Analysis of earthworm sublethal toxic responses to atrazine exposure using 1H nuclear magnetic resonance (NMR)-based metabolomics
Bibliographic record
Abstract
Abstract Atrazine toxicity to earthworms is still not fully understood, particularly at sublethal concentrations. Because of the ubiquity of atrazine in the environment, it is imperative to understand the impacts of atrazine presence to soil-dwelling organisms. To examine this in detail, we used 1H nuclear magnetic resonance (NMR)-based metabolomics to elucidate earthworm (Eisenia fetida) responses after 48 h of atrazine exposure in contact tests. Earthworms were exposed to 4 sublethal concentrations of 362.4, 181.2, 90.6, and 45.3 ng/cm2, which correspond to 1/8th, 1/16th, 1/32nd, and 1/64th of the median lethal concentration (LC50) values, respectively. After exposure, polar metabolites were isolated from earthworm tissues and analyzed using 1H NMR spectroscopy. Sublethal atrazine exposure induced a nonmonotonic response with respect to exposure concentration and caused an overall suppression in earthworm metabolism. Maltose, fumarate, malate, threonine/lactate, adenosine-5′-triphosphate (ATP), betaine, scyllo-inositol, glutamate, arginine, and glutamine were the metabolites identified as most sensitive to atrazine exposure. These observed fluctuations in the metabolic profile suggest that atrazine reduced ATP synthesis and negatively impacted the health of earthworms after acute sublethal exposure. Our study also demonstrates the utility of NMR-based metabolomics for the basic assessment of sublethal toxicity, which can then be used for more targeted approaches with other molecular techniques. Environ Toxicol Chem 2018;37:473–480. © 2017 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".