann, W.; Galla, H.-J.; Sackmann, E. Direct evidence of charge-induced lipid domain structure in model membranes. FEBS Lett. 1977, 78, 169−172. (276) Huang, J.; Swanson, J. E.; Dibble, A. R.; Hinderliter, A. K.; Feigenson, G. W. Nonideal mixing of phosphatidylserine and phosphatidylcholine in the fluid lamellar phase. Biophys. J. 1993, 64, 413−425. (277) Faiß, S.; Kastl, K.; Janshoff, A.; Steinem, C. Formation of irreversibly bound annexin A1 protein domains on POPC/POPS solid supported membranes. Biochim. Biophys. Acta, Biomembr. 2008, 1778, 1601−1610. (278) Menikh, A.; Nyholm, P.-G.; Boggs, J. M. Characterization of the interaction of Ca2+ with hydroxy and non-hydroxy fatty acid species of cerebroside sulfate by Fourier transform infrared spectroscopy and molecular modeling. Biochemistry 1997, 36, 3438−3447. (279) Viani, P.; Cervato, G.; Marchesini, S.; Cestaro, B. Fluorospectroscopic studies of mixtures of distearoylphosphatidylcholine and sulfatides with defined fatty acid compositions. Chem. Phys. Lipids 1986, 39, 41−51. (280) Tupper, S.; Wong, P. T. T.; Tanphaichitr, N. Binding of calcium to sulfogalactosylceramide and the sequential effects on the lipid dynamics. Biochemistry 1992, 31, 11902−11907. (281) Fairn, G. D.; Schieber, N. L.; Ariotti, N.; Murphy, S.; Kuerschner, L.; Webb, R. I.; Grinstein, S.; Parton, R. G. Highresolution mapping reveals topologically distinct cellular pools of phosphatidylserine. J. Cell Biol. 2011, 194, 257−275. (282) Fujimoto, T.; Parmryd, I. Interleaflet coupling, pinning, and leaflet asymmetry  major players in plasma membrane nanodomain formation. Front. Cell Dev. Biol. 2017, 4, 155. (283) McMahon, H. T.; Gallop, J. L. Membrane curvature and mechanisms of dynamic cell membrane remodelling. Nature 2005, 438, 590−596. (284) Chhabra, E. S.; Higgs, H. N. The many faces of actin: matching assembly factors with cellular structures. Nat. Cell Biol. 2007, 9, 1110−1121. (285) Zimmerberg, J.; Hess, S. T. Elongated membrane zones boost interactions of diffusing proteins. Cell 2011, 146, 501−503. (286) Nickels, J. D.; Smith, J. C.; Cheng, X. Lateral organization, bilayer asymmetry, and inter-leaflet coupling of biological membranes. Chem. Phys. Lipids 2015, 192, 87−99. AJ


