Deciphering the Structure, Growth and Assembly of Amyloid-Like Fibrils Using High-Speed Atomic Force Microscopy

Formation of fibrillar structures of proteins that deposit into aggregates has been suggested to play a key role in various neurodegenerative diseases. However mechanisms and dynamics of fibrillization remains to be elucidated. We have previously established that lithostathine, a protein overexpressed in the pre-clinical stages of Alzheimer's disease and present in the pathognomonic lesions associated with this disease, form fibrillar aggregates after its N-terminal truncation. In this paper we visualized, using high-speed atomic force microscopy (HS-AFM), growth and assembly of lithostathine protofibrils under physiological conditions with a time resolution of one image/s. Real-time imaging highlighted a very high velocity of elongation. Formation of fibrils via protofibril lateral association and stacking was also monitored revealing a zipper-like mechanism of association. We also demonstrate that, like other amyloid ß peptides, two lithostathine protofibrils can associate to form helical fibrils. Another striking finding is the propensity of the end of a growing protofibril or fibril to associate with the edge of a second fibril, forming false branching point. Taken together this study provides new clues about fibrillization mechanism of amyloid proteins

Automatic detection of diffusion modes within biological membranes using back-propagation neural network

International audienceAbstractBackgroundSingle particle tracking (SPT) is nowadays one of the most popular technique to probe spatio-temporal dynamics of proteins diffusing within the plasma membrane. Indeed membrane components of eukaryotic cells are very dynamic molecules and can diffuse according to different motion modes. Trajectories are often reconstructed frame-by-frame and dynamic properties often evaluated using mean square displacement (MSD) analysis. However, to get statistically significant results in tracking experiments, analysis of a large number of trajectories is required and new methods facilitating this analysis are still needed.ResultsIn this study we developed a new algorithm based on back-propagation neural network (BPNN) and MSD analysis using a sliding window. The neural network was trained and cross validated with short synthetic trajectories. For simulated and experimental data, the algorithm was shown to accurately discriminate between Brownian, confined and directed diffusion modes within one trajectory, the 3 main of diffusion encountered for proteins diffusing within biological membranes. It does not require a minimum number of observed particle displacements within the trajectory to infer the presence of multiple motion states. The size of the sliding window was small enough to measure local behavior and to detect switches between different diffusion modes for segments as short as 20 frames. It also provides quantitative information from each segment of these trajectories. Besides its ability to detect switches between 3 modes of diffusion, this algorithm is able to analyze simultaneously hundreds of trajectories with a short computational time.ConclusionThis new algorithm, implemented in powerful and handy software, provides a new conceptual and versatile tool, to accurately analyze the dynamic behavior of membrane components

Use of Cyclodextrin for AFM Monitoring of Model Raft Formation

The lipid rafts membrane microdomains, enriched in sphingolipids and cholesterol, are implicated in numerous functions of biological membranes. Using atomic force microscopy, we have examined the effects of cholesterol-loaded methyl-β-cyclodextrin (MβCD-Chl) addition to liquid disordered (l(d))-gel phase separated dioleoylphosphatidylcholine (DOPC)/sphingomyelin (SM) and 1-palmitoyl-2-oleoyl phosphatidylcholine (POPC)/SM supported bilayers. We observed that incubation with MβCD-Chl led to the disappearance of domains with the formation of a homogeneously flat bilayer, most likely in the liquid-ordered (l(o)) state. However, intermediate stages differed with the passage through the coexistence of l(o)-l(d) phases for DOPC/SM samples and of l(o)-gel phases for POPC/SM bilayers. Thus, gel phase SM domains surrounded by a l(o) matrix rich in cholesterol and POPC could be observed just before reaching the uniform l(o) state. This suggests that raft formation in biological membranes could occur not only via liquid-liquid but also via gel-liquid immiscibility. The data also demonstrate that MβCD-Chl as well as the unloaded cyclodextrin MβCD make holes and preferentially extract SM in supported bilayers. This strongly suggests that interpretation of MβCD and MβCD-Chl effects on cell membranes only in terms of cholesterol movements have to be treated with caution

Equivalence between Euler angle conventions for the description of tensorial interactions in liquid NMR: application to different software programs

International audienceLong-range orientational restraints derived from alignment or rotational diffusion tensors have greatly contributed to the expansion of applications in biomolecular NMR. The orientation of the principal axis system of these tensors is usually described by the so-called Euler angles. However, no clear consensus has emerged concerning the convention of the associated orthogonal rotations. As a result, the different programs that derive or predict them have adopted different conventions, which make comparison between their results difficult. Moreover, the rotation schemes are seldom completely described. Here, we summarize the different conventions, determine which ones are adopted by commonly used software packages, and establish the formal equivalencies between the different calculated Euler angles

