IDENTIFICATION OF BIOMARKERS OF SUB-LETHAL ALGAL TOXIN EXPOSURE IN ATLANTIC SALMON (SALMO SALAR) AND CHINOOK SALMON (ONCORHYNCHUS TSHAWYTSCHA) USING DIFFERENTIAL GENE EXPRESSION ANALYSIS
Bibliographic record
Abstract
Atlantic Salmon (Salmo salar) and Chinook Salmon (Oncorhynchus tshawytscha) are important culturally, recreationally, economically, and are a common aquaculture species in Canada and the United States. In the Pacific Northwest, blooms of toxic algae result in millions of dollars annually in losses for salmonid aquaculture producers. Blooms of harmful cyanobacteria produce microcystins which have been linked to net-pen liver disease which has large financial impacts on net pen salmon operations. Microcystin-LR, the most toxic variant, bioaccumulates in the liver and disrupts normal cellular activity by inhibiting protein phosphatases leading to deleterious effects on growth, immune status, and liver function. To minimize economic loss and improve animal welfare, salmonid producers have interest in using biomarkers of sub-lethal microcystin exposure to mitigate these impacts. RNA-sequencing was performed on liver samples from Atlantic and Chinook Salmon fed algal paste containing microcystin-LR in order to evaluate changes in gene expression caused by toxin exposure. The transcriptome response was examined at several time points following exposure to determine the most useful candidate biomarkers and to evaluate windows of detection. These biomarkers can serve as early warning signs that will allow aquaculture managers to decide to harvest early to avoid large mortality events caused by these algal toxins. Additionally, the biomarkers identified in this study have potential to be utilized across salmonid species for fish species broadly. Complementing previous microcystin research on fish, this study reports many differentially expressed genes related to the cell cycle, microtubule integrity, apoptosis, oxidative stress, inflammation, and metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".