Chemical Reviews

Review

(287) Fujiwara, T.; Ritchie, K.; Murakoshi, H.; Jacobson, K.; Kusumi, A. Phospholipids undergo hop diffusion in compartmentalized cell membrane. J. Cell Biol. 2002, 157, 1071−1082. (288) Kwik, J.; Boyle, S.; Fooksman, D.; Margolis, L.; Sheetz, M. P.; Edidin, M. Membrane cholesterol, lateral mobility, and the phosphatidylinositol 4,5-bisphosphate-dependent organization of cell actin. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 13964−13969. (289) Fujita, A.; Cheng, J.; Fujimoto, T. Segregation of GM1 and GM3 clusters in the cell membrane depends on the intact actin cytoskeleton. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 2009, 1791, 388−396. (290) Goswami, D.; Gowrishankar, K.; Bilgrami, S.; Ghosh, S.; Raghupathy, R.; Chadda, R.; Vishwakarma, R.; Rao, M.; Mayor, S. Nanoclusters of GPI-anchored proteins are formed by cortical actindriven activity. Cell 2008, 135, 1085−1097. (291) Arumugam, S.; Bassereau, P. Membrane nanodomains: contribution of curvature and interaction with proteins and cytoskeleton. Essays Biochem. 2015, 57, 109−119. (292) Callan-Jones, A.; Sorre, B.; Bassereau, P. Curvature-driven lipid sorting in biomembranes. Cold Spring Harbor Perspect. Biol. 2011, 3, a004648. (293) Kim, H.-R.; Mun, Y.; Lee, K.-S.; Park, Y.-J.; Park, J.-S.; Park, J.H.; Jeon, B.-N.; Kim, C.-H.; Jun, Y.; Hyun, Y.-M.; et al. T cell microvilli constitute immunological synaptosomes that carry messages to antigen-presenting cells. Nat. Commun. 2018, 9, 3630. (294) Jung, Y.; Riven, I.; Feigelson, S. W.; Kartvelishvily, E.; Tohya, K.; Miyasaka, M.; Alon, R.; Haran, G. Three-dimensional localization of T-cell receptors in relation to microvilli using a combination of superresolution microscopies. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, E5916−E5924. (295) Kollmitzer, B.; Heftberger, P.; Rappolt, M.; Pabst, G. Monolayer spontaneous curvature of raft-forming membrane lipids. Soft Matter 2013, 9, 10877−10884. (296) Eicher, B.; Marquardt, D.; Heberle, F. A.; Letofsky-Papst, I.; Rechberger, G. N.; Appavou, M.-S.; Katsaras, J.; Pabst, G. Intrinsic curvature-mediated transbilayer coupling in asymmetric lipid vesicles. Biophys. J. 2018, 114, 146−157. (297) Baumgart, T.; Das, S.; Webb, W. W. W.; Jenkins, J. T. T. Membrane elasticity in giant vesicles with fluid phase coexistence. Biophys. J. 2005, 89, 1067−1080. (298) Heinrich, M.; Tian, A.; Esposito, C.; Baumgart, T. Dynamic sorting of lipids and proteins in membrane tubes with a moving phase boundary. Proc. Natl. Acad. Sci. U. S. A. 2010, 107, 7208−7213. (299) Schmid, F. Physical mechanisms of micro- and nanodomain formation in multicomponent lipid membranes. Biochim. Biophys. Acta, Biomembr. 2017, 1859, 509−528. (300) Baumgart, T.; Hess, S. T.; Webb, W. W. Imaging coexisting fluid domains in biomembrane models coupling curvature and line tension. Nature 2003, 425, 821−824. (301) Gebhardt, C.; Gruler, H.; Sackmann, E. On domain structure and local curvature in lipid bilayers and biological membranes. Z. Naturforsch., C: J. Biosci. 1977, 32, 581−596. (302) Cheney, P.; Weisgerber, A.; Feuerbach, A.; Knowles, M. Single lipid molecule dynamics on supported lipid bilayers with membrane curvature. Membranes 2017, 7, 15. (303) Black, J. C.; Cheney, P. P.; Campbell, T.; Knowles, M. K. Membrane curvature based lipid sorting using a nanoparticle patterned substrate. Soft Matter 2014, 10, 2016−2023. (304) Berry, R. S. Phase changes of nanosystems. In Handbook of Nanophysics; Sattler, K. D., Ed.; Taylor & Francis Books (CRC Press), 2010. (305) Esfarjani, K.; Mansoori, G. A. Statistical mechanical modeling and its application to nanosystems. In Handbook of Theoretical and Computational Nanotechnology; Rieth, M., Schommers, W., Eds.; American Scientific Publishers, 2005; Vol. X, Chapter 16, pp 1−45. (306) Hill, T. L. Perspective: nanothermodynamics. Nano Lett. 2001, 1, 111−112.

