Salmonid gene expression biomarkers indicative of physiological responses to changes in salinity, temperature, but not dissolved oxygen
Bibliographic record
Abstract
Abstract An organism’s ability to respond effectively to environmental change is critical to their survival. Yet, life stage and overall condition can dictate tolerance thresholds to heightened environmental stressors, such that stress may not be equally felt across individuals within a species. Environmental changes can induce transcriptional responses in an organism, some of which reflect generalized responses, and others are highly specific to the type of change being experienced. Thus, if transcriptional biomarkers specific to a heightened environmental stressor, even under multi-stressor impacts, can be identified, the biomarkers could be then applied in natural environments to determine when and where individuals are experiencing such stressors. Here, we validate candidate gill gene expression biomarkers by experimentally challenging juvenile Chinook salmon (Oncorhynchus tshawytscha ). A sophisticated experimental set-up (four trials) manipulated salinity (freshwater, brackish water, and seawater), temperature (10, 14, and 18°C), and dissolved oxygen (normoxia and hypoxia), in all 18 possible combinations, for up to six days during the pre-smolt, smolt, and de-smolt life stages. In addition, we also describe the changes in juvenile behaviour, plasma variables, gill Na + /K + - ATPase (NKA) activity, body size, body morphology, and skin pigmentation associated with salinity, temperature, dissolved oxygen, mortality, and smolt status. We statistically identified biomarkers specific to salinity and temperature treatments, as well as mortality across multiple stressors and life stages. Similar biomarkers for the dissolved oxygen treatment could not be identified in the data and we discuss our next steps using an RNA-seq study. This work demonstrates the unique power of gene expression biomarkers to identify a specific stressor even under multi-stressor conditions.
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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".