Metabolomic profiles in relation to benchmark polycyclic aromatic compounds (PACs) and trace elements in two seabird species from Arctic Canada
Bibliographic record
Abstract
While exposure of birds to oil-related contaminants has been documented, the related adverse effects this exposure has on Arctic marine birds remain unexplored. Metabolomics can play an important role to explore biologically relevant metabolite biomarkers in relation to different stressors, even at benchmark levels of contamination. The aim of this study was to characterize the metabolomics profiles in relation to polycyclic aromatic compounds (PACs) and trace elements in the liver of two seabird species in the Canadian Arctic. In July 2018, black guillemots (Cepphus grylle) and thick-billed murres (Uria lomvia) were collected by hunters from a region where natural oil seeps occur in the seabed near Qikiqtarjuaq, Nunavut, Canada. A total of 121 metabolites were identified in liver tissue samples using reversed phase and hydrophilic interaction liquid chromatography coupled to high resolution mass spectrometry platforms to detect non-polar and polar metabolites, respectively. Sixty-nine metabolites showed excellent repeatability and linearity and were used to examine possible effects of oil-related contaminants exposure (PACs and trace elements). Metabolites including 3-hydroxy anthranilic acid, adenine, adenosine, adenosine mono-phosphate, ascorbic acid, butyrylcarnitine, cholic acid, guanosine, guanosine mono-phosphate, inosine, norepinephrine and threonine showed significant differences (more than two fold) between the two species. Elevated adenine and adenosine, along with decreased reduced/oxidized glutathione ratio, highlighted the potential for oxidative stress in murres. Lipid peroxidation and superoxide dismutase activity assays also confirmed these metabolomic findings. These results will help to characterize the baseline metabolomic profiles of Arctic seabird species with different foraging behaviour and trace element burden.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".