(307) Veatch, S. L.; Keller, S. L. Seeing spots: complex phase behavior in simple membranes. Biochim. Biophys. Acta, Mol. Cell Res. 2005, 1746, 172−185. (308) Mouritsen, O. G.; Jorgensen, K. Dynamic lipid-bilayer heterogeneity: a mesoscopic vehicle for membrane function? BioEssays 1992, 14, 129−136. (309) Sezgin, E.; Gutmann, T.; Buhl, T.; Dirkx, R.; Grzybek, M.; Coskun, Ü .; Solimena, M.; Simons, K.; Levental, I.; Schwille, P. Adaptive lipid packing and bioactivity in membrane domains. PLoS One 2015, 10, e0123930. (310) Rosetti, C. M.; Mangiarotti, A.; Wilke, N. Sizes of lipid domains: what do we know from artificial lipid membranes? What are the possible shared features with membrane rafts in cells? Biochim. Biophys. Acta, Biomembr. 2017, 1859, 789−802. (311) Cahn, J. W.; Hilliard, J. E. Free energy of a nonuniform system. I. Interfacial free energy. J. Chem. Phys. 1958, 28, 258−267. (312) Wise, S.; Wang, X.; Wang, C. Unconditionally stable schemes for equations of thin film epitaxy. Discret. Contin. Dyn. Syst. 2010, 28, 405−423. (313) Israelachvili, J. N. Intermolecular and Surface Forces, 3rd ed.; Academic Press, 2011. (314) Palmieri, B.; Yamamoto, T.; Brewster, R. C.; Safran, S. A. Line active molecules promote inhomogeneous structures in membranes: theory, simulations and experiments. Adv. Colloid Interface Sci. 2014, 208, 58−65. (315) Brewster, R.; Pincus, P. A.; Safran, S. A. Hybrid lipids as a biological surface-active component. Biophys. J. 2009, 97, 1087−1094. (316) Palmieri, B.; Safran, S. A. Hybrid lipids increase the probability of fluctuating nanodomains in mixed membranes. Langmuir 2013, 29, 5246−5261. (317) Yamamoto, T.; Safran, S. A. Line tension between domains in multicomponent membranes is sensitive to degree of unsaturation of hybrid lipids. Soft Matter 2011, 7, 7021−7033. (318) Towles, K. B.; Dan, N. Line tension and coalescence in heterogeneous membranes. Langmuir 2007, 23, 13053−13058. (319) Seul, M.; Andelman, D. Domain shapes and patterns: the phenomenology of modulated phases. Science 1995, 267, 476−483. (320) Goh, S. L.; Amazon, J. J.; Feigenson, G. W. Toward a better raft model: modulated phases in the four-component bilayer, DSPC/ DOPC/POPC/CHOL. Biophys. J. 2013, 104, 853−862. (321) Frolov, V. A. J.; Chizmadzhev, Y. A.; Cohen, F. S.; Zimmerberg, J. Entropic traps” in the kinetics of phase separation in multicomponent membranes stabilize nanodomains. Biophys. J. 2006, 91, 189−205. (322) Liu, J.; Qi, S.; Groves, J. T.; Chakraborty, A. K. Phase segregation on different length scales in a model cell membrane system. J. Phys. Chem. B 2005, 109, 19960−19969. (323) Travesset, A. Effect of dipolar moments in domain sizes of lipid bilayers and monolayers. J. Chem. Phys. 2006, 125, 084905. (324) Leibler, S.; Andelman, D. Ordered and curved meso-structures in membranes and amphiphilic films. J. Phys. (Paris) 1987, 48, 2013− 2018. (325) Konyakhina, T. M.; Goh, S. L.; Amazon, J.; Heberle, F. A.; Wu, J.; Feigenson, G. W. Control of a nanoscopic-to-macroscopic transition: modulated phases in four-component DSPC/DOPC/ POPC/Chol giant unilamellar vesicles. Biophys. J. 2011, 101, L8−L10. (326) Elson, E. L.; Fried, E.; Dolbow, J. E.; Genin, G. M. Phase separation in biological membranes: integration of theory and experiment. Annu. Rev. Biophys. 2010, 39, 207−226. (327) Fan, J.; Sammalkorpi, M.; Haataja, M. Domain formation in the plasma membrane: roles of nonequilibrium lipid transport and membrane proteins. Phys. Rev. Lett. 2008, 100, 178102. (328) Foret, L. A simple mechanism of raft formation in twocomponent fluid membranes. Europhys. Lett. 2005, 71, 508−514. (329) Turner, M. S.; Sens, P.; Socci, N. D. Nonequilibrium raftlike membrane domains under continuous recycling. Phys. Rev. Lett. 2005, 95, 168301. (330) Gorelik, J.; Shevchuk, A. I.; Frolenkov, G. I.; Diakonov, I. A.; Lab, M. J.; Kros, C. J.; Richardson, G. P.; Vodyanoy, I.; Edwards, C. AK


