An ‘omics approach to investigate the growth effects of environmentally relevant concentrations of guanylurea exposure on Japanese medaka (Oryzias latipes)
Bibliographic record
Abstract
Metformin is a widely prescribed pharmaceutical used in the treatment of numerous human health disorders, including Type 2 Diabetes, and as a results of its widespread use, metformin is thought to be the most prevalent pharmaceutical in the aquatic environment by weight. The removal of metformin during the water treatment process is directly related to the formation of its primary degradation product, guanylurea, generally present at higher concentrations in surface waters relative to metformin. Growth effects observed in 28-day early life stage (ELS) Japanese medaka exposed to guanylurea were found to be similar to growth effects in 28-day ELS medaka exposed to metformin; however, effect concentrations were orders of magnitude below those of metformin. The present study uses a multi-omics approach to investigate potential mechanisms by which low-level, 1 ng · L−1 nominal, guanylurea exposure may lead to altered growth in 28-day post hatch medaka via shotgun metabolomics and proteomics and qPCR. Specifically, analyses show 6 altered metabolites, 66 altered proteins and 2 altered genes. Collectively, metabolomics, proteomics, and gene expression data (using qPCR) indicate that developmental exposure to guanylurea exposure alters a number of important pathways related to the overall health of ELS fish, including biomolecule metabolism, cellular energetics, nervous system function/development, cellular communication and structure, and detoxification of reactive oxygen species, among others. To our knowledge, this is the first study to both report the molecular level effects of guanylurea on non-target aquatic organisms, and to relate molecular-level changes to whole organism effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".