Gene Expression Profiling in Daphnia magna, Part II: Validation of a Copper Specific Gene Expression Signature with Effluent from Two Copper Mines in California

Abstract

Genomic technologies show great potential for classifying disease states and toxicological impacts from exposure to chemicals into functional categories. In environmental monitoring, the ability to classify field samples and predict the pollutants present in these samples could contribute to monitoring efforts and the diagnosis of contaminated sites. Using gene expression analysis, we challenged our custom Daphnia magna cDNA microarray to determine the presence of a specific metal toxicant in blinded field samples collected from two copper mines in California. We compared the gene expression profiles from our field samples to previously established expression profiles for Cu, Cd, and Zn. The expression profiles from the Cu-containing field samples clustered with the laboratory-exposed Cu-specific gene expression profiles and included genes previously identified as copper biomarkers, verifying that gene expression analysis can predict environmental exposure to a specific pollutant. In addition, our study revealed that upstream field samples containing undetectable levels of Cu caused the differential expression of only a few genes, lending support for the concept of a no observed transcriptional effect level (NOTEL). If confirmed by further studies, the NOTEL may play an important role in discriminating polluted and nonpolluted sites in future monitoring efforts.

Linking molecular biomarkers with higher level condition indicators to identify effects of copper exposures on the endangered delta smelt (Hypomesus transpacificus)

“…Copper concentrations used in this study, though high, are not uncommon in Californian surface waters [39], especially the lowest concentration investigated. Results from this study are indicative of short-term exposure responses.…”

Genomic investigation of year‐long and multigenerational exposures of fathead minnow to the munitions compound RDX

“…As a demonstrative exercise using the results of our one-year time series analysis (Tables 2 and 3), we would ascribe a NOTEL for RDX at <0.83 mg/L. However, in contrast to Poynton et al [41] and Lobenhofer et al [43] who utilized a dose series and a single timepoint for assessing the NOTEL, our experimental design included the investigation of differential expression through a time series. This time-series element provided an additional element of scrutiny in assessing functional impacts resulting from changes in gene expression.…”

“…Recently, NOTEL has been proposed as a means of incorporating transcriptomics or gene expression results into a risk assessment framework [41][42][43]. The studies that developed methods to assess NOTEL [41,43] each emphasize the importance of discerning true differentially expressed transcripts from false positives resulting from type II statistical error.…”

“…The studies that developed methods to assess NOTEL [41,43] each emphasize the importance of discerning true differentially expressed transcripts from false positives resulting from type II statistical error. In our study, relatively stringent criteria for discerning differentially expressed transcripts were utilized to compare treatments to ensure high confidence in statistical calls.…”

Toxicogenomics Applied to Ecological Risk Assessment

“…Helbing et al (2007a,b) compared effects of two forms of thyroid hormone and three chemicals that affect thyroid function, methimazole, propylthiouracil, and perchlorate, to identify specific genes and gene expression profiles that could serve as indicators of exposure to thyroidactive chemicals. In a series of non-EDC-related studies, Poynton et al identified distinct gene expression profiles for Daphnia magna exposed to three metals, Cu, Cd, Zn (Poynton et al, 2007), and later demonstrated that blinded field samples collected from copper mining sites clustered with the laboratory-derived Cu-specific expression profile (Poynton et al, 2008). Although limited to profiles for three metals, this was one of the first studies to demonstrate that gene expression analysis could predict environmental exposure to a specific pollutant (Poynton et al, 2008).…”