Chemical Reviews

Review

(351) Ingólfsson, H. I.; Melo, M. N.; Van Eerden, F. J.; Arnarez, C.; Lopez, C. A.; Wassenaar, T. A.; Periole, X.; De Vries, A. H.; Tieleman, D. P.; Marrink, S. J. Lipid organization of the plasma membrane. J. Am. Chem. Soc. 2014, 136, 14554−14559. (352) Ingólfsson, H. I.; Carpenter, T. S.; Bhatia, H.; Bremer, P. T.; Marrink, S. J.; Lightstone, F. C. Computational lipidomics of the neuronal plasma membrane. Biophys. J. 2017, 113, 2271−2280. (353) Schäfer, L. V.; Marrink, S. J. Partitioning of lipids at domain boundaries in model membranes. Biophys. J. 2010, 99, L91−L93. (354) Muddana, H. S.; Chiang, H. H.; Butler, P. J. Tuning membrane phase separation using nonlipid amphiphiles. Biophys. J. 2012, 102, 489−497. (355) Chavent, M.; Duncan, A. L.; Sansom, M. S. P. Molecular dynamics simulations of membrane proteins and their interactions: from nanoscale to mesoscale. Curr. Opin. Struct. Biol. 2016, 40, 8−16. (356) Niemelä, P. S.; Ollila, S.; Hyvönen, M. T.; Karttunen, M.; Vattulainen, I. Assessing the nature of lipid raft membranes. PLoS Comput. Biol. 2007, 3, e34. (357) Marrink, S. J.; Tieleman, D. P. Perspective on the Martini model. Chem. Soc. Rev. 2013, 42, 6801−6822. (358) Sodt, A. J.; Sandar, M. L.; Gawrisch, K.; Pastor, R. W.; Lyman, E. The molecular structure of the liquid-ordered phase of lipid bilayers. J. Am. Chem. Soc. 2014, 136, 725−732. (359) Javanainen, M.; Martinez-Seara, H.; Vattulainen, I. Nanoscale membrane domain formation driven by cholesterol. Sci. Rep. 2017, 7, 1143. (360) Lipowsky, R. Budding of membranes induced by intramembrane domains. J. Phys. II 1992, 2, 1825−1840. (361) Ursell, T. S.; Klug, W. S.; Phillips, R. Morphology and interaction between lipid domains. Proc. Natl. Acad. Sci. U. S. A. 2009, 106, 13301−13306. (362) Ding, W.; Palaiokostas, M.; Wang, W.; Orsi, M. Effects of lipidcomposition on bilayer membranes quantified by all-atom molecular dynamics. J. Phys. Chem. B 2015, 119, 15263−15274. (363) Koldsø, H.; Shorthouse, D.; Hélie, J.; Sansom, M. S. P. Lipid clustering correlates with membrane curvature as revealed by molecular simulations of complex lipid bilayers. PLoS Comput. Biol. 2014, 10, e1003911. (364) Boyd, K. J.; Alder, N. N.; May, E. R. Buckling under pressure: curvature-based lipid segregation and stability modulation in cardiolipin-containing bilayers. Langmuir 2017, 33, 6937−6946. (365) Amazon, J. J.; Feigenson, G. W. Lattice simulations of phase morphology on lipid bilayers: renormalization, membrane shape, and electrostatic dipole interactions. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 2014, 89, 022702 DOI: 10.1103/PhysRevE.89.022702. (366) Baoukina, S.; Rozmanov, D.; Tieleman, D. P. Composition fluctuations in lipid bilayers. Biophys. J. 2017, 113, 2750−2761. (367) Perlmutter, J. D.; Sachs, J. N. Interleaflet interaction and asymmetry in phase separated lipid bilayers: molecular dynamics simulations. J. Am. Chem. Soc. 2011, 133, 6563−6577. (368) May, S. Trans-monolayer coupling of fluid domains in lipid bilayers. Soft Matter 2009, 5, 3148−3156. (369) Gueguen, G.; Destainville, N.; Manghi, M. Mixed lipid bilayers with locally varying spontaneous curvature and bending. Eur. Phys. J. E: Soft Matter Biol. Phys. 2014, 37, 1−13. (370) Honerkamp-Smith, A. R.; Cicuta, P.; Collins, M. D.; Veatch, S. L.; den Nijs, M.; Schick, M.; Keller, S. L. Line tensions, correlation lengths, and critical exponents in lipid membranes near critical points. Biophys. J. 2008, 95, 236−246. (371) Schick, M. Membrane heterogeneity: manifestation of a curvature-induced microemulsion. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 2012, 85, No. 031902, DOI: 10.1103/PhysRevE.85.031902. (372) Sadeghi, S.; Müller, M.; Vink, R. L. C. Raft formation in lipid bilayers coupled to curvature. Biophys. J. 2014, 107, 1591−1600. (373) Meinhardt, S.; Vink, R. L. C.; Schmid, F. Monolayer curvature stabilizes nanoscale raft domains in mixed lipid bilayers. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 4476−4481.

