| Literature DB >> 27141942 |
Hande Ozgen1, Wia Baron2, Dick Hoekstra1, Nicoletta Kahya1.
Abstract
In the central nervous system, oligodendrocytes synthesize a specialized membrane, the myelin membrane, which enwraps the axons in a multilamellar fashion to provide fast action potential conduction and to ensure axonal integrity. When compared to other membranes, the composition of myelin membranes is unique with its relatively high lipid to protein ratio. Their biogenesis is quite complex and requires a tight regulation of sequential events, which are deregulated in demyelinating diseases such as multiple sclerosis. To devise strategies for remedying such defects, it is crucial to understand molecular mechanisms that underlie myelin assembly and dynamics, including the ability of specific lipids to organize proteins and/or mediate protein-protein interactions in healthy versus diseased myelin membranes. The tight regulation of myelin membrane formation has been widely investigated with classical biochemical and cell biological techniques, both in vitro and in vivo. However, our knowledge about myelin membrane dynamics, such as membrane fluidity in conjunction with the movement/diffusion of proteins and lipids in the membrane and the specificity and role of distinct lipid-protein and protein-protein interactions, is limited. Here, we provide an overview of recent findings about the myelin structure in terms of myelin lipids, proteins and membrane microdomains. To give insight into myelin membrane dynamics, we will particularly highlight the application of model membranes and advanced biophysical techniques, i.e., approaches which clearly provide an added value to insight obtained by classical biochemical techniques.Entities:
Keywords: Fluorescence correlation spectroscopy; Membrane microdomains; Model membranes; Myelin biogenesis; Oligodendrocytes
Mesh:
Substances:
Year: 2016 PMID: 27141942 PMCID: PMC4967101 DOI: 10.1007/s00018-016-2228-8
Source DB: PubMed Journal: Cell Mol Life Sci ISSN: 1420-682X Impact factor: 9.261
Major myelin lipids
| Lipid | % total dry weight of myelin | Comments | Reference | Notes | ||
|---|---|---|---|---|---|---|
| Human | Bovine | Rat | ||||
| Cholesterol | 27.7 | 28.1 | 27.3 | Rate limiting for CNS myelination | [ | |
| Galactolipids | ||||||
| Galactosylceramide (GalC) | 22.7 | 24 | 23.3 | Role in OLG maturation | [ | Mainly C(24:1) |
| Sulfatide | 3.8 | 3.6 | 7.1 | Negative regulator of OLG differentiation | [ | Mainly C(24:1) |
aThe mentioned comment are obtained from CGT knock-out mouse studies, therefore these findings are also relevant for the function of sulfatide
Fig. 1Myelin structure. a Schematic model that shows the uncompacted myelin sheath and the enwrapment of axons by myelin and the localization of major myelin proteins. The major myelin protein PLP is represented in red and MBP is represented in green. b Detailed schematic model of myelin membrane organization and the localization of the major myelin lipids GalC, sulfatide, cholesterol and myelin proteins PLP and MBP within the myelin membrane. Note that outerleaflet lipids GalC and sulfatide face each other in enwrapped myelin. c The synthesis scheme of sulfatide and GalC. Note that GalC is synthesized from ceramide by CGT (ceramide glucosyltransferase); sulfatide is subsequently synthesized from GalC by CST (cerebroside sulfotransferase)
Optical microscopical biophysical techniques applied in oligodendrocyte-myelin field in chronological order
| References | Biophysical techniques | Major findings |
|---|---|---|
| [ | FCS | Diffusion coefficient of MOG-eGFP and Bodipy FL-C5 sphingomyelin |
| [ | C-Laurdan | In co-culture systems (neuron-derived signals): |
| FRAP | Formation of highly dynamic GalC clusters in OLGs | |
| STED | Increased number of large GalC clusters in the presence of neurons | |
| FRET | Self-interaction of GalC within the large clusters | |
| [ | FRET | Decrease in Rho activity in Oli-neu cells in the presence of neuronal-conditioned medium |
| C-Laurdan | Increased membrane condensation in the presence of conditioned neuronal medium or RhoGTPase inactivation | |
| [ | RICS, FRAP | Diffusion coefficient of MOG-eGFP in OLN-93 cells |
| [ | FRET | Interaction of 14-kDa MBP with PIP2 (sensed by CFP-PH-PLCδ1) in Oli-neu cells |
| [ | RICS, FRAP | Diffusion coefficient of DiI-C18 in primary OLGs |
| [ | C-Laurdan | Higher lipid order in GUVs prepared from myelin compared to Oli-neu cells |
| FCS | Slower diffusion of DiD in GUVs prepared from wild type animal compared shiverera mouse or CerS2 deficient mouse | |
| FRAP | Slower diffusion of Cell MaskOranged in control OLGs compared to FB1c-treated OLGs | |
| [ | FRAP | Detection of highly mobile MAG in OLGs isolated from shiverer mouse compared to control to show the diffusion barrier function of 14-kDa MBP |
| [ | FRAP | Dynamic role of 21.5-kDa MBP-RFP in OLN-93 cells proliferation |
| [ | FRAP | Decreased 14-kDa MBP diffusion due to its oligomerization |
| FRET | Self association of 14-kDa MBP | |
| [ | s-FCS | Decreased mobility of PLP-eGFP in the presence of sulfatide (rather than GalC) on poly- |
| Increased mobility of PLP-eGFP even in the presence of sulfatide on fibronectin in OLN-93 cells | ||
|
| Increased mobility of 18.5-MBP-eGFP in the presence of only GalC in OLN-93 cells | |
| [ | FRET | Presence of one or more intermediate conformational states of 18.5-kDa MBP |
aMBP-deficient mouse
bMouse model unable to synthesize long acid fatty acyl chain lipids
cInhibitor of sphingolipid synthesis
dMembrane dye
Fig. 2Biophysical applications. a. FRAP (fluorescence recovery after photobleaching) application in a living cell (for more details see the text). The laser beam depicted in red reflects 100 % laser power. The corresponding graph shows the fluorescence recovery after bleaching. b FCS (fluorescence correlation spectroscopy) applications in a living cell (for more details see the text). The laser beam is depicted in orange and the diffusing molecules in red. Fluorescently labeled molecules diffusing through the detection volume give rise to fluorescence fluctuations in time (i) which can be converted to the autocorrelation curve to determine the half decay. By fitting the autocorrelation curve with mathematical models, particle number, diffusion time/coefficient can be calculated (ii). c Schematic representation of RICS (raster image correlation spectroscopy). Temporal information can be extracted from raster scan images as these images are recorded pixel by pixel (for details see [26, 27]). A representative autocorrelation curve, the weighted residuals and corresponding 2D1C fit model is shown from a z-scan RICS measurement for 18.5 kDa MBP-eGFP. d (i) Schematic representation of FRET (fluorescence resonance energy transfer) and FCSS (fluorescence cross correlation spectroscopy). The red fluorophore is excited by laser light, which transfers its energy of the excited photon in a radiation-less manner to the green fluorophore which is thus excited and as a result emits light. For this so called principle of energy transfer, the distance between two fluorophores should be 20 nm or less. (ii) The red and green fluorophore diffuse together through the confocal volume (see b) which reveals cross correlation, depicted by the black cross-correlation curve in the corresponding graph