Series of Concentration-Induced Phase Transitions in Cholesterol/Phosphatidylcholine Mixtures

In lipid membranes, temperature-induced transition from gel-to-fluid phase increases the lateral diffusion of the lipid molecules by three orders of magnitude. In cell membranes, a similar phase change may trigger the communication between the membrane components. Here concentration-induced phase transition properties of our recently developed statistical mechanical model of cholesterol/phospholipid mixtures are investigated. A slight (<1%) decrease in the model parameter values, controlling the lateral interaction energies, reveals the existence of a series of first- or second-order phase transitions. By weakening the lateral interactions first, the proportion of the ordered (i.e., superlattice) phase (Areg) is slightly and continuously decreasing at every cholesterol mole fraction. Then sudden decreases in Areg appear at the 0.18–0.26 range of cholesterol mole fractions. We point out that the sudden changes in Areg represent first- or second-order concentration-induced phase transitions from fluid to superlattice and from superlattice to fluid phase. Sudden changes like these were detected in our previous experiments at 0.2, 0.222, and 0.25 sterol mole fractions in ergosterol/DMPC mixtures. By further decreasing the lateral interactions, the fluid phase will dominate throughout the 0.18–0.26 interval, whereas outside this interval sudden increases in Areg may appear. Lipid composition-induced phase transitions as specified here should have far more important biological implications than temperature- or pressure-induced phase transitions. This is the case because temperature and pressure in cell membranes are largely invariant under physiological conditions

Genetic diversity and population genetics of large lungworms (Dictyocaulus, Nematoda) in wild deer in Hungary

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.Dictyocaulus nematode worms live as parasites in the lower airways of ungulates and can cause significant disease in both wild and farmed hosts. This study represents the first population genetic analysis of large lungworms in wildlife. Specifically, we quantify genetic variation in Dictyocaulus lungworms from wild deer (red deer, fallow deer and roe deer) in Hungary, based on mitochondrial cytochrome c oxidase subunit 1 (cox1) sequence data, using population genetic and phylogenetic analyses. The studied Dictyocaulus taxa display considerable genetic diversity. At least one cryptic species and a new parasite–host relationship are revealed by our molecular study. Population genetic analyses for Dictyocaulus eckerti revealed high gene flow amongst weakly structured spatial populations that utilise the three host deer species considered here. Our results suggest that D. eckerti is a widespread generalist parasite in ungulates, with a diverse genetic backround and high evolutionary potential. In contrast, evidence of cryptic genetic structure at regional geographic scales was observed for Dictyocaulus capreolus, which infects just one host species, suggesting it is a specialist within the studied area. D. capreolus displayed lower genetic diversity overall, with only moderate gene flow compared to the closely related D. eckerti. We suggest that the differing vagility and dispersal behaviour of hosts are important contributing factors to the population structure of lungworms, and possibly other nematode parasites with single-host life cycles. Our findings are of relevance for the management of lungworms in deer farms and wild deer populations.This work was carried out as a part of Zoltán Ács’ PhD thesis on “Distribution and host spectrum of Dictyocaulus lungworms in deer” supported by the Hungarian Government

Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA) each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions

Detection, identification and functional characterisation of plant and microbial volatile organic compounds with inhibitory activity against two plant pathogens