R. W.; Klenerman, D.; et al. Dynamic assembly of surface structures in living cells. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 5819−5822. (331) Garcia-Parajo, M. F.; Cambi, A.; Torreno-Pina, J. a.; Thompson, N.; Jacobson, K. Nanoclustering as a dominant feature of plasma membrane organization. J. Cell Sci. 2014, 127, 4995−5005. (332) Kusumi, A.; Suzuki, K. G. N.; Kasai, R. S.; Ritchie, K.; Fujiwara, T. K. Hierarchical mesoscale domain organization of the plasma membrane. Trends Biochem. Sci. 2011, 36, 604−615. (333) Newman, M. E. J.; Barkema, G. T. Monte Carlo Methods in Statistical Physics; Oxford University Press: New York, 1999. (334) Pleimling, M.; Selke, W. Droplets in the coexistence region of the two-dimensional Ising model. J. Phys. A: Math. Gen. 2000, 33, L199−L202. (335) Mouritsen, O. G.; Boothroyd, A.; Harris, R.; Jan, N.; Lookman, T.; MacDonald, L.; Pink, D. A.; Zuckermann, M. J. Computer simulation of the main gel−fluid phase transition of lipid bilayers. J. Chem. Phys. 1983, 79, 2027−2041. (336) Jørgensen, K.; Sperotto, M. M.; Mouritsen, O. G.; Ipsen, J. H.; Zuckermann, M. J. Phase equilibria and local structure in binary lipid bilayers. Biochim. Biophys. Acta, Biomembr. 1993, 1152, 135−145. (337) Sugár, I. P.; Thompson, T. E.; Biltonen, R. L. Monte Carlo simulation of two-component bilayers: DMPC/DSPC mixtures. Biophys. J. 1999, 76, 2099−2110. (338) Hac, A. E.; Seeger, H. M.; Fidorra, M.; Heimburg, T. Diffusion in two-component lipid membranes - a fluorescence correlation spectroscopy and Monte Carlo simulation study. Biophys. J. 2005, 88, 317−333. (339) Frazier, M. L.; Wright, J. R.; Pokorny, A.; Almeida, P. F. F. Investigation of domain formation in sphingomyelin/cholesterol/ POPC mixtures by fluorescence resonance energy transfer and Monte Carlo simulations. Biophys. J. 2007, 92, 2422−2433. (340) Lis, M.; Pintal, L.; Swiatek, J.; Cwiklik, L. GPU-based massive parallel kawasaki kinetics in the dynamic Monte Carlo simulations of lipid nanodomains. J. Chem. Theory Comput. 2012, 8, 4758−4765. (341) Almeida, P. F.; Best, A.; Hinderliter, A. Monte Carlo simulation of protein-induced lipid demixing in a membrane with interactions derived from experiment. Biophys. J. 2011, 101, 1930− 1937. (342) Ehrig, J.; Petrov, E. P.; Schwille, P. Near-critical fluctuations and cytoskeleton-assisted phase separation lead to subdiffusion in cell membranes. Biophys. J. 2011, 100, 80−89. (343) Ehrig, J.; Petrov, E. P.; Schwille, P. Phase separation and nearcritical fluctuations in two-component lipid membranes: Monte Carlo simulations on experimentally relevant scales. New J. Phys. 2011, 13, 045019. (344) Berger, O.; Edholm, O.; Jähnig, F. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature. Biophys. J. 1997, 72, 2002−2013. (345) Marrink, S. J.; de Vries, A. H.; Mark, A. E. Coarse Grained Model for Semiquantitative Lipid Simulations. J. Phys. Chem. B 2004, 108, 750−760. (346) Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; De Vries, A. H. The MARTINI force field: coarse grained model for biomolecular simulations. J. Phys. Chem. B 2007, 111, 7812−7824. (347) Risselada, H. J.; Marrink, S. J. The molecular face of lipid rafts in model membranes. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 17367− 17372. (348) Risselada, H. J.; Marrink, S. J.; Müller, M. Curvaturedependent elastic properties of liquid-ordered domains result in inverted domain sorting on uniaxially compressed vesicles. Phys. Rev. Lett. 2011, 106, 148102. (349) Stevens, M. J. Complementary matching in domain formation within lipid bilayers. J. Am. Chem. Soc. 2005, 127, 15330−15331. (350) Pantelopulos, G. A.; Nagai, T.; Bandara, A.; Panahi, A.; Straub, J. E. Critical size dependence of domain formation observed in coarsegrained simulations of bilayers composed of ternary lipid mixtures. J. Chem. Phys. 2017, 147, 095101. AL


