Widespread molecular detection of Legionella pneumophila Serogroup 1 in cold water taps across the United States.

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.

A validation study of a rapid field-based rating system for discriminating among flow permanence classes of headwater streams in South Carolina.

Rapid field-based protocols for classifying flow permanence of headwater streams are needed to inform timely regulatory decisions. Such an existing method was developed for and has been used in North Carolina since 1998. The method uses ordinal scoring of 26 geomorphology, hydrology, and biology attributes of streams. The attribute scores are summed and compared to threshold scores to assign a flow permanence class. Our study objective was to evaluate the method's ability to classify the flow permanence of forested stream reaches from Piedmont and Southeastern Plains ecoregions in South Carolina. Ephemeral reaches scored significantly lower than intermittent and perennial reaches, but scores from intermittent and perennial reaches did not differ. Scores collected in the dry and wet seasons were strongly correlated, indicating that the method was seasonally stable. Scores had positive nonlinear relationships with the maximum recorded wet duration and the proportion of the record that reaches were wet, but were not related to drying frequency. Scores of the presence of baseflow in the dry season were more important in flow permanence classification than those from the wet season. Other important attributes and parameters in discriminating flow classes were macrobenthos, rooted upland plants, bankfull width, drainage area, and ecoregion. Although the North Carolina method did not consistently differentiate intermittent from perennial reaches, the indicator-based approach is a strong foundation from which to build a protocol for South Carolina. Adding measures like bankfull width and drainage area, weighting by ecoregion, or shifting thresholds may be warranted modifications for South Carolina.

Comparing predicted estrogen concentrations with measurements in US waters.

The range of exposure rates to the steroidal estrogens estrone (E1), beta-estradiol (E2), estriol (E3), and ethinyl estradiol (EE2) in the aquatic environment was investigated by modeling estrogen introduction via municipal wastewater from sewage plants across the US. Model predictions were compared to published measured concentrations. Predictions were congruent with most of the measurements, but a few measurements of E2 and EE2 exceed those that would be expected from the model, despite very conservative model assumptions of no degradation or in-stream dilution. Although some extreme measurements for EE2 may reflect analytical artifacts, remaining data suggest concentrations of E2 and EE2 may reach twice the 99th percentile predicted from the model. The model and bulk of the measurement data both suggest that cumulative exposure rates to humans are consistently low relative to effect levels, but also suggest that fish exposures to E1, E2, and EE2 sometimes substantially exceed chronic no-effect levels.

Analysis of ecologically relevant pharmaceuticals in wastewater and surface water using selective solid-phase extraction and UPLC-MS/MS.

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.

Inducible expression and pharmacological characterization of the mouse metabotropic glutamate 5b receptor.

The metabotropic glutamate receptor subtype 5 (mGlu5) and glutamatergic neurotransmission are associated with the pathophysiology of disorders such as anxiety, depression or chronic pain. Human and rat mGlu5 receptors have been cloned and characterized previously. We now describe the cloning of the mouse mGlu5b receptor gene from adult mouse brain and its expression using an ecdysone-inducible system. This subtype has an extra 96 bp sequence which is inserted to the cytoplasmic tail and is identical to the insert present in human and rat mGlu5b. Mouse mGlu5b receptor expression was induced in HEK-293EcR cells by incubation with ponasterone A, an analogue of the insect hormone ecdysone. A fluorometric calcium transient assay system was used to characterize the basic pharmacologic profile of an isolated stable cell line. Quisqualic acid was the most potent receptor agonist (EC(50) approximately 7 nM) although the cells also responded to l-glutamic acid and the Group I-selective receptor agonist, 3,5-dihydroxyphenylglycine (3,5-DHPG). The calcium transients stimulated by these agonists were potently inhibited by reference allosteric mGlu5 antagonists - 2-methyl-6-(phenylethynyl)pyridine (MPEP), 3-[(2-methyl-1,3-thiazol-4-yl)ethynyl]pyridine (MTEP) and 3-methoxy-5-(pyridine-2-ylethynyl)pyridine (methoxy-PEPy) (IC(50) ranges: 0.8-66 nM). The availability of this mouse mGlu5b receptor-expressing cell line will facilitate in vitro characterization of mGlu5 receptor-selective agonists or antagonists prior to in vivo pharmacologic testing.

DNA microarray application in ecotoxicology: experimental design, microarray scanning, and factors affecting transcriptional profiles in a small fish species.

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.

DNA microarray-based ecotoxicological biomarker discovery in a small fish model species.

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.

Genome wide in silico SNP-tumor association analysis.

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. METHODS: An in silico SNP-tumor association study was performed utilizing tissue library and SNP information available in NCBI's dbEST (release 092002) and dbSNP (build 106). RESULTS: A total of 4865 SNPs were identified which were present at higher allele frequencies in tumor compared to normal tissues. A subset of 327 (6.7%) SNPs induce amino acid changes to the protein coding sequences. This approach identified several SNPs which have been previously associated with carcinogenesis, as well as a number of SNPs that now warrant further investigation CONCLUSIONS: This novel in silico approach can assist in prioritization of genes and SNPs in the effort to elucidate the genetic mechanisms underlying the development of cancer.

Human members of the eukaryotic protein kinase family.

BACKGROUND: Eukaryotic protein kinases (EPKs) constitute one of the largest recognized protein families represented in the human genome. EPKs, which are similar to each other in sequence, structure and biochemical properties, are important players in virtually every signaling pathway involved in normal development and disease. Near completion of projects to sequence the human genome and transcriptome provide an opportunity to identify and perform sequence analysis on a nearly complete set of human EPKs. RESULTS: Publicly available genetic sequence data were searched for human sequences that potentially represent EPK family members. After removal of duplicates, splice variants and pseudogenes, this search yielded 510 sequences with recognizable similarity to the EPK family. Protein sequences of putative EPK catalytic domains identified in the search were aligned, and a phonogram was constructed based on the alignment. Representative sequence records in GenBank were identified, and derived information about gene mapping and nomenclature was summarized. CONCLUSIONS: This work represents a nearly comprehensive census and early bioinformatics overview of the EPKs encoded in the human genome. Evaluation of the sequence relationships between these proteins contributes contextual information that enhances understanding of individual family members. This curation of human EPK sequences provides tools and a framework for the further characterization of this important class of enzymes.