Integrating Omic Technologies into Aquatic Ecological Risk Assessment and Environmental Monitoring: Hurdles, Achievements, and Future Outlook

Background: In this commentary we present the findings from an international consortium on fish toxicogenomics sponsored by the U.K. Natural Environment Research Council (Fish Toxicogenomics—Moving into Regulation and Monitoring, held 21–23 April 2008 at the Pacific Environmental Science Centre, Vancouver, BC, Canada). Objectives: The consortium from government agencies, academia, and industry addressed three topics: progress in ecotoxicogenomics, regulatory perspectives on roadblocks for practical implementation of toxicogenomics into risk assessment, and dealing with variability in data sets. Discussion: Participants noted that examples of successful application of omic technologies have been identified, but critical studies are needed to relate molecular changes to ecological adverse outcome. Participants made recommendations for the management of technical and biological variation. They also stressed the need for enhanced interdisciplinary training and communication as well as considerable investment into the generation and curation of appropriate reference omic data. Conclusions: The participants concluded that, although there are hurdles to pass on the road to regulatory acceptance, omics technologies are already useful for elucidating modes of action of toxicants and can contribute to the risk assessment process as part of a weight-of-evidence approach

Effects of Acute Exposure to the Non-steroidal Anti-inflammatory Drug Ibuprofen on the Developing North American Bullfrog (<i>Rana catesbeiana</i>) Tadpole

A variety of pharmaceutical chemicals can represent constituents of municipal effluent outflows that are dispersed into aquatic receiving environments worldwide. Increasingly, there is concern as to the potential of such bioactive substances to interact with wildlife species at sensitive life stages and affect their biology. Using a combination of DNA microarray, quantitative real-time polymerase chain reaction, and quantitative nuclease protection assays, we assessed the ability of sub-lethal and environmentally relevant concentrations of ibuprofen (IBF), a non-steroidal anti-inflammatory agent and prevalent environmental contaminant, to function as a disruptor of endocrine-mediated post-embryonic development of the frog. While the LC₅₀ of IBF for pre-metamorphic <i>Rana catesbeiana</i> tadpoles is 41.5 mg/L (95% confidence interval: 32.3–53.5 mg/L), exposure to concentrations in the ppb range elicited molecular responses both <i>in vivo</i> and in organ culture. A nominal concentration of 15 μg/L IBF (actual = 13.7 μg/L) altered the abundance of 26 mRNA transcripts within the liver of exposed pre-metamorphic <i>R. catesbeiana</i> tadpoles within 6 d. IBF-treated animals demonstrated subsequent disruption of thyroid hormone-mediated reprogramming in the liver transcriptome affecting constituents of several metabolic, developmental, and signaling pathways. Cultured tadpole tail fin treated with IBF for 48 h also demonstrated altered mRNA levels at drug concentrations as low as 1.5 μg/L. These observations raise the possibility that IBF may alter the post-embryonic development of anuran species in freshwater environs, where IBF is a persistent or seasonal pollutant

A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis.

Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level for monitoring has stimulated research on chemotyping techniques. In this study, simple wood extraction of endangered rosewoods (Dalbergia spp), amenable to use in the field, produced colorful hues that were suggestive of wood species. A more definitive study was conducted to develop wood species identification procedures using high-resolution quadrupole time-of-flight (QTOF) mass spectrometers interfaced with liquid chromatography (LC), gas chromatography (GC), and Direct Analysis in Real Time (DART). The time consuming process of extracting “identifying” mass spectral ions for species identification, contentious due to their ubiquitous nature, was supplanted by application of machine learning processes. The unbiased software mining of raw data from multiple analytical batches, followed by statistical Random Forest analysis, enabled discrimination between both anatomically and chemotypically similar Dalbergia species. Statistical Principal Component Analysis (PCA) scatterplots with 95% confidence ellipses were visually compelling in showing a differential clustering of Dalbergia from other commonly traded and lookalike wood species. The information rich raw data from GC or LC analyses offered a corroborative, legally defensible, and widely available confirmatory tool in the identification of timber species