RNA Sequencing of Lake Charr Epidermal Mucus to Assess Molecular Effects of Diluted Bitumen Exposure in a Boreal Lake
Bibliographic record
Abstract
Transport of diluted bitumen (dilbit) from Canada’s oil sands region poses risk for leaks and spills of petroleum-derived contaminants into the environment. Exposure of fish to dilbit is known to cause cardiotoxicity, developmental deformities, and impairment in swim performance. However, previous studies have examined the toxicity of dilbit in laboratory settings which does not account for environmental and biological food-web variables that may alter exposure and/or toxicity of dilbit. Moreover, most methods of assessing organism health following oil exposure require lethal sampling. This work is a part of a larger set of experiments where dilbit spills were simulated within enclosures on a lake; the present study assesses the impacts of residual levels of dilbit that may have entered the surrounding lake environment from the enclosures following model spill cleanup. In order to understand the impacts of residual dilbit in an ecosystem setting without use of lethal sampling, epidermal mucus was collected and sequenced from lake charr (Salvelinus namaycush) exposed to residual dilbit in a boreal lake. While concentrations reached a maximum of 2.29 μg/L total polycyclic aromatic compounds (ΣPAC) within surface waters, surface water ΣPAC concentrations generally remained below 1 μg/L. Results of RNA sequencing were compared to sequencing data from mucus collected prior to dilbit additions. Differential gene expression and pathway analyses indicated dysregulation of genes associated with intermediary and energy metabolism as well as a trend in upregulation of cyp1a3 in epidermal mucus following dilbit exposure. Thus, results of the present study suggest that lake charr undergo consistent biological responses after exposure to residual levels of dilbit following a model spill, and that mRNA-based analysis of mucus may be a viable method for non-lethal oil exposure assessment. Overall, the results provide insight on the response of wild fish to very dilute dilbit exposures after a model spill cleanup.
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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".