Evaluation of the Soil Contamination of Tangier (Morocco) by the Determination of BTEX,PCBs, and PAHs

Benzene, toluene, ethylbenzene, and xylenes (BTEX), twelve polycyclic aromatic hydrocarbons (PAHs) and seven polychlorinated biphenyls (PCBs), were selected as pollutants to evaluate the contamination of soils in the urban and industrial areas of Tangier (Morocco). PAHs and PCBs were determined by gas chromatography-mass spectrometry (GC-MS) after a microwave-assisted extraction (MAE) and gel permeation chromatography (GPC) clean-up. BTEX were directly determined by head-space GC-MS. Results obtained in this study show the presence of high levels of BTEX and PAHs in the soil near the urban waste deposit. However, the analysis of pollutants in the other sampling sites provided comprehensive evidence that soils of Tangier city are not contaminated.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Phylogeny and expression profiling of CAD and CAD-like genes in hybrid Populus (P. deltoides × P. nigra): evidence from herbivore damage for subfunctionalization and functional divergence

Background  

Cinnamyl Alcohol Dehydrogenase (CAD) proteins function in lignin biosynthesis and play a critical role in wood development and plant defense against stresses. Previous phylogenetic studies did not include genes from seedless plants and did not reflect the deep evolutionary history of this gene family. We reanalyzed the phylogeny of CAD and CAD-like genes using a representative dataset including lycophyte and bryophyte sequences. Many CAD/CAD-like genes do not seem to be associated with wood development under normal growth conditions. To gain insight into the functional evolution of CAD/CAD-like genes, we analyzed their expression in Populus plant tissues in response to feeding damage by gypsy moth larvae (Lymantria dispar L.). Expression of CAD/CAD-like genes in Populus tissues (xylem, leaves, and barks) was analyzed in herbivore-treated and non-treated plants by real time quantitative RT-PCR.     

In vivo differential effects of fasting, re-feeding, insulin and insulin stimulation time course on insulin signaling pathway components in peripheral tissues

Background: Components of the insulin receptor signaling pathway are probably some of the best studied ones. Even though methods for studying these components are well established, the in vivo effects of different fasting regimens, and the time course of insulin receptor phosphorylation and that of its downstream components in insulin-sensitive peripheral tissues have not been analyzed in detail. Rationale: When assessing insulin signaling, it may be beneficial to drive insulin levels as low as possible by performing an overnight fast before injecting a supra-physiological dose of insulin. Recent studies have shown however that 5 or 6 h fast in mice is sufficient to assess physiological responses to insulin and/or glucose in glucose tolerance tests [1], insulin tolerance tests [2], [3] and [4] and euglycemic hyperinsulinemic clamp studies [5] and [6]. Moreover, mice are nocturnal feeders, with ∼70% of their daily caloric intake occurring during the dark cycle [5], and their metabolic rate is much higher than humans. Therefore, an overnight fast in mice is closer to starvation than just food withdrawal. Thus our aim was to assess insulin signaling components from the insulin receptor to downstream targets IRS1, Akt/PKB, GSK3, Erk1/2 and ribosomal protein S6 in muscle, liver and adipose tissue in 5 h versus 16 h (overnight) fasted mice, and the time course (0-30 min) of these phosphorylation events. We also assessed whether re-feeding under 5 h and 16 h fasting conditions was a more robust stimulus than insulin alone. Conclusions: Our study determines that a short food withdrawal from mice, for a period of 5 h, results in a similar insulin-stimulated response in phosphorylation events as the long overnight fast, presenting a more physiological experimental set up. We also demonstrate that in vivo, insulin-stimulated phosphorylation of its signaling components is different between different peripheral tissues, and depending on the tissue(s) and protein(s) of interest, an appropriate time course should be chosen.     

Phosphatidylinositol 3-kinase and xanthine oxidase regulate nitric oxide and reactive oxygen species productions by apoptotic lymphocyte microparticles in endothelial cells

Microparticles (MPs) are membrane vesicles released during cell activation and apoptosis. We have previously shown that MPs from apoptotic T cells induce endothelial dysfunction, but the mechanisms implicated are not completely elucidated. In this study, we dissect the pathways involved in endothelial cells with respect to both NO and reactive oxygen species (ROS). Incubation of endothelial cells with MPs decreased NO production that was associated with overexpression and phosphorylation of endothelial NO synthase (eNOS). Also, MPs enhanced expression of caveolin-1 and decreased its phosphorylation. Microparticles enhanced ROS by a mechanism sensitive to xanthine oxidase and P-IkappaBalpha inhibitors. PI3K inhibition reduced the effects of MPs on eNOS, but not on caveolin-1, whereas it enhanced the effects of MPs on ROS production. Microparticles stimulated ERK1/2 phosphorylation via a PI3K-depedent mechanism. Inhibition of MEK reversed eNOS phosphorylation but had no effect on ROS production induced by MPs. In vivo injection of MPs in mice impaired endothelial function. In summary, MPs activate pathways related to NO and ROS productions through PI3K, xanthine oxidase, and NF-kappaB pathways. These data underscore the pleiotropic effects of MPs on NO and ROS, leading to an increase oxidative stress that may account for the deleterious effects of MPs on endothelial function.     

A novel approach for clustering proteomics data using Bayesian fast Fourier transform

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.