Probing the interactions of the HIV-1 matrix protein-derived polybasic region with lipid bilayers: insights from AFM imaging and force spectroscopy.

The human immunodeficiency virus type 1 (HIV-1) matrix protein contains a highly basic region, MA-HBR, crucial for various stages of viral replication. To elucidate the interactions between the polybasic peptide MA-HBR and lipid bilayers, we employed liquid-based atomic force microscopy (AFM) imaging and force spectroscopy on lipid bilayers of differing compositions. In 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers, AFM imaging revealed the formation of annulus-shaped protrusions upon exposure to the polybasic peptide, accompanied by distinctive mechanical responses characterized by enhanced bilayer puncture forces. Importantly, our AFM-based force spectroscopy measurements unveiled that MA-HBR induces interleaflet decoupling within the cohesive bilayer organization. This is evidenced by a force discontinuity observed within the bilayer's elastic deformation regime. In POPC/cholesterol bilayers, MA-HBR caused similar yet smaller annular protrusions, demonstrating an intriguing interplay with cholesterol-rich membranes. In contrast, in bilayers containing anionic 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine (POPS) lipids, MA-HBR induced unique annular protrusions, granular nanoparticles, and nanotubules, showcasing its distinctive effects in anionic lipid-enriched environments. Notably, our force spectroscopy data revealed that anionic POPS lipids weakened interleaflet adhesion within the bilayer, resulting in interleaflet decoupling, which potentially contributes to the specific bilayer perturbations induced by MA-HBR. Collectively, our findings highlight the remarkable variations in how the polybasic peptide, MA-HBR, interacts with lipid bilayers of differing compositions, shedding light on its role in host membrane restructuring during HIV-1 infection.

Construction of nano-silica particle clusters and their effects on the shear thickening properties of liquids.

It is of great research significance to prepare a new shear thickening fluid (STF) with a simple process, remarkable thickening effect and excellent impact resistance from the properties of the particles. Inspired by the shear thickening mechanism, nano-silica particle clusters (SPC) with different morphological structures were prepared by the reaction of amino-modified silica with polyethylene glycol diglycidyl ether (PEGDGE), and the structure models of particle clusters were designed through theoretical analysis. The structure of SPC was affected by the degree of amination modification and the molecular weight of PEGDGE, which was analyzed by DLS and TEM. The shear thickening behavior of the fluid was evaluated by steady-state rheology and dynamic-state rheology analysis. The shear thickening behavior of the fluid composed of SPC also changed greatly with the influence of the degree of amination modification and the molecular weight of PEGDGE. In addition, compared with the STF contained original silica, the STF contained SPC could produce a faster and stronger shear thickening response. Therefore, silica particle clusters are not only a promising candidate for the preparation of high-performance shear thickening fluids, but can also be better applied to industrial and scientific fields such as impact protection and shock absorption.

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain.

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

Design and experimental study of a fiber Bragg grating strain sensor with enhanced sensitivity.

A novel high-sensitivity fiber Bragg grating (FBG) strain sensor is reported, to the best of our knowledge. The sensitivity of the sensor is improved by fixing the FBG on an elastic substrate with a sensitization function. The sensitization principle of the designed sensor is introduced, and the mathematical model of the sensor is established. In the static and dynamic experiments of the sensor, the effect of adhesive between the sensor and the measured structure on the sensitivity of the FBG strain sensor is experimentally investigated. The experimental results show that the adhesive with high shear strength is beneficial to the realization of a high-sensitivity sensor. The sensor fixed with planting bar glue can achieve a sensitivity of 9.42 pm/µÎµ, a repeatability error of 4.79%, and a hysteresis error of 3.36%, which is consistent with theoretical and simulation results. The designed high-sensitivity strain sensor has a simple structure, small size, and convenient installation, so it has a good application prospect in micro-strain monitoring.

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images-Case Study of the Small Hilly Areas in Jurong City.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models-random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)-were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Genome-wide identification and expression analysis of the MYB transcription factor in Japanese plum (Prunus salicina).

MYB proteins constitute one of the largest transcription factor families in plants, members of which are involved in various plant physiological and biochemical processes. Japanese plum (Prunus salicina) is one of the important stone fruit crops worldwide. To date, no comprehensive study of the MYB family in Japanese plum has been reported. In this study, we performed genome-wide analysis of MYB genes in Japanese plum including the phylogeny, gene structures, protein motifs, chromosomal locations, collinearity and expression patterns analysis. A total of 96 Japanese plum R2R3-MYB (PsMYB) genes were characterized and distributed on 8 chromosomes at various densities. Collinearity analysis indicated that the segmental duplication events played a crucial role in the expansion of PsMYB genes, and the interspecies synteny analysis revealed the orthologous gene pairs between Japanese plum and other four selected Rosaceae species. The 96 PsMYB genes could be classified into 27 subgroups based on phylogenetic topology, as supported by the conserved gene structures and motif compositions. Further comparative phylogenetic analysis revealed the functional divergence of MYB gene family during evolution, and three subgroups which included only Rasaceae MYB genes were identified. Expression analysis revealed the distinct expression profiles of the PsMYB genes, and further functional predictions found some of them might be associated with the plum fruit quality traits. Our researches provide a global insight into the organization, phylogeny, evolution and expression patterns of the PsMYB genes, and contribute to the greater understanding of their functional roles in Japanese plum.

