Sublethal metabolic responses to contaminant mixture toxicity in <i>Daphnia magna</i>
Bibliographic record
Abstract
Abstract Anthropogenic activity is increasing the presence of contaminants that enter waterways through wastewater effluent and urban and/or agricultural runoff, generally in complex mixtures. Depending on the mode of action of the individual contaminant within the mixture, toxicity can occur due to contaminants having similar or dissimilar modes of action. However, it is unknown how the metabolome responds to sublethal contaminant mixtures in the keystone genus Daphnia. In the present study we examined D. magna metabolic responses to acute sublethal exposure of propranolol, carbamazepine, and perfluorooctanesulfonic acid (PFOS) as well as in binary (propranolol–carbamazepine, propranolol–PFOS, carbamazepine–PFOS) and tertiary mixtures (carbamazepine–propranolol–PFOS), all at 10% of the median lethal concentration of the population (LC50). The metabolome was measured using 1H nuclear magnetic resonance (NMR) and characterized using principal component analysis, regression analysis, and fold changes in metabolite relative to the unexposed (control) group. The averaged principal component analysis scores plots revealed that carbamazepine–PFOS and carbamazepine–propranolol–PFOS exposures were significantly different from the control treatment. After normalizing the toxicity of each contaminant, we found that some metabolites responded monotonically, whereas others displayed a nonmonotonic response with increasing toxicity units. The single contaminant exposures and 2 binary mixtures (propranolol–carbamazepine, and propranolol–PFOS) resulted in minimal changes in the identified metabolites, whereas the carbamazepine–PFOS and carbamazepine–propranolol–PFOS displayed increases in several amino acid metabolites and decreases in glucose. Overall, our results highlight the sensitivity of the metabolome to distinguish the composition of the contaminant mixtures, with some mixtures displaying heightened responses versus others. Environ Toxicol Chem 2018;37:2448–2457. © 2018 SETAC
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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".