Chemical Reviews

Review

(374) Aranda-Espinoza, H.; Berman, A.; Dan, N.; Pincus, P.; Safran, S. Interaction between inclusions embedded in membranes. Biophys. J. 1996, 71, 648−656. (375) Wolff, J.; Komura, S.; Andelman, D. Budding of domains in mixed bilayer membranes. Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys. 2015, 91, No. 012708, DOI: 10.1103/PhysRevE.91.012708. (376) Hu, J.; Weikl, T.; Lipowsky, R. Vesicles with multiple membrane domains. Soft Matter 2011, 7, 6092−6102. (377) Hořejší, V.; Drbal, K.; Cebecauer, M.; Č erný, J.; Brdička, T.; Angelisová, P.; Stockinger, H. GPI-microdomains: a role in signalling via immunoreceptors. Immunol. Today 1999, 20, 356−361. (378) Simons, K.; Ikonen, E. Functional rafts in cell membranes. Nature 1997, 387, 569−572. (379) Edidin, M.; Kuo, S.; Sheetz, M. Lateral movements of membrane glycoproteins restricted by dynamic cytoplasmic barriers. Science 1991, 254, 1379−1382. (380) Kusumi, A.; Ike, H.; Nakada, C.; Murase, K.; Fujiwara, T. Single-molecule tracking of membrane molecules: plasma membrane compartmentalization and dynamic assembly of raft-philic signaling molecules. Semin. Immunol. 2005, 17, 3−21. (381) Takatori, S.; Tatematsu, T.; Cheng, J.; Matsumoto, J.; Akano, T.; Fujimoto, T. Phosphatidylinositol 3,5-bisphosphate-rich membrane domains in endosomes and lysosomes. Traffic 2016, 17, 154− 167. (382) Surviladze, Z.; Harrison, K. a; Murphy, R. C.; Wilson, B. S. FcϵRI and Thy-1 domains have unique protein and lipid compositions. J. Lipid Res. 2007, 48, 1325−1335. (383) Brameshuber, M.; Weghuber, J.; Ruprecht, V.; Gombos, I.; Horváth, I.; Vigh, L.; Eckerstorfer, P.; Kiss, E.; Stockinger, H.; Schütz, G. J. Imaging of mobile long-lived nanoplatforms in the live cell plasma membrane. J. Biol. Chem. 2010, 285, 41765−41771. (384) Lukeš, T.; Glatzová, D.; Kvíčalová, Z.; Levet, F.; Benda, A.; Letschert, S.; Sauer, M.; Brdička, T.; Lasser, T.; Cebecauer, M. Quantifying protein densities on cell membranes using superresolution optical fluctuation imaging. Nat. Commun. 