Protofibril-Fibril Interactions Inhibit Amyloid Fibril Assembly by Obstructing Secondary Nucleation.

Amyloid-ß peptides (Aß) assemble into both rigid amyloid fibrils and metastable oligomers termed AßO or protofibrils. In Alzheimer's disease, Aß fibrils constitute the core of senile plaques, but Aß protofibrils may represent the main toxic species. Aß protofibrils accumulate at the exterior of senile plaques, yet the protofibril-fibril interplay is not well understood. Applying chemical kinetics and atomic force microscopy to the assembly of Aß and lysozyme, protofibrils are observed to bind to the lateral surfaces of amyloid fibrils. When utilizing Aß variants with different critical oligomer concentrations, the interaction inhibits the autocatalytic proliferation of amyloid fibrils by secondary nucleation on the fibril surface. Thus, metastable oligomers antagonize their replacement by amyloid fibrils both by competing for monomers and blocking secondary nucleation sites. The protofibril-fibril interaction governs their temporal evolution and potential to exert specific toxic activities.

Differences in the responses of flow and nutrient load to isolated and coupled future climate and land use changes.

Understanding the differences in the responses of river hydrology and water quality to climate and land use changes is particularly crucial for the development and management of water resources in the future. This study was carried out to assess the isolated and coupled effects of future climate change and land use change on the flow and nutrient load of the Xitiaoxi watershed in southeast China by applying the calibrated Hydrological Simulation Program Fortran model. Four representative concentration pathways released by the Intergovernmental Panel on Climate Change and two projected land use change scenarios were used to simulate future conditions. The results indicate that climate change would result in flow increased with an average variation of 25.2% in the future, and the increased flow would be mainly concentrated on the high flow part of the total flow duration curve. Climate change would also induce seasonal shifts to nutrient load. The effects of land use change showed that nutrient load was more sensitive than flow, made Orthophosphate load increase by 2.8%-154.7%, and flow increase by 7.2%-15.1%. The results for coupled climate and land use changes indicate that flow and nutrient load would be more affected by climate change than by land use change. Climate and land use changes may amplify or weaken each other's effects on flow and nutrient load, which suggests that both should be incorporated into hydrologic models when studying the future conditions. The results of this study can help decision-makers guide management practices that aim to minimize flow and nutrient load.

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region.

Crop-type identification is very important in agricultural regions. Most researchers in this area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to identify crops. This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types. The change detection method was used to analyze spectral and indices information in time series. Significant differences in crop growth status during the growing season were found. Then, three obviously differentiated time features were extracted. Three advanced machine learning algorithms (Support Vector Machine, Artificial Neural Network, and Random Forest, RF) were used to identify the crop types. The results showed that the detection of (Vertical-vertical) VV, (Vertical-horizontal) VH, and Cross Ratio (CR) changes was effective for identifying land cover. Moreover, the red-edge changes were obviously different according to crop growth periods. Sentinel-2 and Landsat-8 showed different normalized difference vegetation index (NDVI) changes also. By using single remote sensing data to classify crops, Sentinel-2 produced the highest overall accuracy (0.91) and Kappa coefficient (0.89). The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91). The RF method had the best performance in terms of identity classification. In addition, the indices feature dominated the classification results. The combination of phenological period information with multi-source remote sensing data can be used to explore a crop area and its status in the growing season. The results of crop classification can be used to analyze the density and distribution of crops. This study can also allow to determine crop growth status, improve crop yield estimation accuracy, and provide a basis for crop management.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Sub-ten-nanometer heterogeneity of solid supported lipid membranes determined by solution atomic force microscopy.

