Transcriptomic responses to thermal and angling stress in wild brook trout from a southern Ontario stream
Bibliographic record
Abstract
Abstract Brook trout (Salvelinus fontinalis) are threatened by emergent and intensifying anthropogenic stressors that have uncertain cumulative effects. Effectively managing and conserving brook trout will require robust and timely information on population health—particularly where human impacts on brook trout are multiple and intense. Advanced molecular genomic tools, such as quantitative PCR assays that identify and characterize stress in fish, may provide such information, and are advancing due to an accumulation of research on transcript-level stress responses in various fishes. We used a version of the Stress Transcriptional Profiling Chip developed by the Genomic Network for Fish Identification, Stress and Health to identify changes in gene transcription related to temperature and catch-and-release angling in wild, small stream brook trout in southern Ontario’s West Credit River. We angled and took non-lethal gill tissue samples from brook trout either immediately or one hour post-capture in both cool, spring conditions and warm, midsummer conditions. Transcript abundances of heat shock transcription factor 1 (hsf1), heat shock cognate 71 kDa protein (hsc70), heat shock protein 70a (hsp70a), metallothionein A (mtA), and 11β-hydroxysteroid dehydrogenase 2 (hsd11b2) increased significantly in thermally stressful, midsummer conditions. Transcript abundances of hsf1 and insulin-like growth factor 1 (igf1) increased after angling in cool, spring conditions, but evidence of angling effects on transcript abundances was generally weak. These results contribute to a growing understanding of transcript-level stress responses in fish, which may be used to monitor brook trout population health locally, and create tools to monitor salmonid population health more broadly.
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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 | 0.000 | 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) | 0.000 | 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 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".