Salmonid smolt caudal fin and liver transcriptome responses to low sulfur marine diesel and high sulfur fuel oil water accommodated fractions for assessing oil spill effects in marine environments
Bibliographic record
Abstract
The environmental impact of oil spills is a critical concern, particularly pertaining to low sulfur marine diesel (LSMD) and high sulfur fuel oil (HSFO) that are commonly involved in coastal spills. Although transcriptomic biomonitoring of sentinel animals can be a powerful tool for assessing biological effects, conventional methods utilize lethal sampling to examine the liver. As a non-lethal alternative, we have previously shown salmonid caudal fin cyp1a1 is significantly responsive to LSMD-derived toxicants. The present study further investigated the transcriptomic biomonitoring potential of coho salmon smolt caudal fin in comparison to liver tissue in the context of LSMD and HSFO seawater accommodated fraction (seaWAF) exposure in cold-water marine environments. Assessing the toxicity of these seaWAFs involved quantifying polycyclic aromatic hydrocarbon (tPAH50) concentrations and generating gene expression profiles. Initial qPCR analyses revealed significant cyp1a1 response in both liver and caudal fin tissues of both genetic sexes to all seaWAF exposures. RNA-Seq analysis, focusing on the highest LSMD and HSFO seaWAF concentrations (28.4±1.8 and 645.08±146.3 µg/L tPAH50, respectively), revealed distinct tissue-specific and genetic sex-independent transcriptomic responses with an overall enrichment of oxidative stress, cell adhesion, and morphogenesis-related pathways. Remarkably, the caudal fin tissue exhibited transcriptomic response patterns comparable to liver tissue, particularly consistent differential expression of 33 gene transcripts in the liver (independent of sex and oil type) and 44 in the caudal fin. The present work underscores the viability of using the caudal fin as a non-lethal alternative to liver sampling for assessing and tracking oil spill exposure in marine environments.
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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) | 0.002 | 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".