Association of Hepatic Gene Expression with Chemical Concentrations in Wild-Collected Double-Crested Cormorant Embryos using an EcoToxChip Gene Array
Bibliographic record
Abstract
Abstract Contaminant monitoring programs use wild bird eggs, but determining whether measured concentrations elicit adverse effects relies on extrapolation from toxicity studies with avian model species. Here, we directly evaluated the relationships between whole embryo contaminant concentrations and mRNA expression in liver tissue of the double-crested cormorant (Nannopterum auritum). Eggs collected from three North American sites (one from Lake Erie and two from the Salish Sea) were artificially incubated until pipping. Hepatic mRNA was analyzed with an EcoToxChip quantitative polymerase chain reaction (qPCR) array containing 354 target genes. The remaining embryo was analyzed for mercury and 95 persistent organic contaminants. Lake Erie embryos had higher concentrations of most organic contaminants than those from the Salish Sea. Sparse partial least-squares regression analysis of contaminant and gene expression data indicated that chlorinated hydrocarbons, especially polychlorinated biphenyls (PCBs), were associated with variation in gene expression. Linear correlations revealed consistent pairwise associations between chlorinated contaminants and the expression of nine genes (seven genes with PCBs and two with β-hexachlorocyclohexane). Partial least-squares discriminant analysis identified embryos from the Lake Erie site accurately. Overall, gene expression in embryos from wild-collected eggs was associated with tissue contaminant concentrations, and thus, transcriptomic measurements may serve to identify individuals or populations affected biologically by contaminants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".