Visually detecting nanoscopic structures in lipid membranes is important for elucidating lipid-lipid interactions, which are suggested to play a role in mediating membrane rafts. We use solution atomic force microscopy (AFM) to study lateral and normal organization in multicomponent lipid membranes supported by mica substrate. Nanoscopic heterogeneity is observed in a three-component system composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/brain-sphingomyelin (bSM)/cholesterol (Chol). We find sub-ten-nanometer correlation lengths that are used to describe membrane lateral organization. In addition, we find that the correlation length is independent on cholesterol concentration, while the height fluctuation (variation) is not. To explore the mechanism that controls the size of membrane heterogeneity, we extend our study to a four-component system composed of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)/POPC/bSM/Chol. By systematically adjusting the relative amount of DOPC and POPC, we obtain macroscopic-to-nanoscopic size transition of membrane heterogeneity. In contrast to the results from vesicle based fluorescence microscopy, we find that the structural transition is continuous both in the lateral and normal directions. We compare our nanoscopic structures to two theoretical models, and find that both the critical fluctuations and the nanodomain models are not sufficient to account for our solution AFM data. Finally, we propose a nanoheterogeneity model that could serve as the organization principle of the observed nanoscopic structures in multicomponent lipid membranes.

Polyglutamine aggregates impair lipid membrane integrity and enhance lipid membrane rigidity.

Lipid membranes are suggested as the primary target of amyloid aggregates. We study aggregates formed by a polyglutamine (polyQ) peptide, and their disruptive effect on lipid membranes. Using solution atomic force microscopy (AFM), we observe polyQ oligomers coexisting with short fibrils, which have a twisted morphology that likely corresponds to two intertwined oligomer strings. Fourier transform infrared spectroscopy reveals that the content of ß-sheet enriched aggregates increases with incubation time. Using fluorescence microscopy, we find that exposure to polyQ aggregates results in deflated morphology of giant unilamellar vesicles. PolyQ aggregates induced membrane disruption is further substantiated by time-dependent calcein leakage from the interior to the exterior of lipid vesicles. Detailed structural and mechanical perturbations of lipid membranes are revealed by solution AFM. We find that membrane disruption by polyQ aggregates proceeds by a two-step process, involving partial and full disruption. In addition to height contrast, the resulting partially and fully disrupted bilayers have distinct rigidity and adhesion force properties compared to the intact bilayer. Specifically, the bilayer rigidity increases as the intact bilayer becomes partially and fully disrupted. Surprisingly, the adhesion force first decreases and then increases during the disruption process. By resolving individual fibrils deposited on bilayer surface, we show that both the length and the number of fibrils can increase with incubation time. Our results highlight that membrane disruption could be the molecular basis of polyQ aggregates induced cytotoxicity.

Modulation of lipid membrane structural and mechanical properties by a peptidomimetic derived from reduced amide scaffold.

Understanding how antimicrobial peptidomimetics interact with lipid membranes is important in battling multidrug resistant bacterial pathogens. We study the effects of a recently reported peptidomimetic on lipid bilayer structural and mechanical properties. The compound referred to as E107-3 is synthesized based on the acylated reduced amide scaffold and has been shown to exhibit good antimicrobial potency. Our vesicle leakage assay indicates that the compound increases lipid bilayer permeability. We use micropipette aspiration to explore the kinetic response of giant unilamellar vesicles (GUVs). Exposure to the compound causes the GUV protrusion length LP to spontaneously increase and then decrease, followed by GUV rupture. Solution atomic force microscopy (AFM) is used to visualize lipid bilayer structural modulation within a nanoscopic regime. Unlike melittin, which produces pore-like structures, the peptidomimetic compound is found to induce nanoscopic heterogeneous structures. Finally, we use AFM-based force spectroscopy to study the impact of the compound on lipid bilayer mechanical properties. We find that incremental addition of the compound to planar lipid bilayers results in a moderate decrease of the bilayer puncture force FP and a 39% decrease of the bilayer area compressibility modulus KA. To explain our experimental data, we propose a membrane interaction model encompassing disruption of lipid chain packing and extraction of lipid molecules. The later action mode is supported by our observation of a double-bilayer structure in the presence of fusogenic calcium ions.

Polycarbonates with Potent and Selective Antimicrobial Activity toward Gram-Positive Bacteria.

The resistance developed by life-threatening bacteria toward conventional antibiotics has become a major concern in public health. To combat antibiotic resistance, there has been a significant interest in the development of antimicrobial cationic polymers due to the ease of synthesis and low manufacturing cost compared to host-defense peptides (HDPs). Herein, we report the design and synthesis of amphiphilic polycarbonates containing primary amino groups. These polymers exhibit potent antimicrobial activity and excellent selectivity to Gram-positive bacteria, including multidrug resistant pathogens. Fluorescence and TEM studies suggest that these polymers are likely to kill bacteria by disrupting bacterial membranes. These polymers also show low tendency to elicit resistance in bacteria. Their further development may lead to new antimicrobial agents combating drug-resistance.

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region.

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure-up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

Atomically detailed lipid bilayer models for the interpretation of small angle neutron and X-ray scattering data.

