Environmental context and contaminant biotransport by Pacific salmon interact to mediate the bioaccumulation of contaminants by stream-resident fish

1. The extent to which environmental context mediates the uptake of biotransported contaminants by stream-resident organisms is not understood. For example, there is no clear understanding of the extent to which contaminant type, instream characteristics, or resident fish identity interact to influence the uptake of contaminants deposited by Pacific salmon (Oncorhynchus spp.) during their spawning runs. 2. To address this uncertainty, we sampled four stream-resident fish species from 13 watersheds of the Laurentian Great Lakes in locations with and without salmon across a gradient of instream and watershed characteristics. We determined the polychlorinated biphenyl (PCB) and mercury (Hg) concentration along with the stable isotope ratio of C and N for each stream-resident fish. 3. We found that stream-resident fish PCB concentrations were 24-fold higher in reaches with salmon and were positively related to δ15N. In contrast, stream-resident fish Hg concentrations were similar or lower in reaches with salmon and either exhibited a negative or no relationship with δ15N. 4. Based upon AICc, stream-resident fish exhibited species-specific PCB concentrations that were positively related to salmon PCB flux. Hg burdens exhibited an interaction between fish length and salmon Hg flux – as salmon Hg inputs increased, Hg levels decreased with increasing resident fish length. We found no support for models that included the mediating influence of instream or watershed factors. Salmon eggs are enriched in PCBs but have very low Hg concentrations, so our results may be driven by the consumption of salmon eggs by stream-resident fish. 5. Synthesis and applications. Our results highlight that contaminants bioaccumulate differently depending on contaminant type, species identity, and the trophic pathway to contamination. Consequently, consideration of the recipient food web and route of exposure is critical to understanding the fate of biotransported contaminants in ecosystems. The transfer of contaminants by migratory organisms represents an understudied stressor in ecology. Effective management of biotransported contaminants will require the delineation of “hot-spots” of biotransport and implementation of best management practices in those watersheds that receive contaminants from spawning salmon

Oil and PCB interactions on the uptake and excretion in midges

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47989/1/128_2005_Article_BF01625535.pd

Effect of nitric oxide on mitochondrial activity of human synovial cells

<p>Abstract</p> <p>Background</p> <p>Nitric oxide (NO) is a messenger implicated in the destruction and inflammation of joint tissues. Cartilage and synovial membrane from patients with rheumatoid arthritis (RA) and osteoarthritis (OA) have high levels of NO. NO is known to modulate various cellular pathways and, thus, inhibit the activity of the mitochondrial respiratory chain (MRC) of chondrocytes and induce the generation of reactive oxygen species (ROS) and cell death in multiple cell types. For these reasons, and because of the importance of the synovial membrane in development of OA pathology, we investigated the effects of NO on survival, mitochondrial function, and activity of fibroblastic human OA synovial cells.</p> <p>Methods</p> <p>Human OA synovia were obtained from eight patients undergoing hip joint replacement. Sodium nitroprusside (SNP) was used as a NO donor compound and cell viability was evaluated by MTT assays. Mitochondrial function was evaluated by analyzing the mitochondrial membrane potential (Δψm) with flow cytometry using the fluorofore DePsipher. ATP levels were measured by luminescence assays, and the activities of the respiratory chain complexes (complex I: NADH CoQ₁reductase, complex II: succinate dehydrogenase, complex III: ubiquinol-cytochrome c reductase, complex IV: cytochrome c oxidase) and citrate synthase (CS) were measured by enzymatic assay. Protein expression analyses were performed by western blot.</p> <p>Results</p> <p>SNP at a concentration of 0.5 mM induced cell death, shown by the MTT method at different time points. The percentages of viable cells at 24, 48 and 72 hours were 86.11 ± 4.9%, 74.31 ± 3.35%, and 43.88 ± 1.43%, respectively, compared to the basal level of 100% (*<it>p </it>< 0.05). SNP at 0.5 mM induced depolarization of the mitochondrial membrane at 12 hours with a decrease in the ratio of polarized cells (basal = 2.48 ± 0.28; SNP 0.5 mM = 1.57 ± 0.11; *<it>p </it>< 0.01). The time course analyses of treatment with SNP at 0.5 mM demonstrated that treatment reliably and significantly reduced intracellular ATP production (68.34 ± 14.3% vs. basal = 100% at 6 hours; *<it>p </it>< 0.05). The analysis of the MRC at 48 hours showed that SNP at 0.5 mM increased the activity of complexes I (basal = 36.47 ± 3.92 mol/min/mg protein, SNP 0.5 mM = 58.08 ± 6.46 mol/min/mg protein; *<it>p </it>< 0.05) and III (basal = 63.87 ± 6.93 mol/min/mg protein, SNP 0.5 mM = 109.15 ± 30.37 mol/min/mg protein; *<it>p </it>< 0.05) but reduced CS activity (basal = 105.06 ± 10.72 mol/min/mg protein, SNP at 0.5 mM = 66.88 ± 6.08 mol/min/mg protein.; *<it>p </it>< 0.05), indicating a decrease in mitochondrial mass. Finally, SNP regulated the expression of proteins related to the cellular cycle; the NO donor decreased bcl-2, mcl-1 and procaspase-3 protein expression.</p> <p>Conclusions</p> <p>This study suggests that NO reduces the survival of OA synoviocytes by regulating mitochondrial functionality, as well as the proteins controlling the cell cycle.</p