La microscopie à force atomique, une technique de pointe appliquée à la biologie

Le microscope à force atomique (AFM) permet l’observation de la surface d’échantillons biologiques dans un tampon physiologique avec des résolutions latérale et verticale qui peuvent atteindre quelques angströms. Il permet d’observer aussi bien des structures biologiques complexes que des molécules uniques. Il s’est avéré être un outil très performant dans l’étude des membranes isolées, biologiques ou artificielles, et de leurs constituants lipidiques et protéiques et il permet également de traduire en image, dans leur état fonctionnel, d’autres structures biologiques comme des complexes nucléoprotéiques. Il peut servir d’instrument de dissection et de manipulation à l’échelle moléculaire et est utile pour évaluer les forces d’interactions intra ou intermoléculaires

CD82 and Gangliosides Tune CD81 Membrane Behavior

International audienceTetraspanins are a family of transmembrane proteins that form a network of protein–protein interactions within the plasma membrane. Within this network, tetraspanin are thought to control the lateral segregation of their partners at the plasma membrane through mechanisms involving specific lipids. Here, we used a single molecule tracking approach to study the membrane behavior of tetraspanins in mammary epithelial cells and demonstrate that despite a common overall behavior, each tetraspanin (CD9, CD81 and CD82) has a specific signature in terms of dynamics. Furthermore, we demonstrated that tetraspanin dynamics on the cell surface are dependent on gangliosides. More specifically, we found that CD82 expression increases the dynamics of CD81 and alters its localization at the plasma membrane, this has no effect on the behavior of CD9. Our results provide new information on the ability of CD82 and gangliosides to differentially modulate the dynamics and organization of tetraspanins at the plasma membrane and highlight that its lipid and protein composition is involved in the dynamical architecture of the tetraspanin web. We predict that CD82 may act as a regulator of the lateral segregation of specific tetraspanins at the plasma membrane while gangliosides could play a crucial role in establishing tetraspanin-enriched areas

Transfer on hydrophobic substrates and AFM imaging of membrane proteins reconstituted in planar lipid bilayers

International audienceThe lipid-layer technique allows reconstituting transmembrane proteins at a high density in microns size planar membranes and suspended to a lipid monolayer at the air/water interface. In this paper, we transferred these membranes onto two hydrophobic substrates for further structural analysis of reconstituted proteins by Atomic Force Microscopy (AFM). We used a mica sheet covered by a lipid monolayer or a sheet of highly oriented pyrolytic graphite (HOPG) to trap the lipid monolayer at the interface and the suspended membranes. In both cases, we succeeded in the transfer of large membrane patches containing densely packed or 2D-crystallized proteins. As a proof of concept, we transferred and imaged the soluble Shiga toxin bound to its lipid ligand and the ATP-binding cassette (ABC) transporter BmrA reconstituted into a planar bilayer. AFM imaging with a lateral resolution in the nanometer range was achieved. Potential applications of this technique in structural biology and nanobiotechnology are discussed

Additional file 2: Figure S2. of Automatic detection of diffusion modes within biological membranes using back-propagation neural network

- Comparison of the percentage of decision using the BPNN, Hidden Markov Modeling (HMM)-Bayes, Bayesian Information Criterion (BIC) or Support Vector Machines (SVM) algorithms. 200 simulated trajectories of 300 frames mimicking diffusion within plasma membranes, including one directed motion segment with velocity randomly ranging from 1 to 3 μm/s and one confinement segment with diameters ranging from 0.5 and 1.2 μm, were analyzed with BPPN, HMM-Bayes, BIC or SVM. Within a trajectory each 50 frames segment is always localized at the same position. The diffusion coefficient D is 0.25 μm2/s and the integration time 100 ms. A 30 nm localization noise Pn was added to the trajectory (see Material and Methods section). The percentage of decision based on BPNN corresponds to the number of positive decision for a specific motion mode detected for a given frame over 200 trajectories and normalized to 1 or-1 for confined (light grey) or directed (dark grey) trajectories, respectively. The HMM-Bayes and the BIC algorithms can only detect directed or confined segments within a trajectory, respectively. The tables at the bottom detail the performance of the 4 algorithms in terms of sensitivity and specificity for detecting confined and directed motion modes in the range of parameters tested in this study (D = 0.25 μm2/s, 1 μm/s < v < 3 μm/s, 0.5 μm < L < 1.2 μm). (PDF 400 kb