We present a new atom density profile (ADP) model and a statistical approach for extracting structural characteristics of lipid bilayers from X-ray and neutron scattering data. Models for five lipids with varying head and tail chemical composition in the fluid phase, 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine (POPC), 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylserine (POPS), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylglycerol (POPG), are optimized using a simplex based method to simultaneously reproduce both neutron and X-ray scattering data. Structural properties are determined using statistical analysis of multiple optimal model structures. The method and models presented make minimal assumptions regarding the atomic configuration, while taking into account the underlying physical properties of the system. The more general model and statistical approach yield data with well defined uncertainties, indicating the precision in determining density profiles, atomic locations, and bilayer structural characteristics. Resulting bilayer structures include regions exhibiting large conformational variation. Due to the increased detail in the model, the results demonstrate the possibility of a distinct hydration layer within the interfacial (backbone) region.

Influenza M2 Transmembrane Domain Senses Membrane Heterogeneity and Enhances Membrane Curvature.

Targeting host cell membranes by M2 of influenza A virus is important for virus invasion and replication. We study the transmembrane domain of M2 (M2TM) interacting with mica-supported planar bilayers and free-standing giant unilamellar vesicles (GUVs). Using solution atomic force microscopy (AFM), we show that the size of M2TM oligomers is dependent on lipid composition. The addition of M2TM to lipid bilayers containing liquid-ordered (Lo) and liquid-disordered (Ld) phases reveals that M2TM preferentially partitions into the Ld phase; phase-dependent partitioning results in a larger rigidity of the Ld phase. We next use fluorescence microscopy to study the effects of M2TM on phase-coexisting GUVs. In particular, M2TM is found to increase GUVs' miscibility transition temperature Tmix. The augmented thermodynamic stability can be accounted for by considering an enhanced energy barrier of lipid mixing between coexisting phases. Our GUV study also shows that M2TM can elicit an array of vesicle shapes mimicking virus budding. M2TM enhanced membrane curvature is consistent with our AFM data, which show altered membrane rigidity and consequently line tension at domain edges. Together, our results highlight that in addition to conducting protons, M2TM can actively regulate membrane heterogeneity and augment membrane curvature.

Interactions of the anticancer drug tamoxifen with lipid membranes.

Interactions of the hydrophobic anticancer drug tamoxifen (TAM) with lipid model membranes were studied using calcein-encapsulated vesicle leakage, attenuated total reflection Fourier transform infrared (FTIR) spectroscopy, small-angle neutron scattering (SANS), atomic force microscopy (AFM) based force spectroscopy, and all-atom molecular dynamics (MD) simulations. The addition of TAM enhances membrane permeability, inducing calcein to translocate from the interior to the exterior of lipid vesicles. A large decrease in the FTIR absorption band's magnitude was observed in the hydrocarbon chain region, suggesting suppressed bond vibrational dynamics. Bilayer thickening was determined from SANS data. Force spectroscopy measurements indicate that the lipid bilayer area compressibility modulus KA is increased by a large amount after the incorporation of TAM. MD simulations show that TAM decreases the lipid area and increases chain order parameters. Moreover, orientational and positional analyses show that TAM exhibits a highly dynamic conformation within the lipid bilayer. Our detailed experimental and computational studies of TAM interacting with model lipid membranes shed new light on membrane modulation by TAM.

Revisiting the bilayer structures of fluid phase phosphatidylglycerol lipids: Accounting for exchangeable hydrogens.

We recently published two papers detailing the structures of fluid phase phosphatidylglycerol (PG) lipid bilayers (Kucerka et al., 2012 J. Phys. Chem. B 116: 232-239; Pan et al., 2012 Biochim. Biophys. Acta Biomembr. 1818: 2135-2148), which were determined using the scattering density profile model. This hybrid experimental/computational technique utilizes molecular dynamics simulations to parse a lipid bilayer into components whose volume probabilities follow simple analytical functional forms. Given the appropriate scattering densities, these volume probabilities are then translated into neutron scattering length density (NSLD) and electron density (ED) profiles, which are used to jointly refine experimentally obtained small angle neutron and X-ray scattering data. However, accurate NSLD and ED profiles can only be obtained if the bilayer's chemical composition is known. Specifically, in the case of neutron scattering, the lipid's exchangeable hydrogens with aqueous D2O must be accounted for, as they can have a measureable effect on the resultant lipid bilayer structures. This was not done in our above-mentioned papers. Here we report on the molecular structures of PG lipid bilayers by appropriately taking into account the exchangeable hydrogens. Analysis indicates that the temperature-averaged PG lipid areas decrease by 1.5 to 3.8Å(2), depending on the lipid's acyl chain length and unsaturation, compared to PG areas when hydrogen exchange was not taken into account.