Volatile organic compounds (VOCs) play crucial ecological roles in interactions among organisms. For example, plant VOCs can act as a powerful deterrent of herbivore insects and pathogens or they can act as resistance inducers to stimulate plant defences. Likewise, bioactive VOCs can be emitted by beneficial microorganisms and they may potentially act as key molecules in the microbe-microbe and plant-microbe communications. However, scarce information is available concerning the role of VOCs produced by grapevine (Vitis vinifera) plants and beneficial bacteria belonging to the Lysobacter genus in defence mechanisms against two important phytopathogenic oomycetes, namely Plasmopara viticola and Phytophthora infestans, which are the causal agents of grapevine downy mildew and potato late blight, respectively. The major objectives of this PhD thesis were the detection, identification and the functional characterization of VOCs from Vitis spp. and Lysobacter spp., in order to better understand their role in plant-microbe and microbe-microbe communications and to identify new active molecules from natural origin to control phytopathogens. In particular, VOCs from resistant and susceptible grapevine genotypes were identified following P. viticola inoculation and their effect as toxic molecules against downy mildew was explored (publications 1 and 2). Likewise, VOCs produced by Lysobacter spp. were identified and characterised, in order to identify microbial VOCs able to inhibit P. infestans growth (publication 3). In order to reach these goals, a headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-MS) and proton transfer reaction time of flight-mass spectrometry (PTR-ToF-MS) have been used. Two downy mildew resistant hybrids (SO4 and Kober 5BB) and the susceptible V. vinifera cultivar Pinot noir were analysed in vitro using PTR-ToF-MS. We found that P. viticola inoculation resulted in a significant increase monoterpene and sesquiterpene emission by resistant genotypes (SO4 and Kober 5BB) and not by the susceptible cultivar (Vitis vinifera Pinot noir; publication 1). Grapevine VOCs were further identified by HS-SPME/GC-MS using greenhouse-grown plants. The four resistant genotypes tested (BC4, Kober 5BB, SO4 and Solaris) showed significantly increased production of VOCs after P. viticola inoculation under greenhouse conditions. Conversely, no significant emission of volatile terpenes was detected from Pinot noir plants after P. viticola inoculation, suggesting that VOCs of resistant genotypes could play an important role in grapevine resistance against downy mildew. The chemical structures of P. viticola-induced VOCs were identified by retention index and the GC-MS spectrum evaluation and VOCs potentially involved in the grapevine resistance were selected according to their emission profiles. Pure compounds were tested against P. viticola by leaf disk assays and different experiments were set up, in order to elucidate the efficacy of pure VOCs both in a liquid suspension of P. viticola sporangia and after application via the gas phase. These experiments revealed six (2-phenylethanol, β-caryophyllene, β-selinene, trans-2-pentenal, 2-ethylfuran, and β-cyclocitral) and four VOCs (2-phenylethanol, trans-2-pentenal, 2-ethylfuran, and β-cyclocitral) which impaired downy mildew symptoms after direct application of liquid suspension and after treatment with VOC enriched air (without direct contact with the leaf tissue), respectively. With these results we demonstrated that VOCs produced by resistant grapevine genotypes are related to post-infection mechanisms and may contribute to grapevine resistance against P. viticola by inhibition of pathogen development (publication 2). In the second part of the PhD project, the volatilome of Lysobacter spp. was characterised for its inhibitory activity against the soil pathogen P. infestans (publication 3). The effect of VOCs emitted by Lysobacter strains was demonstrated in vitro by dual-culture assay and profiles were characterised by HS-SPME/GC-MS and PTR-ToF-MS analysis. Interestingly, the biocontrol activity and VOC profiles of Lysobacter spp. depended on the bacterial growth media. In particular, VOCs with inhibitory properties (pyrazines, pyrrole and decanal) were mainly emitted by Lysobacter type strains grown on a protein-rich medium, demonstrating the importance of the culture medium composition to optimise the biocontrol efficacy of Lysobacter spp. against plant pathogens. In summary, the presented thesis showed that both analytical chemistry techniques used (PTR-ToF-MS and HS-SPME/GC-MS) can be employed synergistically to detect and identify VOCs from different biological matrixes such as leaf tissue or bacterial cultures. The presented thesis also suggested that VOCs contribute to grapevine resistance and they can effectively be used to control economically important plant pathogens such as P. viticola. Furthermore, results generated in this work indicate that nutrient availability may affect the aggressiveness of Lysobacter spp. in the soil to maximise biocontrol efficacy against P. infestans. However, further metabolomic and transcriptomic analyses are required to investigate the VOC-mediated plant defence mechanisms and to characterize metabolic changes and VOC emissions of Lysobacter spp. grown in soil condition

