Lipid peroxidation is essential for α-synuclein-induced cell death.

Parkinson's disease is the second most common neurodegenerative disease and its pathogenesis is closely associated with oxidative stress. Deposition of aggregated α-synuclein (α-Syn) occurs in familial and sporadic forms of Parkinson's disease. Here, we studied the effect of oligomeric α-Syn on one of the major markers of oxidative stress, lipid peroxidation, in primary co-cultures of neurons and astrocytes. We found that oligomeric but not monomeric α-Syn significantly increases the rate of production of reactive oxygen species, subsequently inducing lipid peroxidation in both neurons and astrocytes. Pre-incubation of cells with isotope-reinforced polyunsaturated fatty acids (D-PUFAs) completely prevented the effect of oligomeric α-Syn on lipid peroxidation. Inhibition of lipid peroxidation with D-PUFAs further protected cells from cell death induced by oligomeric α-Syn. Thus, lipid peroxidation induced by misfolding of α-Syn may play an important role in the cellular mechanism of neuronal cell loss in Parkinson's disease. We have found that aggregated α-synuclein-induced production of reactive oxygen species (ROS) that subsequently stimulates lipid peroxidation and cell death in neurons and astrocytes. Specific inhibition of lipid peroxidation by incubation with reinforced polyunsaturated fatty acids (D-PUFAs) completely prevented the effect of α-synuclein on lipid peroxidation and cell death

NOx AND SO2 EMISSIONS OF HUNGARIAN ELECTRIC POWER PLANT BOILERS

Coal fuelled power stations are responsible for about the half of SO2 emission in Hungary. Specific emission values may be 5 to 10 times the ultimate value admitted in the FRG. Introduction of various desulfurization attachment methods has to be endeavoured in coal fired power stations. The share of Hungarian power stations in NOx emission is some lower, about a quarter. The high specific NOx emission attributable to firing methods (construction) is of importance especially for gas firing. Introduction of (primary) firing methods has to be endeavoured, to prevent NOx formation

Deuterated Arachidonic Acid Ameliorates Lipopolysaccharide-Induced Lung Damage in Mice

Arachidonic acid (ARA) is a major component of lipid bilayers as well as the key substrate for the eicosanoid cascades. ARA is readily oxidized, and its non-enzymatic and enzymatic oxidation products induce inflammatory responses in nearly all tissues, including lung tissues. Deuteration at bis-allylic positions substantially decreases the overall rate of ARA oxidation when hydrogen abstraction is an initiating event. To compare the effects of dosing of arachidonic acid (H-ARA) and its bis-allylic hexadeuterated form (D-ARA) on lungs in conventionally healthy mice and in an acute lung injury model, mice were dosed with H-ARA or D-ARA for six weeks through dietary supplementation and then challenged with intranasal lipopolysaccharide (LPS) for subsequent analysis of bronchoalveolar lavage fluid and lung tissue. Dosing on D-ARA resulted in successful incorporation of D-ARA into various tissues. D-ARA significantly reduced LPS-induced adverse effects on alveolar septal thickness and the bronchoalveolar area. Oral deuterated ARA is taken up efficiently and protects against adverse LPS-induced pathology. This suggests novel therapeutic avenues for reducing lung damage during severe infections and other pathological conditions with inflammation in the pulmonary system and other inflammatory diseases

Direct immobilization of DNA probes on non-modified plastics by UV irradiation and integration in microfluidic devices for rapid bioassay

DNA microarrays have become one of the most powerful tools in the field of genomics and medical diagnosis. Recently, there has been increased interest in combining microfluidics with microarrays since this approach offers advantages in terms of portability, reduced analysis time, low consumption of reagents, and increased system integration. Polymers are widely used for microfluidic systems, but fabrication of microarrays on such materials often requires complicated chemical surface modifications, which hinders the integration of microarrays into microfluidic systems. In this paper, we demonstrate that simple UV irradiation can be used to directly immobilize poly(T)poly(C)-tagged DNA oligonucleotide probes on many different types of plastics without any surface modification. On average, five- and fourfold improvement in immobilization and hybridization efficiency have been achieved compared to surface-modified slides with aminated DNA probes. Moreover, the TC tag only costs 30% of the commonly used amino group modifications. Using this microarray fabrication technique, a portable cyclic olefin copolymer biochip containing eight individually addressable microfluidic channels was developed and used for rapid and parallel identification of Avian Influenza Virus by DNA hybridization. The one-step, cost-effective DNA-linking method on non-modified polymers significantly simplifies microarray fabrication procedures and permits great flexibility to plastic material selection, thus making it convenient to integrate microarrays into plastic microfluidic systems

Collective Dynamics of Specific Gene Ensembles Crucial for Neutrophil Differentiation: The Existence of Genome Vehicles Revealed

Cell fate decision remarkably generates specific cell differentiation path among the multiple possibilities that can arise through the complex interplay of high-dimensional genome activities. The coordinated action of thousands of genes to switch cell fate decision has indicated the existence of stable attractors guiding the process. However, origins of the intracellular mechanisms that create “cellular attractor” still remain unknown. Here, we examined the collective behavior of genome-wide expressions for neutrophil differentiation through two different stimuli, dimethyl sulfoxide (DMSO) and all-trans-retinoic acid (atRA). To overcome the difficulties of dealing with single gene expression noises, we grouped genes into ensembles and analyzed their expression dynamics in correlation space defined by Pearson correlation and mutual information. The standard deviation of correlation distributions of gene ensembles reduces when the ensemble size is increased following the inverse square root law, for both ensembles chosen randomly from whole genome and ranked according to expression variances across time. Choosing the ensemble size of 200 genes, we show the two probability distributions of correlations of randomly selected genes for atRA and DMSO responses overlapped after 48 hours, defining the neutrophil attractor. Next, tracking the ranked ensembles' trajectories, we noticed that only certain, not all, fall into the attractor in a fractal-like manner. The removal of these genome elements from the whole genomes, for both atRA and DMSO responses, destroys the attractor providing evidence for the existence of specific genome elements (named “genome vehicle”) responsible for the neutrophil attractor. Notably, within the genome vehicles, genes with low or moderate expression changes, which are often considered noisy and insignificant, are essential components for the creation of the neutrophil attractor. Further investigations along with our findings might provide a comprehensive mechanistic view of cell fate decision

Hybridization thermodynamics of NimbleGen Microarrays

Background While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets. Results We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex. Conclusions This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p