Serum paraoxonase and arylesterase activities in patients with lung cancer in a Turkish population

BACKGROUND: Lung cancer (LC) is the leading cause of cancer-related deaths. Oxidative DNA damage may contribute to the cancer risk. The antioxidant paraoxonase (PON1) is an endogenous free radical scavenger in the human body. The aim of this study was to determine serum PON1 and arylesterase (ARE) activities in patients with newly diagnosed LC. METHODS: This case control study involved a total of 39 patients with newly diagnosed LC (untreated) and same number of age- and sex-matched healthy individuals. Serum PON1 and ARE activities in addition to lipid parameters were measured in both groups. RESULTS: Serum PON1 and ARE activities were found to be lower in patients with LC compared to the controls (p = 0.001 and p = 0.018, respectively). The ratio of PON1/high density lipoprotein (HDL) was significantly lower in the LC group compared to the control one (p = 0.009). There were positive correlations between the serum levels of HDL and PON1 in both the control (r = 0.415, p = 0.009) and the LC groups (r = 0.496, p = 0.001), respectively. PON1 enzyme activity was calculated as three different phenotypes in both groups. In regard to lipid parameters, total cholesterol levels were significantly lower (p = 0.014) in the LC group whereas the other lipid parameters such as HDL, LDL, and triglyceride levels were not significantly different among groups. CONCLUSION: Serum PON1 activity is significantly low in the LC group compared with the healthy controls. Metastasis status and cigarette smoking do not affect serum PON1 activity in the LC patients

pcaGoPromoter - An R Package for Biological and Regulatory Interpretation of Principal Components in Genome-Wide Gene Expression Data

Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms (biological processes), and an indication of the involvement of possible transcription factors

Ozone and PAN formation inside and outside of the Berlin plume - process analysis and numerical process simulation

During the BERLIOZ field phase on 20 July 1998 a 40 km wide ozone-plume 30 to 70 km north of Berlin in the lee of the city was detected. The ozone mixing ratio inside the plume was app. 15 ppb higher than outside, mainly caused by high ozone precursor emissions in Berlin, resulting in a net chemical ozone production of 6.5 ppb h(-1), which overcompensates ozone advection of -3.6 ppb h(-1) and turbulent diffusion of -1.1 ppb h(-1). That means, although more ozone leaves the control volume far in the lee of Berlin than enters it at the leeside cityborder and although turbulent diffusion causes a loss of ozone in the leeside control volume the chemical production inside the volume leads to a net ozone increase. Using a semi-Lagrangian mass budget method to estimate the net ozone production, 5.0 ppb h(-1) are calculated for the plume. This means a fraction of about 20% of ozone in the plume is produced by local emissions, therefore called 'home made' by the Berlin emissions. For the same area KAMM/DRAIS simulations using an observation based initialisation, results in a net production rate between 4.0 and 6.5 ppb h(-1), while the threefold nested EURAD model gives 6.0 ppb h(-1). The process analysis indicates in many cases good agreement (10% or better) between measurements and simulations not only in the ozone concentrations but also with respect to the physical and chemical processes governing the total change. Remaining differences are caused by different resolution in time and space of the models and measurements as well as by errors in the emission calculation.The upwind-downwind differences in PAN concentrations are partly similar to those of ozone, because in the BERLIOZ case they are governed mainly by photochemical production. While in the stable boundary layer at night and windward of Berlin 0.1 to 0.3 ppb are detected, in the centre of the plume at noon concentrations between 0.75 ppb and 1.0 ppb are measured. The O-3/PAN ratio is about 80 to 120 and thus due to the relatively low PAN concentrations significantly higher than found in previous studies. The low PAN formation on 20 July, was mainly restricted by the moderate nonmethane hydrocarbon levels, whereas high PAN concentrations of 3.0 ppb on 21 July, are caused by local production in the boundary layer and by large scale advection aloft