2017, 8, 1731. (385) Owen, D. M.; Williamson, D. J.; Magenau, A.; Gaus, K. Subresolution lipid domains exist in the plasma membrane and regulate protein diffusion and distribution. Nat. Commun. 2012, 3, 1256. (386) Eggeling, C.; Ringemann, C.; Medda, R.; Schwarzmann, G.; Sandhoff, K.; Polyakova, S.; Belov, V. N.; Hein, B.; von Middendorff, C.; Schönle, A.; et al. Direct observation of the nanoscale dynamics of membrane lipids in a living cell. Nature 2009, 457, 1159−1162. (387) Caiolfa, V. R.; Zamai, M.; Malengo, G.; Andolfo, A.; Madsen, C. D.; Sutin, J.; Digman, M. a; Gratton, E.; Blasi, F.; Sidenius, N. Monomer−dimer dynamics and distribution of GPI-anchored uPAR are determined by cell surface protein assemblies. J. Cell Biol. 2007, 179, 1067−1082. (388) Lasserre, R.; Guo, X.-J.; Conchonaud, F.; Hamon, Y.; Hawchar, O.; Bernard, A.-M.; Soudja, S. M.; Lenne, P.-F.; Rigneault, H.; Olive, D.; et al. Raft nanodomains contribute to Akt/ PKB plasma membrane recruitment and activation. Nat. Chem. Biol. 2008, 4, 538−547. (389) Suzuki, K. G. N.; Kasai, R. S.; Hirosawa, K. M.; Nemoto, Y. L.; Ishibashi, M.; Miwa, Y.; Fujiwara, T. K.; Kusumi, A. Transient GPIanchored protein homodimers are units for raft organization and function. Nat. Chem. Biol. 2012, 8, 774−783. (390) Sharma, P.; Varma, R.; Sarasij, R.; Ira; Gousset, K.; Krishnamoorthy, G.; Rao, M.; Mayor, S. Nanoscale organization of multiple GPI-anchored proteins in living cell membranes. Cell 2004, 116, 577−589. (391) Sevcsik, E.; Schütz, G. J. With or without rafts? Alternative views on cell membranes. BioEssays 2016, 38, 129−139. (392) Dupuy, A. D.; Engelman, D. M. Protein area occupancy at the center of the red blood cell membrane. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 2848−2852. (393) Jain, A.; Holthuis, J. C. M. Membrane contact sites, ancient and central hubs of cellular lipid logistics. Biochim. Biophys. Acta, Mol. Cell Res. 2017, 1864, 1450−1458.

(394) Kinnunen, P. K. On the principles of functional ordering in biological membranes. Chem. Phys. Lipids 1991, 57, 375−399.

AM


Membrane Lipid Nanodomains - ACS Publications - American

Recommend Documents