A rapid and sensitive method has been developed for the analysis of 48 human prescription active pharmaceutical ingredients (APIs) and 6 metabolites of interest, utilizing selective solid-phase extraction (SPE) and ultraperformance liquid chromatography in combination with triple quadrupole mass spectrometry (UPLC-MS/MS). The single-cartridge extraction step was developed using a mixed mode reversed-phase/cation-exchange cartridge (Oasis MCX) and validated in both wastewater effluent and surface water. Recoveries for the majority of compounds ranged from 80% to 125%, with relative standard deviations generally below 15%. Analytes were quantified using a multiple injection analysis with four chromatographic runs, with a combined run time of 48 min and SPE-UPLC-MS/MS method detection limits ranging from 1.0 to 51 ng/L. The analysis of seven wastewater effluents and one surface water sample revealed at least one detection for 38 of the 54 compounds, with effluent concentrations ranging from 7 to 2950 ng/L and surface water concentrations ranging from 10 to 140 ng/L. This initial data demonstrates that a significant number of the selected target analytes are present in wastewater treatment plant discharges.

In the United States, 6,868 cases of legionellosis were reported to the Center for Disease Control and Prevention in 2009−2010. Of these reports, it is estimated that 84% are caused by the microorganism Legionella pneumophila Serogroup (Sg) 1. Legionella spp. have been isolated and recovered from a variety of natural freshwater environments. Human exposure to L. pneumophila Sg1 may occur from aerosolization and subsequent inhalation of household and facility water. In this study, two primer/probe sets (one able to detect L. pneumophila and the other L. pneumophila Sg1) were determined to be highly sensitive and selective for their respective targets. Over 272 water samples, collected in 2009 and 2010 from 68 public and private water taps across the United States, were analyzed using the two qPCR assays to evaluate the incidence of L. pneumophila Sg1. Nearly half of the taps showed the presence of L. pneumophila Sg1 in one sampling event, and 16% of taps were positive in more than one sampling event. This study is the first United States survey to document the occurrence and colonization of L. pneumophila Sg1 in cold water delivered from point of use taps.

No abstract

Publicly available genetic sequence data were searched for human sequences that potentially represent protein kinases, important players in virtually every signaling pathway. After removal of duplicates, splice variants and pseudogenes, this search yielded 510 sequences with recognizable similarity to eukaryotic protein kinases.

Background: Carcinogenesis occurs, at least in part, due to the accumulation of mutations in critical genes that control the mechanisms of cell proliferation, differentiation and death. Publicly accessible databases contain millions of expressed sequence tag (EST) and single nucleotide polymorphism (SNP) records, which have the potential to assist in the identification of SNPs overrepresented in tumor tissue.

As potential biomarkers, gene classifiers are gene expression signatures or patterns capable of distinguishing biological samples belonging to different classes or conditions. This is the second of two papers on profiling gene expression in zebrafish (Danio rerio) treated with endocrine-disrupting chemicals of different modes of action, with a focus on comparative analysis of microarray data for gene classifier discovery. Various combinations of gene feature selection/class prediction algorithms were evaluated, with the use of microarray data organized by a chemical stressor or tissue type, for their accuracy in determining the class memberships of independent test samples. Two-way clustering of gene classifiers and treatment conditions offered another alternative to assess the performance of these potential biomarkers. Both gene feature selection methods and class prediction algorithms were shown to be important in identifying successful gene classifiers. The genetic algorithm and support vector machine yielded classifiers with the best prediction accuracy, regardless of sample size, nature of class prediction, and data complexity. A chemical stressor significantly altering the expression of a greater number of genes tended to generate gene classifiers with better performance. All combinations of gene feature selection/class prediction algorithms performed similarly well with data of high signal to noise ratio. Gene classifier discovery and application on the basis of individual sampling and sample data pooling, respectively, were found to enhance class predictions. Gene expression profiles of the top gene classifiers, identified from both microarray and quantitative polymerase chain reaction assays, displayed greater similarity between fadrozole and 17beta-trenbolone than either one to 17alpha-ethinylestradiol. These gene classifiers could serve as potential biomarkers of exposure to specific classes of endocrine disruptors.

No abstract

The research presented here is part of a larger study of the molecular mode of action of endocrine-disrupting chemicals targeting the hypothalamic-pituitary-gonadal axis in zebrafish (Danio rerio). It addresses several issues critical to microarray application in aquatic ecotoxicology: experimental design, microarray scanning, gene expression intensity distribution, and the effect of experimental parameters on the zebrafish transcriptome. Expression profiles from various tissues of individual zebrafish exposed to 17alpha-ethinylestradiol (30 ng/L), fadrozole (25 micro.g/L), or 17beta-trenbolone (3.0 microg/L) for 48 or 96 h were examined with the Agilent Oligo Microarray (G2518A). As a flexible and efficient alternative to the designs commonly used in microarray studies, an unbalanced incomplete block design was found to be well suited for this work, as evidenced by high data reproducibility, low microarray-to-microarray variability, and little gene-specific dye bias. Random scanner noise had little effect on data reproducibility. A low-level, slightly variable Cyanine 3 (Cy3) contaminant was revealed by hyperspectral imaging, suggesting fluorescence contamination as a potential contributor to the large variance associated with weakly expressed genes. Expression intensities of zebrafish genes were skewed toward the lower end of their distribution range, and more weakly expressed genes tended to have larger variances. Tissue type, followed in descending order by gender, chemical treatment, and exposure duration, had the greatest effect on the overall gene expression profiles, a finding potentially critical to experimental design optimization. Overall, congruence was excellent between quantitative polymerase chain reaction results and microarray profiles of 13 genes examined across a subset of 20 pairs of ovarian samples. These findings will help to improve applications of microarrays in future ecotoxicological studies.