Water Chemistry Alters Gene Expression and Physiological End Points of Chronic Waterborne Copper Exposure in Zebrafish, Danio rerio
Bibliographic record
Abstract
This is the first study to implement a genomic approach to ascertain both transcriptional and functional end points of chronic Cu toxicity in fish associated with experimentally manipulated water chemistries. Over 21 d, zebrafish acclimated to softwater (Na(+) = 0.06 mM, Ca(2+) = 0.08 mM, Mg(2+) = 0.03 mM) were exposed to the following: soft-water (Ctrl); 12 microg L(-1) Cu (Cu); 3.3 mM Na(+) (Na); 3.3 mM Na(+) + 12 microg L(-1) Cu (Na + Cu); 3.3 mM Ca(2+) (Ca); or 3.3 mM Ca(2+) + 12 microg L(-1) Cu (Ca + Cu). Although effective at reducing Cu load in all tissues, Na(+) in the presence of Cu did not decrease the degree of oxidative damage, particularly in the gill and gut. In contrast, Ca + Cu treatment decreased Cu accumulation in gill, but not liver or gut, with no reduction in oxidative damage. Transcriptional analysis of candidate genes (atp7a, ctr1, ECaC, esr1) showed principally a down regulation of transcripts with the Cu only treatment, while Ca + Cu treatment restored some of the genes to control levels. Conversely, the Na + Cu treatment had a strong, opposing affect when compared to that of Cu alone. Zebrafish Affymetrix GeneChips revealed significantly clustered patterns of expression. Changes in expression induced by Cu appeared to be opposite to the majority of the other treatments. Our data on the preventative or enhancing effects of Na(+) and Ca(2+) both alone and in the presence of Cu, may, in the future, facilitate the incorporation of gene expression end points into a biotic ligand model predicting chronic Cu toxicity in this tropical model species of genomic importance.
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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".