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

<p>Abstract</p> <p>Background</p> <p>There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶points that are often generated by high throughput experiments.</p> <p>Results</p> <p>To circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 10⁶data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.</p> <p>Conclusions</p> <p>Misty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.</p

Complex Aggregates over Clusters of Elements

Complex aggregates have been proposed as a way to bridge the gap between approaches that handle sets by imposing conditions on specific elements, and approaches that handle them by imposing conditions on aggregated values. A complex aggregate summarises a subset of the elements in a set, where this subset is defined by conditions on the attribute values. In this paper, we present a new type of complex aggregate, where this subset is defined to be a cluster of the set. This is useful if subsets that are relevant for the task at hand are difficult to describe in terms of attribute conditions. This work is motivated from the analysis of flow cytometry data, where the sets are cells, and the subsets are cell populations. We describe two approaches to aggregate over clusters on an abstract level, and validate one of them empirically, motivating future research in this direction

Enhanced Lipid Diffusion and Mixing in Accelerated Molecular Dynamics

Accelerated molecular dynamics (aMD) is an enhanced sampling technique that expedites conformational space sampling by reducing the barriers separating various low-energy states of a system. Here, we present the first application of the aMD method on lipid membranes. Altogether, ∼1.5 μs simulations were performed on three systems: a pure POPC bilayer, a pure DMPC bilayer, and a mixed POPC:DMPC bilayer. Overall, the aMD simulations are found to produce significant speedup in trans–gauche isomerization and lipid lateral diffusion versus those in conventional MD (cMD) simulations. Further comparison of a 70-ns aMD run and a 300-ns cMD run of the mixed POPC:DMPC bilayer shows that the two simulations yield similar lipid mixing behaviors, with aMD generating a 2–3-fold speedup compared to cMD. Our results demonstrate that the aMD method is an efficient approach for the study of bilayer structural and dynamic properties. On the basis of simulations of the three bilayer systems, we also discuss the impact of aMD parameters on various lipid properties, which can be used as a guideline for future aMD simulations of membrane systems

Geometrical properties of gel and fluid clusters in DMPC/DSPC bilayers: Monte Carlo simulation approach using a two-state model.

In this paper the geometrical properties of gel and fluid clusters of equimolar dimyristoylphosphatidylcholine/distearoylphosphatidylcholine (DMPC/DSPC) lipid bilayers are calculated by using an Ising-type model (Sugar, I. P., T. E. Thompson, and R. L. Biltonen. 1999. Biophys. J. 76:2099-2110). The model is able to predict the following properties in agreement with the respective experimental data: the excess heat capacity curves, fluorescence recovery after photobleaching (FRAP) threshold temperatures at different mixing ratios, the most frequent center-to-center distance between DSPC clusters, and the fractal dimension of gel clusters. In agreement with the neutron diffraction and fluorescence microscopy data, the simulations show that below the percolation threshold temperature of gel clusters many nanometer-size gel clusters co-exist with one large gel cluster of size comparable with the membrane surface area. With increasing temperature the calculated effective fractal dimension and capacity dimension of gel and fluid clusters decrease and increase, respectively, within the (0, 2) interval. In the region of the gel-to-fluid transition the following geometrical properties are independent from the temperature and the state of the cluster: 1) the cluster perimeter linearly increases with the number of cluster arms at a rate of 8.2 nm/arm; 2) the average number of inner islands in a cluster increases with increasing cluster size, S, according to a power function of 0.00427 x S(1.3); 3) the following exponential function describes the average size of an inner island versus the size of the host cluster, S: 1 + 1.09(1 - e(-0.0072xS)). By means of the equations describing the average geometry of the clusters the process of the association of clusters is investigated