Evaluation of multiple laboratory performance and variability in analysis of recreational freshwaters by a rapid Escherichia coli qPCR method (Draft Method C)

There is interest in the application of rapid quantitative polymerase chain reaction (qPCR) methods for recreational freshwater quality monitoring of the fecal indicator bacteria Escherichia coli (E. coli). In this study we determined the performance of 21 laboratories in meeting proposed, standardized data quality acceptance (QA) criteria and the variability of target gene copy estimates from these laboratories in analyses of 18 shared surface water samples by a draft qPCR method developed by the U.S. Environmental Protection Agency (EPA) for E. coli. The participating laboratories ranged from academic and government laboratories with more extensive qPCR experience to “new” water quality and public health laboratories with relatively little previous experience in most cases. Failures to meet QA criteria for the method were observed in 24% of the total 376 test sample analyses. Of these failures, 39% came from two of the “new” laboratories. Likely factors contributing to QA failures included deviations in recommended procedures for the storage and preparation of reference and control materials. A master standard curve calibration model was also found to give lower overall variability in log10 target gene copy estimates than the delta-delta Ct (ΔΔCt) calibration model used in previous EPA qPCR methods. However, differences between the mean estimates from the two models were not significant and variability between laboratories was the greatest contributor to overall method variability in either case. Study findings demonstrate the technical feasibility of multiple laboratories implementing this or other qPCR water quality monitoring methods with similar data quality acceptance criteria but suggest that additional practice and/or assistance may be valuable, even for some more generally experienced qPCR laboratories. Special attention should be placed on providing and following explicit guidance on the preparation, storage and handling of reference and control materials

Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches

There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made

Using ArcMap, Google Earth, and Global Positioning Systems to Select and Locate Random Households in Rural Haiti

Background: A remote sensing technique was developed which combines a Geographic Information System (GIS); Google Earth, and Microsoft Excel to identify home locations for a random sample of households in rural Haiti. The method was used to select homes for ethnographic and water quality research in a region of rural Haiti located within 9 km of a local hospital and source of health education in Deschapelles, Haiti. The technique does not require access to governmental records or ground based surveys to collect household location data and can be performed in a rapid, cost-effective manner. Methods: The random selection of households and the location of these households during field surveys were accomplished using GIS, Google Earth, Microsoft Excel, and handheld Garmin GPSmap 76CSx GPS units. Homes were identified and mapped in Google Earth, exported to ArcMap 10.0, and a random list of homes was generated using Microsoft Excel which was then loaded onto handheld GPS units for field location. The development and use of a remote sensing method was essential to the selection and location of random households. Results: A total of 537 homes initially were mapped and a randomized subset of 96 was identified as potential survey locations. Over 96% of the homes mapped using Google Earth imagery were correctly identified as occupied dwellings. Only 3.6% of the occupants of mapped homes visited declined to be interviewed. 16.4% of the homes visited were not occupied at the time of the visit due to work away from the home or market days. A total of 55 households were located using this method during the 10 days of fieldwork in May and June of 2012. Conclusions: The method used to generate and field locate random homes for surveys and water sampling was an effective means of selecting random households in a rural environment lacking geolocation infrastructure. The success rate for locating households using a handheld GPS was excellent and only rarely was local knowledge required to identify and locate households. This method provides an important technique that can be applied to other developing countries where a randomized study design is needed but infrastructure is lacking to implement more traditional participant selection methods